diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.1.2 - Views and CTEs on Databricks.sql b/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.1.2 - Views and CTEs on Databricks.sql deleted file mode 100644 index dbfee90..0000000 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.1.2 - Views and CTEs on Databricks.sql +++ /dev/null @@ -1,278 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC # Views and CTEs on Databricks --- MAGIC In this demonstration, you will create and explore views and common table expressions (CTEs). --- MAGIC --- MAGIC ## Learning Objectives --- MAGIC By the end of this lesson, you will be able to: --- MAGIC * Use Spark SQL DDL to define views --- MAGIC * Run queries that use common table expressions --- MAGIC --- MAGIC --- MAGIC --- MAGIC **Resources** --- MAGIC * [Create View - Databricks Docs](https://docs.databricks.com/spark/latest/spark-sql/language-manual/sql-ref-syntax-ddl-create-view.html) --- MAGIC * [Common Table Expressions - Databricks Docs](https://docs.databricks.com/spark/latest/spark-sql/language-manual/sql-ref-syntax-qry-select-cte.html) - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Classroom Setup --- MAGIC The following script clears out previous runs of this demo and configures some Hive variables that will be used in our SQL queries. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-meta - --- COMMAND ---------- - --- MAGIC %md --- MAGIC We start by creating a table of data we can use for the demonstration. - --- COMMAND ---------- - -CREATE DATABASE IF NOT EXISTS ${c.database}; -USE ${c.database}; - --- mode "FAILFAST" will abort file parsing with a RuntimeException if any malformed lines are encountered -CREATE OR REPLACE TEMPORARY VIEW temp_delays USING CSV OPTIONS ( - path '${c.userhome}/datasets/flights/departuredelays.csv', - header "true", - mode "FAILFAST" -); -CREATE OR REPLACE TABLE external_table LOCATION '${c.userhome}/external_table' AS - SELECT * FROM temp_delays; - -SELECT * FROM external_table; - --- COMMAND ---------- - --- MAGIC %md ## Views --- MAGIC Let's create a view that contains only the data where the origin is 'ABQ' and the destination is 'LAX'. - --- COMMAND ---------- - -CREATE OR REPLACE VIEW view_delays_ABQ_LAX AS -SELECT * FROM external_table WHERE origin = 'ABQ' AND destination = 'LAX'; -SELECT * FROM view_delays_ABQ_LAX; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC To show a list of tables (and views), we use the `SHOW TABLES` command. --- MAGIC --- MAGIC Note that the `view_delays_abq_lax` view is in the list. If we detach from, and reattach to, the cluster and reload the list of tables, view_delays_abq_lax persists. This is because View metadata (name, location, etc.) are stored in the metastore. --- MAGIC --- MAGIC (The command `USE ${c.database};` is used after reattaching to the cluster because state is lost when the SparkSession is deleted) - --- COMMAND ---------- - -USE ${c.database}; -SHOW tables; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Now, let's create a temporary view. The syntax is very similar but adds `TEMPORARY` to the command. - --- COMMAND ---------- - -CREATE OR REPLACE TEMPORARY VIEW temp_view AS -SELECT * FROM external_table WHERE delay > 120 ORDER BY delay ASC; -SELECT * FROM temp_view; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Let's again show list of tables (and views). --- MAGIC --- MAGIC Two things we note are that the `temp_view` view is in the list and that `temp_view` is marked `isTemporary`. --- MAGIC --- MAGIC If we detach from, and reattach to, the cluster and reload the list of tables, `temp_view` is deleted. This is because temporary view metadata (name, location, etc.) are not stored in the metastore. When we detach from the cluster, the Spark session is deleted, which deletes the temporary view. - --- COMMAND ---------- - -USE ${c.database}; -SHOW TABLES; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Let's now create a global temporary view. We add `GLOBAL` to the command. This view is just like the temporary view above, but it is different in one important way. It is added to the `global_temp` database that exists on the cluster. As long as the cluster is running, this database persists, and any notebooks attached to the cluster can access its global temporary views. --- MAGIC --- MAGIC Note when we use global temporary views, we have to prefix them with `global_temp.` since we are accessing the `global_temp` database. - --- COMMAND ---------- - -CREATE OR REPLACE GLOBAL TEMPORARY VIEW global_temp_view_distance AS -SELECT * FROM external_table WHERE distance > 1000; -SELECT * FROM global_temp.global_temp_view_distance; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Again, global temporary views are available to any notebook attached to the cluster, including --- MAGIC - New notebooks --- MAGIC - This notebook, even if it is detached from, and reattached to, the cluster - --- COMMAND ---------- - -SELECT * FROM global_temp.global_temp_view_distance; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC One thing to note is that global temporary views do not show in the list of tables. - --- COMMAND ---------- - -USE ${c.database}; -SHOW TABLES; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Common Table Expressions (CTEs) --- MAGIC CTEs can be used in a variety of contexts. Below, are a few examples of the different ways a CTE can be used in a query. First, an example of making multiple column aliases using a CTE. - --- COMMAND ---------- - -WITH flight_delays( - total_delay_time, - origin_airport, - destination_airport -) AS ( - SELECT - delay, - origin, - destination - FROM - external_table -) -SELECT - * -FROM - flight_delays -WHERE - total_delay_time > 120 - AND origin_airport = "ATL" - AND destination_airport = "DEN"; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Next, is an example of a CTE in a CTE definition. - --- COMMAND ---------- - -WITH lax_bos AS ( - WITH origin_destination (origin_airport, destination_airport) AS ( - SELECT - origin, - destination - from - external_table - ) - SELECT - * - FROM - origin_destination - WHERE - origin_airport = 'LAX' - AND destination_airport = 'BOS' -) -SELECT - count(origin_airport) AS `Total Flights from LAX to BOS` -FROM - lax_bos; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Now, here is an example of a CTE in a subquery. - --- COMMAND ---------- - -SELECT - max(total_delay) AS `Longest Delay (in minutes)` -FROM - ( - WITH delayed_flights(total_delay) AS ( - SELECT - delay - from - external_table - ) - SELECT - * - FROM - delayed_flights - ); - --- COMMAND ---------- - --- MAGIC %md --- MAGIC We can also use a CTE in a subquery expression. - --- COMMAND ---------- - -SELECT - ( - WITH distinct_origins AS ( - SELECT DISTINCT origin FROM external_table - ) - SELECT - count(origin) - FROM - distinct_origins - ) AS `Number of Different Origin Airports`; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Finally, here is a CTE in a CREATE VIEW statement. - --- COMMAND ---------- - -CREATE OR REPLACE VIEW BOS_LAX AS -WITH origin_destination(origin_airport, destination_airport) AS -(SELECT origin, destination FROM external_table) -SELECT * FROM origin_destination -WHERE origin_airport = 'BOS' AND destination_airport = 'LAX'; -SELECT count(origin_airport) AS `Number of Delayed Flights from BOS to LAX` FROM BOS_LAX; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Clean up --- MAGIC We first drop the training database. - --- COMMAND ---------- - -DROP DATABASE ${c.database} CASCADE; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Finally, we delete the working directory. - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.rm(userhome, True) - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.3 - Creating Delta Tables.sql b/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.3 - Creating Delta Tables.sql deleted file mode 100644 index 7c390cb..0000000 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.3 - Creating Delta Tables.sql +++ /dev/null @@ -1,237 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC # Creating Delta Tables --- MAGIC --- MAGIC After extracting data from external data sources, load data into the Lakehouse to ensure that all of the benefits of the Databricks platform can be fully leveraged. --- MAGIC --- MAGIC While different organizations may have varying policies for how data is initially loaded into Databricks, we typically recommend that early tables represent a mostly raw version of the data, and that validation and enrichment occur in later stages. This pattern ensures that even if data doesn't match expectations with regards to data types or column names, no data will be dropped, meaning that programmatic or manual intervention can still salvage data in a partially corrupted or invalid state. --- MAGIC --- MAGIC This lesson will focus primarily on the pattern used to create most tables, `CREATE TABLE _ AS SELECT` (CTAS) statements. --- MAGIC --- MAGIC ##### Objectives --- MAGIC - Use CTAS statements to create Delta Lake tables --- MAGIC - Create new tables from existing views or tables --- MAGIC - Enrich loaded data with additional metadata --- MAGIC - Declare table schema with generated columns and descriptive comments --- MAGIC - Set advanced options to control data location, quality enforcement, and partitioning - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Create an Empty Delta Table --- MAGIC Use the `CREATE TABLE USING` statement to define an empty Delta table in the metastore. --- MAGIC --- MAGIC We won't need to explicitly state `USING DELTA`, as Delta is the default format. - --- COMMAND ---------- - -CREATE OR REPLACE TABLE users (user_id BIGINT, user_first_touch_timestamp BIGINT, email STRING); - -DESCRIBE EXTENDED users - --- COMMAND ---------- - --- MAGIC %md ## Create Table as Select (CTAS) --- MAGIC --- MAGIC `CREATE TABLE AS SELECT` statements create and populate Delta tables using data retrieved from an input query. - --- COMMAND ---------- - -CREATE OR REPLACE TABLE sales AS -SELECT * FROM parquet.`${c.source}/sales/sales.parquet`; - -DESCRIBE EXTENDED sales - --- COMMAND ---------- - --- MAGIC %md Because they inherit schemas from the query data, CTAS statements do **not** support schema declarations. In the following query, the CTAS statement can not provide the options necessary to properly ingest data from CSV files. - --- COMMAND ---------- - -CREATE OR REPLACE TABLE sales_unparsed AS -SELECT * FROM csv.`${c.source}/sales/sales.csv`; - -SELECT * FROM sales_unparsed - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Subset Columns from Existing Tables --- MAGIC We can also use CTAS statements to load data from existing tables and views. --- MAGIC --- MAGIC The following statement creates a new table containing a subset of columns from the `sales` table. Here, we'll presume that we're intentionally leaving out information that potentially identifies the user or that provides itemized purchase details. - --- COMMAND ---------- - -CREATE OR REPLACE TABLE purchases AS -SELECT order_id, transaction_timestamp, purchase_revenue_in_usd -FROM sales; - -SELECT * FROM purchases - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Declare Schema with Generated Columns --- MAGIC --- MAGIC As noted previously, CTAS statements do not support schema declaration. We note above that the timestamp column appears to be some variant of a Unix timestamp, which may not be the most useful for our analysts to derive insights. This is a situation where generated columns would be beneficial. --- MAGIC --- MAGIC Generated columns are a special type of column whose values are automatically generated based on a user-specified function over other columns in the Delta table. Databricks introduced generated columns in DBR 8.3. --- MAGIC --- MAGIC The code below demonstrates creating a new table while: --- MAGIC 1. Specifying column names and types --- MAGIC 1. Adding a [generated column](https://docs.databricks.com/delta/delta-batch.html#deltausegeneratedcolumns) to calculate the date --- MAGIC 1. Providing a descriptive column comment for the generated column - --- COMMAND ---------- - -CREATE OR REPLACE TABLE purchase_dates ( - order_id STRING, - transaction_timestamp STRING, - purchase_revenue_in_usd STRING, - date DATE GENERATED ALWAYS AS ( - cast(cast(transaction_timestamp/1e6 AS TIMESTAMP) AS DATE)) - COMMENT "generated based on `transactions_timestamp` column") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC --- MAGIC Because `date` is a generated column, if we write to `purchase_dates` without providing values for the `date` column, Delta Lake automatically computes them. --- MAGIC --- MAGIC **NOTE**: The cell below configures a setting to allow for generating columns when using a Delta Lake `MERGE` statement. We'll see more on this syntax later in the course. - --- COMMAND ---------- - -SET spark.databricks.delta.schema.autoMerge.enabled=true; - -MERGE INTO purchase_dates a -USING purchases b -ON a.order_id = b.order_id -WHEN NOT MATCHED THEN - INSERT * - --- COMMAND ---------- - --- MAGIC %md The query automatically reads the most recent snapshot of the table for any query; you never need to run `REFRESH TABLE`. - --- COMMAND ---------- - -SELECT * FROM purchase_dates - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Enrich Tables with Additional Options and Metadata --- MAGIC --- MAGIC So far we've only shown creating managed tables, which will store all data associated with a newly created table under the databases default location. For many use cases, these will be the right choice. --- MAGIC --- MAGIC Here, we'll create an external Delta Lake table and provide a number of additional options. The syntax below will: --- MAGIC 1. Add a table comment --- MAGIC 1. Add an arbitrary key-value pair as a table property --- MAGIC 1. Add a date column derived from source table data --- MAGIC 1. Add a column to record current timestamp at time of update --- MAGIC 1. Configure table `LOCATION` path --- MAGIC 1. Partition tables by a column --- MAGIC --- MAGIC **NOTE**: A number of Delta Lake configurations are set using `TBLPROPERTIES`. When using this field as part of an organizational approach to data discovery and auditing, users should be made aware of which keys are leveraged for modifying default Delta Lake behaviors. - --- COMMAND ---------- - -CREATE OR REPLACE TABLE users_pii -COMMENT "Contains PII" -TBLPROPERTIES ('contains_pii' = True) -LOCATION "${c.userhome}/tmp/users_pii" -PARTITIONED BY (first_touch_date) -AS - SELECT *, - cast(cast(user_first_touch_timestamp/1e6 AS TIMESTAMP) AS DATE) first_touch_date, - current_timestamp() updated - FROM parquet.`${c.source}/users/users.parquet`; - --- COMMAND ---------- - --- MAGIC %md Specifying the `LOCATION` path creates an external Delta table that is unmanaged by the Metastore. --- MAGIC --- MAGIC All of the comments and properties for a given table can be reviewed using `DESCRIBE TABLE EXTENDED`. --- MAGIC --- MAGIC **NOTE**: Delta Lake automatically adds several table properties on table creation. - --- COMMAND ---------- - -DESCRIBE EXTENDED users_pii - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Listing the location used for the table reveals that the unique values in the partition column `date` are used to create data directories. - --- COMMAND ---------- - --- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{Paths.userhome}/tmp/users_pii")) - --- COMMAND ---------- - --- MAGIC %md --- MAGIC --- MAGIC Delta Lake automatically uses partitioning and statistics to read the minimum amount of data when there are applicable predicates in the query. - --- COMMAND ---------- - -SELECT * FROM users_pii WHERE first_touch_date = "2020-06-19" - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Add a Table Constraint --- MAGIC --- MAGIC Because Delta Lake enforces schema on write, Databricks can support standard SQL constraint management clauses to ensure the quality and integrity of data added to a table. --- MAGIC --- MAGIC Databricks currently support two types of constraints: --- MAGIC * [`NOT NULL` constraints](https://docs.databricks.com/delta/delta-constraints.html#not-null-constraint) --- MAGIC * [`CHECK` constraints](https://docs.databricks.com/delta/delta-constraints.html#check-constraint) --- MAGIC --- MAGIC In both cases, you must ensure that no data violating the constraint is already in the table prior to defining the constraint. Once a constraint has been added to a table, data violating the constraint will result in write failure. --- MAGIC --- MAGIC Below, we'll add a `CHECK` constraint to the `date` column of our table. Note that `CHECK` constraints look like standard `WHERE` clauses you might use to filter a dataset. - --- COMMAND ---------- - -ALTER TABLE users_pii ADD CONSTRAINT valid_date CHECK (first_touch_date > '2020-01-01'); - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Table constraints are shown in the `TBLPROPERTIES` field, alongside user-specified tags and other Delta Lake metadata. - --- COMMAND ---------- - -DESCRIBE EXTENDED users_pii - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.4 - Writing to Tables.sql b/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.4 - Writing to Tables.sql deleted file mode 100644 index 741acd5..0000000 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.4 - Writing to Tables.sql +++ /dev/null @@ -1,160 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC # Writing to Delta Tables --- MAGIC Use SQL DML statements to perform complete and incremental updates to existing Delta tables. --- MAGIC --- MAGIC ##### Objectives --- MAGIC - Overwrite data tables using `INSERT OVERWRITE` --- MAGIC - Append to a table using `INSERT INTO` --- MAGIC - Append, update, and delete from a table using `MERGE INTO` --- MAGIC - Ingest data incrementally into tables using `COPY INTO` - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-updates - --- COMMAND ---------- - --- MAGIC %md ## Complete Overwrites --- MAGIC --- MAGIC We can use overwrites to atomically replace all of the data in a table. There are multiple benefits to overwriting tables instead of deleting and recreating tables: --- MAGIC - Overwriting a table is much faster because it doesn’t need to list the directory recursively or delete any files. --- MAGIC - The old version of the table still exists; can easily retrieve the old data using Time Travel. --- MAGIC - It’s an atomic operation. Concurrent queries can still read the table while you are deleting the table. --- MAGIC - Due to ACID transaction guarantees, if overwriting the table fails, the table will be in its previous state. --- MAGIC --- MAGIC The following cells demonstrate two ways to overwrite data. --- MAGIC --- MAGIC 1. Using the `CREATE OR REPLACE TABLE` statement --- MAGIC 2. Using the `INSERT OVERWRITE` statement - --- COMMAND ---------- - -CREATE OR REPLACE TABLE events AS -SELECT * FROM parquet.`${c.source}/events/events.parquet` - --- COMMAND ---------- - -INSERT OVERWRITE sales -SELECT * FROM parquet.`${c.source}/sales/sales.parquet` - --- COMMAND ---------- - --- MAGIC %md This keeps history of the previous table, but rewrites all data. - --- COMMAND ---------- - -DESCRIBE HISTORY sales - --- COMMAND ---------- - --- MAGIC %md ## Append Rows --- MAGIC --- MAGIC We can use `INSERT INTO` to atomically append new rows to an existing Delta table. This allows for incremental updates to existing tables, which is much more efficient than overwriting each time. --- MAGIC --- MAGIC Append new sale records to the `sales` table using `INSERT INTO`. - --- COMMAND ---------- - -INSERT INTO sales -SELECT * FROM parquet.`${c.source}/sales/sales-30m.parquet` - --- COMMAND ---------- - --- MAGIC %md ## Merge Updates --- MAGIC --- MAGIC You can upsert data from a source table, view, or DataFrame into a target Delta table using the `MERGE` SQL operation. Delta Lake supports inserts, updates and deletes in `MERGE`, and supports extended syntax beyond the SQL standards to facilitate advanced use cases. --- MAGIC ``` --- MAGIC MERGE INTO target a --- MAGIC USING source b --- MAGIC ON --- MAGIC WHEN MATCHED THEN --- MAGIC WHEN NOT MATCHED THEN --- MAGIC ``` --- MAGIC We will use the `MERGE` operation to update historic users data with updated emails and new users. - --- COMMAND ---------- - -CREATE OR REPLACE TEMP VIEW users_update AS -SELECT *, current_timestamp() updated FROM parquet.`${c.source}/users/users-30m.parquet` - --- COMMAND ---------- - --- MAGIC %md As we implemented the `users` table as a Type 1 SCD Delta table with an `updated` field, we can leverage this field while performing a merge operation. Let's make sure that records that are updated OR inserted have the same timestamp. This operation will be completed as a single batch to avoid potentially leaving our table in a corrupt state. - --- COMMAND ---------- - -MERGE INTO users a -USING users_update b -ON a.user_id = b.user_id -WHEN MATCHED AND a.email IS NULL AND b.email IS NOT NULL THEN - UPDATE SET email = b.email, updated = b.updated -WHEN NOT MATCHED THEN INSERT * - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Insert-Only Merge for Deduplication --- MAGIC --- MAGIC A common ETL use case is to collect logs into Delta table by appending them to a table. However, often the sources can generate duplicate log records and downstream deduplication steps are needed to take care of them. With merge, you can avoid inserting the duplicate records. --- MAGIC --- MAGIC When merging new events parsed from `events_raw`, confirm that an identical record isn't already in the `events` table. --- MAGIC ``` --- MAGIC MERGE INTO target a --- MAGIC USING source b --- MAGIC ON a.data = b.data --- MAGIC WHEN NOT MATCHED THEN --- MAGIC INSERT * --- MAGIC ``` --- MAGIC By default, the merge operation searches the entire Delta table to find matches in the source table. One way to speed up merge is to reduce the search space by adding known constraints in the match condition. It will also reduce the chances of conflicts with other concurrent operations. --- MAGIC ``` --- MAGIC events.country = 'USA' AND events.date = current_date() - INTERVAL 7 DAYS --- MAGIC ``` - --- COMMAND ---------- - -MERGE INTO events a -USING events_update b -ON a.user_id = b.user_id AND a.event_timestamp = b.event_timestamp -WHEN NOT MATCHED AND b.traffic_source = 'email' THEN - INSERT * - --- COMMAND ---------- - --- MAGIC %md ## Load Incrementally --- MAGIC Use `COPY INTO` to incrementally load data from external systems. --- MAGIC - Data schema should be consistent --- MAGIC - Duplicate records should try to be excluded or handled downstream --- MAGIC - Potentially much cheaper than full table scan for data that grows predictably --- MAGIC - Leveraged by many data ingestion partners --- MAGIC --- MAGIC Update the `sales` delta table by incrementally loading data from an external location where a number of new transactions arrive during a 30 minute window. Each sale should only be recorded once, at the time that the transaction is processed. Use a method that allows idempotent execution to avoid processing data multiple times. - --- COMMAND ---------- - -COPY INTO sales -FROM "${c.source}/sales/sales-30m.parquet" -FILEFORMAT = PARQUET - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.6 - Cleaning Data.sql b/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.6 - Cleaning Data.sql deleted file mode 100644 index e374db3..0000000 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.6 - Cleaning Data.sql +++ /dev/null @@ -1,169 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md # Cleaning Data --- MAGIC --- MAGIC Apply a number of common transformations to clean data with Spark SQL. --- MAGIC --- MAGIC ##### Objectives --- MAGIC - Summarize datasets and describe null behaviors --- MAGIC - Retrieve and remove duplicates based on select columns --- MAGIC - Validate datasets for expected counts, missing values, and duplicate records --- MAGIC - Apply common transformations to clean and transform data - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-updates - --- COMMAND ---------- - --- MAGIC %md --- MAGIC We'll work with new users records in `users_update` table for this lesson. - --- COMMAND ---------- - -SELECT * FROM users_update - --- COMMAND ---------- - --- MAGIC %md ## Transformations --- MAGIC As we inspect and clean our data, we'll need to construct various column expressions and queries to express transformations to apply on our dataset. --- MAGIC - Column expressions are constructed from existing columns, operators, and built-in Spark SQL functions. They can be used in `SELECT` statements to express transformations that create new columns from datasets. --- MAGIC - Along with `SELECT`, many additional query commands can be used to express transformations in Spark SQL, including `WHERE`, `DISTINCT`, `ORDER BY`, `GROUP BY`, etc. - --- COMMAND ---------- - --- MAGIC %md ## Inspect Data --- MAGIC We'll inspect `users_update` for missing values and duplicate records. - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ### Missing Values --- MAGIC Count the number of missing values in each column of `users_update`. Note that nulls behave incorrectly in some math functions, including `count`. --- MAGIC --- MAGIC #### Null Behavior with `count()` --- MAGIC - `count(col)` skips `NULL` values when counting specific columns or expressions. --- MAGIC - `count(*)` is a special case that counts the total number of rows without skipping `NULL` values --- MAGIC --- MAGIC To count null values, use the `count_if` function or `WHERE` clause to provide a condition that filters for records where the value `IS NULL`. - --- COMMAND ---------- - -SELECT - count_if(user_id IS NULL) missing_user_ids, - count_if(user_first_touch_timestamp IS NULL) missing_timestamps, - count_if(email IS NULL) missing_emails -FROM users_update - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ### Distinct Records --- MAGIC - `DISTINCT *` returns rows with duplicates removed. --- MAGIC - `DISTINCT col` returns unique values in column `col` --- MAGIC --- MAGIC Note that the count for distinct users is greater than the count for distinct rows; rows containing `NULL` values were skipped from processing distinct rows. `count(*)` is the only case where `count()` includes records with `NULL` values. - --- COMMAND ---------- - -SELECT - count(user_id) total_ids, count(DISTINCT user_id) unique_ids, - count(email) total_emails, count(DISTINCT email) unique_emails, - count(*) total_rows, count(DISTINCT *) unique_non_null_rows -FROM users_update - --- COMMAND ---------- - --- MAGIC %md ## Deduplicate Rows --- MAGIC --- MAGIC Use `DISTINCT *` to remove true duplicate records (rows with same values for all columns). - --- COMMAND ---------- - -SELECT DISTINCT * FROM users_update - --- COMMAND ---------- - --- MAGIC %md #### Deduplicate Based on Specific Columns --- MAGIC --- MAGIC Use `GROUP BY` to remove duplicate records based on select columns. The query below groups rows by `user_id` to deduplicate rows based on values from this column. --- MAGIC --- MAGIC The `max()` aggregate function is used on the `email` column as a hack to capture non-null emails when multiple records are present. - --- COMMAND ---------- - -CREATE OR REPLACE TEMP VIEW deduped_users AS -SELECT user_id, user_first_touch_timestamp, max(email) email, max(updated) updated -FROM users_update -GROUP BY user_id, user_first_touch_timestamp; - -SELECT count(*) FROM deduped_users - --- COMMAND ---------- - --- MAGIC %md ## Validate Datasets --- MAGIC Let's check our datasets for expected counts, missing values, and duplicate records. --- MAGIC Validation can be performed with simple filters and `WHERE` clauses (and `COUNT_IF` statements). - --- COMMAND ---------- - --- MAGIC %md Validate that the `user_id` for each row is unique. - --- COMMAND ---------- - -SELECT max(row_count) <= 1 no_duplicate_ids FROM ( - SELECT user_id, count(*) row_count - FROM deduped_users - GROUP BY user_id) - --- COMMAND ---------- - --- MAGIC %md Confirm that each email is associated with at most one `user_id`. - --- COMMAND ---------- - -SELECT max(user_id_count) <= 1 at_most_one_id FROM ( - SELECT email, count(user_id) user_id_count - FROM deduped_users - WHERE email IS NOT NULL - GROUP BY email) - --- COMMAND ---------- - --- MAGIC %md ## Date Format and Regex --- MAGIC - Format datetimes as strings --- MAGIC - Use `regexp_extract` to extract domains from the email column using regex - --- COMMAND ---------- - -SELECT *, - date_format(first_touch, "MMM d, yyyy") first_touch_date, - date_format(first_touch, "HH:mm:ss") first_touch_time, - regexp_extract(email, "(?<=@)[^.]+(?=\.)", 0) email_domain -FROM ( - SELECT *, - CAST(user_first_touch_timestamp / 1e6 AS timestamp) first_touch - FROM deduped_users -) - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.7 - Reshaping Data.sql b/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.7 - Reshaping Data.sql deleted file mode 100644 index b5c7f27..0000000 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.7 - Reshaping Data.sql +++ /dev/null @@ -1,207 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC # Reshaping Data --- MAGIC --- MAGIC Combine datasets and lookup tables with various join types and strategies. --- MAGIC Reshape with pivot tables. --- MAGIC --- MAGIC ##### Objectives --- MAGIC - Combine datasets using different types of joins --- MAGIC - Join records to a pre-existing lookup table --- MAGIC - Examine and provide hints for join strategies --- MAGIC - Reshape data using pivot tables - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-cleaned - --- COMMAND ---------- - --- MAGIC %md ## Explode Arrays --- MAGIC Use the `explode` array function to explode arrays in the item field of events. - --- COMMAND ---------- - -SELECT *, explode(items) item FROM events - --- COMMAND ---------- - --- MAGIC %md ## Collect Arrays --- MAGIC Explode and flatten complex data using various array functions - --- COMMAND ---------- - -SELECT user_id, - flatten(collect_set(items.item_id)) cart_history, - collect_set(event_name) event_history -FROM (SELECT *, explode(items) item FROM events) -GROUP BY user_id - --- COMMAND ---------- - --- MAGIC %md ## Join a Lookup Table --- MAGIC --- MAGIC Lookup tables are normally small, historical tables used to enrich new data passing through an ETL pipeline. --- MAGIC --- MAGIC In this example, we will use a small lookup table to get details for each item sold by this retailer. --- MAGIC --- MAGIC **Inner Join:** Selects rows that have matching values in both relations. This is the default join in Spark SQL. - --- COMMAND ---------- - -CREATE OR REPLACE VIEW sales_enriched AS -SELECT * -FROM (SELECT *, explode(items) item FROM sales) a -INNER JOIN item_lookup b -ON a.item.item_id = b.item_id; - -SELECT * FROM sales_enriched - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Examine Join Strategy --- MAGIC Use the `EXPLAIN` command to view the physical plan used to execute the query. --- MAGIC Look for BroadcastHashJoin or BroadcastExchange. - --- COMMAND ---------- - -EXPLAIN FORMATTED -SELECT * FROM sales_enriched - --- COMMAND ---------- - --- MAGIC %md --- MAGIC By default, Spark performed a broadcast join rather than a shuffle join. That is, it broadcasted `item_lookup` to the larger `sales`, replicating the smaller dataset on each node of our cluster. This avoided having to move the larger dataset across the cluster. - --- COMMAND ---------- - --- MAGIC %md `autoBroadcastJoinThreshold` --- MAGIC --- MAGIC We can access configuration settings to take a look at the broadcast join threshold. This specifies the maximum size in bytes for a table that broadcasts to worker nodes. - --- COMMAND ---------- - -SET spark.sql.autoBroadcastJoinThreshold - --- COMMAND ---------- - --- MAGIC %md Re-examine physical plan when join is executed while broadcasting is disabled --- MAGIC 1. Drop threshold to `-1` to disable broadcasting --- MAGIC 1. Explain join --- MAGIC --- MAGIC Now notice the lack of broadcast in the query physical plan. - --- COMMAND ---------- - -SET spark.sql.autoBroadcastJoinThreshold=-1 - --- COMMAND ---------- - --- MAGIC %md Notice a sort merge join is performed, rather than a broadcast join. - --- COMMAND ---------- - -EXPLAIN FORMATTED -SELECT * FROM sales_enriched - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Broadcast Join Hint --- MAGIC Use a join hint to suggest broadcasting the lookup table for the join. --- MAGIC --- MAGIC The join side with this hint will be broadcast regardless of `autoBroadcastJoinThreshold`. - --- COMMAND ---------- - -EXPLAIN FORMATTED -SELECT /*+ BROADCAST(b) */ * -FROM (SELECT *, explode(items) item FROM sales) a -INNER JOIN item_lookup b -ON a.item.item_id = b.item_id - --- COMMAND ---------- - --- MAGIC %md Reset the original threshold. - --- COMMAND ---------- - -SET spark.sql.autoBroadcastJoinThreshold=10485760b - --- COMMAND ---------- - --- MAGIC %md ## Pivot Tables --- MAGIC The `PIVOT` clause is used for data perspective. We can get the aggregated values based on specific column values, which will be turned to multiple columns used in `SELECT` clause. The `PIVOT` clause can be specified after the table name or subquery. --- MAGIC --- MAGIC **`SELECT * FROM ()`**: The `SELECT` statement inside the parentheses is the input for this table. --- MAGIC --- MAGIC **`PIVOT`**: The first argument in the clause is an aggregate function and the column to be aggregated. Then, we specify the pivot column in the `FOR` subclause. The `IN` operator contains the pivot column values.
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC Use `PIVOT` to create a new `transactions` table that flattens out the information contained in the `sales` table and joins this with `users` table. --- MAGIC --- MAGIC Join these tables on email address, but without propagating this email address forward (to avoid potential PII exposure in downstream tables). - --- COMMAND ---------- - -CREATE OR REPLACE TABLE transactions AS - -SELECT * FROM ( - SELECT - user_id, - order_id, - transaction_timestamp, - total_item_quantity, - purchase_revenue_in_usd, - unique_items, - a.item.item_id item_id, - a.item.quantity quantity - FROM sales_enriched a - INNER JOIN users b - ON a.email = b.email -) PIVOT ( - sum(quantity) FOR item_id in ( - 'P_FOAM_K', - 'M_STAN_Q', - 'P_FOAM_S', - 'M_PREM_Q', - 'M_STAN_F', - 'M_STAN_T', - 'M_PREM_K', - 'M_PREM_F', - 'M_STAN_K', - 'M_PREM_T', - 'P_DOWN_S', - 'P_DOWN_K' - ) -) - --- COMMAND ---------- - -SELECT * FROM transactions - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.8 - Nested Data & Advanced Transformations.sql b/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.8 - Nested Data & Advanced Transformations.sql deleted file mode 100644 index 720f905..0000000 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.8 - Nested Data & Advanced Transformations.sql +++ /dev/null @@ -1,159 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md # Advanced Transformations --- MAGIC --- MAGIC Manipulate nested data and work with advanced functions in SQL. --- MAGIC --- MAGIC ##### Objectives --- MAGIC - Flatten nested data --- MAGIC - Apply advanced functions to transform data - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-transactions - --- COMMAND ---------- - --- MAGIC %md We will work with the `events_raw` table we loaded earlier from `event-kafka.json`. - --- COMMAND ---------- - -SELECT * FROM events_raw - --- COMMAND ---------- - --- MAGIC %md Earlier in the module, we merged a clean version of this new dataset, `events_update` into our original `events` dataset. The raw events data was cleaned by parsing and flattening the relevant JSON data in the `value` column, and then removing duplicate event records. We'll go through that process here. - --- COMMAND ---------- - --- MAGIC %md ## Parse JSON --- MAGIC --- MAGIC Parse JSON string with `from_json` and schema definition. - --- COMMAND ---------- - -CREATE OR REPLACE TEMP VIEW events_raw_json AS -SELECT from_json(cast(value as STRING), ("device STRING, ecommerce STRUCT< purchase_revenue_in_usd: DOUBLE, total_item_quantity: BIGINT, unique_items: BIGINT>, event_name STRING, event_previous_timestamp BIGINT, event_timestamp BIGINT, geo STRUCT< city: STRING, state: STRING>, items ARRAY< STRUCT< coupon: STRING, item_id: STRING, item_name: STRING, item_revenue_in_usd: DOUBLE, price_in_usd: DOUBLE, quantity: BIGINT>>, traffic_source STRING, user_first_touch_timestamp BIGINT, user_id STRING")) json -FROM events_raw; - --- COMMAND ---------- - --- MAGIC %md ## Flatten Nested Data --- MAGIC Deduplicate events by `user_id` and `event_timestamp`. --- MAGIC --- MAGIC Promote subfields with `json.*` to flatten data. - --- COMMAND ---------- - -CREATE OR REPLACE TEMP VIEW deduped_events AS -WITH deduped_events_raw AS ( - SELECT max(json) json FROM events_raw_json - GROUP BY json.user_id, json.event_timestamp -) -SELECT json.* FROM deduped_events_raw - --- COMMAND ---------- - --- MAGIC %md ## Higher Order Functions --- MAGIC Higher order functions in Spark SQL allow you to work directly with complex data types. When working with hierarchical data, records are frequently stored as array or map type objects. Higher-order functions allow you to transform data while preserving the original structure. --- MAGIC --- MAGIC Higher order functions include: --- MAGIC - `FILTER` filters an array using the given lambda function. --- MAGIC - `EXIST` tests whether a statement is true for one or more elements in an array. --- MAGIC - `TRANSFORM` uses the given lambda function to transform all elements in an array. --- MAGIC - `REDUCE` takes two lambda functions to reduce the elements of an array to a single value by merging the elements into a buffer, and the apply a finishing function on the final buffer. - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Filter --- MAGIC Remove items that are not king-sized from all records in our `items` column. We can use the `FILTER` function to create a new column that excludes that value from each array. --- MAGIC --- MAGIC **`FILTER (items, i -> i.item_id LIKE "%K") AS king_items`** --- MAGIC --- MAGIC In the statement above: --- MAGIC - **`FILTER`** : the name of the higher-order function
--- MAGIC - **`items`** : the name of our input array
--- MAGIC - **`i`** : the name of the iterator variable. You choose this name and then use it in the lambda function. It iterates over the array, cycling each value into the function one at a time.
--- MAGIC - **`->`** : Indicates the start of a function
--- MAGIC - **`i.item_id LIKE "%K"`** : This is the function. Each value is checked to see if it ends with the capital letter K. If it is, it gets filtered into the new column, `king_items` - --- COMMAND ---------- - --- filter for sales of only king sized items -SELECT - order_id, - items, - FILTER (items, i -> i.item_id LIKE "%K") AS king_items -FROM sales - --- COMMAND ---------- - --- MAGIC %md --- MAGIC You may write a filter that produces a lot of empty arrays in the created column. When that happens, it can be useful to use a `WHERE` clause to show only non-empty array values in the returned column. --- MAGIC --- MAGIC In this example, we accomplish that by using a subquery (a query within a query). They are useful for performing an operation in multiple steps. In this case, we're using it to create the named column that we will use with a `WHERE` clause. - --- COMMAND ---------- - -CREATE OR REPLACE TEMP VIEW king_size_sales AS - -SELECT order_id, king_items -FROM ( - SELECT - order_id, - FILTER (items, i -> i.item_id LIKE "%K") AS king_items - FROM sales) -WHERE size(king_items) > 0; - -SELECT * FROM king_size_sales - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Transform --- MAGIC Built-in functions are designed to operate on a single, simple data type within a cell; they cannot process array values. `TRANSFORM` can be particularly useful when you want to apply an existing function to each element in an array. --- MAGIC --- MAGIC Compute the total revenue from king-sized items per order. --- MAGIC --- MAGIC **`TRANSFORM(king_items, k -> CAST(k.item_revenue_in_usd * 100 AS INT)) AS item_revenues`** --- MAGIC --- MAGIC In the statement above, for each value in the input array, we extract the item's revenue value, multiply it by 100, and cast the result to integer. Note that we're using the same kind as references as in the previous command, but we name the iterator with a new variable, **`k`**. - --- COMMAND ---------- - --- get total revenue from king items per order -CREATE OR REPLACE TEMP VIEW king_item_revenues AS - -SELECT - order_id, - king_items, - TRANSFORM ( - king_items, - k -> CAST(k.item_revenue_in_usd * 100 AS INT) - ) AS item_revenues -FROM king_size_sales; - -SELECT * FROM king_item_revenues - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.9 - Views in the Lakehouse.sql b/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.9 - Views in the Lakehouse.sql deleted file mode 100644 index 01c4908..0000000 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.9 - Views in the Lakehouse.sql +++ /dev/null @@ -1,144 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md # Views in the Lakehouse --- MAGIC --- MAGIC Register views to create stable, secure queries in the Lakehouse. --- MAGIC --- MAGIC In this lesson, we'll give a quick overview of how stored views are created and managed. --- MAGIC --- MAGIC --- MAGIC Rather than trying to capture every possible metric in our view, we'll create a summary of values that might be of interest to our analysts. --- MAGIC --- MAGIC 0. Views as saved queries --- MAGIC 0. Aliasing tables to views --- MAGIC 0. Dynamic views - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-transactions - --- COMMAND ---------- - --- MAGIC %md ## Views as saved queries --- MAGIC --- MAGIC A Spark DataFrame and a view are nearly identical constructs. By calling `EXPLAIN` on our DataFrame, we can see that our source table is a set of instructions to deserialize the files containing our data. - --- COMMAND ---------- - -EXPLAIN FORMATTED SELECT * FROM transactions - --- COMMAND ---------- - --- MAGIC %md ## Aliasing Tables to Views --- MAGIC --- MAGIC #### Examine Clickpath Data --- MAGIC Define logic that creates or updates a table that aggregates the number of times each user took a particular action and then join this information with the flattened view of transactions created earlier. This will combine data from the `events` and `transactions` tables in order to create a record of all actions a user took on the site and what their final order looked like. --- MAGIC --- MAGIC This `clickpaths` view should contain all the fields from your `transactions` table, as well as a count of every `event_name` in its own column. Each user that completed a purchase should have a single row in the final table. - --- COMMAND ---------- - -CREATE OR REPLACE VIEW clickpaths AS - WITH user_event_counts AS ( - SELECT * FROM ( - SELECT user_id user, event_name - FROM events - ) PIVOT ( count(*) FOR event_name IN ( - "cart", "pillows", "login", "main", "careers", "guest", "faq", "down", "warranty", "finalize", - "register", "shipping_info", "checkout", "mattresses", "add_item", "press", "email_coupon", - "cc_info", "foam", "reviews", "original", "delivery", "premium" ))) -SELECT * -FROM user_event_counts a -JOIN transactions b - ON a.user = b.user_id; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC We can see that our view is simply storing the Spark plan for our query. - --- COMMAND ---------- - -EXPLAIN FORMATTED SELECT * FROM clickpaths - --- COMMAND ---------- - --- MAGIC %md --- MAGIC When we execute a query against this view, we will process the plan to generate the logically correct result. --- MAGIC --- MAGIC Note that while the data may end up in the Delta Cache, this result is not guaranteed to be persisted, and is only cached for the currently active cluster. - --- COMMAND ---------- - -SELECT * FROM clickpaths WHERE login = True - --- COMMAND ---------- - -SELECT * -FROM clickpaths -WHERE user_id = "UA000000102360871" - --- COMMAND ---------- - --- MAGIC %md ## Dynamic Views --- MAGIC Databricks dynamic views allow user or group identity ACLs to be applied to data at the column (or row) level. --- MAGIC --- MAGIC Database administrators can configure data access privileges to disallow access to a source table and only allow users to query a redacted view. Users with sufficient privileges will be able to see all fields, while restricted users will be shown arbitrary results, as defined at view creation. - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Consider our `sales` table with the following columns. - --- COMMAND ---------- - -DESCRIBE sales - --- COMMAND ---------- - -CREATE OR REPLACE VIEW sales_view AS -SELECT - order_id, - transaction_timestamp, - CASE - WHEN is_member('ade_demo') THEN email - ELSE 'REDACTED' - END AS email, - CASE - WHEN is_member('ade_demo') THEN purchase_revenue_in_usd - ELSE 'REDACTED' - END AS purchase_revenue_in_usd -FROM sales - --- COMMAND ---------- - -SELECT * FROM sales_view - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Now when we query from `sales_view`, only members of the group `ade_demo` will be able to see results in plain text. --- MAGIC --- MAGIC **NOTE:** You may not have privileges to create groups or assign membership. Your instructor should be able to demonstrate how group membership will change query results. - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.1.3L - Databases, Tables & Views Lab.sql b/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.1.3L - Databases, Tables & Views Lab.sql deleted file mode 100644 index 59aa522..0000000 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.1.3L - Databases, Tables & Views Lab.sql +++ /dev/null @@ -1,340 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC # Databases, Tables, and Views Lab --- MAGIC --- MAGIC ## Learning Objectives --- MAGIC **In this lab, you will create and explore interactions between various relational entities, including:** --- MAGIC --- MAGIC - Databases --- MAGIC - Tables (managed and external) --- MAGIC - Views (views, temp views, and global temp views) --- MAGIC --- MAGIC **Resources** --- MAGIC * [Databases and Tables - Databricks Docs](https://docs.databricks.com/user-guide/tables.html) --- MAGIC * [Managed and Unmanaged Tables](https://docs.databricks.com/user-guide/tables.html#managed-and-unmanaged-tables) --- MAGIC * [Creating a Table with the UI](https://docs.databricks.com/user-guide/tables.html#create-a-table-using-the-ui) --- MAGIC * [Create a Local Table](https://docs.databricks.com/user-guide/tables.html#create-a-local-table) --- MAGIC * [Saving to Persistent Tables](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html#saving-to-persistent-tables) - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ### Getting Started --- MAGIC --- MAGIC Run the following cell to configure variables and datasets for this lesson. - --- COMMAND ---------- - --- MAGIC %run ../../Includes/setup-meta - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Overview of the Data --- MAGIC --- MAGIC The data include multiple entries from a selection of weather stations, including average temperatures recorded in either Fahrenheit or Celsius. The schema for the table: --- MAGIC --- MAGIC |ColumnName | DataType| Description| --- MAGIC |------------|---------|------------| --- MAGIC |NAME |string | Station name | --- MAGIC |STATION |string | Unique ID | --- MAGIC |LATITUDE |float | Latitude | --- MAGIC |LONGITUDE |float | Longitude | --- MAGIC |ELEVATION |float | Elevation | --- MAGIC |DATE |date | YYYY-MM-DD | --- MAGIC |UNIT |string | Temperature units | --- MAGIC |TAVG |float | Average temperature | --- MAGIC --- MAGIC This data is stored in the Parquet format; preview the data with the query below. - --- COMMAND ---------- - -SELECT * -FROM parquet.`${c.userhome}/datasets/weather` - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Create a Database --- MAGIC --- MAGIC Create a database in the default location using the `{c.database}` variable defined in setup script. - --- COMMAND ---------- - --- TODO - - ${c.database} - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Change to Your New Database --- MAGIC --- MAGIC `USE` your newly created database. - --- COMMAND ---------- - --- TODO - - ${c.database} - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Create a Managed Table --- MAGIC Use a CTAS statement to create a managed table named `weather_managed`. - --- COMMAND ---------- - --- TODO - - -SELECT * -FROM parquet.`${c.userhome}/datasets/weather` - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Create an External Table --- MAGIC --- MAGIC Recall that an external table differs from a managed table through specification of a location. Create an external table called `weather_external` below. - --- COMMAND ---------- - --- TODO - - -LOCATION "${c.userhome}/lab/external" -AS SELECT * -FROM parquet.`${c.userhome}/datasets/weather` - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Examine Table Details --- MAGIC Use the SQL command `DESCRIBE EXTENDED table_name` to examine the two weather tables. - --- COMMAND ---------- - -DESCRIBE EXTENDED weather_managed - --- COMMAND ---------- - -DESCRIBE EXTENDED weather_external - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Run the following helper code to extract and compare the table locations. - --- COMMAND ---------- - --- MAGIC %python --- MAGIC def getTableLocation(tableName): --- MAGIC return spark.sql(f"DESCRIBE EXTENDED {tableName}").select("data_type").filter("col_name = 'Location'").first()[0] - --- COMMAND ---------- - --- MAGIC %python --- MAGIC managedTablePath = getTableLocation("weather_managed") --- MAGIC externalTablePath = getTableLocation("weather_external") --- MAGIC --- MAGIC print(f"""The weather_managed table is saved at: --- MAGIC --- MAGIC {managedTablePath} --- MAGIC --- MAGIC The weather_external table is saved at: --- MAGIC --- MAGIC {externalTablePath}""") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC List the contents of these directories to confirm that data exists in both locations. - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.ls(managedTablePath) - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.ls(externalTablePath) - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ### Check Directory Contents after Dropping Database and All Tables --- MAGIC The `CASCADE` keyword will accomplish this. --- MAGIC --- MAGIC **NOTE**: You will encounter an error when listing your `managedTablePath` - --- COMMAND ---------- - --- TODO - - ${c.database} - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.ls(managedTablePath) - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.ls(externalTablePath) - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.ls(userhome) - --- COMMAND ---------- - --- MAGIC %md --- MAGIC **This highlights the main differences between managed and external tables.** By default, the files associated with managed tables will be stored to this location on the root DBFS storage linked to the workspace, and will be deleted when a table is dropped. --- MAGIC --- MAGIC Files for external tables will be persisted in the location provided at table creation, preventing users from inadvertently deleting underlying files. **External tables can easily be migrated to other databases or renamed, but these operations with managed tables will require rewriting ALL underlying files.** - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Create a Database with a Specified Path --- MAGIC --- MAGIC Assuming you dropped your database in the last step, you can use the same `database` name. - --- COMMAND ---------- - -CREATE DATABASE ${c.database} LOCATION '${c.userhome}/${c.database}' - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Recreate your `weather_managed` table in this new database and print out the location of this table. - --- COMMAND ---------- - --- TODO - - - --- COMMAND ---------- - --- MAGIC %python --- MAGIC getTableLocation("weather_managed") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC While here we're using the `userhome` directory created on the DBFS root, _any_ object store can be used as the database directory. **Defining database directories for groups of users can greatly reduce the chances of accidental data exfiltration**. - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Views and their Scoping --- MAGIC --- MAGIC Using the provided `AS` clause, register: --- MAGIC - a view named `celsius` --- MAGIC - a temporary view named `celsius_temp` --- MAGIC - a global temp view named `celsius_global` - --- COMMAND ---------- - --- TODO - - -AS (SELECT * - FROM weather_managed - WHERE UNIT = "C") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Now create a temporary view. - --- COMMAND ---------- - --- TODO - - -AS (SELECT * - FROM weather_managed - WHERE UNIT = "C") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Now register a global temp view. - --- COMMAND ---------- - --- TODO - - -AS (SELECT * - FROM weather_managed - WHERE UNIT = "C") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Views will be displayed alongside tables when listing from the catalog. - --- COMMAND ---------- - -SHOW TABLES - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Note the following: --- MAGIC - The view is associated with the current database. This view will be available to any user that can access this database and will persist between sessions. --- MAGIC - The temp view is not associated with any database. The temp view is ephemeral and is only accessible in the current SparkSession. --- MAGIC - The global temp view does not appear in our catalog. **Global temp views will always register to the `global_temp` database**. The `global_temp` database is ephemeral but tied to the lifetime of the cluster; however, it is only accessible by notebooks attached to the same cluster on which it was created. - --- COMMAND ---------- - -SELECT * FROM global_temp.celsius_global - --- COMMAND ---------- - --- MAGIC %md --- MAGIC While no job was triggered when defining these views, a job is triggered _each time_ a query is executed against the view. - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Clean Up --- MAGIC Drop the database and all tables to clean up your workspace. - --- COMMAND ---------- - -DROP DATABASE ${c.database} CASCADE - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Synopsis --- MAGIC --- MAGIC In this lab we: --- MAGIC - Created and deleted databases --- MAGIC - Explored behavior of managed and external tables --- MAGIC - Learned about the scoping of views - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.3.3L - Python for SQL Lab.py b/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.3.3L - Python for SQL Lab.py deleted file mode 100644 index 3267c26..0000000 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.3.3L - Python for SQL Lab.py +++ /dev/null @@ -1,232 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Just Enough Python for Databricks SQL Lab -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, students should be able to: -# MAGIC * Review basic Python code and describe expected outcomes of code execution -# MAGIC * Reason through control flow statements in Python functions -# MAGIC * Add parameters to a SQL query by wrapping it in a Python function - -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Reviewing Python Basics -# MAGIC -# MAGIC In the previous notebook, we briefly explored using `spark.sql()` to execute arbitrary SQL commands from Python. -# MAGIC -# MAGIC Look at the following 3 cells. Before executing each cell, identify: -# MAGIC 1. The expected output of cell execution -# MAGIC 1. What logic is being executed -# MAGIC 1. Changes to the resultant state of the environment -# MAGIC -# MAGIC Then execute the cells, compare the results to your expectations, and see the explanations below. - -# COMMAND ---------- - -course = "python_for_sql" - -# COMMAND ---------- - -spark.sql(f"SELECT '{course}'") - -# COMMAND ---------- - -display(spark.sql(f"SELECT '{course}'")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC 1. `Cmd 3` assigns a string to a variable. When a variable assignment is successful, no output is displayed to the notebook. A new variable is added to the current execution environment. -# MAGIC 1. `Cmd 4` executes a SQL query and returns the results as a DataFrame. In this case, the SQL query is just to select a string, so no changes to our environment occur. When a returned DataFrame is not captured, the schema for the DataFrame is displayed alongside the word `DataFrame`. -# MAGIC 1. `Cmd 5` executes the same SQL query and displays the returned DataFrame. This combination of `display()` and `spark.sql()` most closely mirrors executing logic in a `%sql` cell; the results will always be printed in a formatted table, assuming results are returned by the query; some queries will instead manipulate tables or databases, in which case the work `OK` will print to show successful execution. In this case, no changes to our environment occur from running this code. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Setting Up a Development Environment -# MAGIC -# MAGIC Throughout this course, we use logic similar to the follow cell to capture information about the user currently executing the notebook and create an isolated development database. -# MAGIC -# MAGIC The `re` library is the [standard Python library for regex](https://docs.python.org/3/library/re.html). -# MAGIC -# MAGIC Databricks SQL has a special method to capture the username of the `current_user()`; and the `.first()[0]` code is a quick hack to capture the first row of the first column of a query executed with `spark.sql()` (in this case, we do this safely knowing that there will only be 1 row and 1 column). -# MAGIC -# MAGIC All other logic below is just string formatting. - -# COMMAND ---------- - -import re - -username = spark.sql("SELECT current_user()").first()[0] -userhome = f"dbfs:/user/{username}/{course}" -database = f"""{course}_{re.sub("[^a-zA-Z0-9]", "_", username)}_db""" - -print(f""" -username: {username} -userhome: {userhome} -database: {database} -""") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Below, we add a simple control flow statement to this logic to create and use this user-specific database. Optionally, we will reset this database and drop all of the contents on repeat execution. (Note the the default mode is `"reset"`). - -# COMMAND ---------- - -def create_database(course, mode="reset"): - import re - - username = spark.sql("SELECT current_user()").first()[0] - userhome = f"dbfs:/user/{username}/{course}" - database = f"""{course}_{re.sub("[^a-zA-Z0-9]", "_", username)}_db""" - - print(f""" - username: {username} - userhome: {userhome} - database: {database} - """) - - if mode == "reset": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - spark.sql(f""" - CREATE DATABASE IF NOT EXISTS {database} - LOCATION '{userhome}' - """) - spark.sql(f"USE {database}") - -create_database(course) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC While this logic as defined is geared toward isolating students in shared workspaces for instructional purposes, the same basic design could be leveraged for testing new logic in an isolated environment before pushing to production. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Handling Errors Gracefully -# MAGIC -# MAGIC Review the logic in the function below. -# MAGIC -# MAGIC Note that we've just declared a new database that currently contains no tables. - -# COMMAND ---------- - -def query_or_make_demo_table(table): - try: - display(spark.sql(f"SELECT * FROM {table}")) - except: - spark.sql(f""" - CREATE TABLE {table} - (id INT, name STRING, value DOUBLE, state STRING) - """) - spark.sql(f""" - INSERT INTO {table} - VALUES (1, "Yve", 1.0, "CA"), - (2, "Omar", 2.5, "NY"), - (3, "Elia", 3.3, "OH"), - (4, "Rebecca", 4.7, "TX"), - (5, "Ameena", 5.3, "CA"), - (6, "Ling", 6.6, "NY"), - (7, "Pedro", 7.1, "KY") - """) - display(spark.sql(f"SELECT * FROM {table}")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Try to identify the following before executing the next cell: -# MAGIC 1. The expected output of cell execution -# MAGIC 1. What logic is being executed -# MAGIC 1. Changes to the resultant state of the environment - -# COMMAND ---------- - -query_or_make_demo_table("demo_table") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Now answer the same three questions before running the same query below. - -# COMMAND ---------- - -query_or_make_demo_table("demo_table") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC - On the first execution, the table `demo_table` did not yet exist. As such, the attempt to return the contents of the table created an error, which resulted in our `except` block of logic executing. This block: -# MAGIC 1. Created the table -# MAGIC 1. Inserted values -# MAGIC 1. Returned the contents of the table -# MAGIC - On the second execution, the table `demo_table` already exists, and so the first query in the `try` block executes without error. As a result, we just display the results of the query without modifying anything in our environment. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Adapting SQL to Python -# MAGIC Let's consider the following SQL query against our demo table created above. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT id, value -# MAGIC FROM demo_table -# MAGIC WHERE state = "CA" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's use this simple example to practice creating a Python function that adds optional functionality. -# MAGIC -# MAGIC Our target function will: -# MAGIC * Always return only the `id` and `value` column from the a table named `demo_table` -# MAGIC * Allow filtering results by state, but default to all states -# MAGIC * Optionally return the query result object (a PySpark DataFrame) -# MAGIC -# MAGIC Stretch Goal: -# MAGIC * Add logic to check that if the value passed for the `state` filter contains two uppercase letters -# MAGIC -# MAGIC Some starter logic has been provided below. - -# COMMAND ---------- - -# TODO -def preview_values(state=None, return_results=False): - query = - if state is not None: - - -# COMMAND ---------- - -# MAGIC %md -# MAGIC The assert statements below can be used to check whether or not your function works as intended. - -# COMMAND ---------- - -import pyspark.sql.dataframe - -assert preview_values(return_results=True).columns == ["id", "value"], "Query should only return `id` and `value` columns" -assert preview_values() == None, "Function should not return anything by default" -assert type(preview_values(return_results=True)) == pyspark.sql.dataframe.DataFrame, "Function should optionally return the DataFrame results" -assert preview_values(state="OH", return_results=True).first()[0] == 3, "Function should allow filtering by state" - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.1.1 - Structured Streaming Concepts.py b/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.1.1 - Structured Streaming Concepts.py deleted file mode 100644 index 6d9fa0f..0000000 --- a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.1.1 - Structured Streaming Concepts.py +++ /dev/null @@ -1,559 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Structured Streaming Concepts -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, you should be able to: -# MAGIC * Describe the programming model used by Spark Structured Streaming -# MAGIC * Configure required options to perform a streaming read on a source -# MAGIC * Describe the requirements for end-to-end fault tolerance -# MAGIC * Configure required options to perform a streaming write to a sink -# MAGIC * Interact with streaming queries and stop active streams -# MAGIC -# MAGIC ## Datasets Used -# MAGIC The source contains smartphone accelerometer samples from devices and users with the following columns: -# MAGIC -# MAGIC | Field | Description | -# MAGIC | ------------- | ----------- | -# MAGIC | Arrival_Time | time data was received | -# MAGIC | Creation_Time | event time | -# MAGIC | Device | type of Model | -# MAGIC | Index | unique identifier of event | -# MAGIC | Model | i.e Nexus4 | -# MAGIC | User | unique user identifier | -# MAGIC | geolocation | city & country | -# MAGIC | gt | transportation mode | -# MAGIC | id | unused null field | -# MAGIC | x | acceleration in x-dir | -# MAGIC | y | acceleration in y-dir | -# MAGIC | z | acceleration in z-dir | - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Getting Started -# MAGIC -# MAGIC Run the following cell to configure our "classroom." - -# COMMAND ---------- - -# MAGIC %run ../Includes/classic-setup $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Micro-Batches as a Table -# MAGIC -# MAGIC For more information, see the analogous section in the [Structured Streaming Programming Guide](http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#basic-concepts) (from which several images have been borrowed). -# MAGIC -# MAGIC Spark Structured Streaming approaches streaming data by modeling it as a series of continuous appends to an unbounded table. While similar to defining **micro-batch** logic, this model allows incremental queries to be defined against streaming sources as if they were static input (though the fact that the input is an unbounded tables does impose some constraints). -# MAGIC -# MAGIC -# MAGIC -# MAGIC ### Basic Concepts -# MAGIC -# MAGIC - The developer defines an **input table** by configuring a streaming read against a **source**. The syntax for doing this is similar to working with static data. -# MAGIC - A **query** is defined against the input table. Both the DataFrames API and Spark SQL can be used to easily define transformations and actions against the input table. -# MAGIC - This logical query on the input table generates the **results table**. The results table contains the incremental state information of the stream. -# MAGIC - The **output** of a streaming pipeline will persist updates to the results table by writing to an external **sink**. Generally, a sink will be a durable system such as files or a pub/sub messaging bus. -# MAGIC - New rows are appended to the input table for each **trigger interval**. These new rows are essentially analogous to micro-batch transactions and will be automatically propagated through the results table to the sink. -# MAGIC -# MAGIC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## End-to-end Fault Tolerance -# MAGIC -# MAGIC Structured Streaming ensures end-to-end exactly-once fault-tolerance guarantees through _checkpointing_ (discussed below) and Write Ahead Logs. -# MAGIC -# MAGIC Structured Streaming sources, sinks, and the underlying execution engine work together to track the progress of stream processing. If a failure occurs, the streaming engine attempts to restart and/or reprocess the data. -# MAGIC For best practices on recovering from a failed streaming query see docs. -# MAGIC -# MAGIC This approach _only_ works if the streaming source is replayable; replayable sources include cloud-based object storage and pub/sub messaging services. -# MAGIC -# MAGIC At a high level, the underlying streaming mechanism relies on a couple approaches: -# MAGIC -# MAGIC * First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval. -# MAGIC * Next, the streaming sinks are designed to be _idempotent_—that is, multiple writes of the same data (as identified by the offset) do _not_ result in duplicates being written to the sink. -# MAGIC -# MAGIC Taken together, replayable data sources and idempotent sinks allow Structured Streaming to ensure **end-to-end, exactly-once semantics** under any failure condition. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Reading a Stream -# MAGIC -# MAGIC The `spark.readStream()` method returns a `DataStreamReader` used to configure and query the stream. -# MAGIC -# MAGIC Configuring a streaming read on a source requires: -# MAGIC * The schema of the data -# MAGIC * **NOTE**: Some streaming sources allow for schema inference -# MAGIC * The `format` of the source file format or named connector -# MAGIC * **NOTE**: `delta` is the default format for all reads and writes in Databricks -# MAGIC * Additional source-specific configuration options. For example: -# MAGIC * [`cloudFiles`](https://docs.databricks.com/spark/latest/structured-streaming/auto-loader-s3.html) -# MAGIC * Kafka -# MAGIC * The name of the source table or the location of the files in object storage -# MAGIC -# MAGIC Below, we define a streaming read against a source (represented by `dataSource`) consisting of files from cloud storage. -# MAGIC -# MAGIC **NOTE**: We can think of this `DataStreamReader` as an incremental temp view defined against an ever-appending source table. Just as with a temp view, we only store the query plan when we set up an incremental read. It's not until we query results that we'll see compute happen. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ### The Schema -# MAGIC -# MAGIC Working with `cloudFiles` allows Databricks to automatically infer the schema from most file sources. -# MAGIC -# MAGIC Once data is loaded into a Delta Lake table, all schema for downstream incremental reads will be grabbed automatically from the table metadata. -# MAGIC -# MAGIC Here, we'll provide an explicit schema for our data. - -# COMMAND ---------- - -schema = """Arrival_Time BIGINT, - Creation_Time BIGINT, - Device STRING, - Index BIGINT, - Model STRING, - User STRING, - geolocation STRUCT< - city: STRING, - country: STRING>, - gt STRING, - Id BIGINT, - x DOUBLE, - y DOUBLE, - z DOUBLE""" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Creating a Streaming Temp View -# MAGIC -# MAGIC Below we pull all of the above concepts together to define a streaming read. -# MAGIC -# MAGIC If we were continuing to build out our query with PySpark, we would capture this as a DataFrame. Instead, we use `createOrReplaceTempView` to create an entity that can be queried locally with SQL. - -# COMMAND ---------- - -(spark - .readStream - .schema(schema) - .format("cloudFiles") - .option("cloudFiles.format", "json") - .load(dataSource) - .createOrReplaceTempView("streaming_tmp_vw") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Comparing to Static Reads -# MAGIC -# MAGIC The above logic provides us with more or less the same result as the static query below. - -# COMMAND ---------- - -spark.sql(f"CREATE OR REPLACE TEMP VIEW static_tmp_vw AS SELECT * FROM json.`{dataSource}`") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC When we query a static read on data, we display the results of the query at a point in time. -# MAGIC -# MAGIC **NOTE**: The `display(spark.table())` pattern shown in the next cell is the same as executing a `SELECT * FROM` for a table or view. Later, we'll see that this allows us to pass streaming temp views back to the DataFrame API to write out a stream. - -# COMMAND ---------- - -display(spark.table("static_tmp_vw")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC But when we execute a query on a streaming temporary view, we'll continue to update the results of the query as new data arrives in the source. -# MAGIC -# MAGIC Think of a query executed against a streaming temp view as an **always-on incremental query**. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT * FROM streaming_tmp_vw - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Before continuing, click `Stop Execution` at the top of the notebook, `Cancel` immediately under the cell, or run the following cell to stop all active streaming queries. - -# COMMAND ---------- - -for s in spark.streams.active: - print("Stopping " + s.id) - s.stop() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Working with Streaming Data -# MAGIC We can execute most transformation against streaming temp views the same way we would with static data. Here, we'll run a simple aggregation to get counts of records for each `device`. -# MAGIC -# MAGIC Because we are querying a streaming temp view, this becomes a streaming query that executes indefinitely, rather than completing after retrieving a single set of results. For streaming queries like this, Databricks Notebooks include interactive dashboards that allow users to monitor streaming performance. Explore this below. -# MAGIC -# MAGIC ![](https://files.training.databricks.com/images/adbcore/streaming-dashboard.png) -# MAGIC -# MAGIC One important note regarding this example: this is merely displaying an aggregation of input as seen by the stream. **None of these records are being persisted anywhere at this point.** - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT device, COUNT(device) AS total_records -# MAGIC FROM streaming_tmp_vw -# MAGIC GROUP BY device - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Before continuing, click `Stop Execution` at the top of the notebook, `Cancel` immediately under the cell, or run the following cell to stop all active streaming queries. - -# COMMAND ---------- - -for s in spark.streams.active: - print("Stopping " + s.id) - s.stop() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Persisting Streaming Results -# MAGIC -# MAGIC In order to persist incremental results, we need to pass our logic back to the PySpark Structured Streaming DataFrames API. -# MAGIC -# MAGIC Above, we created a temp view from a PySpark streaming DataFrame. If we create another temp view from the results of a query against a streaming temp view, we'll again have a streaming temp view. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC CREATE OR REPLACE TEMP VIEW device_counts_tmp_vw AS ( -# MAGIC SELECT device, COUNT(device) AS total_records -# MAGIC FROM streaming_tmp_vw -# MAGIC GROUP BY device -# MAGIC ) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Writing a Stream -# MAGIC -# MAGIC To persist the results of a streaming query, we must write them out to durable storage. The `DataFrame.writeStream` method returns a `DataStreamWriter` used to configure the output. -# MAGIC -# MAGIC There are a number of required parameters to configure a streaming write: -# MAGIC * The `format` of the **output sink**; see documentation -# MAGIC * The location of the **checkpoint directory** -# MAGIC * The **output mode** -# MAGIC * Configurations specific to the output sink, such as: -# MAGIC * Kafka -# MAGIC * A custom sink via `writeStream.foreach(...)` -# MAGIC -# MAGIC Once the configuration is completed, we trigger the job with a call to `.table()`. If we didn't want to create a table and instead wanted to write directly to storage, we would use `.start()` instead. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Checkpointing -# MAGIC -# MAGIC Databricks creates checkpoints by storing the current state of your streaming job to cloud storage. -# MAGIC -# MAGIC Checkpointing combines with write ahead logs to allow a terminated stream to be restarted and continue from where it left off. -# MAGIC -# MAGIC Checkpoints cannot be shared between separate streams. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Output Modes -# MAGIC -# MAGIC Streaming jobs have output modes similar to static/batch workloads. [More details here](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#output-modes). -# MAGIC -# MAGIC | Mode | Example | Notes | -# MAGIC | ------------- | ----------- | --- | -# MAGIC | **Append** | `.outputMode("append")` | Only the new rows appended to the Result Table since the last trigger are written to the sink. This is the default. | -# MAGIC | **Complete** | `.outputMode("complete")` | The entire updated Result Table is written to the sink. The individual sink implementation decides how to handle writing the entire table. | -# MAGIC | **Update** | `.outputMode("update")` | Only the rows in the Result Table that were updated since the last trigger will be outputted to the sink.| -# MAGIC -# MAGIC **NOTE**: Not all sinks will support `update` mode. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ### Defining the Trigger Interval -# MAGIC -# MAGIC When defining a streaming write, the `trigger` method specifies when the system should process the next set of data.. -# MAGIC -# MAGIC | Trigger Type | Example | Notes | -# MAGIC |----------------------------------------|-----------|-------------| -# MAGIC | Unspecified | | The query will be executed as soon as the system has completed processing the previous query (this is the default) | -# MAGIC | Fixed interval micro-batches | `.trigger(processingTime="2 minutes")` | The query will be executed in micro-batches and kicked off at the user-specified intervals | -# MAGIC | One-time micro-batch | `.trigger(once=True)` | The query will execute _only one_ micro-batch to process all the available data and then stop on its own | -# MAGIC | Continuous w/fixed checkpoint interval | `.trigger(continuous="1 second")` | The query will be executed in a low-latency, continuous processing mode. _EXPERIMENTAL_ | -# MAGIC -# MAGIC Note that triggers are specified when defining how data will be written to a sink and control the frequency of micro-batches. By default, Spark will automatically detect and process all data in the source that has been added since the last trigger; some sources allow configuration to limit the size of each micro-batch. -# MAGIC -# MAGIC Read this blog post to learn more about using one-time triggers to simplify CDC with a hybrid streaming/batch design. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Pulling It All Together -# MAGIC -# MAGIC The code below demonstrates using `spark.table()` to load data from a streaming temp view back to a DataFrame. Note that Spark will always load streaming views as a streaming DataFrame and static views as static DataFrames (meaning that incremental processing must be defined with read logic to support incremental writing). - -# COMMAND ---------- - -streamingQuery = (spark.table("device_counts_tmp_vw") - .writeStream - .option("checkpointLocation", checkpointPath) - .outputMode("complete") - .trigger(processingTime='10 seconds') - .table("device_counts") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Querying the Output -# MAGIC Now let's query the output we've written from SQL. Because the result is a table, we only need to deserialize the data to return the results. -# MAGIC -# MAGIC Because we are now querying a table (not a streaming DataFrame), the following will **not** be a streaming query. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT * -# MAGIC FROM device_counts - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Debugging with the Memory Sink -# MAGIC -# MAGIC The **memory** sink can be a useful tool for debugging. It provides a quick and easy sink requiring no setup. The output is stored as an in-memory table, with a name defined using `queryName`. -# MAGIC -# MAGIC This should be used only for debugging purposes with low data volumes, since the entire output is collected and stored in the driver’s memory. - -# COMMAND ---------- - -streamingQueryMem = (spark.table("streaming_tmp_vw") - .writeStream - .format("memory") # memory = store in-memory table (for testing only) - .queryName("streaming_query_mem") # name of the in-memory table - .outputMode("append") - .start() -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's examine the contents of the in-memory table with the same query used previously. Like the previous query we ran against the Delta output, this will **not** be a streaming query. In this case, we are simply querying the in-memory table established by the memory sink in the previous cell. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT device, COUNT(device) AS total_records -# MAGIC FROM streaming_query_mem -# MAGIC GROUP BY device - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Interacting with Streaming Queries -# MAGIC -# MAGIC -# MAGIC the logic defined above, data is read from JSON files and then saved out in the Delta Lake format. Note that because Delta creates a new version for each transaction, when working with streaming data this will mean that the Delta table creates a new version for each trigger interval in which new data is processed. [More info on streaming with Delta](https://docs.databricks.com/delta/delta-streaming.html#table-streaming-reads-and-writes). - -# COMMAND ---------- - -# MAGIC %md -# MAGIC The `recentProgress` attribute allows access to metadata about recently processed micro-batches. Let's dump the contents for the streaming query created earlier. - -# COMMAND ---------- - -streamingQuery.recentProgress - -# COMMAND ---------- - -# MAGIC %md -# MAGIC In addition to referencing `StreamingQuery` objects returned by `writeStream`, as we did above, we can iterate on the `streams.active` attribute in `SparkSession` to identify all active streaming queries. - -# COMMAND ---------- - -for s in spark.streams.active: # Iterate over all streams - print(s.id) # Print the stream's id - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's iterate on all active streams and stop them. This is an important thing to do here, for if you don't then your cluster will run indefinitely! -# MAGIC -# MAGIC After running the following cell, feel free to examine the cells earlier that initiated streaming queries; notice they have both been canceled. - -# COMMAND ---------- - -for s in spark.streams.active: - print("Stopping " + s.id) - s.stop() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Incremental Ingestion with Auto Loader -# MAGIC -# MAGIC Incremental ETL is important since it allows us to deal solely with new data that has been encountered since the last ingestion. Reliably processing only the new data is key to achieving scalability. -# MAGIC -# MAGIC Ingesting into a Delta Lake table from a data lake is a common use case that has traditionally been challenging to properly set up, typically relying on the integration of always-on services like Kafka to track the files that have been ingested, and to monitor cloud storage for new file arrivals. Databricks Auto Loader abstracts all this and provides an easy-to-use mechanism for incrementally and efficiently processing new data files as they arrive in cloud file storage, in the form of a structured streaming source. -# MAGIC -# MAGIC Given an input directory path on the cloud file storage, the `cloudFiles` source automatically processes new files as they arrive, with the option of also processing existing files in that directory. For full details, refer to the documentation. -# MAGIC -# MAGIC **Due to the benefits and scalability that Auto Loader delivers, Databricks recommends its use as general best practice when ingesting data from cloud storage.** - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Reset the output directory in preparation to stream data using Auto Loader. - -# COMMAND ---------- - -# MAGIC %run ../Includes/classic-setup $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Reading Data with Auto Loader -# MAGIC -# MAGIC An example invocation of Auto Loader is provided below. Comparing against the standard streaming read from earlier, notice the following differences: -# MAGIC -# MAGIC * Specify a `format` of `cloudFiles` -# MAGIC * Specify the underlying format of the data using the `cloudFiles.format` option -# MAGIC * The `dataLandingLocation` source below represents a cloud storage location from where data is being ingested - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ### Schema Inference and Evolution -# MAGIC -# MAGIC As mentioned earlier, every streaming DataFrame must have a schema. But Auto Loader can be configured to take a more active role in inferring and maintaining the schema of the data as it evolves. -# MAGIC -# MAGIC By omitting a schema specification, Auto Loader will detect the schema based on the data seen on the input. Specifying the `cloudFiles.schemaLocation` option allows Auto Loader to track the schema, thereby improving performances and ensuring stability of the schema across stream restart. A common pattern is to use `checkpointLocation` for this purpose. -# MAGIC -# MAGIC There must be data present for schema inference to work; otherwise you must specify a schema. -# MAGIC -# MAGIC **Schema evolution** allows changes to a schema in response to data that changes over time. This can be an important feature in some use cases. - -# COMMAND ---------- - -incrementalStreamingDF = (spark - .readStream - .format("cloudFiles") - .option("cloudFiles.format", "json") - .option("cloudFiles.schemaLocation", checkpointPath) - .load(dataLandingLocation) -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Writing the output also takes a similar form as the previous streaming case. Note the following differences: -# MAGIC * Specify `mergeSchema` option to activate schema evolution. If any changes to the schema occur over time, the schema is adapted rather than rejecting the write. This can be useful in some use cases. -# MAGIC * Omitting the trigger to allow the query to continue running, ingesting new data as it arrives. If you wish to schedule your ETL process to run in batch mode, consider using a one-time trigger instead. - -# COMMAND ---------- - -(incrementalStreamingDF - .writeStream - .format("delta") - .option("checkpointLocation", checkpointPath) - .option("mergeSchema", "true") - .table(outputTable) -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Querying the Output -# MAGIC By now the following query against the output table will likely seem familiar. Run it a few times, and it will become apparent that nothing changes, as no data is arriving in our simulated cloud storage. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT Device,COUNT(Device) AS Count -# MAGIC FROM ${c.outputTable} -# MAGIC GROUP BY Device - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Land New Data -# MAGIC Run the following cell to simulate the arrival of new data in our cloud storage. Each time you execute the cell below, a new file will be written to our source directory. Following this cell, observe the stream monitor above, and notice the impact on the results when re-running the query. - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Clean Up -# MAGIC Stop active streams and remove created resources before continuing. - -# COMMAND ---------- - -# MAGIC %run ../Includes/classic-setup $mode="clean" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC

Summary

-# MAGIC -# MAGIC We used `readStream` to stream input from a variety of sources, including Databricks Auto Loader. Auto Loader augments Structured Streaming functionality by providing an easy-to-use interface of performing incremental ETL from cloud storage. -# MAGIC -# MAGIC We also explored various options for consuming, writing and querying the streamed input data. -# MAGIC -# MAGIC Finally, we explored the array of active streams maintained in the `SparkSession` object. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC

Additional Topics & Resources

-# MAGIC -# MAGIC * Structured Streaming Programming Guide
-# MAGIC * A Deep Dive into Structured Streaming by Tathagata Das. This is an excellent video describing how Structured Streaming works. -# MAGIC * Failed Streaming Query Recovery Best Practices for Recovery. -# MAGIC * Continuous Processing Mode Lowest possible latency stream processing. Currently Experimental. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.1.2 - Windows and Watermarks.py b/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.1.2 - Windows and Watermarks.py deleted file mode 100644 index e9a8b3d..0000000 --- a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.1.2 - Windows and Watermarks.py +++ /dev/null @@ -1,310 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Windows and Watermarks -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, you should be able to: -# MAGIC * Explain why some methods will not work on streaming data -# MAGIC * Use windows to aggregate over chunks of data rather than all data -# MAGIC * Compare tumbling windows and sliding windows -# MAGIC * Apply watermarking to manage state - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Getting Started -# MAGIC -# MAGIC Run the following cell to configure our "classroom." - -# COMMAND ---------- - -# MAGIC %run ../Includes/classic-setup $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Configure Streaming Read -# MAGIC -# MAGIC This lesson uses the same data as the previous notebook, again loaded with AutoLoader. -# MAGIC -# MAGIC The code below registers a streaming DataFrame (which we'll use again in a moment) and a streaming temp view. -# MAGIC -# MAGIC Note the use of the `selectExpr` method, which allows multiple SQL operations to be configured on a per column basis in PySpark DataFrames. Here, we're simplifying the data to be dealt with by selecting only two columns: -# MAGIC * `Creation_Time`, originally encoded in nanoseconds, is converted to unixtime and renamed to `creation_time` -# MAGIC * `gt` is renamed to `action` - -# COMMAND ---------- - -from pyspark.sql.functions import col - -schema = """Arrival_Time BIGINT, - Creation_Time BIGINT, - Device STRING, - Index BIGINT, - Model STRING, - User STRING, - geolocation STRUCT< - city: STRING, - country: STRING>, - gt STRING, - Id BIGINT, - x DOUBLE, - y DOUBLE, - z DOUBLE""" - -streamingDF = (spark - .readStream - .format("cloudFiles") - .option("cloudFiles.format", "json") - .schema(schema) - .load(dataLandingLocation) - .selectExpr("cast(Creation_Time/1E9 AS timestamp) AS creation_time", "gt AS action") -) - -streamingDF.createOrReplaceTempView("streaming_tmp_vw") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Unsupported Operations -# MAGIC -# MAGIC Most operations on a streaming DataFrame are identical to a static DataFrame. There are some exceptions to this. -# MAGIC -# MAGIC Consider the model of the data as a constantly appending table. Sorting is one of a handful of operations that is either too complex or logically not possible to do when working with streaming data. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT * FROM streaming_tmp_vw -# MAGIC ORDER BY creation_time DESC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Streaming Aggregations -# MAGIC -# MAGIC Continuous applications often require near real-time decisions on real-time, aggregated statistics. -# MAGIC -# MAGIC Some examples include -# MAGIC * Aggregating errors in data from IoT devices by type -# MAGIC * Detecting anomalous behavior in a server's log file by aggregating by country -# MAGIC * Performing behavior analysis on instant messages via hash tags -# MAGIC -# MAGIC While these streaming aggregates may need to reference historic trends, analytics will generally be calculated over discrete units of time. Spark Structured Streaming supports time-based **windows** on streaming DataFrames to make these calculations easy. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ### What is Time? -# MAGIC -# MAGIC Multiple times may be associated with each streaming event. Consider the discrete differences between the time at which the event data was: -# MAGIC - Generated -# MAGIC - Written to the streaming source -# MAGIC - Processed into Spark -# MAGIC -# MAGIC Each of these times will be recorded from the system clock of the machine running the process, with discrepancies and latencies being introduced due to many different causes. -# MAGIC -# MAGIC Generally speaking, most analytics will be interested in the time the data was generated. As such, this lesson will focus on timestamps recorded at the time of data generation, which we will refer to as the **event time**. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Windowing -# MAGIC -# MAGIC Defining windows on a time series field imposes a time range constraint on an otherwise unbounded input. This allows users to utilize this field for aggregations in the same way they would use distinct values when calling `GROUP BY`. Spark maintains a state table with aggregates for each user-defined bucket of time. -# MAGIC -# MAGIC Spark supports three types of windows: -# MAGIC -# MAGIC * **Tumbling windows**: fixed-size windows, regularly recurring windows that do not overlap. Each event will be aggregated into only one window. -# MAGIC * **Sliding windows**: fixed-size windows, regularly recurring windows that overlap. Each event may be aggregated into multiple windows. -# MAGIC * **Session windows**: dynamic windows whose start time and duration depends on the inputs. An event will trigger the start of a window that will, in general, continue until a predetermined duration after the last event received. -# MAGIC -# MAGIC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC The following diagram illustrates in greater detail the concept of sliding windows and how events received at various times will be aggregated into the various windows (assuming that the slide duration is less than the window duration, which leads to overlapping windows): -# MAGIC -# MAGIC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ### Consuming with a Windowed Aggregation -# MAGIC -# MAGIC Let's consume the stream from SQL in a windowed aggregation using the SQL `window` function, which accepts a timestamp column and a window duration to define the tumbling windows. An optional third argument specifies a slide duration that allows the definition of a sliding window. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT -# MAGIC window.start AS start, -# MAGIC action, -# MAGIC count(action) AS count -# MAGIC FROM streaming_tmp_vw -# MAGIC GROUP BY -# MAGIC window(creation_time, '1 hour'), -# MAGIC action -# MAGIC ORDER BY -# MAGIC start, -# MAGIC action - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Once a batch of data has loaded, render the results as a bar graph with the following settings: -# MAGIC -# MAGIC * **Keys** is set to `start` -# MAGIC * **Series groupings** is set to `action` -# MAGIC * **Values** is set to `count` - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Land New Data -# MAGIC Recall that our stream has been set up for incremental ingestion. Invoke the following cell a few times to simulate the arrival of new data. Note the impact on the results reported above. - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Performance Considerations -# MAGIC Because aggregation is a wide transformation, it will trigger a shuffle. Configuring the number of partitions can reduce the number of tasks and properly balance the workload for the cluster. -# MAGIC -# MAGIC In most cases, a 1-to-1 mapping of partitions to cores is ideal for streaming applications. The code below sets the number of partitions to 4, which maps perfectly to a cluster with 4 cores. - -# COMMAND ---------- - -spark.conf.set("spark.sql.shuffle.partitions", 4) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Watermarking -# MAGIC -# MAGIC -# MAGIC When aggregating with an unbounded input, Spark's fault-tolerant state management naturally incurs some processing overhead. To keep these overheads bounded within acceptable limits, the size of the state data should not grow indefinitely. However, with sliding windows, the number of windows/groups will grow indefinitely, and so can the size of state (proportional to the number of groups). To bound the state size, we have to be able to drop old aggregates that are not going to be updated anymore. We achieve this using **watermarking**. -# MAGIC -# MAGIC Watermarking allows users to define a cutoff threshold for how much state should be maintained. This cutoff is calculated against the most recently seen event time. Data arriving after this threshold will be discarded. -# MAGIC -# MAGIC The `withWatermark` method allows users to easily define this cutoff threshold. -# MAGIC -# MAGIC Note that there is no built-in support for watermarking in Spark SQL, but we can define this in PySpark before creating a temp view, as shown below. - -# COMMAND ---------- - -(streamingDF - .withWatermark("creation_time", "2 hours") # Specify a 2-hour watermark - .createOrReplaceTempView("watermarked_tmp_vw") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC By directing our windowed aggregation at this new temp view, we can easily achieve the same outcome while managing state information. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT -# MAGIC window.start AS start, -# MAGIC action, -# MAGIC count(action) AS count -# MAGIC FROM watermarked_tmp_vw -# MAGIC GROUP BY -# MAGIC window(creation_time, '1 hour'), -# MAGIC action -# MAGIC ORDER BY -# MAGIC start, -# MAGIC action - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC -# MAGIC ## Example Details -# MAGIC -# MAGIC The threshold is always calculated against the max event time seen. -# MAGIC -# MAGIC In the example above, -# MAGIC * The in-memory state is limited to two hours of historic data. -# MAGIC * Data arriving more than 2 hours late should be dropped. -# MAGIC * Data received within 2 hours of being generated will never be dropped. -# MAGIC -# MAGIC Caution This guarantee is strict in only one direction. Data delayed by more than 2 hours is not guaranteed to be dropped; it may or may not get aggregated. The more delayed the data is, the less likely the engine is going to process it. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Writing Results -# MAGIC -# MAGIC Previously we used `spark.table()` to pass SQL logic stored in temp views back to a DataFrame to write out streaming results. -# MAGIC -# MAGIC Below, we instead use `spark.sql()` and pass the entire SQL query. - -# COMMAND ---------- - -(spark.sql(""" - SELECT - window.start AS start, - action, - count(action) AS count - FROM watermarked_tmp_vw - GROUP BY - window(creation_time, '1 hour'), - action - ORDER BY - start, - action -""").writeStream - .option("checkpointLocation", checkpointPath) - .outputMode("complete") - .table("action_counts") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Clean Up - -# COMMAND ---------- - -# MAGIC %run ../Includes/classic-setup $mode="clean" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Summary -# MAGIC -# MAGIC * A handful of operations valid for static DataFrames will not work with streaming data -# MAGIC * Windows allow users to define time-based buckets for aggregating streaming data -# MAGIC * Watermarking allows users to manage the amount of state being calculated with each trigger and define how late-arriving data should be handled - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.3.3 - Pipeline Results.py b/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.3.3 - Pipeline Results.py deleted file mode 100644 index 79ea039..0000000 --- a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.3.3 - Pipeline Results.py +++ /dev/null @@ -1,54 +0,0 @@ -# Databricks notebook source -# MAGIC %md -# MAGIC # Exploring the Results of a DLT Pipeline -# MAGIC -# MAGIC This Notebook explores the execution results of a DLT pipeline. - -# COMMAND ---------- - -# MAGIC %run ../Includes/dlt-setup $course="dlt_demo" - -# COMMAND ---------- - -storage_location = userhome - -# COMMAND ---------- - -dbutils.fs.ls(storage_location) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC The `system` directory captures events associated with the pipeline. - -# COMMAND ---------- - -dbutils.fs.ls(f"{storage_location}/system/events") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC These event logs are stored as a Delta table. Let's query the table. - -# COMMAND ---------- - -display(spark.sql(f"SELECT * FROM delta.`{storage_location}/system/events`")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's view the contents of the *tables* directory. - -# COMMAND ---------- - -dbutils.fs.ls(f"{storage_location}/tables") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's query the gold table. - -# COMMAND ---------- - -display(spark.sql(f"SELECT * FROM {database}.sales_order_in_la")) - diff --git a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.1.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py b/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.1.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py deleted file mode 100644 index 4bcfe08..0000000 --- a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.1.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py +++ /dev/null @@ -1,121 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Using Auto Loader and Structured Streaming with Spark SQL -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lab, you will be able to: -# MAGIC * Ingest data using Auto Loader -# MAGIC * Aggregate streaming data -# MAGIC * Stream data to a Delta table - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Setup -# MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. - -# COMMAND ---------- - -# MAGIC %run ../../Includes/classic-setup $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Configure Streaming Read -# MAGIC -# MAGIC This lab uses a collection of customer-related CSV data from DBFS found in */databricks-datasets/retail-org/customers/*. -# MAGIC -# MAGIC Read this data using Auto Loader using its schema inference (use `customersCheckpointPath` to store the schema info). Create a streaming temporary view called `customers_raw_temp`. - -# COMMAND ---------- - -# TODO -customersCheckpointPath = userhome + "/customersCheckpoint" - -(spark - .readStream - - .load("/databricks-datasets/retail-org/customers/") - .createOrReplaceTempView("customers_raw_temp") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Define a streaming aggregation -# MAGIC -# MAGIC Using CTAS syntax, define a new streaming view called `customer_count_by_state_temp` that counts the number of customers per `state`, in a field called `customer_count`. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC -- TODO -# MAGIC CREATE OR REPLACE TEMPORARY VIEW customer_count_by_state_temp AS -# MAGIC SELECT -# MAGIC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Write aggregated data to a Delta table -# MAGIC -# MAGIC Stream data from the `customer_count_by_state_temp` view to a Delta table called `customer_count_by_state`. - -# COMMAND ---------- - -# TODO -customersCountCheckpointPath = userhome + "/customersCountCheckpoint" - -(spark - - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Query the results -# MAGIC -# MAGIC Query the `customer_count_by_state` table (this will not be a streaming query). Plot the results as a bar graph and also using the map plot. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC -- TODO - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Wrapping Up -# MAGIC -# MAGIC Run the following cell to remove the database and all data associated with this lab. - -# COMMAND ---------- - -# MAGIC %run ../../Includes/classic-setup $mode="clean" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC By completing this lab, you should now feel comfortable: -# MAGIC * Using PySpark to configure Auto Loader for incremental data ingestion -# MAGIC * Using Spark SQL to aggregate streaming data -# MAGIC * Streaming data to a Delta table - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.2.3L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py b/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.2.3L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py deleted file mode 100644 index d75c4a1..0000000 --- a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.2.3L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py +++ /dev/null @@ -1,181 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Propagating Incremental Updates with Structured Streaming and Delta Lake -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lab, you will be able to: -# MAGIC * Apply your knowledge of structured streaming and Auto Loader to implement a simple multi-hop architecture - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Setup -# MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. - -# COMMAND ---------- - -# MAGIC %run ../../Includes/classic-setup $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Ingest data -# MAGIC -# MAGIC This lab uses a collection of customer-related CSV data from DBFS found in */databricks-datasets/retail-org/customers/*. -# MAGIC -# MAGIC Read this data using Auto Loader using its schema inference (use `customersCheckpointPath` to store the schema info). Stream the raw data to a Delta table called `bronze`. - -# COMMAND ---------- - -# TODO -customersCheckpointPath = userhome + "/customersCheckpoint" - -(spark - .readStream - - .load("/databricks-datasets/retail-org/customers/") - .writeStream - - .table("bronze") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC Let's create a streaming temporary view into the bronze table, so that we can perform transforms using SQL. - -# COMMAND ---------- - -(spark - .readStream - .table("bronze") - .createOrReplaceTempView("bronze_temp")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Clean and enhance data -# MAGIC -# MAGIC Using CTAS syntax, define a new streaming view called `bronze_enhanced_temp` that does the following: -# MAGIC * Skips records with a null `postcode` (set to zero) -# MAGIC * Inserts a column called `receipt_time` containing a current timestamp -# MAGIC * Inserts a column called `source_file` intaining the input filename - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC -- TODO -# MAGIC CREATE OR REPLACE TEMPORARY VIEW bronze_enhanced_temp AS -# MAGIC SELECT -# MAGIC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Silver table -# MAGIC -# MAGIC Stream the data from `bronze_enhanced_temp` to a table called `silver`. - -# COMMAND ---------- - -# TODO -silverCheckpointPath = userhome + "/silverCheckpoint" - -(spark.table("bronze_enhanced_temp") - - .table("silver")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC Let's create a streaming temporary view into the silver table, so that we can perform business-level using SQL. - -# COMMAND ---------- - -(spark - .readStream - .table("silver") - .createOrReplaceTempView("silver_temp")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Gold tables -# MAGIC -# MAGIC Using CTAS syntax, define a new streaming view called `customer_count_temp` that counts customers per state. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC -- TODO -# MAGIC CREATE OR REPLACE TEMPORARY VIEW customer_count_by_state_temp AS -# MAGIC SELECT -# MAGIC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Finally, stream the data from the `customer_count_by_state_temp` view to a Delta table called `gold_customer_count_by_state`. - -# COMMAND ---------- - -# TODO -customersCountCheckpointPath = userhome + "/customersCountCheckpoint" - -(spark - .table("customer_count_by_state_temp") - .writeStream - - .table("gold_customer_count_by_state")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Query the results -# MAGIC -# MAGIC Query the `gold_customer_count_by_state` table (this will not be a streaming query). Plot the results as a bar graph and also using the map plot. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT * FROM gold_customer_count_by_state - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Wrapping Up -# MAGIC -# MAGIC Run the following cell to remove the database and all data associated with this lab. - -# COMMAND ---------- - -# MAGIC %run ../../Includes/classic-setup $mode="clean" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC By completing this lab, you should now feel comfortable: -# MAGIC * Using PySpark to configure Auto Loader for incremental data ingestion -# MAGIC * Using Spark SQL to aggregate streaming data -# MAGIC * Streaming data to a Delta table - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.4L - Intro & Config Lab.py b/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.4L - Intro & Config Lab.py deleted file mode 100644 index b0c6cf7..0000000 --- a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.4L - Intro & Config Lab.py +++ /dev/null @@ -1,188 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Lab: Migrating SQL Notebooks to Delta Live Tables -# MAGIC -# MAGIC This notebook dictates an overall structure for the lab exercise, configures the environment for the lab, provides simulated data streaming, and performs cleanup once you are done. A notebook like this is not typically needed in a production pipeline scenario. -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, you should be able to: -# MAGIC * Convert existing data pipelines to Delta Live Tables - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Datasets Used -# MAGIC -# MAGIC This demo uses simplified artificially generated medical data. The schema of our two datasets is represented below. Note that we will be manipulating these schema during various steps. -# MAGIC -# MAGIC #### Recordings -# MAGIC The main dataset uses heart rate recordings from medical devices delivered in the JSON format. -# MAGIC -# MAGIC | Field | Type | -# MAGIC | --- | --- | -# MAGIC | device_id | int | -# MAGIC | mrn | long | -# MAGIC | time | double | -# MAGIC | heartrate | double | -# MAGIC -# MAGIC #### PII -# MAGIC These data will later be joined with a static table of patient information stored in an external system to identify patients by name. -# MAGIC -# MAGIC | Field | Type | -# MAGIC | --- | --- | -# MAGIC | mrn | long | -# MAGIC | name | string | - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Getting Started -# MAGIC -# MAGIC Begin by running the following cell to configure the lab environment. - -# COMMAND ---------- - -# MAGIC %run "../../Includes/dlt-setup" $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Land Initial Data -# MAGIC Seed the landing zone with some data before proceeding. You will re-run this command to land additional data later. - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create and Configure a Pipeline -# MAGIC -# MAGIC 1. Click the **Jobs** button on the sidebar, then select the **Delta Live Tables** tab. -# MAGIC 1. Click **Create Pipeline**. -# MAGIC 1. Fill in a **Pipeline Name** of your choosing. -# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the notebook `3.3.4 - LAB - Migrating a SQL Pipeline to DLT`. -# MAGIC 1. Run the cell below to generate values for **source**, **Target** and **Storage Location**. (All of these will include your current username). -# MAGIC * Click `Add configuration`; enter the word `source` in the **Key** field and the output printed next to `source` below in the value field. -# MAGIC * Enter the database name printed next to `Target` below in the **Target** field. -# MAGIC * Enter the location printed next to `Storage Location` below in the **Storage Location** field. -# MAGIC 1. Set **Pipeline Mode** to **Triggered**. -# MAGIC 1. Disable autoscaling. -# MAGIC 1. Set the number of wokers to 1. -# MAGIC 1. Click **Create**. - -# COMMAND ---------- - -storage_location = userhome + "/output" -print(f"source : {dataLandingLocation.split(':')[1]}") -print(f"Target: {database}") -print(f"Storage Location: {storage_location.split(':')[1]}") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Run your Pipeline -# MAGIC -# MAGIC Select **Development** mode, which accelerates the development lifecycle by reusing the same cluster across runs. It will also turn off automatic retries when jobs fail. -# MAGIC -# MAGIC Click **Start** to begin the first update to your table. -# MAGIC -# MAGIC Delta Live Tables will automatically deploy all the necessary infrastructure and resolve the dependencies between all datasets. -# MAGIC -# MAGIC **NOTE**: The first table update make take several minutes as relationships are resolved and infrastructure deploys. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Open and Complete DLT Pipeline Notebook -# MAGIC -# MAGIC You will perform your work in this companion Notebook, which you will ultimately deploy as a pipeline. -# MAGIC -# MAGIC Open the Notebook and, following the guidelines provided therein, fill in the cells where prompted to implement a multi-hop architecture similar to the one we worked with in the previous section. -# MAGIC -# MAGIC **NOTE**: As a first step to preparing your pipeline, run the following cell to obtain the cloud file location. Substitue this value for the text that reads ``. This value will be unique within the workspace to your user identity to prevent possible interference between users within the same workspace. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Troubleshooting Code in Development Mode -# MAGIC -# MAGIC Don't despair if your pipeline fails the first time. Delta Live Tables is in active development, and error messages are improving all the time. -# MAGIC -# MAGIC Because relationships between tables are mapped as a DAG, error messages will often indicate that a dataset isn't found. -# MAGIC -# MAGIC Let's consider our DAG below: -# MAGIC -# MAGIC -# MAGIC -# MAGIC If the error message `Dataset not found: 'recordings_parsed'` is raised, there may be several culprits: -# MAGIC 1. The logic defining `recordings_parsed` is invalid -# MAGIC 1. There is an error reading from `recordings_bronze` -# MAGIC 1. A typo exists in either `recordings_parsed` or `recordings_bronze` -# MAGIC -# MAGIC The safest way to identify the culprit is to iteratively add table/view definitions back into your DAG starting from your initial ingestion tables. You can simply comment out later table/view definitions and uncomment these between runs. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Display Results -# MAGIC -# MAGIC Assuming your pipeline runs successfully, display the contents of the gold table. -# MAGIC -# MAGIC **NOTE**: Because we specified a value for **Target**, tables are published to the specified database. Without a **Target** specification, we would need to query the table based on its underlying location in DBFS (relative to the **Storage Location**). - -# COMMAND ---------- - -spark.sql(f"SELECT * FROM {database}daily_patient_avg") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Trigger another file arrival with the following cell. Feel free to run it a couple more times if desired. Following this, run the pipeline again and view the results. Feel free to re-run the cell above to gain an updated view of the `daily_patient_avg` table. - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Wrapping Up -# MAGIC -# MAGIC Ensure that you delete your pipeline from the DLT UI, and run the following cell to clean up the files and tables that were created as part of the lab setup and execution. - -# COMMAND ---------- - -# MAGIC %run "../../Includes/dlt-setup" $mode="clean" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Summary -# MAGIC -# MAGIC In this lab, you learned to convert an existing data pipeline to a Delta Live Tables SQL pipeline, and deployed that pipeline using the DLT UI. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Additional Topics & Resources -# MAGIC -# MAGIC * Delta Live Tables Documentation -# MAGIC * Delta Live Tables Demo - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/0 - Task Orchestration with Databricks Jobs.py b/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/0 - Task Orchestration with Databricks Jobs.py deleted file mode 100644 index ad80e31..0000000 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/0 - Task Orchestration with Databricks Jobs.py +++ /dev/null @@ -1,115 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Orchestrating Jobs with Databricks -# MAGIC -# MAGIC New updates to the Databricks Jobs UI have added the ability to schedule multiple tasks as part of a job, allowing Databricks Jobs to fully handle orchestration for most production workloads. -# MAGIC -# MAGIC Here, we'll start by reviewing the steps for scheduling a notebook as a triggered standalone job, and then add a dependent job using a DLT pipeline. -# MAGIC -# MAGIC -# MAGIC By the end of this lesson, you should feel confident: -# MAGIC * Scheduling a notebook as a Databricks Job -# MAGIC * Describing job scheduling options and differences between cluster types -# MAGIC * Review Job Runs to track progress and see results -# MAGIC * Scheduling a DLT pipeline as a Databricks Job -# MAGIC * Configuring linear dependencies between tasks using the Databricks Jobs UI -# MAGIC -# MAGIC ## Schedule a Notebook Job -# MAGIC -# MAGIC When using the Jobs UI to orchestrate a workload with multiple tasks, you'll always begin by scheduling a single task. -# MAGIC -# MAGIC Here, we'll start by scheduling the notebook `1 - Reset`. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. -# MAGIC 1. Click the blue `Create Job` button -# MAGIC 1. Configure the task: -# MAGIC 1. Enter `reset` for the task name -# MAGIC 1. Select the notebook `1 - Reset` using the notebook picker -# MAGIC 1. Select an Existing All Purpose Cluster from the **Cluster** dropdown -# MAGIC 1. Click **Create** -# MAGIC -# MAGIC **Note**: When selecting your all purpose cluster, you will get a warning about how this will be billed as all purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. -# MAGIC -# MAGIC Click the blue **Run now** button in the top right to start the job. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Review Run -# MAGIC -# MAGIC As currently scheduled, our single notebook provides identical performance to the legacy Databricks Jobs UI, which only allowed a single notebook to be scheduled. -# MAGIC -# MAGIC From the **Runs** tab, clicking on the start time field will display a preview of the notebook with results. If the job is still running, this will be under **Active Runs**, and the displayed notebook will ocassionaly update to show progress throughout execution. If it has already completed, it will be under **Completed Runs** and just display the static results of the executed notebook. -# MAGIC -# MAGIC The notebook scheduled using the magic command `%run` to call an additional notebook using a relative path. Note that while not covered in this course, [new functionality added to Databricks Repos allows loading Python modules using relative paths](https://docs.databricks.com/repos.html#work-with-non-notebook-files-in-a-databricks-repo). -# MAGIC -# MAGIC The actual outcome of the scheduled notebook is to reset the output of the DLT pipeline configured earlier in the course, as well as to print out the necessary variables used to configure this pipeline for users that may not have coded along previously. -# MAGIC -# MAGIC Before continuing to the next step, make sure you either have access to a - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Chron Scheduling of Databricks Jobs -# MAGIC -# MAGIC Note that on the right hand side of the Jobs UI, directly under the **Job Details** section is a section labeled **Schedule**. -# MAGIC -# MAGIC Click on the **Edit schedule** button to explore scheduling options. -# MAGIC -# MAGIC Changing the **Schedule type** field from **Manual** to **Scheduled** will bring up a chron scheduling UI. -# MAGIC -# MAGIC This UI provides extensive options for setting up chronological scheduling of your Jobs. Settings configured with the UI can also be output in cron syntax, which can be editted if custom configuration not available with the UI is needed. -# MAGIC -# MAGIC At this time, we'll leave our job set with **Manual** scheduling. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Schedule a DLT Pipeline as a Task -# MAGIC -# MAGIC In this step, we'll add a DLT pipeline to execute after the success of the task we configured in the previous step. -# MAGIC -# MAGIC **NOTE**: This step assumes that the DLT pipeline describe in the lab for module 3 of this course was configured successfully. If this is not the case, instructions are included for configuring this DLT pipeline in the run output of the `reset` notebook executed above. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar -# MAGIC 1. Select the job you defined above by clicking on the name -# MAGIC 1. At the top left of your screen, you'll see the **Runs** tab is currently selected; click the **Tasks** tab. -# MAGIC 1. Click the large blue circle with a **+** at the center bottom of the screen to add a new task -# MAGIC 1. Specify the **Task name** as `dlt` -# MAGIC 1. From **Type**, select `Pipeline` -# MAGIC 1. Click the **Pipeline** field and select the DLT pipeline you configured previously -# MAGIC 1. Note that the **Depends on** field defaults to your previously defined task -# MAGIC 1. Click the blue **Create task** button -# MAGIC -# MAGIC You should now see a screen with 2 boxes and a downward arrow between them. Your `reset` task will be at the top, leading into your `dlt` task. This visualization represents the dependencies between these tasks. -# MAGIC -# MAGIC Click **Run now** to execute your job. -# MAGIC -# MAGIC **NOTE**: You may need to wait a few minutes as infrastructure for your DLT pipeline is deployed. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Review Multi-Task Run Results -# MAGIC -# MAGIC Clicking into the job run will replicate the UI showing both tasks. The visualizations for tasks will update in real time to reflect which tasks are actively running, and will change colors if task failure occur. Clicking on a task box will render the scheduled notebook in the UI. (You can think of this as just an additional layer of orchestration on top of the previous Databricks Jobs UI, if that helps; note that if you have workloads scheduling jobs with the CLI or REST API, [the JSON structure used to configure and get results about jobs has seen similar updates to the UI](https://docs.databricks.com/dev-tools/api/latest/jobs.html)). -# MAGIC -# MAGIC **NOTE**: At this time, DLT pipelines scheduled as tasks do not directly render results in the Runs GUI; instead, you will be directed back to the DLT Pipeline GUI for the scheduled Pipeline. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/1- Reset.py b/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/1- Reset.py deleted file mode 100644 index 7f54ee9..0000000 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/1- Reset.py +++ /dev/null @@ -1,48 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %run ../../Includes/dlt-setup $course="dlt_demo" $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC If you have not previously configured this DLT pipeline successfully, the following cell prints out two values that will be used during the configuration steps that follow. - -# COMMAND ---------- - -print(f"Target: {database}") -print(f"Storage location: {userhome.split(':')[1]}") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create and configure a pipeline -# MAGIC -# MAGIC The instructions below refer to the same pipeline created during the previous codealong for DLT; if you successfully configured this notebook previously, you should not need to reconfigure this pipeline now. -# MAGIC -# MAGIC -# MAGIC Steps: -# MAGIC 1. Click the **Jobs** button on the sidebar, -# MAGIC 1. Select the **Delta Live Tables** tab. -# MAGIC 1. Click **Create Pipeline**. -# MAGIC 1. Fill in a **Pipeline Name** of your choosing. -# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the companion notebook called **2 - DLT Job**. -# MAGIC 1. In the **Target** field, specify the database name printed out next to **Target** in the cell above. (This should follow the pattern `dbacademy__dlt_demo`) -# MAGIC 1. In the **Storage location** field, copy the directory as printed above. -# MAGIC 1. For **Pipeline Mode**, select **Triggered** -# MAGIC 1. Uncheck the **Enable autoscaling** box, and set the number of workers to 1., -# MAGIC 1. Click **Create**. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/0 - Lab Instructions.py b/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/0 - Lab Instructions.py deleted file mode 100644 index 98bc5c7..0000000 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/0 - Lab Instructions.py +++ /dev/null @@ -1,91 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Orchestrating Jobs with Databricks -# MAGIC -# MAGIC In this lab, you'll be configuring a multi-task job comprising of: -# MAGIC * A notebook that lands a new batch of data in a storage directory -# MAGIC * A Delta Live Table pipeline that processes this data through a series of tables -# MAGIC * A notebook that queries the gold table produced by this pipeline as well as various metrics output by DLT -# MAGIC -# MAGIC By the end of this lab, you should feel confident: -# MAGIC * Scheduling a notebook as a Databricks Job -# MAGIC * Scheduling a DLT pipeline as a Databricks Job -# MAGIC * Configuring linear dependencies between tasks using the Databricks Jobs UI -# MAGIC -# MAGIC ## Schedule a Notebook Job -# MAGIC -# MAGIC When using the Jobs UI to orchestrate a workload with multiple tasks, you'll always begin by scheduling a single task. -# MAGIC -# MAGIC Here, we'll start by scheduling the notebook `1 - Batch Job`. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. -# MAGIC 1. Click the blue `Create Job` button -# MAGIC 1. Configure the task: -# MAGIC 1. Enter `Batch-Job` for the task name -# MAGIC 1. Select the notebook `1 - Batch Job` using the notebook picker -# MAGIC 1. Select an Existing All Purpose Cluster from the **Cluster** dropdown -# MAGIC 1. Click **Create** -# MAGIC -# MAGIC **Note**: When selecting your all purpose cluster, you will get a warning about how this will be billed as all purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. -# MAGIC -# MAGIC Click the blue **Run now** button in the top right to confirm that you have successfully configured this task. From the **Runs** tab, clicking on the start time field will pull up the notebook with results. -# MAGIC -# MAGIC ## Schedule a DLT Pipeline as a Task -# MAGIC -# MAGIC In this step, we'll add a DLT pipeline to execute after the success of the task we configured in the previous step. -# MAGIC -# MAGIC **NOTE**: This step assumes that the DLT pipeline describe in the lab for module 3 of this course was configured successfully. If this is not the case, instructions are included for configuring this DLT pipeline in the run output of the `Batch-Job` notebook executed above. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar -# MAGIC 1. Select the job you defined above by clicking on the name (this should have the name `Batch-Job`) -# MAGIC 1. At the top left of your screen, you'll see the **Runs** tab is currently selected; click the **Tasks** tab. -# MAGIC 1. Click the large blue circle with a **+** at the center bottom of the screen to add a new task -# MAGIC 1. Specify the **Task name** as `DLT-Pipeline` -# MAGIC 1. From **Type**, select `Pipeline` -# MAGIC 1. Click the **Pipeline** field and select the DLT pipeline you configured previously -# MAGIC 1. Note that the **Depends on** field defaults to your previously defined task -# MAGIC 1. Click the blue **Create task** button -# MAGIC -# MAGIC You should now see a screen with 2 boxes and a downward arrow between them. Your `Batch-Job` task will be at the top, leading into your `DLT-Pipeline` task. This visualization represents the dependencies between these tasks. -# MAGIC -# MAGIC Before clicking **Run now**, click the job name in the top left and provide something unique and descriptive, like `-MTJ-lab` -# MAGIC -# MAGIC **NOTE**: You may need to wait a few minutes as infrastructure for your DLT pipeline is deployed. Feel free to skip clicking **Run now** until the next task is configured if you don't want to wait. -# MAGIC -# MAGIC ## Schedule an Additional Notebook Task -# MAGIC -# MAGIC An additional notebook has been provided which queries some of the DLT metrics and the gold table defined in the DLT pipeline. We'll add this as a final task in our job. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the **Tasks** tab of the job you've been configuring -# MAGIC 1. Click the blue **+** button to add another task -# MAGIC 1. Specify the **Task name** as `Query-Results` -# MAGIC 1. Leave the **Type** set to `Notebook` -# MAGIC 1. Select the notebook `3 - Query Results Job` using the notebook picker -# MAGIC 1. Note that the **Depends on** field defaults to your previously defined task -# MAGIC 1. Select an Existing All Purpose Cluster from the **Cluster** dropdown -# MAGIC 1. Click the blue **Create task** button -# MAGIC -# MAGIC Click the blue **Run now** button in the top right of the screen to run this job. -# MAGIC -# MAGIC From the **Runs** tab, you will be able to click on the start time for this run under the **Active runs** section and visually track task progress. -# MAGIC -# MAGIC Once all your tasks have succeeded, review the contents of each task to confirm expected behavior. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/1 - Batch Job.py b/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/1 - Batch Job.py deleted file mode 100644 index c8a6033..0000000 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/1 - Batch Job.py +++ /dev/null @@ -1,35 +0,0 @@ -# Databricks notebook source -# MAGIC %run ../../Includes/dlt-setup - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create and Configure a Pipeline -# MAGIC -# MAGIC **NOTE**: This lab is configured to work with the DLT pipeline completed as part of the DLT lab in the previous module. If you have not successfully completed this lab, follow the instructions below to configure a pipeline using specified notebook. -# MAGIC -# MAGIC Instructions for configuring DLT pipeline: -# MAGIC 1. Click the **Jobs** button on the sidebar, then select the **Delta Live Tables** tab. -# MAGIC 1. Click **Create Pipeline**. -# MAGIC 1. Fill in a **Pipeline Name** of your choosing. -# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the notebook `4.1.2 - DLT Job`. -# MAGIC 1. Run the cell below to generate values for **source**, **Target** and **Storage Location**. (All of these will include your current username). -# MAGIC * Click `Add configuration`; enter the word `source` in the **Key** field and the output printed next to `source` below in the value field. -# MAGIC * Enter the database name printed next to `Target` below in the **Target** field. -# MAGIC * Enter the location printed next to `Storage Location` below in the **Storage Location** field. -# MAGIC 1. Set **Pipeline Mode** to **Triggered**. -# MAGIC 1. Disable autoscaling. -# MAGIC 1. Set the number of wokers to 1. -# MAGIC 1. Click **Create**. - -# COMMAND ---------- - -storage_location = userhome + "/output" -print(f"source : {dataLandingLocation.split(':')[1]}") -print(f"Target: {database}") -print(f"Storage Location: {storage_location.split(':')[1]}") - diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/3 - Query Results Job.py b/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/3 - Query Results Job.py deleted file mode 100644 index a2d7e55..0000000 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/3 - Query Results Job.py +++ /dev/null @@ -1,62 +0,0 @@ -# Databricks notebook source -# MAGIC %run ../../Includes/dlt-setup - -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Exploring the Results of a DLT Pipeline -# MAGIC -# MAGIC This Notebook explores the execution results of a DLT pipeline. Before proceeding, you will need one piece of information specific to your pipeline instance: the location in DBFS where results are stored. Because we did not specify a value for **Storage Location** when creating the pipeline, DLT automatically created a folder for us. Obtain this information as follows. -# MAGIC -# MAGIC Click **Settings** on the **Pipeline Details** page. This provides a JSON representation of the pipeline configuration. Copy the value specified for **storage** and substitute for `` throughout the rest of this Notebook. -# MAGIC -# MAGIC Generally, and particularly in production systems, you will specify **Storage Location** in your pipeline configurations to have full control of where pipeline results are stored. - -# COMMAND ---------- - -storage_location = userhome + "/output" - -# COMMAND ---------- - -dbutils.fs.ls(storage_location) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC The `system` directory captures events associated with the pipeline. - -# COMMAND ---------- - -dbutils.fs.ls(f"{storage_location}/system/events") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC These event logs are stored as a Delta table. Let's query the table. - -# COMMAND ---------- - -display(spark.sql(f"SELECT * FROM delta.`{storage_location}/system/events`")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's view the contents of the *tables* directory. - -# COMMAND ---------- - -dbutils.fs.ls(f"{storage_location}/tables") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's query the gold table. - -# COMMAND ---------- - -display(spark.sql(f"SELECT * FROM {database}.daily_patient_avg")) - -# COMMAND ---------- - -database - diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.3.2 - LAB - Configuring Privileges for Production Data and Derived Tables.py b/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.3.2 - LAB - Configuring Privileges for Production Data and Derived Tables.py deleted file mode 100644 index 79093dc..0000000 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.3.2 - LAB - Configuring Privileges for Production Data and Derived Tables.py +++ /dev/null @@ -1,203 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Configuring Privileges for Production Data and Derived Tables -# MAGIC -# MAGIC The instructions as detailed below are provided for pairs of users to explore how Table ACLs on Databricks work. It leverages Databricks SQL and the Data Explorer to accomplish these tasks, and assumes that neither user has admin privileges for the workspace. An admin will need to have previously granted `CREATE` and `USAGE` privileges on a catalog for users to be able to create databases in Databricksd SQL -# MAGIC -# MAGIC By the end of this lesson, you should be able to: -# MAGIC * Use Data Explorer to navigate relational entities -# MAGIC * Configure permissions for tables and views with Data Explorer -# MAGIC * Configure minimal permissions to allow for table discovery and querying -# MAGIC * Change ownership for databases, tables, and views created in DBSQL - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Exchange User Names with your Partner -# MAGIC If you are not in a workspace where you usernames correspond with your email address, make sure your partner has your username. They will need this when assigning privileges and searching for your database at later steps. -# MAGIC -# MAGIC The following query will print your username. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT current_user() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Generate Setup Statements -# MAGIC -# MAGIC The following cell uses Python to extract username of the present user and format this into several statements used to create databases, tables, and views. -# MAGIC -# MAGIC Both students should execute the following cell. Successful execution will print out a series of formatted SQL queries, which can be copied into the DBSQL query editor and executed. - -# COMMAND ---------- - -def generate_query(course, mode="reset"): - import re - import random - - username = spark.sql("SELECT current_user()").first()[0] - userhome = f"dbfs:/user/{username}/{course}" - database = f"""dbacademy_{re.sub("[^a-zA-Z0-9]", "_", username)}_{course}""" - - if mode == "reset": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - - print(f""" -CREATE DATABASE IF NOT EXISTS {database} -LOCATION '{userhome}'; - -USE {database}; - -CREATE TABLE beans -(name STRING, color STRING, grams FLOAT, delicious BOOLEAN); - -INSERT INTO beans -VALUES ('black', 'black', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('lentils', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('jelly', 'rainbow', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('pinto', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('green', 'green', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('beanbag chair', 'white', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('lentils', 'green', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('kidney', 'red', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('castor', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}); - -CREATE VIEW tasty_beans -AS SELECT * FROM beans WHERE delicious = true; - """) -generate_query("acls_lab") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Steps: -# MAGIC * Run the cell above -# MAGIC * Copy the entire output to your clipboard -# MAGIC * Navigate to the Databricks SQL workspace -# MAGIC * Make sure that a DBSQL endpoint is running -# MAGIC * Use the left sidebar to select the **SQL Editor** -# MAGIC * Paste the query above and click the blue **Run** in the top right -# MAGIC -# MAGIC **NOTE**: You will need to be connected to a DBSQL endpoint to execute these queries successfully. If you cannot connect to a DBSQL endpoint, you will need to contact your administrator to give you access. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Find Your Database -# MAGIC In the Data Explorer, find the database you created earlier (this should follow the pattern `dbacademy__acls_lab`). -# MAGIC -# MAGIC Clicking on the database name should display a list of the contained tables and views on the left hand side. On the right, you'll see some details about the database, including the **Owner** and **Location**. -# MAGIC -# MAGIC Click the **Permissions** tab to review who presently has permissions (depending on your workspace configuration, some permissions may have been inherited from settings on the catalog). - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Change Database Permissions -# MAGIC -# MAGIC Step: -# MAGIC 1. Make sure you have the **Permissions** tab selected for the database -# MAGIC 1. Click the blue **Grant** button -# MAGIC 1. Select the **USAGE**, **SELECT**, and **READ_METADATA** options -# MAGIC 1. Enter the username of your partner in the field at the top. -# MAGIC 1. Click **OK** -# MAGIC -# MAGIC Confirm with your partner that you can each see each others' databases and tables. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Run a Query to Confirm -# MAGIC -# MAGIC By granting `USAGE`, `SELECT`, and `READ_METADATA` on your database, your partner should now be able to freely query the tables and views in this database, but will not be able to create new tables OR modify your data. -# MAGIC -# MAGIC In the SQL Editor, each user should run a series of queries to confirm this behavior in the database they were just added to. -# MAGIC -# MAGIC **Make sure you specify your partner's database while running the queries below.** -# MAGIC -# MAGIC Queries to execute: -# MAGIC * `SELECT * FROM .beans` -# MAGIC * `SELECT * FROM .tasty_beans` -# MAGIC * `SELECT * FROM .beans MINUS SELECT * FROM .tasty_beans` -# MAGIC * ``` -# MAGIC UPDATE .beans -# MAGIC SET color = 'pink' -# MAGIC WHERE name = 'black' -# MAGIC ``` -# MAGIC -# MAGIC **NOTE**: These first 3 queries should succeed, but the last should fail. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Execute a Query to Generate the Union of Your Beans -# MAGIC -# MAGIC Modify the query below to specify the `beans` tables in each of your databases. -# MAGIC -# MAGIC ``` -# MAGIC SELECT * FROM .beans -# MAGIC UNION ALL TABLE .beans -# MAGIC ``` -# MAGIC -# MAGIC **NOTE**: Because random values were inserted for the `grams` and `delicious` columns, you should see 2 distinct rows for each `name`, `color` pair. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Register a Derivative View to Your Database -# MAGIC -# MAGIC Modify the query below to register the results of the previous query to your database. -# MAGIC -# MAGIC ``` -# MAGIC CREATE VIEW .our_beans AS -# MAGIC SELECT * FROM .beans -# MAGIC UNION ALL TABLE .beans -# MAGIC ``` - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Query Your Partner's View -# MAGIC -# MAGIC Once your partner has successfully completed the previous step, run the following query against each of your tables; you should get the same results: -# MAGIC -# MAGIC ``` -# MAGIC SELECT name, color, delicious, sum(grams) -# MAGIC FROM our_beans -# MAGIC GROUP BY name, color, delicious -# MAGIC ``` - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Add Modify Permissions -# MAGIC -# MAGIC Now try to drop each other's `beans` tables. At the moment, this shouldn't work. -# MAGIC -# MAGIC Using the Data Explorer, add the `MODIFY` permission for your `beans` table for your partner. -# MAGIC -# MAGIC Again, attempt to drop your partner's `beans` table. This time, it should succeed. -# MAGIC -# MAGIC Try to re-execute queries against any of the views of tables you'd previously queried in this lab. -# MAGIC -# MAGIC **NOTE**: If steps were completed successfully, none of your previous queries should work, as the data referenced by your views has been permanently deleted. This demonstrates the risks associated with providing `MODIFY` privileges to users on data that will be used in production applications and dashboards. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.1 - Last Mile ETL with DBSQL.py b/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.1 - Last Mile ETL with DBSQL.py deleted file mode 100644 index 453f786..0000000 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.1 - Last Mile ETL with DBSQL.py +++ /dev/null @@ -1,271 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Last Mile ETL with Databricks SQL -# MAGIC -# MAGIC Before we continue, let's do a recap of some of the things we've learned so far: -# MAGIC 1. The Databricks workspace contains a suite of tools to simplify the data engineering development lifecycle -# MAGIC 1. Databricks notebooks allow users to mix SQL with other programming languages to define ETL workloads -# MAGIC 1. Delta Lake provides ACID compliant transactions and makes incremental data processing easy in the Lakehouse -# MAGIC 1. Delta Live Tables extends the SQL syntax to support many design patterns in the Lakehouse, and simplifies infrastructure deployment -# MAGIC 1. Multi-task jobs allows for full task orchestration, adding dependencies while scheduling a mix of notebooks and DLT pipelines -# MAGIC 1. Databricks SQL allows users to edit and execute SQL queries, build visualizations, and define dashboards -# MAGIC 1. Data Explorer simplifies managing Table ACLs, making Lakehouse data available to SQL analysts (soon to be expanded greatly by Unity Catalog) -# MAGIC -# MAGIC In this section, we'll focus on exploring more DBSQL functionality to support production workloads. -# MAGIC -# MAGIC We'll start by focusing on leveraging Databricks SQL to configure queries that support last mile ETL for analytics. Note that while we'll be using the Databricks SQL UI for this demo, SQL Endpoints [integrate with a number of other tools to allow external query execution](https://docs.databricks.com/integrations/partners.html, as well as having [full API support for executing arbitrary queries programmatically](https://docs.databricks.com/sql/api/index.html). -# MAGIC -# MAGIC From these query results, we'll generate a series of visualizations, which we'll combine into a dashboard. -# MAGIC -# MAGIC Finally, we'll walk through scheduling updates for queries and dashboards, and demonstrate setting alerts to help monitor the state of production datasets over time. -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, you will feel confident: -# MAGIC * Using Databricks SQL as a tool to support production ETL tasks backing analytic workloads -# MAGIC * Configuring SQL queries and visualizations with the Databricks SQL Editor -# MAGIC * Creating dashboards in Databricks SQL -# MAGIC * Scheduling updates for queries and dashboards -# MAGIC * Setting alerts for SQL queries - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Run Setup Script -# MAGIC The following cells runs a notebook that defines a class we'll use to generate SQL queries. - -# COMMAND ---------- - -# MAGIC %run ../Includes/query_generator - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Executing the following cell will reset the database set variables for later query formatting. You can remove the `mode="reset"` argument if you wish to print out the queries without resetting the target database. - -# COMMAND ---------- - -Gen = QueryGenerator(course="4_4", mode="reset") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Demo Database -# MAGIC Execute the following cell and copy the results into the Databricks SQL Editor. -# MAGIC -# MAGIC These queries: -# MAGIC * Create a new database -# MAGIC * Declare two tables (we'll use these for loading data) -# MAGIC * Declare two functions (we'll use these for generating data) -# MAGIC -# MAGIC Once copied, execute the query using the **Run** button. - -# COMMAND ---------- - -Gen.config() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC **NOTE**: The queries above are only designed to be run once after resetting the demo completely to reconfigure the environment. Users will need to have `CREATE` and `USAGE` permissions on the catalog to execute them. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Query to Load Data -# MAGIC Execute the cell below to print out a formatted SQL query for loading data in the `user_ping` table created in the previous step. -# MAGIC -# MAGIC Save this query with the name **Load Ping Data**. -# MAGIC -# MAGIC Run this query to load a batch of data. - -# COMMAND ---------- - -Gen.load() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Executing the query should load some data and return a preview of the data in the table. -# MAGIC -# MAGIC **NOTE**: Random numbers are being used to define and load data, so each user will have slightly different values present. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Set a Query Refresh Schedule -# MAGIC -# MAGIC Steps: -# MAGIC * Locate the **Refresh Schedule** field at the bottom right of the SQL query editor box; click the blue **Never** -# MAGIC * Use the drop down to change to Refresh every **1 minute** -# MAGIC * For **Ends**, click the **On** radio button -# MAGIC * Select tomorrow's date -# MAGIC * Click **OK** - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Query to Track Total Records -# MAGIC -# MAGIC Execute the cell below to print out a formatted SQL query to track total records in the `user_ping` table. -# MAGIC -# MAGIC Save this query with the name **User Counts**. -# MAGIC -# MAGIC Run the query to calculate the current results. - -# COMMAND ---------- - -Gen.user_counts() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Bar Graph Visualization -# MAGIC -# MAGIC Steps: -# MAGIC * Click the **Add Visualization** button -# MAGIC * Click on the name (should default to something like `Visualization 1`) and change the name to **Total User Records** -# MAGIC * Set `user_id` for the **X Column** -# MAGIC * Set `total_records` for the **Y Columns** -# MAGIC * Click **Save** - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a New Dashboard -# MAGIC -# MAGIC Steps: -# MAGIC * Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. -# MAGIC * Click the **Create new dashboard** option -# MAGIC * Name your dashboard **User Ping Summary ``** -# MAGIC * Click **Save** to create the new dashboard -# MAGIC * Your newly created dashboard should now be selected as the target; click **OK** to add your visualization - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Query to Calculate the Recent Average Ping -# MAGIC -# MAGIC Execute the cell below to print out a formatted SQL query to calculate the average ping observed per user over a 3 minute window. -# MAGIC -# MAGIC Save this query with the name **Avg Ping**. -# MAGIC -# MAGIC Run the query to calculate the current results. - -# COMMAND ---------- - -Gen.avg_ping() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Add a Line Plot Visualization to your Dashboard -# MAGIC -# MAGIC Steps: -# MAGIC * Click the **Add Visualization** button -# MAGIC * Click on the name (should default to something like `Visualization 1`) and change the name to **Avg User Ping** -# MAGIC * Select `Line` for the **Visualization Type** -# MAGIC * Set `end_time` for the **X Column** -# MAGIC * Set `avg_ping` for the **Y Columns** -# MAGIC * Set `user_id` for the **Group by** -# MAGIC * Click **Save** -# MAGIC * Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. -# MAGIC * Select the dashboard you created earlier -# MAGIC * Click **OK** to add your visualization - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Query to Report Summary Statistics -# MAGIC -# MAGIC Execute the cell below to print out a formatted SQL query that summarizes all records for a user. -# MAGIC -# MAGIC Save this query with the name **Ping Summary**. -# MAGIC -# MAGIC Run the query to calculate the current results. - -# COMMAND ---------- - -Gen.summary() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Add the Summary Table to your Dashboard -# MAGIC -# MAGIC Steps: -# MAGIC * Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. -# MAGIC * Select the dashboard you created earlier -# MAGIC * Click **OK** to add your visualization - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Review and Refresh your Dashboard -# MAGIC -# MAGIC Steps: -# MAGIC * Use the left side bar to navigate to **Dashboards** -# MAGIC * Find the dashboard you've added your queries to -# MAGIC * Click the blue **Refresh** button to update your dashboard -# MAGIC * Click the **Schedule** button to review dashboard scheduling options -# MAGIC * Note that scheduling a dashboard to update will execute all queries associated with that dashboard -# MAGIC * Do not schedule the dashboard at this time - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Share your Dashboard -# MAGIC -# MAGIC Steps: -# MAGIC * Click the blue **Share** button -# MAGIC * Select **All Users** from the top field -# MAGIC * Choose **Can Run** from the right field -# MAGIC * Click **Add** -# MAGIC * Change the **Credentials** to **Run as viewer** -# MAGIC -# MAGIC **NOTE**: At present, no other users should have any permissions to run your dashboard, as they have not been granted permissions to the underlying databases and tables using Table ACLs. If you wish other users to be able to trigger updates to your dashboard, you will either need to grant them permissions to **Run as owner** or add permissions for the tables referenced in your queries. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Set Up an Alert -# MAGIC -# MAGIC Steps: -# MAGIC * Use the left side bar to navigate to **Alerts** -# MAGIC * Click **Create Alert** in the top right -# MAGIC * Click the field at the top left of the screen to give the alert a name **` Count Check`** -# MAGIC * Select your **User Counts** query -# MAGIC * For the **Trigger when** options, configure: -# MAGIC * **Value column**: `total_records` -# MAGIC * **Condition**: `>` -# MAGIC * **Threshold**: 15 -# MAGIC * For **Refresh**, select **Never** -# MAGIC * Click **Create Alert** -# MAGIC * On the next screen, click the blue **Refresh** in the top right to evaluate the alert - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Review Alert Destination Options -# MAGIC -# MAGIC -# MAGIC -# MAGIC Steps: -# MAGIC * From the preview of your alert, click the blue **Add** button to the right of **Destinations** on the right side of the screen -# MAGIC * At the bottom of the window that pops up, locate the and click the blue text in the message **Create new destinations in Alert Destinations** -# MAGIC * Review the available alerting options - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/0 - Instructions and Configuration.py b/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/0 - Instructions and Configuration.py deleted file mode 100644 index 0def76c..0000000 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/0 - Instructions and Configuration.py +++ /dev/null @@ -1,276 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## End-to-End ETL in the Lakehouse -# MAGIC -# MAGIC In this notebook, you will pull together concepts learned throughout the course to complete an example data pipeline. -# MAGIC -# MAGIC The following is a non-exhaustive list of skills and tasks necessary to successfully complete this exercise: -# MAGIC * Using Databricks notebooks to write queries in SQL and Python -# MAGIC * Creating and modifying databases, tables, and views -# MAGIC * Using Auto Loader and Spark Structured Streaming for incremental data processing in a multi-hop architecture -# MAGIC * Using Delta Live Table SQL syntax -# MAGIC * Configuring a Delta Live Table pipeline for continuous processing -# MAGIC * Using Databricks Jobs to orchestrate tasks from notebooks stored in Repos -# MAGIC * Setting chronological scheduling for Databricks Jobs -# MAGIC * Defining queries in Databricks SQL -# MAGIC * Creating visualizations in Databricks SQL -# MAGIC * Defining Databricks SQL dashboards to review metrics and results - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Run Setup -# MAGIC Run the following cell to reset all the databases and diretories associated with this lab. - -# COMMAND ---------- - -# MAGIC %run "../../Includes/dlt-setup" $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Land Initial Data -# MAGIC Seed the landing zone with some data before proceeding. - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create and Configure a DLT Pipeline -# MAGIC **NOTE**: The main difference between the instructions here and in previous labs with DLT is that in this instance, we will be setting up our pipeline for **Continuous** execution in **Production** mode. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Click the **Jobs** button on the sidebar, then select the **Delta Live Tables** tab. -# MAGIC 1. Click **Create Pipeline**. -# MAGIC 1. Fill in a **Pipeline Name** of your choosing. -# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the notebook `1 - DLT Task`. -# MAGIC 1. Run the cell below to generate values for **source**, **Target** and **Storage Location**. (All of these will include your current username). -# MAGIC * Click `Add configuration`; enter the word `source` in the **Key** field and the output printed next to `source` below in the value field. -# MAGIC * Enter the database name printed next to `Target` below in the **Target** field. -# MAGIC * Enter the location printed next to `Storage Location` below in the **Storage Location** field. -# MAGIC 1. Set **Pipeline Mode** to **Continuous**. -# MAGIC 1. Disable autoscaling. -# MAGIC 1. Set the number of wokers to 1. -# MAGIC 1. Click **Create**. -# MAGIC -# MAGIC In the UI that populates, change from **Development** to **Production** mode. This should begin the deployment of infrastructure. - -# COMMAND ---------- - -storage_location = userhome + "/output" -print(f"source : {dataLandingLocation.split(':')[1]}") -print(f"Target: {database}") -print(f"Storage Location: {storage_location.split(':')[1]}") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Schedule a Notebook Job -# MAGIC -# MAGIC Our DLT pipeline is setup to process data as soon as it arrives. We'll schedule a notebook to land a new batch of data each minute so we can see this functionality in action. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. -# MAGIC 1. Click the blue `Create Job` button -# MAGIC 1. Configure the task: -# MAGIC 1. Enter `Land-Data` for the task name -# MAGIC 1. Select the notebook `2 - Land New Data` using the notebook picker -# MAGIC 1. Select an Existing All Purpose Cluster from the **Cluster** dropdown -# MAGIC 1. Click **Create** -# MAGIC -# MAGIC **Note**: When selecting your all purpose cluster, you will get a warning about how this will be billed as all purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. -# MAGIC -# MAGIC ## Set a Chronological Schedule for your Job -# MAGIC Steps: -# MAGIC * On the right hand side of the Jobs UI, locate **Schedule** section. -# MAGIC * Click on the **Edit schedule** button to explore scheduling options. -# MAGIC * Change **Schedule type** field from **Manual** to **Scheduled** will bring up a chron scheduling UI. -# MAGIC * Set the schedule to update every **2 minutes** -# MAGIC * Click **Save** -# MAGIC -# MAGIC **NOTE**: If you wish, you can click **Run now** to trigger the first run, or wait until the top of the next minute to make sure your scheduling has worked successfully. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Register DLT Event Metrics for Querying with DBSQL -# MAGIC -# MAGIC The following cell prints out SQL statements to register the DLT event logs to your target database for querying in DBSQL. -# MAGIC -# MAGIC Execute the output code with the DBSQL Query Editor to register these tables and views. Explore each and make note of the logged event metrics. - -# COMMAND ---------- - -print(f""" -CREATE TABLE IF NOT EXISTS {database}.dlt_events -LOCATION '{storage_location}/system/events'; - -CREATE VIEW IF NOT EXISTS {database}.dlt_success AS -SELECT * FROM {database}.dlt_events -WHERE details:flow_progress:metrics IS NOT NULL; - -CREATE VIEW IF NOT EXISTS {database}.dlt_metrics AS -SELECT timestamp, origin.flow_name, details -FROM {database}.dlt_success -ORDER BY timestamp DESC; -""") - - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Define a Query on the Gold Table -# MAGIC -# MAGIC The `daily_patient_avg` table is automatically updated each time a new batch of data is processed through the DLT pipeline. Each time a query is executed against this table, DBSQL will confirm if there is a newer version and then materialize results from the newest available version. -# MAGIC -# MAGIC Run the following cell to print out a query with your database name. Save this as a DBSQL query. - -# COMMAND ---------- - -print(f"SELECT * FROM {database}.daily_patient_avg") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Add a Line Plot Visualization -# MAGIC -# MAGIC To track trends in patient averages over time, create a line plot and add it to a new dashboard. -# MAGIC -# MAGIC Create a line plot with the following settings: -# MAGIC * **X Column**: `date` -# MAGIC * **Y Columns**: `avg_heartrate` -# MAGIC * **Group By**: `name` -# MAGIC -# MAGIC Add this visualization to a dashboard. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Track Data Processing Progress -# MAGIC -# MAGIC The code below extracts the `flow_name`, `timestamp`, and `num_output_rows` from the DLT event logs. -# MAGIC -# MAGIC Save this query in DBSQL, then define a bar plot visualization that shows: -# MAGIC * **X Column**: `timestamp` -# MAGIC * **Y Columns**: `num_output_rows` -# MAGIC * **Group By**: `flow_name` -# MAGIC -# MAGIC Add your visualization to your dashboard. - -# COMMAND ---------- - -print(f""" -SELECT flow_name, timestamp, int(details:flow_progress:metrics:num_output_rows) num_output_rows -FROM {database}.dlt_metrics -ORDER BY timestamp DESC -""") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Refresh your Dashboard and Track Results -# MAGIC -# MAGIC The `Land-Data` notebook scheduled with Jobs above has 12 batches of data, each representing a month of recordings for our small sampling of patients. As configured per our instructions, it should take just over 20 minutes for all of these batches of data to be triggered and processed (we scheduled the Databricks Job to run every 2 minutes, and batches of data will process through our pipeline very quickly after initial ingestion). -# MAGIC -# MAGIC Refresh your dashboard and review your visualizations to see how many batches of data have been processed. (If you followed the instructions as outlined here, there should be 12 distinct flow updates tracked by your DLT metrics.) If all source data has not yet been processed, you can go back to the Databricks Jobs UI and manually trigger additional batches. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Execute a Query to Repair Broken Data -# MAGIC -# MAGIC Review the code that defined the `recordings_enriched` table to identify the filter applied for the quality check. -# MAGIC -# MAGIC In the cell below, write a query that returns all the records from the `recordings_bronze` table that were refused by this quality check. - -# COMMAND ---------- - -# TODO -display(spark.sql(f"")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC For the purposes of our demo, let's assume that thorough manual review of our data and systems has demonstrated that occassionally otherwise valid heartrate recordings are returned as negative values. -# MAGIC -# MAGIC Run the following query to examine these same rows with the negative sign removed. - -# COMMAND ---------- - -display(spark.sql(f"SELECT abs(heartrate), * FROM {database}.recordings_bronze WHERE heartrate <= 0")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC To complete our dataset, we wish to insert these fixed records into the silver `recordings_enriched` table. -# MAGIC -# MAGIC Use the cell below to update the query used in the DLT pipeline to execute this repair. -# MAGIC -# MAGIC **NOTE**: Make sure you update the code to only process those records that were previously rejected due to the quality check. - -# COMMAND ---------- - -# TODO -# CREATE INCREMENTAL LIVE TABLE recordings_enriched -# (CONSTRAINT positive_heartrate EXPECT (heartrate > 0) ON VIOLATION DROP ROW) -# AS SELECT -# CAST(a.device_id AS INTEGER) device_id, -# CAST(a.mrn AS LONG) mrn, -# CAST(a.heartrate AS DOUBLE) heartrate, -# CAST(from_unixtime(a.time, 'yyyy-MM-dd HH:mm:ss') AS TIMESTAMP) time, -# b.name -# FROM STREAM(live.recordings_bronze) a -# INNER JOIN STREAM(live.pii) b -# ON a.mrn = b.mrn - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Use the cell below to manually or programmatically confirm that this update has been successful. -# MAGIC -# MAGIC (The total number of records in the `recordings_bronze` should now be equal to the total records in `recordings_enriched`). - -# COMMAND ---------- - -# TODO - - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Consider Production Data Permissions -# MAGIC -# MAGIC Note that while our manual repair of the data was successful, as the owner of these datasets, by default we have permissions to modify or delete these data from any location we're executing code. -# MAGIC -# MAGIC To put this another way: our current permissions would allow us to change or drop our production tables permanently if an errant SQL query is accidentally executed with the current user's permissions (or if other users are granted similar permissions). -# MAGIC -# MAGIC While for the purposes of this lab, we desired to have full permissions on our data, as we move code from development to production, it is safer to leverage [service principals](https://docs.databricks.com/administration-guide/users-groups/service-principals.html) when scheduling Jobs and DLT Pipelines to avoid accidental data modifications. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Shut Down Production Infrastructure -# MAGIC -# MAGIC Note that Databricks Jobs, DLT Pipelines, and scheduled DBSQL queries and dashboards are all designed to provide sustained execution of production code. In this end-to-end demo, you were instructed to configure a Job and Pipeline for continuous data processing. To prevent these workloads from continuing to execute, you should **Pause** your Databricks Job and **Stop** your DLT pipeline. Deleting these assets will also ensure that production infrastructure is terminated. -# MAGIC -# MAGIC **NOTE**: All instructions for DBSQL asset scheduling in previous lessons instructed users to set the update schedule to end tomorrow. You may choose to go back and also cancel these updates to prevent DBSQL endpoints from staying on until that time. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/2 - Land New Data.py b/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/2 - Land New Data.py deleted file mode 100644 index 466f683..0000000 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/2 - Land New Data.py +++ /dev/null @@ -1,8 +0,0 @@ -# Databricks notebook source -# MAGIC %run ../../Includes/dlt-setup - -# COMMAND ---------- - -File.newData() - - diff --git a/Data-Engineering-With-Databricks/Includes/Reset.py b/Data-Engineering-With-Databricks/Includes/Reset.py deleted file mode 100644 index 7b1e766..0000000 --- a/Data-Engineering-With-Databricks/Includes/Reset.py +++ /dev/null @@ -1,21 +0,0 @@ -# Databricks notebook source -import pyspark.sql.functions as F -import re - -course_name = "eltsql" - -username = spark.sql("SELECT current_user()").first()[0] -clean_username = re.sub("[^a-zA-Z0-9]", "_", username) -database = f"""{clean_username}_dbacademy_{course_name}""" -userhome = f"dbfs:/user/{username}/dbacademy/{course_name}" - -print(f"username: {username}") -print(f"clean_username: {clean_username}") -print(f"database: {database}") -print(f"userhome: {userhome}") - -dbutils.fs.rm(userhome, True) - -print(f"Dropping the database {database}") -spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - diff --git a/Data-Engineering-With-Databricks/Includes/classic-setup.py b/Data-Engineering-With-Databricks/Includes/classic-setup.py deleted file mode 100644 index 7f69e33..0000000 --- a/Data-Engineering-With-Databricks/Includes/classic-setup.py +++ /dev/null @@ -1,71 +0,0 @@ -# Databricks notebook source -for stream in spark.streams.active: - stream.stop() - -# COMMAND ---------- - -import pyspark.sql.functions as F -import re - -course_name = "dewd" - -username = spark.sql("SELECT current_user()").first()[0] -clean_username = re.sub("[^a-zA-Z0-9]", "_", username) -database = f"dbacademy_{clean_username}_{course_name}" - -userhome = f"dbfs:/user/{username}/dbacademy/{course_name}" - -print(f""" -username: {username} -userhome: {userhome} -database: {database}""") - -dbutils.widgets.text("mode", "setup") -mode = dbutils.widgets.get("mode") - -if mode == "reset" or mode == "clean": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - -if mode != "clean": - spark.sql(f"CREATE DATABASE IF NOT EXISTS {database}") - spark.sql(f"USE {database}") - -# COMMAND ---------- - -# MAGIC %run ./mount-datasets - -# COMMAND ---------- - -outputPath = userhome + "/streaming-concepts" -checkpointPath = outputPath + "/checkpoint" - -# original dataset -dataSource = "/mnt/training/definitive-guide/data/activity-json/streaming" - -# data landing location; files will be copies from original dataset one at a time for incremental ingestion use case -dataLandingLocation = outputPath + "/landing-zone" - -outputTable = "bronze_table" - -spark.conf.set('c.outputTable', outputTable) - -# COMMAND ---------- - -class FileArrival: - def __init__(self, dataSource, landingZone): - self.sourceFiles = dbutils.fs.ls(dataSource) - dbutils.fs.mkdirs(landingZone) - self.landingZone = landingZone - self.fileID = 0 - - def newData(self, numFiles=1): - for i in range(numFiles): - dbutils.fs.cp(self.sourceFiles[self.fileID].path, self.landingZone) - self.fileID+=1 - -# COMMAND ---------- - -File = FileArrival(dataSource, dataLandingLocation) -File.newData() - diff --git a/Data-Engineering-With-Databricks/Includes/dlt-setup.py b/Data-Engineering-With-Databricks/Includes/dlt-setup.py deleted file mode 100644 index 2c76be0..0000000 --- a/Data-Engineering-With-Databricks/Includes/dlt-setup.py +++ /dev/null @@ -1,80 +0,0 @@ -# Databricks notebook source -for stream in spark.streams.active: - stream.stop() - -# COMMAND ---------- - -import pyspark.sql.functions as F -import re - -dbutils.widgets.text("course", "dewd") -course_name = dbutils.widgets.get("course") - -username = spark.sql("SELECT current_user()").first()[0] -clean_username = re.sub("[^a-zA-Z0-9]", "_", username) -database = f"dbacademy_{clean_username}_{course_name}" - -userhome = f"dbfs:/user/{username}/dbacademy/{course_name}" - -print(f""" -username: {username} -userhome: {userhome} -database: {database}""") - -dbutils.widgets.text("mode", "setup") -mode = dbutils.widgets.get("mode") - -if mode == "reset" or mode == "clean": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - -# COMMAND ---------- - -# MAGIC %run ./mount-datasets - -# COMMAND ---------- - -sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) - -# COMMAND ---------- - -dataSource = "/mnt/training/healthcare" - -dataLandingLocation = userhome + "/source" -bronzePath = userhome + "/bronze" -recordingsParsedPath = userhome + "/silver/recordings_parsed" -recordingsEnrichedPath = userhome + "/silver/recordings_enriched" -dailyAvgPath = userhome + "/gold/dailyAvg" - -checkpointPath = userhome + "/checkpoints" -bronzeCheckpoint = userhome + "/checkpoints/bronze" -recordingsParsedCheckpoint = userhome + "/checkpoints/recordings_parsed" -recordingsEnrichedCheckpoint = userhome + "/checkpoints/recordings_enriched" -dailyAvgCheckpoint = userhome + "/checkpoints/dailyAvgPath" - -# COMMAND ---------- - -class FileArrival: - def __init__(self): - self.source = dataSource + "/tracker/streaming/" - self.userdir = dataLandingLocation + "/" - try: - self.curr_mo = 1 + int(max([x[1].split(".")[0] for x in dbutils.fs.ls(self.userdir)])) - except: - self.curr_mo = 1 - - def newData(self, continuous=False): - if self.curr_mo > 12: - print("Data source exhausted\n") - elif continuous == True: - while self.curr_mo <= 12: - curr_file = f"{self.curr_mo:02}.json" - dbutils.fs.cp(self.source + curr_file, self.userdir + curr_file) - self.curr_mo += 1 - else: - curr_file = f"{str(self.curr_mo).zfill(2)}.json" - dbutils.fs.cp(self.source + curr_file, self.userdir + curr_file) - self.curr_mo += 1 - -File = FileArrival() - diff --git a/Data-Engineering-With-Databricks/Includes/multi-hop-setup.py b/Data-Engineering-With-Databricks/Includes/multi-hop-setup.py deleted file mode 100644 index fdc0645..0000000 --- a/Data-Engineering-With-Databricks/Includes/multi-hop-setup.py +++ /dev/null @@ -1,80 +0,0 @@ -# Databricks notebook source -for stream in spark.streams.active: - stream.stop() - -# COMMAND ---------- - -import pyspark.sql.functions as F -import re - -course_name = "dewd" - -username = spark.sql("SELECT current_user()").first()[0] -clean_username = re.sub("[^a-zA-Z0-9]", "_", username) -database = f"dbacademy_{clean_username}_{course_name}" - -userhome = f"dbfs:/user/{username}/dbacademy/{course_name}" - -print(f""" -username: {username} -userhome: {userhome} -database: {database}""") - -dbutils.widgets.text("mode", "setup") -mode = dbutils.widgets.get("mode") - -if mode == "reset" or mode == "clean": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - -if mode != "clean": - spark.sql(f"CREATE DATABASE IF NOT EXISTS {database}") - spark.sql(f"USE {database}") - -# COMMAND ---------- - -# MAGIC %run ./mount-datasets - -# COMMAND ---------- - -sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) - -# COMMAND ---------- - -dataSource = "/mnt/training/healthcare" - -dataLandingLocation = userhome + "/source" -bronzePath = userhome + "/bronze" -recordingsParsedPath = userhome + "/silver/recordings_parsed" -recordingsEnrichedPath = userhome + "/silver/recordings_enriched" -dailyAvgPath = userhome + "/gold/dailyAvg" - -checkpointPath = userhome + "/checkpoints" -bronzeCheckpoint = userhome + "/checkpoints/bronze" -recordingsParsedCheckpoint = userhome + "/checkpoints/recordings_parsed" -recordingsEnrichedCheckpoint = userhome + "/checkpoints/recordings_enriched" -dailyAvgCheckpoint = userhome + "/checkpoints/dailyAvgPath" - -# COMMAND ---------- - -class FileArrival: - def __init__(self): - self.source = dataSource + "/tracker/streaming/" - self.userdir = dataLandingLocation + "/" - self.curr_mo = 1 - - def newData(self, continuous=False): - if self.curr_mo > 12: - print("Data source exhausted\n") - elif continuous == True: - while self.curr_mo <= 12: - curr_file = f"{self.curr_mo:02}.json" - dbutils.fs.cp(self.source + curr_file, self.userdir + curr_file) - self.curr_mo += 1 - else: - curr_file = f"{str(self.curr_mo).zfill(2)}.json" - dbutils.fs.cp(self.source + curr_file, self.userdir + curr_file) - self.curr_mo += 1 - -File = FileArrival() - diff --git a/Data-Engineering-With-Databricks/Includes/query_generator.py b/Data-Engineering-With-Databricks/Includes/query_generator.py deleted file mode 100644 index 14b40b3..0000000 --- a/Data-Engineering-With-Databricks/Includes/query_generator.py +++ /dev/null @@ -1,88 +0,0 @@ -# Databricks notebook source -class QueryGenerator: - def __init__(self, course, mode="normal"): - import re - import random - self.username = spark.sql("SELECT current_user()").first()[0] - self.userhome = f"dbfs:/user/{self.username}/{course}" - self.database = f"""dbacademy_{re.sub("[^a-zA-Z0-9]", "_", self.username)}_{course}""" - - if mode == "reset": - spark.sql(f"DROP DATABASE IF EXISTS {self.database} CASCADE") - dbutils.fs.rm(self.userhome, True) - - def config(self): - print(f""" -CREATE DATABASE {self.database} -LOCATION '{self.userhome}'; - -USE {self.database}; - -CREATE TABLE user_ping -(user_id STRING, ping INTEGER, time TIMESTAMP); - -CREATE TABLE user_ids (user_id STRING); - -INSERT INTO user_ids VALUES -("potato_luver"), -("beanbag_lyfe"), -("default_username"), -("the_king"), -("n00b"), -("frodo"), -("data_the_kid"), -("el_matador"), -("the_wiz"); - -CREATE FUNCTION get_ping() - RETURNS INT - RETURN int(rand() * 250); - -CREATE FUNCTION is_active() - RETURNS BOOLEAN - RETURN CASE - WHEN rand() > .25 THEN true - ELSE false - END; - """) - - def load(self): - print(f""" -INSERT INTO {self.database}.user_ping -SELECT *, - {self.database}.get_ping() ping, - current_timestamp() time -FROM {self.database}.user_ids -WHERE {self.database}.is_active()=true; - -SELECT * FROM {self.database}.user_ping; - """) - - def user_counts(self): - print(f""" -SELECT user_id, count(*) total_records -FROM {self.database}.user_ping -GROUP BY user_id -ORDER BY - total_records DESC, - user_id ASC;; - """) - - def avg_ping(self): - print(f""" -SELECT user_id, window.end end_time, mean(ping) avg_ping -FROM {self.database}.user_ping -GROUP BY user_id, window(time, '3 minutes') -ORDER BY - end_time DESC, - user_id ASC; - """) - - def summary(self): - print(f""" -SELECT user_id, min(time) first_seen, max(time) last_seen, count(*) total_records, avg(ping) total_avg_ping -FROM {self.database}.user_ping -GROUP BY user_id -ORDER BY user_id ASC - """) - diff --git a/Data-Engineering-With-Databricks/Includes/setup-cleaned.py b/Data-Engineering-With-Databricks/Includes/setup-cleaned.py deleted file mode 100644 index 09cb1e2..0000000 --- a/Data-Engineering-With-Databricks/Includes/setup-cleaned.py +++ /dev/null @@ -1,56 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./setup-updates - -# COMMAND ---------- - -def merge_deduped_users(): - spark.sql(f""" - CREATE OR REPLACE TEMP VIEW deduped_users AS - SELECT user_id, user_first_touch_timestamp, max(email) email, max(updated) updated - FROM users_update - GROUP BY user_id, user_first_touch_timestamp - """) - - spark.sql(f""" - MERGE INTO users a - USING deduped_users b - ON a.user_id = b.user_id - WHEN MATCHED AND a.email IS NULL AND b.email IS NOT NULL THEN - UPDATE SET email = b.email, updated = b.updated - WHEN NOT MATCHED THEN INSERT * - """) - - -# COMMAND ---------- - -def merge_events_update(): - spark.sql(f""" - MERGE INTO events a - USING events_update b - ON a.user_id = b.user_id AND a.event_timestamp = b.event_timestamp - WHEN NOT MATCHED AND b.traffic_source = 'email' THEN - INSERT * - """) - - -# COMMAND ---------- - -def merge_sales_update(): - spark.sql(f""" - COPY INTO sales - FROM "{Paths.source}/sales/sales-30m.parquet" - FILEFORMAT = PARQUET - """) - - -# COMMAND ---------- - -merge_deduped_users() -merge_events_update() -merge_sales_update() - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC CREATE OR REPLACE TABLE item_lookup AS -# MAGIC SELECT * FROM parquet.`${c.source}/products/products.parquet` diff --git a/Data-Engineering-With-Databricks/Includes/setup-external.py b/Data-Engineering-With-Databricks/Includes/setup-external.py deleted file mode 100644 index 6acbf65..0000000 --- a/Data-Engineering-With-Databricks/Includes/setup-external.py +++ /dev/null @@ -1,21 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./setup - -# COMMAND ---------- - -dbutils.fs.rm(f"{Paths.source}/sales/sales.csv", True) -dbutils.fs.cp(f"{Paths.source_uri}/sales/sales.csv", f"{Paths.source}/sales/sales.csv", True) - -(spark - .read - .format("parquet") - .load(f"{Paths.source}/users/users.parquet") - .repartition(1) - .write - .format("org.apache.spark.sql.jdbc") - .option("url", f"jdbc:sqlite:/{username}_ecommerce.db") - .option("dbtable", "users") - .mode("overwrite") - .save()) - - diff --git a/Data-Engineering-With-Databricks/Includes/setup-load.py b/Data-Engineering-With-Databricks/Includes/setup-load.py deleted file mode 100644 index 8ed10a1..0000000 --- a/Data-Engineering-With-Databricks/Includes/setup-load.py +++ /dev/null @@ -1,26 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./setup - -# COMMAND ---------- - -def load_historical(): - spark.sql(f""" - CREATE OR REPLACE TABLE events AS - SELECT * FROM parquet.`{Paths.source}/events/events.parquet` - """) - - spark.sql(f""" - CREATE OR REPLACE TABLE users AS - SELECT *, current_timestamp() updated FROM parquet.`{Paths.source}/users/users.parquet` - """) - - spark.sql(f""" - CREATE OR REPLACE TABLE sales AS - SELECT * FROM parquet.`{Paths.source}/sales/sales.parquet` - """) - - -# COMMAND ---------- - -load_historical() - diff --git a/Data-Engineering-With-Databricks/Includes/setup-meta.py b/Data-Engineering-With-Databricks/Includes/setup-meta.py deleted file mode 100644 index 05a5aa0..0000000 --- a/Data-Engineering-With-Databricks/Includes/setup-meta.py +++ /dev/null @@ -1,11 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./sql-setup $course="meta" $mode="cleanup" - -# COMMAND ---------- - -URI = "wasbs://courseware@dbacademy.blob.core.windows.net/databases_tables_and_views_on_databricks/v02" - -# COMMAND ---------- - -dbutils.fs.cp(URI, f"{userhome}/datasets", True) - diff --git a/Data-Engineering-With-Databricks/Includes/setup-transactions.py b/Data-Engineering-With-Databricks/Includes/setup-transactions.py deleted file mode 100644 index 93da748..0000000 --- a/Data-Engineering-With-Databricks/Includes/setup-transactions.py +++ /dev/null @@ -1,43 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./setup-cleaned - -# COMMAND ---------- - -def create_transactions(): - spark.sql(f""" - CREATE OR REPLACE TABLE transactions AS - SELECT * FROM ( - SELECT - user_id, - order_id, - transaction_timestamp, - total_item_quantity, - purchase_revenue_in_usd, - unique_items, - a.items_exploded.item_id item_id, - a.items_exploded.quantity quantity - FROM - ( SELECT *, explode(items) items_exploded FROM sales ) a - INNER JOIN users b - ON a.email = b.email - ) PIVOT ( - sum(quantity) FOR item_id in ( - 'P_FOAM_K', - 'M_STAN_Q', - 'P_FOAM_S', - 'M_PREM_Q', - 'M_STAN_F', - 'M_STAN_T', - 'M_PREM_K', - 'M_PREM_F', - 'M_STAN_K', - 'M_PREM_T', - 'P_DOWN_S', - 'P_DOWN_K' - ) - ) - """) - -create_transactions() - - diff --git a/Data-Engineering-With-Databricks/Includes/setup-updates.py b/Data-Engineering-With-Databricks/Includes/setup-updates.py deleted file mode 100644 index 9db7eaa..0000000 --- a/Data-Engineering-With-Databricks/Includes/setup-updates.py +++ /dev/null @@ -1,62 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./setup-load - -# COMMAND ---------- - -def load_events_raw(): - spark.sql(f""" - CREATE TABLE IF NOT EXISTS events_json - (key BINARY, offset INT, partition BIGINT, timestamp BIGINT, topic STRING, value BINARY) - USING JSON OPTIONS (path = "{Paths.source}/events/events-kafka.json"); - """) - - spark.sql(f""" - CREATE OR REPLACE TABLE events_raw - (key BINARY, offset BIGINT, partition BIGINT, timestamp BIGINT, topic STRING, value BINARY); - """) - - spark.sql(f""" - INSERT INTO events_raw - SELECT * FROM events_json - """) - - -# COMMAND ---------- - -# lesson: nested data & advanced transformations -# Last Lab & Writing to Delta -def create_events_update(): - spark.sql(f""" - CREATE OR REPLACE TEMP VIEW events_raw_json AS - SELECT from_json(cast(value as STRING), ("device STRING, ecommerce STRUCT< purchase_revenue_in_usd: DOUBLE, total_item_quantity: BIGINT, unique_items: BIGINT>, event_name STRING, event_previous_timestamp BIGINT, event_timestamp BIGINT, geo STRUCT< city: STRING, state: STRING>, items ARRAY< STRUCT< coupon: STRING, item_id: STRING, item_name: STRING, item_revenue_in_usd: DOUBLE, price_in_usd: DOUBLE, quantity: BIGINT>>, traffic_source STRING, user_first_touch_timestamp BIGINT, user_id STRING")) json - FROM events_raw - """) - - spark.sql(f""" - CREATE OR REPLACE TEMP VIEW events_update AS - WITH deduped_events_raw AS ( - SELECT max(json) json FROM events_raw_json - GROUP BY json.user_id, json.event_timestamp - ) - SELECT json.* FROM deduped_events_raw - """) - - -# COMMAND ---------- - -# lesson: Writing delta -def create_users_update(): - spark.sql(f""" - CREATE OR REPLACE TEMP VIEW users_update AS - SELECT *, current_timestamp() updated - FROM parquet.`{Paths.source}/users/users-30m.parquet` - """) - - -# COMMAND ---------- - -load_events_raw() -create_events_update() -create_users_update() - - diff --git a/Data-Engineering-With-Databricks/Includes/setup.py b/Data-Engineering-With-Databricks/Includes/setup.py deleted file mode 100644 index 32ef8d1..0000000 --- a/Data-Engineering-With-Databricks/Includes/setup.py +++ /dev/null @@ -1,98 +0,0 @@ -# Databricks notebook source -import pyspark.sql.functions as F -import re - -class BuildEnvironmentVariables: - - def __init__(self, username): - self.course_name = "eltsql" - self.source_uri = "wasbs://courseware@dbacademy.blob.core.windows.net/elt-with-spark-sql/v01" - - self.username = username - self.working_dir = f"dbfs:/user/{self.username}/dbacademy/{self.course_name}" - self.userhome = self.working_dir # TEMPORARY BACKWARDS COMPATABILITY - - clean_username = re.sub("[^a-zA-Z0-9]", "_", self.username) - self.database_name = f"{clean_username}_dbacademy_{self.course_name}" - self.database_location = f"{self.working_dir}/db" - - self.source = f"{self.working_dir}/source_datasets" - self.base_path=f"{self.working_dir}/tables" - - self.sales_table_path = f"{self.base_path}/sales" - self.users_table_path = f"{self.base_path}/users" - self.events_raw_table_path = f"{self.base_path}/events_raw" - self.events_clean_table_path = f"{self.base_path}/events_clean" - self.transactions_table_path = f"{self.base_path}/transactions" - self.clickpaths_table_path = f"{self.base_path}/clickpaths" - - def set_hive_variables(self): - for (k, v) in self.__dict__.items(): - spark.sql(f"SET c.{k} = {v}") - - def __repr__(self): - return self.__dict__.__repr__().replace(", ", ",\n") - - -# COMMAND ---------- - -username = spark.sql("SELECT current_user()").first()[0] -dbacademy_env = BuildEnvironmentVariables(username) -Paths = dbacademy_env # Temporary backwards compatability - -# Hack for backwards compatability -username = dbacademy_env.username -database = dbacademy_env.database_name -userhome = dbacademy_env.working_dir - -print(f"username: {username}") -print(f"database: {database}") -print(f"userhome: {userhome}") - -# print(f"dbacademy_env: Databricks Academy configuration object") -# print(f"dbacademy_env.username: {dbacademy_env.username}") -# print(f"dbacademy_env.database_name: {dbacademy_env.database_name}") -# print(f"dbacademy_env.working_dir: {dbacademy_env.working_dir}") - - -# COMMAND ---------- - -def path_exists(path): - try: - return len(dbutils.fs.ls(path)) >= 0 - except Exception: - return False - -dbutils.widgets.text("mode", "default") -mode = dbutils.widgets.get("mode") - -if mode == "reset" or mode == "cleanup": - # Drop the database and remove all data for both reset and cleanup - print(f"Removing the database {database}") - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - - print(f"Removing previously generated datasets from\n{dbacademy_env.working_dir}") - dbutils.fs.rm(dbacademy_env.working_dir, True) - -if mode != "cleanup": - # We are not cleaning up so we want to setup the environment - - # RESET is in case we want to force a reset - # not-existing for net-new install - if mode == "reset" or not path_exists(dbacademy_env.source): - print(f"\nInstalling datasets to\n{dbacademy_env.source}") - print(f"""\nNOTE: The datasets that we are installing are located in Washington, USA - depending on the - region that your workspace is in, this operation can take as little as 3 minutes and - upwards to 6 minutes, but this is a one-time operation.""") - - dbutils.fs.cp(dbacademy_env.source_uri, dbacademy_env.source, True) - print(f"""\nThe install of the datasets completed successfully.""") - - # Create the database and use it. - spark.sql(f"CREATE DATABASE IF NOT EXISTS {dbacademy_env.database_name} LOCATION '{dbacademy_env.database_location}'") - spark.sql(f"USE {dbacademy_env.database_name}") - - # Once the database is created, init the hive variables - dbacademy_env.set_hive_variables() - - diff --git a/Data-Engineering-With-Databricks/Includes/sql-setup.py b/Data-Engineering-With-Databricks/Includes/sql-setup.py deleted file mode 100644 index 6d1d83a..0000000 --- a/Data-Engineering-With-Databricks/Includes/sql-setup.py +++ /dev/null @@ -1,32 +0,0 @@ -# Databricks notebook source -import pyspark.sql.functions as F -import re - -dbutils.widgets.text("course", "dewd") -course = dbutils.widgets.get("course") -username = spark.sql("SELECT current_user()").collect()[0][0] -userhome = f"dbfs:/user/{username}/{course}" -database = f"""{course}_{re.sub("[^a-zA-Z0-9]", "_", username)}_db""" -print(f""" -username: {username} -userhome: {userhome} -database: {database}""") - -spark.sql(f"SET c.username = {username}") -spark.sql(f"SET c.userhome = {userhome}") -spark.sql(f"SET c.database = {database}") - -dbutils.widgets.text("mode", "setup") -mode = dbutils.widgets.get("mode") - -if mode == "reset": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - spark.sql(f"CREATE DATABASE IF NOT EXISTS {database} LOCATION '{userhome}'") - spark.sql(f"USE {database}") - -if mode == "cleanup": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - - diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.1.2 - Views and CTEs on Databricks.sql b/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.1.2 - Views and CTEs on Databricks.sql deleted file mode 100644 index dbfee90..0000000 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.1.2 - Views and CTEs on Databricks.sql +++ /dev/null @@ -1,278 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC # Views and CTEs on Databricks --- MAGIC In this demonstration, you will create and explore views and common table expressions (CTEs). --- MAGIC --- MAGIC ## Learning Objectives --- MAGIC By the end of this lesson, you will be able to: --- MAGIC * Use Spark SQL DDL to define views --- MAGIC * Run queries that use common table expressions --- MAGIC --- MAGIC --- MAGIC --- MAGIC **Resources** --- MAGIC * [Create View - Databricks Docs](https://docs.databricks.com/spark/latest/spark-sql/language-manual/sql-ref-syntax-ddl-create-view.html) --- MAGIC * [Common Table Expressions - Databricks Docs](https://docs.databricks.com/spark/latest/spark-sql/language-manual/sql-ref-syntax-qry-select-cte.html) - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Classroom Setup --- MAGIC The following script clears out previous runs of this demo and configures some Hive variables that will be used in our SQL queries. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-meta - --- COMMAND ---------- - --- MAGIC %md --- MAGIC We start by creating a table of data we can use for the demonstration. - --- COMMAND ---------- - -CREATE DATABASE IF NOT EXISTS ${c.database}; -USE ${c.database}; - --- mode "FAILFAST" will abort file parsing with a RuntimeException if any malformed lines are encountered -CREATE OR REPLACE TEMPORARY VIEW temp_delays USING CSV OPTIONS ( - path '${c.userhome}/datasets/flights/departuredelays.csv', - header "true", - mode "FAILFAST" -); -CREATE OR REPLACE TABLE external_table LOCATION '${c.userhome}/external_table' AS - SELECT * FROM temp_delays; - -SELECT * FROM external_table; - --- COMMAND ---------- - --- MAGIC %md ## Views --- MAGIC Let's create a view that contains only the data where the origin is 'ABQ' and the destination is 'LAX'. - --- COMMAND ---------- - -CREATE OR REPLACE VIEW view_delays_ABQ_LAX AS -SELECT * FROM external_table WHERE origin = 'ABQ' AND destination = 'LAX'; -SELECT * FROM view_delays_ABQ_LAX; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC To show a list of tables (and views), we use the `SHOW TABLES` command. --- MAGIC --- MAGIC Note that the `view_delays_abq_lax` view is in the list. If we detach from, and reattach to, the cluster and reload the list of tables, view_delays_abq_lax persists. This is because View metadata (name, location, etc.) are stored in the metastore. --- MAGIC --- MAGIC (The command `USE ${c.database};` is used after reattaching to the cluster because state is lost when the SparkSession is deleted) - --- COMMAND ---------- - -USE ${c.database}; -SHOW tables; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Now, let's create a temporary view. The syntax is very similar but adds `TEMPORARY` to the command. - --- COMMAND ---------- - -CREATE OR REPLACE TEMPORARY VIEW temp_view AS -SELECT * FROM external_table WHERE delay > 120 ORDER BY delay ASC; -SELECT * FROM temp_view; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Let's again show list of tables (and views). --- MAGIC --- MAGIC Two things we note are that the `temp_view` view is in the list and that `temp_view` is marked `isTemporary`. --- MAGIC --- MAGIC If we detach from, and reattach to, the cluster and reload the list of tables, `temp_view` is deleted. This is because temporary view metadata (name, location, etc.) are not stored in the metastore. When we detach from the cluster, the Spark session is deleted, which deletes the temporary view. - --- COMMAND ---------- - -USE ${c.database}; -SHOW TABLES; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Let's now create a global temporary view. We add `GLOBAL` to the command. This view is just like the temporary view above, but it is different in one important way. It is added to the `global_temp` database that exists on the cluster. As long as the cluster is running, this database persists, and any notebooks attached to the cluster can access its global temporary views. --- MAGIC --- MAGIC Note when we use global temporary views, we have to prefix them with `global_temp.` since we are accessing the `global_temp` database. - --- COMMAND ---------- - -CREATE OR REPLACE GLOBAL TEMPORARY VIEW global_temp_view_distance AS -SELECT * FROM external_table WHERE distance > 1000; -SELECT * FROM global_temp.global_temp_view_distance; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Again, global temporary views are available to any notebook attached to the cluster, including --- MAGIC - New notebooks --- MAGIC - This notebook, even if it is detached from, and reattached to, the cluster - --- COMMAND ---------- - -SELECT * FROM global_temp.global_temp_view_distance; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC One thing to note is that global temporary views do not show in the list of tables. - --- COMMAND ---------- - -USE ${c.database}; -SHOW TABLES; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Common Table Expressions (CTEs) --- MAGIC CTEs can be used in a variety of contexts. Below, are a few examples of the different ways a CTE can be used in a query. First, an example of making multiple column aliases using a CTE. - --- COMMAND ---------- - -WITH flight_delays( - total_delay_time, - origin_airport, - destination_airport -) AS ( - SELECT - delay, - origin, - destination - FROM - external_table -) -SELECT - * -FROM - flight_delays -WHERE - total_delay_time > 120 - AND origin_airport = "ATL" - AND destination_airport = "DEN"; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Next, is an example of a CTE in a CTE definition. - --- COMMAND ---------- - -WITH lax_bos AS ( - WITH origin_destination (origin_airport, destination_airport) AS ( - SELECT - origin, - destination - from - external_table - ) - SELECT - * - FROM - origin_destination - WHERE - origin_airport = 'LAX' - AND destination_airport = 'BOS' -) -SELECT - count(origin_airport) AS `Total Flights from LAX to BOS` -FROM - lax_bos; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Now, here is an example of a CTE in a subquery. - --- COMMAND ---------- - -SELECT - max(total_delay) AS `Longest Delay (in minutes)` -FROM - ( - WITH delayed_flights(total_delay) AS ( - SELECT - delay - from - external_table - ) - SELECT - * - FROM - delayed_flights - ); - --- COMMAND ---------- - --- MAGIC %md --- MAGIC We can also use a CTE in a subquery expression. - --- COMMAND ---------- - -SELECT - ( - WITH distinct_origins AS ( - SELECT DISTINCT origin FROM external_table - ) - SELECT - count(origin) - FROM - distinct_origins - ) AS `Number of Different Origin Airports`; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Finally, here is a CTE in a CREATE VIEW statement. - --- COMMAND ---------- - -CREATE OR REPLACE VIEW BOS_LAX AS -WITH origin_destination(origin_airport, destination_airport) AS -(SELECT origin, destination FROM external_table) -SELECT * FROM origin_destination -WHERE origin_airport = 'BOS' AND destination_airport = 'LAX'; -SELECT count(origin_airport) AS `Number of Delayed Flights from BOS to LAX` FROM BOS_LAX; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Clean up --- MAGIC We first drop the training database. - --- COMMAND ---------- - -DROP DATABASE ${c.database} CASCADE; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Finally, we delete the working directory. - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.rm(userhome, True) - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.3 - Creating Delta Tables.sql b/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.3 - Creating Delta Tables.sql deleted file mode 100644 index 7c390cb..0000000 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.3 - Creating Delta Tables.sql +++ /dev/null @@ -1,237 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC # Creating Delta Tables --- MAGIC --- MAGIC After extracting data from external data sources, load data into the Lakehouse to ensure that all of the benefits of the Databricks platform can be fully leveraged. --- MAGIC --- MAGIC While different organizations may have varying policies for how data is initially loaded into Databricks, we typically recommend that early tables represent a mostly raw version of the data, and that validation and enrichment occur in later stages. This pattern ensures that even if data doesn't match expectations with regards to data types or column names, no data will be dropped, meaning that programmatic or manual intervention can still salvage data in a partially corrupted or invalid state. --- MAGIC --- MAGIC This lesson will focus primarily on the pattern used to create most tables, `CREATE TABLE _ AS SELECT` (CTAS) statements. --- MAGIC --- MAGIC ##### Objectives --- MAGIC - Use CTAS statements to create Delta Lake tables --- MAGIC - Create new tables from existing views or tables --- MAGIC - Enrich loaded data with additional metadata --- MAGIC - Declare table schema with generated columns and descriptive comments --- MAGIC - Set advanced options to control data location, quality enforcement, and partitioning - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Create an Empty Delta Table --- MAGIC Use the `CREATE TABLE USING` statement to define an empty Delta table in the metastore. --- MAGIC --- MAGIC We won't need to explicitly state `USING DELTA`, as Delta is the default format. - --- COMMAND ---------- - -CREATE OR REPLACE TABLE users (user_id BIGINT, user_first_touch_timestamp BIGINT, email STRING); - -DESCRIBE EXTENDED users - --- COMMAND ---------- - --- MAGIC %md ## Create Table as Select (CTAS) --- MAGIC --- MAGIC `CREATE TABLE AS SELECT` statements create and populate Delta tables using data retrieved from an input query. - --- COMMAND ---------- - -CREATE OR REPLACE TABLE sales AS -SELECT * FROM parquet.`${c.source}/sales/sales.parquet`; - -DESCRIBE EXTENDED sales - --- COMMAND ---------- - --- MAGIC %md Because they inherit schemas from the query data, CTAS statements do **not** support schema declarations. In the following query, the CTAS statement can not provide the options necessary to properly ingest data from CSV files. - --- COMMAND ---------- - -CREATE OR REPLACE TABLE sales_unparsed AS -SELECT * FROM csv.`${c.source}/sales/sales.csv`; - -SELECT * FROM sales_unparsed - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Subset Columns from Existing Tables --- MAGIC We can also use CTAS statements to load data from existing tables and views. --- MAGIC --- MAGIC The following statement creates a new table containing a subset of columns from the `sales` table. Here, we'll presume that we're intentionally leaving out information that potentially identifies the user or that provides itemized purchase details. - --- COMMAND ---------- - -CREATE OR REPLACE TABLE purchases AS -SELECT order_id, transaction_timestamp, purchase_revenue_in_usd -FROM sales; - -SELECT * FROM purchases - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Declare Schema with Generated Columns --- MAGIC --- MAGIC As noted previously, CTAS statements do not support schema declaration. We note above that the timestamp column appears to be some variant of a Unix timestamp, which may not be the most useful for our analysts to derive insights. This is a situation where generated columns would be beneficial. --- MAGIC --- MAGIC Generated columns are a special type of column whose values are automatically generated based on a user-specified function over other columns in the Delta table. Databricks introduced generated columns in DBR 8.3. --- MAGIC --- MAGIC The code below demonstrates creating a new table while: --- MAGIC 1. Specifying column names and types --- MAGIC 1. Adding a [generated column](https://docs.databricks.com/delta/delta-batch.html#deltausegeneratedcolumns) to calculate the date --- MAGIC 1. Providing a descriptive column comment for the generated column - --- COMMAND ---------- - -CREATE OR REPLACE TABLE purchase_dates ( - order_id STRING, - transaction_timestamp STRING, - purchase_revenue_in_usd STRING, - date DATE GENERATED ALWAYS AS ( - cast(cast(transaction_timestamp/1e6 AS TIMESTAMP) AS DATE)) - COMMENT "generated based on `transactions_timestamp` column") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC --- MAGIC Because `date` is a generated column, if we write to `purchase_dates` without providing values for the `date` column, Delta Lake automatically computes them. --- MAGIC --- MAGIC **NOTE**: The cell below configures a setting to allow for generating columns when using a Delta Lake `MERGE` statement. We'll see more on this syntax later in the course. - --- COMMAND ---------- - -SET spark.databricks.delta.schema.autoMerge.enabled=true; - -MERGE INTO purchase_dates a -USING purchases b -ON a.order_id = b.order_id -WHEN NOT MATCHED THEN - INSERT * - --- COMMAND ---------- - --- MAGIC %md The query automatically reads the most recent snapshot of the table for any query; you never need to run `REFRESH TABLE`. - --- COMMAND ---------- - -SELECT * FROM purchase_dates - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Enrich Tables with Additional Options and Metadata --- MAGIC --- MAGIC So far we've only shown creating managed tables, which will store all data associated with a newly created table under the databases default location. For many use cases, these will be the right choice. --- MAGIC --- MAGIC Here, we'll create an external Delta Lake table and provide a number of additional options. The syntax below will: --- MAGIC 1. Add a table comment --- MAGIC 1. Add an arbitrary key-value pair as a table property --- MAGIC 1. Add a date column derived from source table data --- MAGIC 1. Add a column to record current timestamp at time of update --- MAGIC 1. Configure table `LOCATION` path --- MAGIC 1. Partition tables by a column --- MAGIC --- MAGIC **NOTE**: A number of Delta Lake configurations are set using `TBLPROPERTIES`. When using this field as part of an organizational approach to data discovery and auditing, users should be made aware of which keys are leveraged for modifying default Delta Lake behaviors. - --- COMMAND ---------- - -CREATE OR REPLACE TABLE users_pii -COMMENT "Contains PII" -TBLPROPERTIES ('contains_pii' = True) -LOCATION "${c.userhome}/tmp/users_pii" -PARTITIONED BY (first_touch_date) -AS - SELECT *, - cast(cast(user_first_touch_timestamp/1e6 AS TIMESTAMP) AS DATE) first_touch_date, - current_timestamp() updated - FROM parquet.`${c.source}/users/users.parquet`; - --- COMMAND ---------- - --- MAGIC %md Specifying the `LOCATION` path creates an external Delta table that is unmanaged by the Metastore. --- MAGIC --- MAGIC All of the comments and properties for a given table can be reviewed using `DESCRIBE TABLE EXTENDED`. --- MAGIC --- MAGIC **NOTE**: Delta Lake automatically adds several table properties on table creation. - --- COMMAND ---------- - -DESCRIBE EXTENDED users_pii - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Listing the location used for the table reveals that the unique values in the partition column `date` are used to create data directories. - --- COMMAND ---------- - --- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{Paths.userhome}/tmp/users_pii")) - --- COMMAND ---------- - --- MAGIC %md --- MAGIC --- MAGIC Delta Lake automatically uses partitioning and statistics to read the minimum amount of data when there are applicable predicates in the query. - --- COMMAND ---------- - -SELECT * FROM users_pii WHERE first_touch_date = "2020-06-19" - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Add a Table Constraint --- MAGIC --- MAGIC Because Delta Lake enforces schema on write, Databricks can support standard SQL constraint management clauses to ensure the quality and integrity of data added to a table. --- MAGIC --- MAGIC Databricks currently support two types of constraints: --- MAGIC * [`NOT NULL` constraints](https://docs.databricks.com/delta/delta-constraints.html#not-null-constraint) --- MAGIC * [`CHECK` constraints](https://docs.databricks.com/delta/delta-constraints.html#check-constraint) --- MAGIC --- MAGIC In both cases, you must ensure that no data violating the constraint is already in the table prior to defining the constraint. Once a constraint has been added to a table, data violating the constraint will result in write failure. --- MAGIC --- MAGIC Below, we'll add a `CHECK` constraint to the `date` column of our table. Note that `CHECK` constraints look like standard `WHERE` clauses you might use to filter a dataset. - --- COMMAND ---------- - -ALTER TABLE users_pii ADD CONSTRAINT valid_date CHECK (first_touch_date > '2020-01-01'); - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Table constraints are shown in the `TBLPROPERTIES` field, alongside user-specified tags and other Delta Lake metadata. - --- COMMAND ---------- - -DESCRIBE EXTENDED users_pii - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.4 - Writing to Tables.sql b/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.4 - Writing to Tables.sql deleted file mode 100644 index 741acd5..0000000 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.4 - Writing to Tables.sql +++ /dev/null @@ -1,160 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC # Writing to Delta Tables --- MAGIC Use SQL DML statements to perform complete and incremental updates to existing Delta tables. --- MAGIC --- MAGIC ##### Objectives --- MAGIC - Overwrite data tables using `INSERT OVERWRITE` --- MAGIC - Append to a table using `INSERT INTO` --- MAGIC - Append, update, and delete from a table using `MERGE INTO` --- MAGIC - Ingest data incrementally into tables using `COPY INTO` - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-updates - --- COMMAND ---------- - --- MAGIC %md ## Complete Overwrites --- MAGIC --- MAGIC We can use overwrites to atomically replace all of the data in a table. There are multiple benefits to overwriting tables instead of deleting and recreating tables: --- MAGIC - Overwriting a table is much faster because it doesn’t need to list the directory recursively or delete any files. --- MAGIC - The old version of the table still exists; can easily retrieve the old data using Time Travel. --- MAGIC - It’s an atomic operation. Concurrent queries can still read the table while you are deleting the table. --- MAGIC - Due to ACID transaction guarantees, if overwriting the table fails, the table will be in its previous state. --- MAGIC --- MAGIC The following cells demonstrate two ways to overwrite data. --- MAGIC --- MAGIC 1. Using the `CREATE OR REPLACE TABLE` statement --- MAGIC 2. Using the `INSERT OVERWRITE` statement - --- COMMAND ---------- - -CREATE OR REPLACE TABLE events AS -SELECT * FROM parquet.`${c.source}/events/events.parquet` - --- COMMAND ---------- - -INSERT OVERWRITE sales -SELECT * FROM parquet.`${c.source}/sales/sales.parquet` - --- COMMAND ---------- - --- MAGIC %md This keeps history of the previous table, but rewrites all data. - --- COMMAND ---------- - -DESCRIBE HISTORY sales - --- COMMAND ---------- - --- MAGIC %md ## Append Rows --- MAGIC --- MAGIC We can use `INSERT INTO` to atomically append new rows to an existing Delta table. This allows for incremental updates to existing tables, which is much more efficient than overwriting each time. --- MAGIC --- MAGIC Append new sale records to the `sales` table using `INSERT INTO`. - --- COMMAND ---------- - -INSERT INTO sales -SELECT * FROM parquet.`${c.source}/sales/sales-30m.parquet` - --- COMMAND ---------- - --- MAGIC %md ## Merge Updates --- MAGIC --- MAGIC You can upsert data from a source table, view, or DataFrame into a target Delta table using the `MERGE` SQL operation. Delta Lake supports inserts, updates and deletes in `MERGE`, and supports extended syntax beyond the SQL standards to facilitate advanced use cases. --- MAGIC ``` --- MAGIC MERGE INTO target a --- MAGIC USING source b --- MAGIC ON --- MAGIC WHEN MATCHED THEN --- MAGIC WHEN NOT MATCHED THEN --- MAGIC ``` --- MAGIC We will use the `MERGE` operation to update historic users data with updated emails and new users. - --- COMMAND ---------- - -CREATE OR REPLACE TEMP VIEW users_update AS -SELECT *, current_timestamp() updated FROM parquet.`${c.source}/users/users-30m.parquet` - --- COMMAND ---------- - --- MAGIC %md As we implemented the `users` table as a Type 1 SCD Delta table with an `updated` field, we can leverage this field while performing a merge operation. Let's make sure that records that are updated OR inserted have the same timestamp. This operation will be completed as a single batch to avoid potentially leaving our table in a corrupt state. - --- COMMAND ---------- - -MERGE INTO users a -USING users_update b -ON a.user_id = b.user_id -WHEN MATCHED AND a.email IS NULL AND b.email IS NOT NULL THEN - UPDATE SET email = b.email, updated = b.updated -WHEN NOT MATCHED THEN INSERT * - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Insert-Only Merge for Deduplication --- MAGIC --- MAGIC A common ETL use case is to collect logs into Delta table by appending them to a table. However, often the sources can generate duplicate log records and downstream deduplication steps are needed to take care of them. With merge, you can avoid inserting the duplicate records. --- MAGIC --- MAGIC When merging new events parsed from `events_raw`, confirm that an identical record isn't already in the `events` table. --- MAGIC ``` --- MAGIC MERGE INTO target a --- MAGIC USING source b --- MAGIC ON a.data = b.data --- MAGIC WHEN NOT MATCHED THEN --- MAGIC INSERT * --- MAGIC ``` --- MAGIC By default, the merge operation searches the entire Delta table to find matches in the source table. One way to speed up merge is to reduce the search space by adding known constraints in the match condition. It will also reduce the chances of conflicts with other concurrent operations. --- MAGIC ``` --- MAGIC events.country = 'USA' AND events.date = current_date() - INTERVAL 7 DAYS --- MAGIC ``` - --- COMMAND ---------- - -MERGE INTO events a -USING events_update b -ON a.user_id = b.user_id AND a.event_timestamp = b.event_timestamp -WHEN NOT MATCHED AND b.traffic_source = 'email' THEN - INSERT * - --- COMMAND ---------- - --- MAGIC %md ## Load Incrementally --- MAGIC Use `COPY INTO` to incrementally load data from external systems. --- MAGIC - Data schema should be consistent --- MAGIC - Duplicate records should try to be excluded or handled downstream --- MAGIC - Potentially much cheaper than full table scan for data that grows predictably --- MAGIC - Leveraged by many data ingestion partners --- MAGIC --- MAGIC Update the `sales` delta table by incrementally loading data from an external location where a number of new transactions arrive during a 30 minute window. Each sale should only be recorded once, at the time that the transaction is processed. Use a method that allows idempotent execution to avoid processing data multiple times. - --- COMMAND ---------- - -COPY INTO sales -FROM "${c.source}/sales/sales-30m.parquet" -FILEFORMAT = PARQUET - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.6 - Cleaning Data.sql b/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.6 - Cleaning Data.sql deleted file mode 100644 index e374db3..0000000 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.6 - Cleaning Data.sql +++ /dev/null @@ -1,169 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md # Cleaning Data --- MAGIC --- MAGIC Apply a number of common transformations to clean data with Spark SQL. --- MAGIC --- MAGIC ##### Objectives --- MAGIC - Summarize datasets and describe null behaviors --- MAGIC - Retrieve and remove duplicates based on select columns --- MAGIC - Validate datasets for expected counts, missing values, and duplicate records --- MAGIC - Apply common transformations to clean and transform data - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-updates - --- COMMAND ---------- - --- MAGIC %md --- MAGIC We'll work with new users records in `users_update` table for this lesson. - --- COMMAND ---------- - -SELECT * FROM users_update - --- COMMAND ---------- - --- MAGIC %md ## Transformations --- MAGIC As we inspect and clean our data, we'll need to construct various column expressions and queries to express transformations to apply on our dataset. --- MAGIC - Column expressions are constructed from existing columns, operators, and built-in Spark SQL functions. They can be used in `SELECT` statements to express transformations that create new columns from datasets. --- MAGIC - Along with `SELECT`, many additional query commands can be used to express transformations in Spark SQL, including `WHERE`, `DISTINCT`, `ORDER BY`, `GROUP BY`, etc. - --- COMMAND ---------- - --- MAGIC %md ## Inspect Data --- MAGIC We'll inspect `users_update` for missing values and duplicate records. - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ### Missing Values --- MAGIC Count the number of missing values in each column of `users_update`. Note that nulls behave incorrectly in some math functions, including `count`. --- MAGIC --- MAGIC #### Null Behavior with `count()` --- MAGIC - `count(col)` skips `NULL` values when counting specific columns or expressions. --- MAGIC - `count(*)` is a special case that counts the total number of rows without skipping `NULL` values --- MAGIC --- MAGIC To count null values, use the `count_if` function or `WHERE` clause to provide a condition that filters for records where the value `IS NULL`. - --- COMMAND ---------- - -SELECT - count_if(user_id IS NULL) missing_user_ids, - count_if(user_first_touch_timestamp IS NULL) missing_timestamps, - count_if(email IS NULL) missing_emails -FROM users_update - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ### Distinct Records --- MAGIC - `DISTINCT *` returns rows with duplicates removed. --- MAGIC - `DISTINCT col` returns unique values in column `col` --- MAGIC --- MAGIC Note that the count for distinct users is greater than the count for distinct rows; rows containing `NULL` values were skipped from processing distinct rows. `count(*)` is the only case where `count()` includes records with `NULL` values. - --- COMMAND ---------- - -SELECT - count(user_id) total_ids, count(DISTINCT user_id) unique_ids, - count(email) total_emails, count(DISTINCT email) unique_emails, - count(*) total_rows, count(DISTINCT *) unique_non_null_rows -FROM users_update - --- COMMAND ---------- - --- MAGIC %md ## Deduplicate Rows --- MAGIC --- MAGIC Use `DISTINCT *` to remove true duplicate records (rows with same values for all columns). - --- COMMAND ---------- - -SELECT DISTINCT * FROM users_update - --- COMMAND ---------- - --- MAGIC %md #### Deduplicate Based on Specific Columns --- MAGIC --- MAGIC Use `GROUP BY` to remove duplicate records based on select columns. The query below groups rows by `user_id` to deduplicate rows based on values from this column. --- MAGIC --- MAGIC The `max()` aggregate function is used on the `email` column as a hack to capture non-null emails when multiple records are present. - --- COMMAND ---------- - -CREATE OR REPLACE TEMP VIEW deduped_users AS -SELECT user_id, user_first_touch_timestamp, max(email) email, max(updated) updated -FROM users_update -GROUP BY user_id, user_first_touch_timestamp; - -SELECT count(*) FROM deduped_users - --- COMMAND ---------- - --- MAGIC %md ## Validate Datasets --- MAGIC Let's check our datasets for expected counts, missing values, and duplicate records. --- MAGIC Validation can be performed with simple filters and `WHERE` clauses (and `COUNT_IF` statements). - --- COMMAND ---------- - --- MAGIC %md Validate that the `user_id` for each row is unique. - --- COMMAND ---------- - -SELECT max(row_count) <= 1 no_duplicate_ids FROM ( - SELECT user_id, count(*) row_count - FROM deduped_users - GROUP BY user_id) - --- COMMAND ---------- - --- MAGIC %md Confirm that each email is associated with at most one `user_id`. - --- COMMAND ---------- - -SELECT max(user_id_count) <= 1 at_most_one_id FROM ( - SELECT email, count(user_id) user_id_count - FROM deduped_users - WHERE email IS NOT NULL - GROUP BY email) - --- COMMAND ---------- - --- MAGIC %md ## Date Format and Regex --- MAGIC - Format datetimes as strings --- MAGIC - Use `regexp_extract` to extract domains from the email column using regex - --- COMMAND ---------- - -SELECT *, - date_format(first_touch, "MMM d, yyyy") first_touch_date, - date_format(first_touch, "HH:mm:ss") first_touch_time, - regexp_extract(email, "(?<=@)[^.]+(?=\.)", 0) email_domain -FROM ( - SELECT *, - CAST(user_first_touch_timestamp / 1e6 AS timestamp) first_touch - FROM deduped_users -) - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.7 - Reshaping Data.sql b/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.7 - Reshaping Data.sql deleted file mode 100644 index b5c7f27..0000000 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.7 - Reshaping Data.sql +++ /dev/null @@ -1,207 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC # Reshaping Data --- MAGIC --- MAGIC Combine datasets and lookup tables with various join types and strategies. --- MAGIC Reshape with pivot tables. --- MAGIC --- MAGIC ##### Objectives --- MAGIC - Combine datasets using different types of joins --- MAGIC - Join records to a pre-existing lookup table --- MAGIC - Examine and provide hints for join strategies --- MAGIC - Reshape data using pivot tables - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-cleaned - --- COMMAND ---------- - --- MAGIC %md ## Explode Arrays --- MAGIC Use the `explode` array function to explode arrays in the item field of events. - --- COMMAND ---------- - -SELECT *, explode(items) item FROM events - --- COMMAND ---------- - --- MAGIC %md ## Collect Arrays --- MAGIC Explode and flatten complex data using various array functions - --- COMMAND ---------- - -SELECT user_id, - flatten(collect_set(items.item_id)) cart_history, - collect_set(event_name) event_history -FROM (SELECT *, explode(items) item FROM events) -GROUP BY user_id - --- COMMAND ---------- - --- MAGIC %md ## Join a Lookup Table --- MAGIC --- MAGIC Lookup tables are normally small, historical tables used to enrich new data passing through an ETL pipeline. --- MAGIC --- MAGIC In this example, we will use a small lookup table to get details for each item sold by this retailer. --- MAGIC --- MAGIC **Inner Join:** Selects rows that have matching values in both relations. This is the default join in Spark SQL. - --- COMMAND ---------- - -CREATE OR REPLACE VIEW sales_enriched AS -SELECT * -FROM (SELECT *, explode(items) item FROM sales) a -INNER JOIN item_lookup b -ON a.item.item_id = b.item_id; - -SELECT * FROM sales_enriched - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Examine Join Strategy --- MAGIC Use the `EXPLAIN` command to view the physical plan used to execute the query. --- MAGIC Look for BroadcastHashJoin or BroadcastExchange. - --- COMMAND ---------- - -EXPLAIN FORMATTED -SELECT * FROM sales_enriched - --- COMMAND ---------- - --- MAGIC %md --- MAGIC By default, Spark performed a broadcast join rather than a shuffle join. That is, it broadcasted `item_lookup` to the larger `sales`, replicating the smaller dataset on each node of our cluster. This avoided having to move the larger dataset across the cluster. - --- COMMAND ---------- - --- MAGIC %md `autoBroadcastJoinThreshold` --- MAGIC --- MAGIC We can access configuration settings to take a look at the broadcast join threshold. This specifies the maximum size in bytes for a table that broadcasts to worker nodes. - --- COMMAND ---------- - -SET spark.sql.autoBroadcastJoinThreshold - --- COMMAND ---------- - --- MAGIC %md Re-examine physical plan when join is executed while broadcasting is disabled --- MAGIC 1. Drop threshold to `-1` to disable broadcasting --- MAGIC 1. Explain join --- MAGIC --- MAGIC Now notice the lack of broadcast in the query physical plan. - --- COMMAND ---------- - -SET spark.sql.autoBroadcastJoinThreshold=-1 - --- COMMAND ---------- - --- MAGIC %md Notice a sort merge join is performed, rather than a broadcast join. - --- COMMAND ---------- - -EXPLAIN FORMATTED -SELECT * FROM sales_enriched - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Broadcast Join Hint --- MAGIC Use a join hint to suggest broadcasting the lookup table for the join. --- MAGIC --- MAGIC The join side with this hint will be broadcast regardless of `autoBroadcastJoinThreshold`. - --- COMMAND ---------- - -EXPLAIN FORMATTED -SELECT /*+ BROADCAST(b) */ * -FROM (SELECT *, explode(items) item FROM sales) a -INNER JOIN item_lookup b -ON a.item.item_id = b.item_id - --- COMMAND ---------- - --- MAGIC %md Reset the original threshold. - --- COMMAND ---------- - -SET spark.sql.autoBroadcastJoinThreshold=10485760b - --- COMMAND ---------- - --- MAGIC %md ## Pivot Tables --- MAGIC The `PIVOT` clause is used for data perspective. We can get the aggregated values based on specific column values, which will be turned to multiple columns used in `SELECT` clause. The `PIVOT` clause can be specified after the table name or subquery. --- MAGIC --- MAGIC **`SELECT * FROM ()`**: The `SELECT` statement inside the parentheses is the input for this table. --- MAGIC --- MAGIC **`PIVOT`**: The first argument in the clause is an aggregate function and the column to be aggregated. Then, we specify the pivot column in the `FOR` subclause. The `IN` operator contains the pivot column values.
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC Use `PIVOT` to create a new `transactions` table that flattens out the information contained in the `sales` table and joins this with `users` table. --- MAGIC --- MAGIC Join these tables on email address, but without propagating this email address forward (to avoid potential PII exposure in downstream tables). - --- COMMAND ---------- - -CREATE OR REPLACE TABLE transactions AS - -SELECT * FROM ( - SELECT - user_id, - order_id, - transaction_timestamp, - total_item_quantity, - purchase_revenue_in_usd, - unique_items, - a.item.item_id item_id, - a.item.quantity quantity - FROM sales_enriched a - INNER JOIN users b - ON a.email = b.email -) PIVOT ( - sum(quantity) FOR item_id in ( - 'P_FOAM_K', - 'M_STAN_Q', - 'P_FOAM_S', - 'M_PREM_Q', - 'M_STAN_F', - 'M_STAN_T', - 'M_PREM_K', - 'M_PREM_F', - 'M_STAN_K', - 'M_PREM_T', - 'P_DOWN_S', - 'P_DOWN_K' - ) -) - --- COMMAND ---------- - -SELECT * FROM transactions - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.8 - Nested Data & Advanced Transformations.sql b/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.8 - Nested Data & Advanced Transformations.sql deleted file mode 100644 index 720f905..0000000 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.8 - Nested Data & Advanced Transformations.sql +++ /dev/null @@ -1,159 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md # Advanced Transformations --- MAGIC --- MAGIC Manipulate nested data and work with advanced functions in SQL. --- MAGIC --- MAGIC ##### Objectives --- MAGIC - Flatten nested data --- MAGIC - Apply advanced functions to transform data - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-transactions - --- COMMAND ---------- - --- MAGIC %md We will work with the `events_raw` table we loaded earlier from `event-kafka.json`. - --- COMMAND ---------- - -SELECT * FROM events_raw - --- COMMAND ---------- - --- MAGIC %md Earlier in the module, we merged a clean version of this new dataset, `events_update` into our original `events` dataset. The raw events data was cleaned by parsing and flattening the relevant JSON data in the `value` column, and then removing duplicate event records. We'll go through that process here. - --- COMMAND ---------- - --- MAGIC %md ## Parse JSON --- MAGIC --- MAGIC Parse JSON string with `from_json` and schema definition. - --- COMMAND ---------- - -CREATE OR REPLACE TEMP VIEW events_raw_json AS -SELECT from_json(cast(value as STRING), ("device STRING, ecommerce STRUCT< purchase_revenue_in_usd: DOUBLE, total_item_quantity: BIGINT, unique_items: BIGINT>, event_name STRING, event_previous_timestamp BIGINT, event_timestamp BIGINT, geo STRUCT< city: STRING, state: STRING>, items ARRAY< STRUCT< coupon: STRING, item_id: STRING, item_name: STRING, item_revenue_in_usd: DOUBLE, price_in_usd: DOUBLE, quantity: BIGINT>>, traffic_source STRING, user_first_touch_timestamp BIGINT, user_id STRING")) json -FROM events_raw; - --- COMMAND ---------- - --- MAGIC %md ## Flatten Nested Data --- MAGIC Deduplicate events by `user_id` and `event_timestamp`. --- MAGIC --- MAGIC Promote subfields with `json.*` to flatten data. - --- COMMAND ---------- - -CREATE OR REPLACE TEMP VIEW deduped_events AS -WITH deduped_events_raw AS ( - SELECT max(json) json FROM events_raw_json - GROUP BY json.user_id, json.event_timestamp -) -SELECT json.* FROM deduped_events_raw - --- COMMAND ---------- - --- MAGIC %md ## Higher Order Functions --- MAGIC Higher order functions in Spark SQL allow you to work directly with complex data types. When working with hierarchical data, records are frequently stored as array or map type objects. Higher-order functions allow you to transform data while preserving the original structure. --- MAGIC --- MAGIC Higher order functions include: --- MAGIC - `FILTER` filters an array using the given lambda function. --- MAGIC - `EXIST` tests whether a statement is true for one or more elements in an array. --- MAGIC - `TRANSFORM` uses the given lambda function to transform all elements in an array. --- MAGIC - `REDUCE` takes two lambda functions to reduce the elements of an array to a single value by merging the elements into a buffer, and the apply a finishing function on the final buffer. - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Filter --- MAGIC Remove items that are not king-sized from all records in our `items` column. We can use the `FILTER` function to create a new column that excludes that value from each array. --- MAGIC --- MAGIC **`FILTER (items, i -> i.item_id LIKE "%K") AS king_items`** --- MAGIC --- MAGIC In the statement above: --- MAGIC - **`FILTER`** : the name of the higher-order function
--- MAGIC - **`items`** : the name of our input array
--- MAGIC - **`i`** : the name of the iterator variable. You choose this name and then use it in the lambda function. It iterates over the array, cycling each value into the function one at a time.
--- MAGIC - **`->`** : Indicates the start of a function
--- MAGIC - **`i.item_id LIKE "%K"`** : This is the function. Each value is checked to see if it ends with the capital letter K. If it is, it gets filtered into the new column, `king_items` - --- COMMAND ---------- - --- filter for sales of only king sized items -SELECT - order_id, - items, - FILTER (items, i -> i.item_id LIKE "%K") AS king_items -FROM sales - --- COMMAND ---------- - --- MAGIC %md --- MAGIC You may write a filter that produces a lot of empty arrays in the created column. When that happens, it can be useful to use a `WHERE` clause to show only non-empty array values in the returned column. --- MAGIC --- MAGIC In this example, we accomplish that by using a subquery (a query within a query). They are useful for performing an operation in multiple steps. In this case, we're using it to create the named column that we will use with a `WHERE` clause. - --- COMMAND ---------- - -CREATE OR REPLACE TEMP VIEW king_size_sales AS - -SELECT order_id, king_items -FROM ( - SELECT - order_id, - FILTER (items, i -> i.item_id LIKE "%K") AS king_items - FROM sales) -WHERE size(king_items) > 0; - -SELECT * FROM king_size_sales - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Transform --- MAGIC Built-in functions are designed to operate on a single, simple data type within a cell; they cannot process array values. `TRANSFORM` can be particularly useful when you want to apply an existing function to each element in an array. --- MAGIC --- MAGIC Compute the total revenue from king-sized items per order. --- MAGIC --- MAGIC **`TRANSFORM(king_items, k -> CAST(k.item_revenue_in_usd * 100 AS INT)) AS item_revenues`** --- MAGIC --- MAGIC In the statement above, for each value in the input array, we extract the item's revenue value, multiply it by 100, and cast the result to integer. Note that we're using the same kind as references as in the previous command, but we name the iterator with a new variable, **`k`**. - --- COMMAND ---------- - --- get total revenue from king items per order -CREATE OR REPLACE TEMP VIEW king_item_revenues AS - -SELECT - order_id, - king_items, - TRANSFORM ( - king_items, - k -> CAST(k.item_revenue_in_usd * 100 AS INT) - ) AS item_revenues -FROM king_size_sales; - -SELECT * FROM king_item_revenues - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.9 - Views in the Lakehouse.sql b/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.9 - Views in the Lakehouse.sql deleted file mode 100644 index 01c4908..0000000 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.9 - Views in the Lakehouse.sql +++ /dev/null @@ -1,144 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md # Views in the Lakehouse --- MAGIC --- MAGIC Register views to create stable, secure queries in the Lakehouse. --- MAGIC --- MAGIC In this lesson, we'll give a quick overview of how stored views are created and managed. --- MAGIC --- MAGIC --- MAGIC Rather than trying to capture every possible metric in our view, we'll create a summary of values that might be of interest to our analysts. --- MAGIC --- MAGIC 0. Views as saved queries --- MAGIC 0. Aliasing tables to views --- MAGIC 0. Dynamic views - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Run Setup --- MAGIC --- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. - --- COMMAND ---------- - --- MAGIC %run ../Includes/setup-transactions - --- COMMAND ---------- - --- MAGIC %md ## Views as saved queries --- MAGIC --- MAGIC A Spark DataFrame and a view are nearly identical constructs. By calling `EXPLAIN` on our DataFrame, we can see that our source table is a set of instructions to deserialize the files containing our data. - --- COMMAND ---------- - -EXPLAIN FORMATTED SELECT * FROM transactions - --- COMMAND ---------- - --- MAGIC %md ## Aliasing Tables to Views --- MAGIC --- MAGIC #### Examine Clickpath Data --- MAGIC Define logic that creates or updates a table that aggregates the number of times each user took a particular action and then join this information with the flattened view of transactions created earlier. This will combine data from the `events` and `transactions` tables in order to create a record of all actions a user took on the site and what their final order looked like. --- MAGIC --- MAGIC This `clickpaths` view should contain all the fields from your `transactions` table, as well as a count of every `event_name` in its own column. Each user that completed a purchase should have a single row in the final table. - --- COMMAND ---------- - -CREATE OR REPLACE VIEW clickpaths AS - WITH user_event_counts AS ( - SELECT * FROM ( - SELECT user_id user, event_name - FROM events - ) PIVOT ( count(*) FOR event_name IN ( - "cart", "pillows", "login", "main", "careers", "guest", "faq", "down", "warranty", "finalize", - "register", "shipping_info", "checkout", "mattresses", "add_item", "press", "email_coupon", - "cc_info", "foam", "reviews", "original", "delivery", "premium" ))) -SELECT * -FROM user_event_counts a -JOIN transactions b - ON a.user = b.user_id; - --- COMMAND ---------- - --- MAGIC %md --- MAGIC We can see that our view is simply storing the Spark plan for our query. - --- COMMAND ---------- - -EXPLAIN FORMATTED SELECT * FROM clickpaths - --- COMMAND ---------- - --- MAGIC %md --- MAGIC When we execute a query against this view, we will process the plan to generate the logically correct result. --- MAGIC --- MAGIC Note that while the data may end up in the Delta Cache, this result is not guaranteed to be persisted, and is only cached for the currently active cluster. - --- COMMAND ---------- - -SELECT * FROM clickpaths WHERE login = True - --- COMMAND ---------- - -SELECT * -FROM clickpaths -WHERE user_id = "UA000000102360871" - --- COMMAND ---------- - --- MAGIC %md ## Dynamic Views --- MAGIC Databricks dynamic views allow user or group identity ACLs to be applied to data at the column (or row) level. --- MAGIC --- MAGIC Database administrators can configure data access privileges to disallow access to a source table and only allow users to query a redacted view. Users with sufficient privileges will be able to see all fields, while restricted users will be shown arbitrary results, as defined at view creation. - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Consider our `sales` table with the following columns. - --- COMMAND ---------- - -DESCRIBE sales - --- COMMAND ---------- - -CREATE OR REPLACE VIEW sales_view AS -SELECT - order_id, - transaction_timestamp, - CASE - WHEN is_member('ade_demo') THEN email - ELSE 'REDACTED' - END AS email, - CASE - WHEN is_member('ade_demo') THEN purchase_revenue_in_usd - ELSE 'REDACTED' - END AS purchase_revenue_in_usd -FROM sales - --- COMMAND ---------- - -SELECT * FROM sales_view - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Now when we query from `sales_view`, only members of the group `ade_demo` will be able to see results in plain text. --- MAGIC --- MAGIC **NOTE:** You may not have privileges to create groups or assign membership. Your instructor should be able to demonstrate how group membership will change query results. - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.1.3L - Databases, Tables & Views Lab.sql b/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.1.3L - Databases, Tables & Views Lab.sql deleted file mode 100644 index dc18781..0000000 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.1.3L - Databases, Tables & Views Lab.sql +++ /dev/null @@ -1,339 +0,0 @@ --- Databricks notebook source --- MAGIC %md-sandbox --- MAGIC --- MAGIC
--- MAGIC Databricks Learning --- MAGIC
- --- COMMAND ---------- - --- MAGIC %md --- MAGIC # Databases, Tables, and Views Lab --- MAGIC --- MAGIC ## Learning Objectives --- MAGIC **In this lab, you will create and explore interactions between various relational entities, including:** --- MAGIC --- MAGIC - Databases --- MAGIC - Tables (managed and external) --- MAGIC - Views (views, temp views, and global temp views) --- MAGIC --- MAGIC **Resources** --- MAGIC * [Databases and Tables - Databricks Docs](https://docs.databricks.com/user-guide/tables.html) --- MAGIC * [Managed and Unmanaged Tables](https://docs.databricks.com/user-guide/tables.html#managed-and-unmanaged-tables) --- MAGIC * [Creating a Table with the UI](https://docs.databricks.com/user-guide/tables.html#create-a-table-using-the-ui) --- MAGIC * [Create a Local Table](https://docs.databricks.com/user-guide/tables.html#create-a-local-table) --- MAGIC * [Saving to Persistent Tables](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html#saving-to-persistent-tables) - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ### Getting Started --- MAGIC --- MAGIC Run the following cell to configure variables and datasets for this lesson. - --- COMMAND ---------- - --- MAGIC %run ../../Includes/setup-meta - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Overview of the Data --- MAGIC --- MAGIC The data include multiple entries from a selection of weather stations, including average temperatures recorded in either Fahrenheit or Celsius. The schema for the table: --- MAGIC --- MAGIC |ColumnName | DataType| Description| --- MAGIC |------------|---------|------------| --- MAGIC |NAME |string | Station name | --- MAGIC |STATION |string | Unique ID | --- MAGIC |LATITUDE |float | Latitude | --- MAGIC |LONGITUDE |float | Longitude | --- MAGIC |ELEVATION |float | Elevation | --- MAGIC |DATE |date | YYYY-MM-DD | --- MAGIC |UNIT |string | Temperature units | --- MAGIC |TAVG |float | Average temperature | --- MAGIC --- MAGIC This data is stored in the Parquet format; preview the data with the query below. - --- COMMAND ---------- - -SELECT * -FROM parquet.`${c.userhome}/datasets/weather` - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Create a Database --- MAGIC --- MAGIC Create a database in the default location using the `{c.database}` variable defined in setup script. - --- COMMAND ---------- - --- ANSWER -CREATE DATABASE IF NOT EXISTS ${c.database} - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Change to Your New Database --- MAGIC --- MAGIC `USE` your newly created database. - --- COMMAND ---------- - --- ANSWER -USE ${c.database} - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Create a Managed Table --- MAGIC Use a CTAS statement to create a managed table named `weather_managed`. - --- COMMAND ---------- - --- ANSWER - -CREATE TABLE weather_managed AS -SELECT * -FROM parquet.`${c.userhome}/datasets/weather` - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Create an External Table --- MAGIC --- MAGIC Recall that an external table differs from a managed table through specification of a location. Create an external table called `weather_external` below. - --- COMMAND ---------- - --- ANSWER - -CREATE TABLE weather_external -LOCATION "${c.userhome}/lab/external" -AS SELECT * -FROM parquet.`${c.userhome}/datasets/weather` - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Examine Table Details --- MAGIC Use the SQL command `DESCRIBE EXTENDED table_name` to examine the two weather tables. - --- COMMAND ---------- - -DESCRIBE EXTENDED weather_managed - --- COMMAND ---------- - -DESCRIBE EXTENDED weather_external - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Run the following helper code to extract and compare the table locations. - --- COMMAND ---------- - --- MAGIC %python --- MAGIC def getTableLocation(tableName): --- MAGIC return spark.sql(f"DESCRIBE EXTENDED {tableName}").select("data_type").filter("col_name = 'Location'").first()[0] - --- COMMAND ---------- - --- MAGIC %python --- MAGIC managedTablePath = getTableLocation("weather_managed") --- MAGIC externalTablePath = getTableLocation("weather_external") --- MAGIC --- MAGIC print(f"""The weather_managed table is saved at: --- MAGIC --- MAGIC {managedTablePath} --- MAGIC --- MAGIC The weather_external table is saved at: --- MAGIC --- MAGIC {externalTablePath}""") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC List the contents of these directories to confirm that data exists in both locations. - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.ls(managedTablePath) - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.ls(externalTablePath) - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ### Check Directory Contents after Dropping Database and All Tables --- MAGIC The `CASCADE` keyword will accomplish this. --- MAGIC --- MAGIC **NOTE**: You will encounter an error when listing your `managedTablePath` - --- COMMAND ---------- - --- ANSWER -DROP DATABASE ${c.database} CASCADE - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.ls(managedTablePath) - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.ls(externalTablePath) - --- COMMAND ---------- - --- MAGIC %python --- MAGIC dbutils.fs.ls(userhome) - --- COMMAND ---------- - --- MAGIC %md --- MAGIC **This highlights the main differences between managed and external tables.** By default, the files associated with managed tables will be stored to this location on the root DBFS storage linked to the workspace, and will be deleted when a table is dropped. --- MAGIC --- MAGIC Files for external tables will be persisted in the location provided at table creation, preventing users from inadvertently deleting underlying files. **External tables can easily be migrated to other databases or renamed, but these operations with managed tables will require rewriting ALL underlying files.** - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Create a Database with a Specified Path --- MAGIC --- MAGIC Assuming you dropped your database in the last step, you can use the same `database` name. - --- COMMAND ---------- - -CREATE DATABASE ${c.database} LOCATION '${c.userhome}/${c.database}' - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Recreate your `weather_managed` table in this new database and print out the location of this table. - --- COMMAND ---------- - --- ANSWER - -CREATE TABLE weather_managed AS -SELECT * -FROM parquet.`${c.userhome}/datasets/weather` - --- COMMAND ---------- - --- MAGIC %python --- MAGIC getTableLocation("weather_managed") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC While here we're using the `userhome` directory created on the DBFS root, _any_ object store can be used as the database directory. **Defining database directories for groups of users can greatly reduce the chances of accidental data exfiltration**. - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Views and their Scoping --- MAGIC --- MAGIC Using the provided `AS` clause, register: --- MAGIC - a view named `celsius` --- MAGIC - a temporary view named `celsius_temp` --- MAGIC - a global temp view named `celsius_global` - --- COMMAND ---------- - --- ANSWER - -CREATE OR REPLACE VIEW celsius -AS (SELECT * - FROM weather_managed - WHERE UNIT = "C") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Now create a temporary view. - --- COMMAND ---------- - --- ANSWER - -CREATE OR REPLACE TEMP VIEW celsius_temp -AS (SELECT * - FROM weather_managed - WHERE UNIT = "C") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Now register a global temp view. - --- COMMAND ---------- - --- ANSWER - -CREATE OR REPLACE GLOBAL TEMP VIEW celsius_global -AS (SELECT * - FROM weather_managed - WHERE UNIT = "C") - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Views will be displayed alongside tables when listing from the catalog. - --- COMMAND ---------- - -SHOW TABLES - --- COMMAND ---------- - --- MAGIC %md --- MAGIC Note the following: --- MAGIC - The view is associated with the current database. This view will be available to any user that can access this database and will persist between sessions. --- MAGIC - The temp view is not associated with any database. The temp view is ephemeral and is only accessible in the current SparkSession. --- MAGIC - The global temp view does not appear in our catalog. **Global temp views will always register to the `global_temp` database**. The `global_temp` database is ephemeral but tied to the lifetime of the cluster; however, it is only accessible by notebooks attached to the same cluster on which it was created. - --- COMMAND ---------- - -SELECT * FROM global_temp.celsius_global - --- COMMAND ---------- - --- MAGIC %md --- MAGIC While no job was triggered when defining these views, a job is triggered _each time_ a query is executed against the view. - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Clean Up --- MAGIC Drop the database and all tables to clean up your workspace. - --- COMMAND ---------- - -DROP DATABASE ${c.database} CASCADE - --- COMMAND ---------- - --- MAGIC %md --- MAGIC ## Synopsis --- MAGIC --- MAGIC In this lab we: --- MAGIC - Created and deleted databases --- MAGIC - Explored behavior of managed and external tables --- MAGIC - Learned about the scoping of views - --- COMMAND ---------- - --- MAGIC %md-sandbox --- MAGIC © 2022 Databricks, Inc. All rights reserved.
--- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
--- MAGIC
--- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.3.3L - Python for SQL Lab.py b/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.3.3L - Python for SQL Lab.py deleted file mode 100644 index 1c1c6e4..0000000 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.3.3L - Python for SQL Lab.py +++ /dev/null @@ -1,238 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Just Enough Python for Databricks SQL Lab -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, students should be able to: -# MAGIC * Review basic Python code and describe expected outcomes of code execution -# MAGIC * Reason through control flow statements in Python functions -# MAGIC * Add parameters to a SQL query by wrapping it in a Python function - -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Reviewing Python Basics -# MAGIC -# MAGIC In the previous notebook, we briefly explored using `spark.sql()` to execute arbitrary SQL commands from Python. -# MAGIC -# MAGIC Look at the following 3 cells. Before executing each cell, identify: -# MAGIC 1. The expected output of cell execution -# MAGIC 1. What logic is being executed -# MAGIC 1. Changes to the resultant state of the environment -# MAGIC -# MAGIC Then execute the cells, compare the results to your expectations, and see the explanations below. - -# COMMAND ---------- - -course = "python_for_sql" - -# COMMAND ---------- - -spark.sql(f"SELECT '{course}'") - -# COMMAND ---------- - -display(spark.sql(f"SELECT '{course}'")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC 1. `Cmd 3` assigns a string to a variable. When a variable assignment is successful, no output is displayed to the notebook. A new variable is added to the current execution environment. -# MAGIC 1. `Cmd 4` executes a SQL query and returns the results as a DataFrame. In this case, the SQL query is just to select a string, so no changes to our environment occur. When a returned DataFrame is not captured, the schema for the DataFrame is displayed alongside the word `DataFrame`. -# MAGIC 1. `Cmd 5` executes the same SQL query and displays the returned DataFrame. This combination of `display()` and `spark.sql()` most closely mirrors executing logic in a `%sql` cell; the results will always be printed in a formatted table, assuming results are returned by the query; some queries will instead manipulate tables or databases, in which case the work `OK` will print to show successful execution. In this case, no changes to our environment occur from running this code. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Setting Up a Development Environment -# MAGIC -# MAGIC Throughout this course, we use logic similar to the follow cell to capture information about the user currently executing the notebook and create an isolated development database. -# MAGIC -# MAGIC The `re` library is the [standard Python library for regex](https://docs.python.org/3/library/re.html). -# MAGIC -# MAGIC Databricks SQL has a special method to capture the username of the `current_user()`; and the `.first()[0]` code is a quick hack to capture the first row of the first column of a query executed with `spark.sql()` (in this case, we do this safely knowing that there will only be 1 row and 1 column). -# MAGIC -# MAGIC All other logic below is just string formatting. - -# COMMAND ---------- - -import re - -username = spark.sql("SELECT current_user()").first()[0] -userhome = f"dbfs:/user/{username}/{course}" -database = f"""{course}_{re.sub("[^a-zA-Z0-9]", "_", username)}_db""" - -print(f""" -username: {username} -userhome: {userhome} -database: {database} -""") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Below, we add a simple control flow statement to this logic to create and use this user-specific database. Optionally, we will reset this database and drop all of the contents on repeat execution. (Note the the default mode is `"reset"`). - -# COMMAND ---------- - -def create_database(course, mode="reset"): - import re - - username = spark.sql("SELECT current_user()").first()[0] - userhome = f"dbfs:/user/{username}/{course}" - database = f"""{course}_{re.sub("[^a-zA-Z0-9]", "_", username)}_db""" - - print(f""" - username: {username} - userhome: {userhome} - database: {database} - """) - - if mode == "reset": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - spark.sql(f""" - CREATE DATABASE IF NOT EXISTS {database} - LOCATION '{userhome}' - """) - spark.sql(f"USE {database}") - -create_database(course) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC While this logic as defined is geared toward isolating students in shared workspaces for instructional purposes, the same basic design could be leveraged for testing new logic in an isolated environment before pushing to production. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Handling Errors Gracefully -# MAGIC -# MAGIC Review the logic in the function below. -# MAGIC -# MAGIC Note that we've just declared a new database that currently contains no tables. - -# COMMAND ---------- - -def query_or_make_demo_table(table): - try: - display(spark.sql(f"SELECT * FROM {table}")) - except: - spark.sql(f""" - CREATE TABLE {table} - (id INT, name STRING, value DOUBLE, state STRING) - """) - spark.sql(f""" - INSERT INTO {table} - VALUES (1, "Yve", 1.0, "CA"), - (2, "Omar", 2.5, "NY"), - (3, "Elia", 3.3, "OH"), - (4, "Rebecca", 4.7, "TX"), - (5, "Ameena", 5.3, "CA"), - (6, "Ling", 6.6, "NY"), - (7, "Pedro", 7.1, "KY") - """) - display(spark.sql(f"SELECT * FROM {table}")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Try to identify the following before executing the next cell: -# MAGIC 1. The expected output of cell execution -# MAGIC 1. What logic is being executed -# MAGIC 1. Changes to the resultant state of the environment - -# COMMAND ---------- - -query_or_make_demo_table("demo_table") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Now answer the same three questions before running the same query below. - -# COMMAND ---------- - -query_or_make_demo_table("demo_table") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC - On the first execution, the table `demo_table` did not yet exist. As such, the attempt to return the contents of the table created an error, which resulted in our `except` block of logic executing. This block: -# MAGIC 1. Created the table -# MAGIC 1. Inserted values -# MAGIC 1. Returned the contents of the table -# MAGIC - On the second execution, the table `demo_table` already exists, and so the first query in the `try` block executes without error. As a result, we just display the results of the query without modifying anything in our environment. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Adapting SQL to Python -# MAGIC Let's consider the following SQL query against our demo table created above. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT id, value -# MAGIC FROM demo_table -# MAGIC WHERE state = "CA" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's use this simple example to practice creating a Python function that adds optional functionality. -# MAGIC -# MAGIC Our target function will: -# MAGIC * Always return only the `id` and `value` column from the a table named `demo_table` -# MAGIC * Allow filtering results by state, but default to all states -# MAGIC * Optionally return the query result object (a PySpark DataFrame) -# MAGIC -# MAGIC Stretch Goal: -# MAGIC * Add logic to check that if the value passed for the `state` filter contains two uppercase letters -# MAGIC -# MAGIC Some starter logic has been provided below. - -# COMMAND ---------- - -# ANSWER - -def preview_values(state=None, return_results=False): - query = "SELECT id, value FROM demo_table" - if state is not None: -# assert state == state.upper() and len(state) == 2, "Please use standard 2 letter uppercase state abbreviations" - query += f" WHERE state = '{state}'" - query_results = spark.sql(query) - display(query_results) - if return_results == True: - return query_results - -# COMMAND ---------- - -# MAGIC %md -# MAGIC The assert statements below can be used to check whether or not your function works as intended. - -# COMMAND ---------- - -import pyspark.sql.dataframe - -assert preview_values(return_results=True).columns == ["id", "value"], "Query should only return `id` and `value` columns" -assert preview_values() == None, "Function should not return anything by default" -assert type(preview_values(return_results=True)) == pyspark.sql.dataframe.DataFrame, "Function should optionally return the DataFrame results" -assert preview_values(state="OH", return_results=True).first()[0] == 3, "Function should allow filtering by state" - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.1.1 - Structured Streaming Concepts.py b/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.1.1 - Structured Streaming Concepts.py deleted file mode 100644 index 6d9fa0f..0000000 --- a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.1.1 - Structured Streaming Concepts.py +++ /dev/null @@ -1,559 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Structured Streaming Concepts -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, you should be able to: -# MAGIC * Describe the programming model used by Spark Structured Streaming -# MAGIC * Configure required options to perform a streaming read on a source -# MAGIC * Describe the requirements for end-to-end fault tolerance -# MAGIC * Configure required options to perform a streaming write to a sink -# MAGIC * Interact with streaming queries and stop active streams -# MAGIC -# MAGIC ## Datasets Used -# MAGIC The source contains smartphone accelerometer samples from devices and users with the following columns: -# MAGIC -# MAGIC | Field | Description | -# MAGIC | ------------- | ----------- | -# MAGIC | Arrival_Time | time data was received | -# MAGIC | Creation_Time | event time | -# MAGIC | Device | type of Model | -# MAGIC | Index | unique identifier of event | -# MAGIC | Model | i.e Nexus4 | -# MAGIC | User | unique user identifier | -# MAGIC | geolocation | city & country | -# MAGIC | gt | transportation mode | -# MAGIC | id | unused null field | -# MAGIC | x | acceleration in x-dir | -# MAGIC | y | acceleration in y-dir | -# MAGIC | z | acceleration in z-dir | - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Getting Started -# MAGIC -# MAGIC Run the following cell to configure our "classroom." - -# COMMAND ---------- - -# MAGIC %run ../Includes/classic-setup $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Micro-Batches as a Table -# MAGIC -# MAGIC For more information, see the analogous section in the [Structured Streaming Programming Guide](http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#basic-concepts) (from which several images have been borrowed). -# MAGIC -# MAGIC Spark Structured Streaming approaches streaming data by modeling it as a series of continuous appends to an unbounded table. While similar to defining **micro-batch** logic, this model allows incremental queries to be defined against streaming sources as if they were static input (though the fact that the input is an unbounded tables does impose some constraints). -# MAGIC -# MAGIC -# MAGIC -# MAGIC ### Basic Concepts -# MAGIC -# MAGIC - The developer defines an **input table** by configuring a streaming read against a **source**. The syntax for doing this is similar to working with static data. -# MAGIC - A **query** is defined against the input table. Both the DataFrames API and Spark SQL can be used to easily define transformations and actions against the input table. -# MAGIC - This logical query on the input table generates the **results table**. The results table contains the incremental state information of the stream. -# MAGIC - The **output** of a streaming pipeline will persist updates to the results table by writing to an external **sink**. Generally, a sink will be a durable system such as files or a pub/sub messaging bus. -# MAGIC - New rows are appended to the input table for each **trigger interval**. These new rows are essentially analogous to micro-batch transactions and will be automatically propagated through the results table to the sink. -# MAGIC -# MAGIC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## End-to-end Fault Tolerance -# MAGIC -# MAGIC Structured Streaming ensures end-to-end exactly-once fault-tolerance guarantees through _checkpointing_ (discussed below) and Write Ahead Logs. -# MAGIC -# MAGIC Structured Streaming sources, sinks, and the underlying execution engine work together to track the progress of stream processing. If a failure occurs, the streaming engine attempts to restart and/or reprocess the data. -# MAGIC For best practices on recovering from a failed streaming query see docs. -# MAGIC -# MAGIC This approach _only_ works if the streaming source is replayable; replayable sources include cloud-based object storage and pub/sub messaging services. -# MAGIC -# MAGIC At a high level, the underlying streaming mechanism relies on a couple approaches: -# MAGIC -# MAGIC * First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval. -# MAGIC * Next, the streaming sinks are designed to be _idempotent_—that is, multiple writes of the same data (as identified by the offset) do _not_ result in duplicates being written to the sink. -# MAGIC -# MAGIC Taken together, replayable data sources and idempotent sinks allow Structured Streaming to ensure **end-to-end, exactly-once semantics** under any failure condition. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Reading a Stream -# MAGIC -# MAGIC The `spark.readStream()` method returns a `DataStreamReader` used to configure and query the stream. -# MAGIC -# MAGIC Configuring a streaming read on a source requires: -# MAGIC * The schema of the data -# MAGIC * **NOTE**: Some streaming sources allow for schema inference -# MAGIC * The `format` of the source file format or named connector -# MAGIC * **NOTE**: `delta` is the default format for all reads and writes in Databricks -# MAGIC * Additional source-specific configuration options. For example: -# MAGIC * [`cloudFiles`](https://docs.databricks.com/spark/latest/structured-streaming/auto-loader-s3.html) -# MAGIC * Kafka -# MAGIC * The name of the source table or the location of the files in object storage -# MAGIC -# MAGIC Below, we define a streaming read against a source (represented by `dataSource`) consisting of files from cloud storage. -# MAGIC -# MAGIC **NOTE**: We can think of this `DataStreamReader` as an incremental temp view defined against an ever-appending source table. Just as with a temp view, we only store the query plan when we set up an incremental read. It's not until we query results that we'll see compute happen. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ### The Schema -# MAGIC -# MAGIC Working with `cloudFiles` allows Databricks to automatically infer the schema from most file sources. -# MAGIC -# MAGIC Once data is loaded into a Delta Lake table, all schema for downstream incremental reads will be grabbed automatically from the table metadata. -# MAGIC -# MAGIC Here, we'll provide an explicit schema for our data. - -# COMMAND ---------- - -schema = """Arrival_Time BIGINT, - Creation_Time BIGINT, - Device STRING, - Index BIGINT, - Model STRING, - User STRING, - geolocation STRUCT< - city: STRING, - country: STRING>, - gt STRING, - Id BIGINT, - x DOUBLE, - y DOUBLE, - z DOUBLE""" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Creating a Streaming Temp View -# MAGIC -# MAGIC Below we pull all of the above concepts together to define a streaming read. -# MAGIC -# MAGIC If we were continuing to build out our query with PySpark, we would capture this as a DataFrame. Instead, we use `createOrReplaceTempView` to create an entity that can be queried locally with SQL. - -# COMMAND ---------- - -(spark - .readStream - .schema(schema) - .format("cloudFiles") - .option("cloudFiles.format", "json") - .load(dataSource) - .createOrReplaceTempView("streaming_tmp_vw") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Comparing to Static Reads -# MAGIC -# MAGIC The above logic provides us with more or less the same result as the static query below. - -# COMMAND ---------- - -spark.sql(f"CREATE OR REPLACE TEMP VIEW static_tmp_vw AS SELECT * FROM json.`{dataSource}`") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC When we query a static read on data, we display the results of the query at a point in time. -# MAGIC -# MAGIC **NOTE**: The `display(spark.table())` pattern shown in the next cell is the same as executing a `SELECT * FROM` for a table or view. Later, we'll see that this allows us to pass streaming temp views back to the DataFrame API to write out a stream. - -# COMMAND ---------- - -display(spark.table("static_tmp_vw")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC But when we execute a query on a streaming temporary view, we'll continue to update the results of the query as new data arrives in the source. -# MAGIC -# MAGIC Think of a query executed against a streaming temp view as an **always-on incremental query**. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT * FROM streaming_tmp_vw - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Before continuing, click `Stop Execution` at the top of the notebook, `Cancel` immediately under the cell, or run the following cell to stop all active streaming queries. - -# COMMAND ---------- - -for s in spark.streams.active: - print("Stopping " + s.id) - s.stop() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Working with Streaming Data -# MAGIC We can execute most transformation against streaming temp views the same way we would with static data. Here, we'll run a simple aggregation to get counts of records for each `device`. -# MAGIC -# MAGIC Because we are querying a streaming temp view, this becomes a streaming query that executes indefinitely, rather than completing after retrieving a single set of results. For streaming queries like this, Databricks Notebooks include interactive dashboards that allow users to monitor streaming performance. Explore this below. -# MAGIC -# MAGIC ![](https://files.training.databricks.com/images/adbcore/streaming-dashboard.png) -# MAGIC -# MAGIC One important note regarding this example: this is merely displaying an aggregation of input as seen by the stream. **None of these records are being persisted anywhere at this point.** - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT device, COUNT(device) AS total_records -# MAGIC FROM streaming_tmp_vw -# MAGIC GROUP BY device - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Before continuing, click `Stop Execution` at the top of the notebook, `Cancel` immediately under the cell, or run the following cell to stop all active streaming queries. - -# COMMAND ---------- - -for s in spark.streams.active: - print("Stopping " + s.id) - s.stop() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Persisting Streaming Results -# MAGIC -# MAGIC In order to persist incremental results, we need to pass our logic back to the PySpark Structured Streaming DataFrames API. -# MAGIC -# MAGIC Above, we created a temp view from a PySpark streaming DataFrame. If we create another temp view from the results of a query against a streaming temp view, we'll again have a streaming temp view. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC CREATE OR REPLACE TEMP VIEW device_counts_tmp_vw AS ( -# MAGIC SELECT device, COUNT(device) AS total_records -# MAGIC FROM streaming_tmp_vw -# MAGIC GROUP BY device -# MAGIC ) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Writing a Stream -# MAGIC -# MAGIC To persist the results of a streaming query, we must write them out to durable storage. The `DataFrame.writeStream` method returns a `DataStreamWriter` used to configure the output. -# MAGIC -# MAGIC There are a number of required parameters to configure a streaming write: -# MAGIC * The `format` of the **output sink**; see documentation -# MAGIC * The location of the **checkpoint directory** -# MAGIC * The **output mode** -# MAGIC * Configurations specific to the output sink, such as: -# MAGIC * Kafka -# MAGIC * A custom sink via `writeStream.foreach(...)` -# MAGIC -# MAGIC Once the configuration is completed, we trigger the job with a call to `.table()`. If we didn't want to create a table and instead wanted to write directly to storage, we would use `.start()` instead. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Checkpointing -# MAGIC -# MAGIC Databricks creates checkpoints by storing the current state of your streaming job to cloud storage. -# MAGIC -# MAGIC Checkpointing combines with write ahead logs to allow a terminated stream to be restarted and continue from where it left off. -# MAGIC -# MAGIC Checkpoints cannot be shared between separate streams. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Output Modes -# MAGIC -# MAGIC Streaming jobs have output modes similar to static/batch workloads. [More details here](https://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#output-modes). -# MAGIC -# MAGIC | Mode | Example | Notes | -# MAGIC | ------------- | ----------- | --- | -# MAGIC | **Append** | `.outputMode("append")` | Only the new rows appended to the Result Table since the last trigger are written to the sink. This is the default. | -# MAGIC | **Complete** | `.outputMode("complete")` | The entire updated Result Table is written to the sink. The individual sink implementation decides how to handle writing the entire table. | -# MAGIC | **Update** | `.outputMode("update")` | Only the rows in the Result Table that were updated since the last trigger will be outputted to the sink.| -# MAGIC -# MAGIC **NOTE**: Not all sinks will support `update` mode. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ### Defining the Trigger Interval -# MAGIC -# MAGIC When defining a streaming write, the `trigger` method specifies when the system should process the next set of data.. -# MAGIC -# MAGIC | Trigger Type | Example | Notes | -# MAGIC |----------------------------------------|-----------|-------------| -# MAGIC | Unspecified | | The query will be executed as soon as the system has completed processing the previous query (this is the default) | -# MAGIC | Fixed interval micro-batches | `.trigger(processingTime="2 minutes")` | The query will be executed in micro-batches and kicked off at the user-specified intervals | -# MAGIC | One-time micro-batch | `.trigger(once=True)` | The query will execute _only one_ micro-batch to process all the available data and then stop on its own | -# MAGIC | Continuous w/fixed checkpoint interval | `.trigger(continuous="1 second")` | The query will be executed in a low-latency, continuous processing mode. _EXPERIMENTAL_ | -# MAGIC -# MAGIC Note that triggers are specified when defining how data will be written to a sink and control the frequency of micro-batches. By default, Spark will automatically detect and process all data in the source that has been added since the last trigger; some sources allow configuration to limit the size of each micro-batch. -# MAGIC -# MAGIC Read this blog post to learn more about using one-time triggers to simplify CDC with a hybrid streaming/batch design. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Pulling It All Together -# MAGIC -# MAGIC The code below demonstrates using `spark.table()` to load data from a streaming temp view back to a DataFrame. Note that Spark will always load streaming views as a streaming DataFrame and static views as static DataFrames (meaning that incremental processing must be defined with read logic to support incremental writing). - -# COMMAND ---------- - -streamingQuery = (spark.table("device_counts_tmp_vw") - .writeStream - .option("checkpointLocation", checkpointPath) - .outputMode("complete") - .trigger(processingTime='10 seconds') - .table("device_counts") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Querying the Output -# MAGIC Now let's query the output we've written from SQL. Because the result is a table, we only need to deserialize the data to return the results. -# MAGIC -# MAGIC Because we are now querying a table (not a streaming DataFrame), the following will **not** be a streaming query. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT * -# MAGIC FROM device_counts - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Debugging with the Memory Sink -# MAGIC -# MAGIC The **memory** sink can be a useful tool for debugging. It provides a quick and easy sink requiring no setup. The output is stored as an in-memory table, with a name defined using `queryName`. -# MAGIC -# MAGIC This should be used only for debugging purposes with low data volumes, since the entire output is collected and stored in the driver’s memory. - -# COMMAND ---------- - -streamingQueryMem = (spark.table("streaming_tmp_vw") - .writeStream - .format("memory") # memory = store in-memory table (for testing only) - .queryName("streaming_query_mem") # name of the in-memory table - .outputMode("append") - .start() -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's examine the contents of the in-memory table with the same query used previously. Like the previous query we ran against the Delta output, this will **not** be a streaming query. In this case, we are simply querying the in-memory table established by the memory sink in the previous cell. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT device, COUNT(device) AS total_records -# MAGIC FROM streaming_query_mem -# MAGIC GROUP BY device - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Interacting with Streaming Queries -# MAGIC -# MAGIC -# MAGIC the logic defined above, data is read from JSON files and then saved out in the Delta Lake format. Note that because Delta creates a new version for each transaction, when working with streaming data this will mean that the Delta table creates a new version for each trigger interval in which new data is processed. [More info on streaming with Delta](https://docs.databricks.com/delta/delta-streaming.html#table-streaming-reads-and-writes). - -# COMMAND ---------- - -# MAGIC %md -# MAGIC The `recentProgress` attribute allows access to metadata about recently processed micro-batches. Let's dump the contents for the streaming query created earlier. - -# COMMAND ---------- - -streamingQuery.recentProgress - -# COMMAND ---------- - -# MAGIC %md -# MAGIC In addition to referencing `StreamingQuery` objects returned by `writeStream`, as we did above, we can iterate on the `streams.active` attribute in `SparkSession` to identify all active streaming queries. - -# COMMAND ---------- - -for s in spark.streams.active: # Iterate over all streams - print(s.id) # Print the stream's id - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's iterate on all active streams and stop them. This is an important thing to do here, for if you don't then your cluster will run indefinitely! -# MAGIC -# MAGIC After running the following cell, feel free to examine the cells earlier that initiated streaming queries; notice they have both been canceled. - -# COMMAND ---------- - -for s in spark.streams.active: - print("Stopping " + s.id) - s.stop() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Incremental Ingestion with Auto Loader -# MAGIC -# MAGIC Incremental ETL is important since it allows us to deal solely with new data that has been encountered since the last ingestion. Reliably processing only the new data is key to achieving scalability. -# MAGIC -# MAGIC Ingesting into a Delta Lake table from a data lake is a common use case that has traditionally been challenging to properly set up, typically relying on the integration of always-on services like Kafka to track the files that have been ingested, and to monitor cloud storage for new file arrivals. Databricks Auto Loader abstracts all this and provides an easy-to-use mechanism for incrementally and efficiently processing new data files as they arrive in cloud file storage, in the form of a structured streaming source. -# MAGIC -# MAGIC Given an input directory path on the cloud file storage, the `cloudFiles` source automatically processes new files as they arrive, with the option of also processing existing files in that directory. For full details, refer to the documentation. -# MAGIC -# MAGIC **Due to the benefits and scalability that Auto Loader delivers, Databricks recommends its use as general best practice when ingesting data from cloud storage.** - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Reset the output directory in preparation to stream data using Auto Loader. - -# COMMAND ---------- - -# MAGIC %run ../Includes/classic-setup $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Reading Data with Auto Loader -# MAGIC -# MAGIC An example invocation of Auto Loader is provided below. Comparing against the standard streaming read from earlier, notice the following differences: -# MAGIC -# MAGIC * Specify a `format` of `cloudFiles` -# MAGIC * Specify the underlying format of the data using the `cloudFiles.format` option -# MAGIC * The `dataLandingLocation` source below represents a cloud storage location from where data is being ingested - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ### Schema Inference and Evolution -# MAGIC -# MAGIC As mentioned earlier, every streaming DataFrame must have a schema. But Auto Loader can be configured to take a more active role in inferring and maintaining the schema of the data as it evolves. -# MAGIC -# MAGIC By omitting a schema specification, Auto Loader will detect the schema based on the data seen on the input. Specifying the `cloudFiles.schemaLocation` option allows Auto Loader to track the schema, thereby improving performances and ensuring stability of the schema across stream restart. A common pattern is to use `checkpointLocation` for this purpose. -# MAGIC -# MAGIC There must be data present for schema inference to work; otherwise you must specify a schema. -# MAGIC -# MAGIC **Schema evolution** allows changes to a schema in response to data that changes over time. This can be an important feature in some use cases. - -# COMMAND ---------- - -incrementalStreamingDF = (spark - .readStream - .format("cloudFiles") - .option("cloudFiles.format", "json") - .option("cloudFiles.schemaLocation", checkpointPath) - .load(dataLandingLocation) -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Writing the output also takes a similar form as the previous streaming case. Note the following differences: -# MAGIC * Specify `mergeSchema` option to activate schema evolution. If any changes to the schema occur over time, the schema is adapted rather than rejecting the write. This can be useful in some use cases. -# MAGIC * Omitting the trigger to allow the query to continue running, ingesting new data as it arrives. If you wish to schedule your ETL process to run in batch mode, consider using a one-time trigger instead. - -# COMMAND ---------- - -(incrementalStreamingDF - .writeStream - .format("delta") - .option("checkpointLocation", checkpointPath) - .option("mergeSchema", "true") - .table(outputTable) -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Querying the Output -# MAGIC By now the following query against the output table will likely seem familiar. Run it a few times, and it will become apparent that nothing changes, as no data is arriving in our simulated cloud storage. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT Device,COUNT(Device) AS Count -# MAGIC FROM ${c.outputTable} -# MAGIC GROUP BY Device - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Land New Data -# MAGIC Run the following cell to simulate the arrival of new data in our cloud storage. Each time you execute the cell below, a new file will be written to our source directory. Following this cell, observe the stream monitor above, and notice the impact on the results when re-running the query. - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Clean Up -# MAGIC Stop active streams and remove created resources before continuing. - -# COMMAND ---------- - -# MAGIC %run ../Includes/classic-setup $mode="clean" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC

Summary

-# MAGIC -# MAGIC We used `readStream` to stream input from a variety of sources, including Databricks Auto Loader. Auto Loader augments Structured Streaming functionality by providing an easy-to-use interface of performing incremental ETL from cloud storage. -# MAGIC -# MAGIC We also explored various options for consuming, writing and querying the streamed input data. -# MAGIC -# MAGIC Finally, we explored the array of active streams maintained in the `SparkSession` object. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC

Additional Topics & Resources

-# MAGIC -# MAGIC * Structured Streaming Programming Guide
-# MAGIC * A Deep Dive into Structured Streaming by Tathagata Das. This is an excellent video describing how Structured Streaming works. -# MAGIC * Failed Streaming Query Recovery Best Practices for Recovery. -# MAGIC * Continuous Processing Mode Lowest possible latency stream processing. Currently Experimental. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.1.2 - Windows and Watermarks.py b/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.1.2 - Windows and Watermarks.py deleted file mode 100644 index e9a8b3d..0000000 --- a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.1.2 - Windows and Watermarks.py +++ /dev/null @@ -1,310 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Windows and Watermarks -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, you should be able to: -# MAGIC * Explain why some methods will not work on streaming data -# MAGIC * Use windows to aggregate over chunks of data rather than all data -# MAGIC * Compare tumbling windows and sliding windows -# MAGIC * Apply watermarking to manage state - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Getting Started -# MAGIC -# MAGIC Run the following cell to configure our "classroom." - -# COMMAND ---------- - -# MAGIC %run ../Includes/classic-setup $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Configure Streaming Read -# MAGIC -# MAGIC This lesson uses the same data as the previous notebook, again loaded with AutoLoader. -# MAGIC -# MAGIC The code below registers a streaming DataFrame (which we'll use again in a moment) and a streaming temp view. -# MAGIC -# MAGIC Note the use of the `selectExpr` method, which allows multiple SQL operations to be configured on a per column basis in PySpark DataFrames. Here, we're simplifying the data to be dealt with by selecting only two columns: -# MAGIC * `Creation_Time`, originally encoded in nanoseconds, is converted to unixtime and renamed to `creation_time` -# MAGIC * `gt` is renamed to `action` - -# COMMAND ---------- - -from pyspark.sql.functions import col - -schema = """Arrival_Time BIGINT, - Creation_Time BIGINT, - Device STRING, - Index BIGINT, - Model STRING, - User STRING, - geolocation STRUCT< - city: STRING, - country: STRING>, - gt STRING, - Id BIGINT, - x DOUBLE, - y DOUBLE, - z DOUBLE""" - -streamingDF = (spark - .readStream - .format("cloudFiles") - .option("cloudFiles.format", "json") - .schema(schema) - .load(dataLandingLocation) - .selectExpr("cast(Creation_Time/1E9 AS timestamp) AS creation_time", "gt AS action") -) - -streamingDF.createOrReplaceTempView("streaming_tmp_vw") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Unsupported Operations -# MAGIC -# MAGIC Most operations on a streaming DataFrame are identical to a static DataFrame. There are some exceptions to this. -# MAGIC -# MAGIC Consider the model of the data as a constantly appending table. Sorting is one of a handful of operations that is either too complex or logically not possible to do when working with streaming data. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT * FROM streaming_tmp_vw -# MAGIC ORDER BY creation_time DESC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Streaming Aggregations -# MAGIC -# MAGIC Continuous applications often require near real-time decisions on real-time, aggregated statistics. -# MAGIC -# MAGIC Some examples include -# MAGIC * Aggregating errors in data from IoT devices by type -# MAGIC * Detecting anomalous behavior in a server's log file by aggregating by country -# MAGIC * Performing behavior analysis on instant messages via hash tags -# MAGIC -# MAGIC While these streaming aggregates may need to reference historic trends, analytics will generally be calculated over discrete units of time. Spark Structured Streaming supports time-based **windows** on streaming DataFrames to make these calculations easy. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ### What is Time? -# MAGIC -# MAGIC Multiple times may be associated with each streaming event. Consider the discrete differences between the time at which the event data was: -# MAGIC - Generated -# MAGIC - Written to the streaming source -# MAGIC - Processed into Spark -# MAGIC -# MAGIC Each of these times will be recorded from the system clock of the machine running the process, with discrepancies and latencies being introduced due to many different causes. -# MAGIC -# MAGIC Generally speaking, most analytics will be interested in the time the data was generated. As such, this lesson will focus on timestamps recorded at the time of data generation, which we will refer to as the **event time**. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Windowing -# MAGIC -# MAGIC Defining windows on a time series field imposes a time range constraint on an otherwise unbounded input. This allows users to utilize this field for aggregations in the same way they would use distinct values when calling `GROUP BY`. Spark maintains a state table with aggregates for each user-defined bucket of time. -# MAGIC -# MAGIC Spark supports three types of windows: -# MAGIC -# MAGIC * **Tumbling windows**: fixed-size windows, regularly recurring windows that do not overlap. Each event will be aggregated into only one window. -# MAGIC * **Sliding windows**: fixed-size windows, regularly recurring windows that overlap. Each event may be aggregated into multiple windows. -# MAGIC * **Session windows**: dynamic windows whose start time and duration depends on the inputs. An event will trigger the start of a window that will, in general, continue until a predetermined duration after the last event received. -# MAGIC -# MAGIC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC The following diagram illustrates in greater detail the concept of sliding windows and how events received at various times will be aggregated into the various windows (assuming that the slide duration is less than the window duration, which leads to overlapping windows): -# MAGIC -# MAGIC - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ### Consuming with a Windowed Aggregation -# MAGIC -# MAGIC Let's consume the stream from SQL in a windowed aggregation using the SQL `window` function, which accepts a timestamp column and a window duration to define the tumbling windows. An optional third argument specifies a slide duration that allows the definition of a sliding window. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT -# MAGIC window.start AS start, -# MAGIC action, -# MAGIC count(action) AS count -# MAGIC FROM streaming_tmp_vw -# MAGIC GROUP BY -# MAGIC window(creation_time, '1 hour'), -# MAGIC action -# MAGIC ORDER BY -# MAGIC start, -# MAGIC action - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Once a batch of data has loaded, render the results as a bar graph with the following settings: -# MAGIC -# MAGIC * **Keys** is set to `start` -# MAGIC * **Series groupings** is set to `action` -# MAGIC * **Values** is set to `count` - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Land New Data -# MAGIC Recall that our stream has been set up for incremental ingestion. Invoke the following cell a few times to simulate the arrival of new data. Note the impact on the results reported above. - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ### Performance Considerations -# MAGIC Because aggregation is a wide transformation, it will trigger a shuffle. Configuring the number of partitions can reduce the number of tasks and properly balance the workload for the cluster. -# MAGIC -# MAGIC In most cases, a 1-to-1 mapping of partitions to cores is ideal for streaming applications. The code below sets the number of partitions to 4, which maps perfectly to a cluster with 4 cores. - -# COMMAND ---------- - -spark.conf.set("spark.sql.shuffle.partitions", 4) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Watermarking -# MAGIC -# MAGIC -# MAGIC When aggregating with an unbounded input, Spark's fault-tolerant state management naturally incurs some processing overhead. To keep these overheads bounded within acceptable limits, the size of the state data should not grow indefinitely. However, with sliding windows, the number of windows/groups will grow indefinitely, and so can the size of state (proportional to the number of groups). To bound the state size, we have to be able to drop old aggregates that are not going to be updated anymore. We achieve this using **watermarking**. -# MAGIC -# MAGIC Watermarking allows users to define a cutoff threshold for how much state should be maintained. This cutoff is calculated against the most recently seen event time. Data arriving after this threshold will be discarded. -# MAGIC -# MAGIC The `withWatermark` method allows users to easily define this cutoff threshold. -# MAGIC -# MAGIC Note that there is no built-in support for watermarking in Spark SQL, but we can define this in PySpark before creating a temp view, as shown below. - -# COMMAND ---------- - -(streamingDF - .withWatermark("creation_time", "2 hours") # Specify a 2-hour watermark - .createOrReplaceTempView("watermarked_tmp_vw") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC By directing our windowed aggregation at this new temp view, we can easily achieve the same outcome while managing state information. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT -# MAGIC window.start AS start, -# MAGIC action, -# MAGIC count(action) AS count -# MAGIC FROM watermarked_tmp_vw -# MAGIC GROUP BY -# MAGIC window(creation_time, '1 hour'), -# MAGIC action -# MAGIC ORDER BY -# MAGIC start, -# MAGIC action - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC -# MAGIC ## Example Details -# MAGIC -# MAGIC The threshold is always calculated against the max event time seen. -# MAGIC -# MAGIC In the example above, -# MAGIC * The in-memory state is limited to two hours of historic data. -# MAGIC * Data arriving more than 2 hours late should be dropped. -# MAGIC * Data received within 2 hours of being generated will never be dropped. -# MAGIC -# MAGIC Caution This guarantee is strict in only one direction. Data delayed by more than 2 hours is not guaranteed to be dropped; it may or may not get aggregated. The more delayed the data is, the less likely the engine is going to process it. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Writing Results -# MAGIC -# MAGIC Previously we used `spark.table()` to pass SQL logic stored in temp views back to a DataFrame to write out streaming results. -# MAGIC -# MAGIC Below, we instead use `spark.sql()` and pass the entire SQL query. - -# COMMAND ---------- - -(spark.sql(""" - SELECT - window.start AS start, - action, - count(action) AS count - FROM watermarked_tmp_vw - GROUP BY - window(creation_time, '1 hour'), - action - ORDER BY - start, - action -""").writeStream - .option("checkpointLocation", checkpointPath) - .outputMode("complete") - .table("action_counts") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Clean Up - -# COMMAND ---------- - -# MAGIC %run ../Includes/classic-setup $mode="clean" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Summary -# MAGIC -# MAGIC * A handful of operations valid for static DataFrames will not work with streaming data -# MAGIC * Windows allow users to define time-based buckets for aggregating streaming data -# MAGIC * Watermarking allows users to manage the amount of state being calculated with each trigger and define how late-arriving data should be handled - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.3.3 - Pipeline Results.py b/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.3.3 - Pipeline Results.py deleted file mode 100644 index 79ea039..0000000 --- a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.3.3 - Pipeline Results.py +++ /dev/null @@ -1,54 +0,0 @@ -# Databricks notebook source -# MAGIC %md -# MAGIC # Exploring the Results of a DLT Pipeline -# MAGIC -# MAGIC This Notebook explores the execution results of a DLT pipeline. - -# COMMAND ---------- - -# MAGIC %run ../Includes/dlt-setup $course="dlt_demo" - -# COMMAND ---------- - -storage_location = userhome - -# COMMAND ---------- - -dbutils.fs.ls(storage_location) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC The `system` directory captures events associated with the pipeline. - -# COMMAND ---------- - -dbutils.fs.ls(f"{storage_location}/system/events") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC These event logs are stored as a Delta table. Let's query the table. - -# COMMAND ---------- - -display(spark.sql(f"SELECT * FROM delta.`{storage_location}/system/events`")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's view the contents of the *tables* directory. - -# COMMAND ---------- - -dbutils.fs.ls(f"{storage_location}/tables") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's query the gold table. - -# COMMAND ---------- - -display(spark.sql(f"SELECT * FROM {database}.sales_order_in_la")) - diff --git a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.1.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py b/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.1.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py deleted file mode 100644 index 65eedff..0000000 --- a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.1.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py +++ /dev/null @@ -1,133 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Using Auto Loader and Structured Streaming with Spark SQL -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lab, you will be able to: -# MAGIC * Ingest data using Auto Loader -# MAGIC * Aggregate streaming data -# MAGIC * Stream data to a Delta table - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Setup -# MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. - -# COMMAND ---------- - -# MAGIC %run ../../Includes/classic-setup $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Configure Streaming Read -# MAGIC -# MAGIC This lab uses a collection of customer-related CSV data from DBFS found in */databricks-datasets/retail-org/customers/*. -# MAGIC -# MAGIC Read this data using Auto Loader using its schema inference (use `customersCheckpointPath` to store the schema info). Create a streaming temporary view called `customers_raw_temp`. - -# COMMAND ---------- - -# ANSWER -customersCheckpointPath = userhome + "/customersCheckpoint" - -(spark - .readStream - .format("cloudFiles") - .option("cloudFiles.format", "csv") - .option("cloudFiles.schemaLocation", customersCheckpointPath) - .load("/databricks-datasets/retail-org/customers/") - .createOrReplaceTempView("customers_raw_temp") -) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Define a streaming aggregation -# MAGIC -# MAGIC Using CTAS syntax, define a new streaming view called `customer_count_by_state_temp` that counts the number of customers per `state`, in a field called `customer_count`. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC -- ANSWER -# MAGIC CREATE OR REPLACE TEMPORARY VIEW customer_count_by_state_temp AS -# MAGIC SELECT -# MAGIC state, -# MAGIC count(state) AS customer_count -# MAGIC FROM customers_raw_temp -# MAGIC GROUP BY -# MAGIC state - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Write aggregated data to a Delta table -# MAGIC -# MAGIC Stream data from the `customer_count_by_state_temp` view to a Delta table called `customer_count_by_state`. - -# COMMAND ---------- - -# ANSWER -customersCountCheckpointPath = userhome + "/customersCountCheckpoint" - -(spark - .table("customer_count_by_state_temp") - .writeStream - .format("delta") - .option("checkpointLocation", customersCountCheckpointPath) - .outputMode("complete") - .table("customer_count_by_state")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Query the results -# MAGIC -# MAGIC Query the `customer_count_by_state` table (this will not be a streaming query). Plot the results as a bar graph and also using the map plot. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC -- ANSWER -# MAGIC SELECT * FROM customer_count_by_state - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Wrapping Up -# MAGIC -# MAGIC Run the following cell to remove the database and all data associated with this lab. - -# COMMAND ---------- - -# MAGIC %run ../../Includes/classic-setup $mode="clean" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC By completing this lab, you should now feel comfortable: -# MAGIC * Using PySpark to configure Auto Loader for incremental data ingestion -# MAGIC * Using Spark SQL to aggregate streaming data -# MAGIC * Streaming data to a Delta table - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.2.3L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py b/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.2.3L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py deleted file mode 100644 index 115589a..0000000 --- a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.2.3L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py +++ /dev/null @@ -1,193 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Propagating Incremental Updates with Structured Streaming and Delta Lake -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lab, you will be able to: -# MAGIC * Apply your knowledge of structured streaming and Auto Loader to implement a simple multi-hop architecture - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Setup -# MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. - -# COMMAND ---------- - -# MAGIC %run ../../Includes/classic-setup $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Ingest data -# MAGIC -# MAGIC This lab uses a collection of customer-related CSV data from DBFS found in */databricks-datasets/retail-org/customers/*. -# MAGIC -# MAGIC Read this data using Auto Loader using its schema inference (use `customersCheckpointPath` to store the schema info). Stream the raw data to a Delta table called `bronze`. - -# COMMAND ---------- - -# ANSWER -customersCheckpointPath = userhome + "/customersCheckpoint" - -(spark - .readStream - .format("cloudFiles") - .option("cloudFiles.format", "csv") - .option("cloudFiles.schemaLocation", customersCheckpointPath) - .load("/databricks-datasets/retail-org/customers/") - .writeStream - .format("delta") - .option("checkpointLocation", customersCheckpointPath) - .outputMode("append") - .table("bronze")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC Let's create a streaming temporary view into the bronze table, so that we can perform transforms using SQL. - -# COMMAND ---------- - -(spark - .readStream - .table("bronze") - .createOrReplaceTempView("bronze_temp")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Clean and enhance data -# MAGIC -# MAGIC Using CTAS syntax, define a new streaming view called `bronze_enhanced_temp` that does the following: -# MAGIC * Skips records with a null `postcode` (set to zero) -# MAGIC * Inserts a column called `receipt_time` containing a current timestamp -# MAGIC * Inserts a column called `source_file` intaining the input filename - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC -- ANSWER -# MAGIC CREATE OR REPLACE TEMPORARY VIEW bronze_enhanced_temp AS -# MAGIC SELECT -# MAGIC *, current_timestamp() receipt_time, input_file_name() source_file -# MAGIC FROM bronze_temp -# MAGIC WHERE postcode > 0 - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Silver table -# MAGIC -# MAGIC Stream the data from `bronze_enhanced_temp` to a table called `silver`. - -# COMMAND ---------- - -# ANSWER -silverCheckpointPath = userhome + "/silverCheckpoint" - -(spark.table("bronze_enhanced_temp") - .writeStream - .format("delta") - .option("checkpointLocation", silverCheckpointPath) - .outputMode("append") - .table("silver")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC Let's create a streaming temporary view into the silver table, so that we can perform business-level using SQL. - -# COMMAND ---------- - -(spark - .readStream - .table("silver") - .createOrReplaceTempView("silver_temp")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Gold tables -# MAGIC -# MAGIC Using CTAS syntax, define a new streaming view called `customer_count_temp` that counts customers per state. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC -- ANSWER -# MAGIC CREATE OR REPLACE TEMPORARY VIEW customer_count_by_state_temp AS -# MAGIC SELECT state, count(state) AS customer_count -# MAGIC FROM silver_temp -# MAGIC GROUP BY -# MAGIC state - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Finally, stream the data from the `customer_count_by_state_temp` view to a Delta table called `gold_customer_count_by_state`. - -# COMMAND ---------- - -# ANSWER -customersCountCheckpointPath = userhome + "/customersCountCheckpoint" - -(spark - .table("customer_count_by_state_temp") - .writeStream - .format("delta") - .option("checkpointLocation", customersCountCheckpointPath) - .outputMode("complete") - .table("gold_customer_count_by_state")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## Query the results -# MAGIC -# MAGIC Query the `gold_customer_count_by_state` table (this will not be a streaming query). Plot the results as a bar graph and also using the map plot. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT * FROM gold_customer_count_by_state - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Wrapping Up -# MAGIC -# MAGIC Run the following cell to remove the database and all data associated with this lab. - -# COMMAND ---------- - -# MAGIC %run ../../Includes/classic-setup $mode="clean" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC By completing this lab, you should now feel comfortable: -# MAGIC * Using PySpark to configure Auto Loader for incremental data ingestion -# MAGIC * Using Spark SQL to aggregate streaming data -# MAGIC * Streaming data to a Delta table - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.4L - Intro & Config Lab.py b/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.4L - Intro & Config Lab.py deleted file mode 100644 index b0c6cf7..0000000 --- a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.4L - Intro & Config Lab.py +++ /dev/null @@ -1,188 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Lab: Migrating SQL Notebooks to Delta Live Tables -# MAGIC -# MAGIC This notebook dictates an overall structure for the lab exercise, configures the environment for the lab, provides simulated data streaming, and performs cleanup once you are done. A notebook like this is not typically needed in a production pipeline scenario. -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, you should be able to: -# MAGIC * Convert existing data pipelines to Delta Live Tables - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Datasets Used -# MAGIC -# MAGIC This demo uses simplified artificially generated medical data. The schema of our two datasets is represented below. Note that we will be manipulating these schema during various steps. -# MAGIC -# MAGIC #### Recordings -# MAGIC The main dataset uses heart rate recordings from medical devices delivered in the JSON format. -# MAGIC -# MAGIC | Field | Type | -# MAGIC | --- | --- | -# MAGIC | device_id | int | -# MAGIC | mrn | long | -# MAGIC | time | double | -# MAGIC | heartrate | double | -# MAGIC -# MAGIC #### PII -# MAGIC These data will later be joined with a static table of patient information stored in an external system to identify patients by name. -# MAGIC -# MAGIC | Field | Type | -# MAGIC | --- | --- | -# MAGIC | mrn | long | -# MAGIC | name | string | - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Getting Started -# MAGIC -# MAGIC Begin by running the following cell to configure the lab environment. - -# COMMAND ---------- - -# MAGIC %run "../../Includes/dlt-setup" $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Land Initial Data -# MAGIC Seed the landing zone with some data before proceeding. You will re-run this command to land additional data later. - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create and Configure a Pipeline -# MAGIC -# MAGIC 1. Click the **Jobs** button on the sidebar, then select the **Delta Live Tables** tab. -# MAGIC 1. Click **Create Pipeline**. -# MAGIC 1. Fill in a **Pipeline Name** of your choosing. -# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the notebook `3.3.4 - LAB - Migrating a SQL Pipeline to DLT`. -# MAGIC 1. Run the cell below to generate values for **source**, **Target** and **Storage Location**. (All of these will include your current username). -# MAGIC * Click `Add configuration`; enter the word `source` in the **Key** field and the output printed next to `source` below in the value field. -# MAGIC * Enter the database name printed next to `Target` below in the **Target** field. -# MAGIC * Enter the location printed next to `Storage Location` below in the **Storage Location** field. -# MAGIC 1. Set **Pipeline Mode** to **Triggered**. -# MAGIC 1. Disable autoscaling. -# MAGIC 1. Set the number of wokers to 1. -# MAGIC 1. Click **Create**. - -# COMMAND ---------- - -storage_location = userhome + "/output" -print(f"source : {dataLandingLocation.split(':')[1]}") -print(f"Target: {database}") -print(f"Storage Location: {storage_location.split(':')[1]}") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Run your Pipeline -# MAGIC -# MAGIC Select **Development** mode, which accelerates the development lifecycle by reusing the same cluster across runs. It will also turn off automatic retries when jobs fail. -# MAGIC -# MAGIC Click **Start** to begin the first update to your table. -# MAGIC -# MAGIC Delta Live Tables will automatically deploy all the necessary infrastructure and resolve the dependencies between all datasets. -# MAGIC -# MAGIC **NOTE**: The first table update make take several minutes as relationships are resolved and infrastructure deploys. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Open and Complete DLT Pipeline Notebook -# MAGIC -# MAGIC You will perform your work in this companion Notebook, which you will ultimately deploy as a pipeline. -# MAGIC -# MAGIC Open the Notebook and, following the guidelines provided therein, fill in the cells where prompted to implement a multi-hop architecture similar to the one we worked with in the previous section. -# MAGIC -# MAGIC **NOTE**: As a first step to preparing your pipeline, run the following cell to obtain the cloud file location. Substitue this value for the text that reads ``. This value will be unique within the workspace to your user identity to prevent possible interference between users within the same workspace. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Troubleshooting Code in Development Mode -# MAGIC -# MAGIC Don't despair if your pipeline fails the first time. Delta Live Tables is in active development, and error messages are improving all the time. -# MAGIC -# MAGIC Because relationships between tables are mapped as a DAG, error messages will often indicate that a dataset isn't found. -# MAGIC -# MAGIC Let's consider our DAG below: -# MAGIC -# MAGIC -# MAGIC -# MAGIC If the error message `Dataset not found: 'recordings_parsed'` is raised, there may be several culprits: -# MAGIC 1. The logic defining `recordings_parsed` is invalid -# MAGIC 1. There is an error reading from `recordings_bronze` -# MAGIC 1. A typo exists in either `recordings_parsed` or `recordings_bronze` -# MAGIC -# MAGIC The safest way to identify the culprit is to iteratively add table/view definitions back into your DAG starting from your initial ingestion tables. You can simply comment out later table/view definitions and uncomment these between runs. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Display Results -# MAGIC -# MAGIC Assuming your pipeline runs successfully, display the contents of the gold table. -# MAGIC -# MAGIC **NOTE**: Because we specified a value for **Target**, tables are published to the specified database. Without a **Target** specification, we would need to query the table based on its underlying location in DBFS (relative to the **Storage Location**). - -# COMMAND ---------- - -spark.sql(f"SELECT * FROM {database}daily_patient_avg") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Trigger another file arrival with the following cell. Feel free to run it a couple more times if desired. Following this, run the pipeline again and view the results. Feel free to re-run the cell above to gain an updated view of the `daily_patient_avg` table. - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Wrapping Up -# MAGIC -# MAGIC Ensure that you delete your pipeline from the DLT UI, and run the following cell to clean up the files and tables that were created as part of the lab setup and execution. - -# COMMAND ---------- - -# MAGIC %run "../../Includes/dlt-setup" $mode="clean" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Summary -# MAGIC -# MAGIC In this lab, you learned to convert an existing data pipeline to a Delta Live Tables SQL pipeline, and deployed that pipeline using the DLT UI. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Additional Topics & Resources -# MAGIC -# MAGIC * Delta Live Tables Documentation -# MAGIC * Delta Live Tables Demo - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/0 - Task Orchestration with Databricks Jobs.py b/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/0 - Task Orchestration with Databricks Jobs.py deleted file mode 100644 index ad80e31..0000000 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/0 - Task Orchestration with Databricks Jobs.py +++ /dev/null @@ -1,115 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Orchestrating Jobs with Databricks -# MAGIC -# MAGIC New updates to the Databricks Jobs UI have added the ability to schedule multiple tasks as part of a job, allowing Databricks Jobs to fully handle orchestration for most production workloads. -# MAGIC -# MAGIC Here, we'll start by reviewing the steps for scheduling a notebook as a triggered standalone job, and then add a dependent job using a DLT pipeline. -# MAGIC -# MAGIC -# MAGIC By the end of this lesson, you should feel confident: -# MAGIC * Scheduling a notebook as a Databricks Job -# MAGIC * Describing job scheduling options and differences between cluster types -# MAGIC * Review Job Runs to track progress and see results -# MAGIC * Scheduling a DLT pipeline as a Databricks Job -# MAGIC * Configuring linear dependencies between tasks using the Databricks Jobs UI -# MAGIC -# MAGIC ## Schedule a Notebook Job -# MAGIC -# MAGIC When using the Jobs UI to orchestrate a workload with multiple tasks, you'll always begin by scheduling a single task. -# MAGIC -# MAGIC Here, we'll start by scheduling the notebook `1 - Reset`. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. -# MAGIC 1. Click the blue `Create Job` button -# MAGIC 1. Configure the task: -# MAGIC 1. Enter `reset` for the task name -# MAGIC 1. Select the notebook `1 - Reset` using the notebook picker -# MAGIC 1. Select an Existing All Purpose Cluster from the **Cluster** dropdown -# MAGIC 1. Click **Create** -# MAGIC -# MAGIC **Note**: When selecting your all purpose cluster, you will get a warning about how this will be billed as all purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. -# MAGIC -# MAGIC Click the blue **Run now** button in the top right to start the job. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Review Run -# MAGIC -# MAGIC As currently scheduled, our single notebook provides identical performance to the legacy Databricks Jobs UI, which only allowed a single notebook to be scheduled. -# MAGIC -# MAGIC From the **Runs** tab, clicking on the start time field will display a preview of the notebook with results. If the job is still running, this will be under **Active Runs**, and the displayed notebook will ocassionaly update to show progress throughout execution. If it has already completed, it will be under **Completed Runs** and just display the static results of the executed notebook. -# MAGIC -# MAGIC The notebook scheduled using the magic command `%run` to call an additional notebook using a relative path. Note that while not covered in this course, [new functionality added to Databricks Repos allows loading Python modules using relative paths](https://docs.databricks.com/repos.html#work-with-non-notebook-files-in-a-databricks-repo). -# MAGIC -# MAGIC The actual outcome of the scheduled notebook is to reset the output of the DLT pipeline configured earlier in the course, as well as to print out the necessary variables used to configure this pipeline for users that may not have coded along previously. -# MAGIC -# MAGIC Before continuing to the next step, make sure you either have access to a - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Chron Scheduling of Databricks Jobs -# MAGIC -# MAGIC Note that on the right hand side of the Jobs UI, directly under the **Job Details** section is a section labeled **Schedule**. -# MAGIC -# MAGIC Click on the **Edit schedule** button to explore scheduling options. -# MAGIC -# MAGIC Changing the **Schedule type** field from **Manual** to **Scheduled** will bring up a chron scheduling UI. -# MAGIC -# MAGIC This UI provides extensive options for setting up chronological scheduling of your Jobs. Settings configured with the UI can also be output in cron syntax, which can be editted if custom configuration not available with the UI is needed. -# MAGIC -# MAGIC At this time, we'll leave our job set with **Manual** scheduling. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Schedule a DLT Pipeline as a Task -# MAGIC -# MAGIC In this step, we'll add a DLT pipeline to execute after the success of the task we configured in the previous step. -# MAGIC -# MAGIC **NOTE**: This step assumes that the DLT pipeline describe in the lab for module 3 of this course was configured successfully. If this is not the case, instructions are included for configuring this DLT pipeline in the run output of the `reset` notebook executed above. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar -# MAGIC 1. Select the job you defined above by clicking on the name -# MAGIC 1. At the top left of your screen, you'll see the **Runs** tab is currently selected; click the **Tasks** tab. -# MAGIC 1. Click the large blue circle with a **+** at the center bottom of the screen to add a new task -# MAGIC 1. Specify the **Task name** as `dlt` -# MAGIC 1. From **Type**, select `Pipeline` -# MAGIC 1. Click the **Pipeline** field and select the DLT pipeline you configured previously -# MAGIC 1. Note that the **Depends on** field defaults to your previously defined task -# MAGIC 1. Click the blue **Create task** button -# MAGIC -# MAGIC You should now see a screen with 2 boxes and a downward arrow between them. Your `reset` task will be at the top, leading into your `dlt` task. This visualization represents the dependencies between these tasks. -# MAGIC -# MAGIC Click **Run now** to execute your job. -# MAGIC -# MAGIC **NOTE**: You may need to wait a few minutes as infrastructure for your DLT pipeline is deployed. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Review Multi-Task Run Results -# MAGIC -# MAGIC Clicking into the job run will replicate the UI showing both tasks. The visualizations for tasks will update in real time to reflect which tasks are actively running, and will change colors if task failure occur. Clicking on a task box will render the scheduled notebook in the UI. (You can think of this as just an additional layer of orchestration on top of the previous Databricks Jobs UI, if that helps; note that if you have workloads scheduling jobs with the CLI or REST API, [the JSON structure used to configure and get results about jobs has seen similar updates to the UI](https://docs.databricks.com/dev-tools/api/latest/jobs.html)). -# MAGIC -# MAGIC **NOTE**: At this time, DLT pipelines scheduled as tasks do not directly render results in the Runs GUI; instead, you will be directed back to the DLT Pipeline GUI for the scheduled Pipeline. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/1- Reset.py b/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/1- Reset.py deleted file mode 100644 index 7f54ee9..0000000 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/1- Reset.py +++ /dev/null @@ -1,48 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %run ../../Includes/dlt-setup $course="dlt_demo" $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC If you have not previously configured this DLT pipeline successfully, the following cell prints out two values that will be used during the configuration steps that follow. - -# COMMAND ---------- - -print(f"Target: {database}") -print(f"Storage location: {userhome.split(':')[1]}") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create and configure a pipeline -# MAGIC -# MAGIC The instructions below refer to the same pipeline created during the previous codealong for DLT; if you successfully configured this notebook previously, you should not need to reconfigure this pipeline now. -# MAGIC -# MAGIC -# MAGIC Steps: -# MAGIC 1. Click the **Jobs** button on the sidebar, -# MAGIC 1. Select the **Delta Live Tables** tab. -# MAGIC 1. Click **Create Pipeline**. -# MAGIC 1. Fill in a **Pipeline Name** of your choosing. -# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the companion notebook called **2 - DLT Job**. -# MAGIC 1. In the **Target** field, specify the database name printed out next to **Target** in the cell above. (This should follow the pattern `dbacademy__dlt_demo`) -# MAGIC 1. In the **Storage location** field, copy the directory as printed above. -# MAGIC 1. For **Pipeline Mode**, select **Triggered** -# MAGIC 1. Uncheck the **Enable autoscaling** box, and set the number of workers to 1., -# MAGIC 1. Click **Create**. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/0 - Lab Instructions.py b/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/0 - Lab Instructions.py deleted file mode 100644 index 98bc5c7..0000000 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/0 - Lab Instructions.py +++ /dev/null @@ -1,91 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Orchestrating Jobs with Databricks -# MAGIC -# MAGIC In this lab, you'll be configuring a multi-task job comprising of: -# MAGIC * A notebook that lands a new batch of data in a storage directory -# MAGIC * A Delta Live Table pipeline that processes this data through a series of tables -# MAGIC * A notebook that queries the gold table produced by this pipeline as well as various metrics output by DLT -# MAGIC -# MAGIC By the end of this lab, you should feel confident: -# MAGIC * Scheduling a notebook as a Databricks Job -# MAGIC * Scheduling a DLT pipeline as a Databricks Job -# MAGIC * Configuring linear dependencies between tasks using the Databricks Jobs UI -# MAGIC -# MAGIC ## Schedule a Notebook Job -# MAGIC -# MAGIC When using the Jobs UI to orchestrate a workload with multiple tasks, you'll always begin by scheduling a single task. -# MAGIC -# MAGIC Here, we'll start by scheduling the notebook `1 - Batch Job`. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. -# MAGIC 1. Click the blue `Create Job` button -# MAGIC 1. Configure the task: -# MAGIC 1. Enter `Batch-Job` for the task name -# MAGIC 1. Select the notebook `1 - Batch Job` using the notebook picker -# MAGIC 1. Select an Existing All Purpose Cluster from the **Cluster** dropdown -# MAGIC 1. Click **Create** -# MAGIC -# MAGIC **Note**: When selecting your all purpose cluster, you will get a warning about how this will be billed as all purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. -# MAGIC -# MAGIC Click the blue **Run now** button in the top right to confirm that you have successfully configured this task. From the **Runs** tab, clicking on the start time field will pull up the notebook with results. -# MAGIC -# MAGIC ## Schedule a DLT Pipeline as a Task -# MAGIC -# MAGIC In this step, we'll add a DLT pipeline to execute after the success of the task we configured in the previous step. -# MAGIC -# MAGIC **NOTE**: This step assumes that the DLT pipeline describe in the lab for module 3 of this course was configured successfully. If this is not the case, instructions are included for configuring this DLT pipeline in the run output of the `Batch-Job` notebook executed above. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar -# MAGIC 1. Select the job you defined above by clicking on the name (this should have the name `Batch-Job`) -# MAGIC 1. At the top left of your screen, you'll see the **Runs** tab is currently selected; click the **Tasks** tab. -# MAGIC 1. Click the large blue circle with a **+** at the center bottom of the screen to add a new task -# MAGIC 1. Specify the **Task name** as `DLT-Pipeline` -# MAGIC 1. From **Type**, select `Pipeline` -# MAGIC 1. Click the **Pipeline** field and select the DLT pipeline you configured previously -# MAGIC 1. Note that the **Depends on** field defaults to your previously defined task -# MAGIC 1. Click the blue **Create task** button -# MAGIC -# MAGIC You should now see a screen with 2 boxes and a downward arrow between them. Your `Batch-Job` task will be at the top, leading into your `DLT-Pipeline` task. This visualization represents the dependencies between these tasks. -# MAGIC -# MAGIC Before clicking **Run now**, click the job name in the top left and provide something unique and descriptive, like `-MTJ-lab` -# MAGIC -# MAGIC **NOTE**: You may need to wait a few minutes as infrastructure for your DLT pipeline is deployed. Feel free to skip clicking **Run now** until the next task is configured if you don't want to wait. -# MAGIC -# MAGIC ## Schedule an Additional Notebook Task -# MAGIC -# MAGIC An additional notebook has been provided which queries some of the DLT metrics and the gold table defined in the DLT pipeline. We'll add this as a final task in our job. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the **Tasks** tab of the job you've been configuring -# MAGIC 1. Click the blue **+** button to add another task -# MAGIC 1. Specify the **Task name** as `Query-Results` -# MAGIC 1. Leave the **Type** set to `Notebook` -# MAGIC 1. Select the notebook `3 - Query Results Job` using the notebook picker -# MAGIC 1. Note that the **Depends on** field defaults to your previously defined task -# MAGIC 1. Select an Existing All Purpose Cluster from the **Cluster** dropdown -# MAGIC 1. Click the blue **Create task** button -# MAGIC -# MAGIC Click the blue **Run now** button in the top right of the screen to run this job. -# MAGIC -# MAGIC From the **Runs** tab, you will be able to click on the start time for this run under the **Active runs** section and visually track task progress. -# MAGIC -# MAGIC Once all your tasks have succeeded, review the contents of each task to confirm expected behavior. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/1 - Batch Job.py b/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/1 - Batch Job.py deleted file mode 100644 index c8a6033..0000000 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/1 - Batch Job.py +++ /dev/null @@ -1,35 +0,0 @@ -# Databricks notebook source -# MAGIC %run ../../Includes/dlt-setup - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create and Configure a Pipeline -# MAGIC -# MAGIC **NOTE**: This lab is configured to work with the DLT pipeline completed as part of the DLT lab in the previous module. If you have not successfully completed this lab, follow the instructions below to configure a pipeline using specified notebook. -# MAGIC -# MAGIC Instructions for configuring DLT pipeline: -# MAGIC 1. Click the **Jobs** button on the sidebar, then select the **Delta Live Tables** tab. -# MAGIC 1. Click **Create Pipeline**. -# MAGIC 1. Fill in a **Pipeline Name** of your choosing. -# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the notebook `4.1.2 - DLT Job`. -# MAGIC 1. Run the cell below to generate values for **source**, **Target** and **Storage Location**. (All of these will include your current username). -# MAGIC * Click `Add configuration`; enter the word `source` in the **Key** field and the output printed next to `source` below in the value field. -# MAGIC * Enter the database name printed next to `Target` below in the **Target** field. -# MAGIC * Enter the location printed next to `Storage Location` below in the **Storage Location** field. -# MAGIC 1. Set **Pipeline Mode** to **Triggered**. -# MAGIC 1. Disable autoscaling. -# MAGIC 1. Set the number of wokers to 1. -# MAGIC 1. Click **Create**. - -# COMMAND ---------- - -storage_location = userhome + "/output" -print(f"source : {dataLandingLocation.split(':')[1]}") -print(f"Target: {database}") -print(f"Storage Location: {storage_location.split(':')[1]}") - diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/3 - Query Results Job.py b/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/3 - Query Results Job.py deleted file mode 100644 index a2d7e55..0000000 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/3 - Query Results Job.py +++ /dev/null @@ -1,62 +0,0 @@ -# Databricks notebook source -# MAGIC %run ../../Includes/dlt-setup - -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Exploring the Results of a DLT Pipeline -# MAGIC -# MAGIC This Notebook explores the execution results of a DLT pipeline. Before proceeding, you will need one piece of information specific to your pipeline instance: the location in DBFS where results are stored. Because we did not specify a value for **Storage Location** when creating the pipeline, DLT automatically created a folder for us. Obtain this information as follows. -# MAGIC -# MAGIC Click **Settings** on the **Pipeline Details** page. This provides a JSON representation of the pipeline configuration. Copy the value specified for **storage** and substitute for `` throughout the rest of this Notebook. -# MAGIC -# MAGIC Generally, and particularly in production systems, you will specify **Storage Location** in your pipeline configurations to have full control of where pipeline results are stored. - -# COMMAND ---------- - -storage_location = userhome + "/output" - -# COMMAND ---------- - -dbutils.fs.ls(storage_location) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC The `system` directory captures events associated with the pipeline. - -# COMMAND ---------- - -dbutils.fs.ls(f"{storage_location}/system/events") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC These event logs are stored as a Delta table. Let's query the table. - -# COMMAND ---------- - -display(spark.sql(f"SELECT * FROM delta.`{storage_location}/system/events`")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's view the contents of the *tables* directory. - -# COMMAND ---------- - -dbutils.fs.ls(f"{storage_location}/tables") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Let's query the gold table. - -# COMMAND ---------- - -display(spark.sql(f"SELECT * FROM {database}.daily_patient_avg")) - -# COMMAND ---------- - -database - diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.3.2 - LAB - Configuring Privileges for Production Data and Derived Tables.py b/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.3.2 - LAB - Configuring Privileges for Production Data and Derived Tables.py deleted file mode 100644 index 79093dc..0000000 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.3.2 - LAB - Configuring Privileges for Production Data and Derived Tables.py +++ /dev/null @@ -1,203 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Configuring Privileges for Production Data and Derived Tables -# MAGIC -# MAGIC The instructions as detailed below are provided for pairs of users to explore how Table ACLs on Databricks work. It leverages Databricks SQL and the Data Explorer to accomplish these tasks, and assumes that neither user has admin privileges for the workspace. An admin will need to have previously granted `CREATE` and `USAGE` privileges on a catalog for users to be able to create databases in Databricksd SQL -# MAGIC -# MAGIC By the end of this lesson, you should be able to: -# MAGIC * Use Data Explorer to navigate relational entities -# MAGIC * Configure permissions for tables and views with Data Explorer -# MAGIC * Configure minimal permissions to allow for table discovery and querying -# MAGIC * Change ownership for databases, tables, and views created in DBSQL - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Exchange User Names with your Partner -# MAGIC If you are not in a workspace where you usernames correspond with your email address, make sure your partner has your username. They will need this when assigning privileges and searching for your database at later steps. -# MAGIC -# MAGIC The following query will print your username. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT current_user() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Generate Setup Statements -# MAGIC -# MAGIC The following cell uses Python to extract username of the present user and format this into several statements used to create databases, tables, and views. -# MAGIC -# MAGIC Both students should execute the following cell. Successful execution will print out a series of formatted SQL queries, which can be copied into the DBSQL query editor and executed. - -# COMMAND ---------- - -def generate_query(course, mode="reset"): - import re - import random - - username = spark.sql("SELECT current_user()").first()[0] - userhome = f"dbfs:/user/{username}/{course}" - database = f"""dbacademy_{re.sub("[^a-zA-Z0-9]", "_", username)}_{course}""" - - if mode == "reset": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - - print(f""" -CREATE DATABASE IF NOT EXISTS {database} -LOCATION '{userhome}'; - -USE {database}; - -CREATE TABLE beans -(name STRING, color STRING, grams FLOAT, delicious BOOLEAN); - -INSERT INTO beans -VALUES ('black', 'black', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('lentils', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('jelly', 'rainbow', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('pinto', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('green', 'green', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('beanbag chair', 'white', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('lentils', 'green', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('kidney', 'red', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), -('castor', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}); - -CREATE VIEW tasty_beans -AS SELECT * FROM beans WHERE delicious = true; - """) -generate_query("acls_lab") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Steps: -# MAGIC * Run the cell above -# MAGIC * Copy the entire output to your clipboard -# MAGIC * Navigate to the Databricks SQL workspace -# MAGIC * Make sure that a DBSQL endpoint is running -# MAGIC * Use the left sidebar to select the **SQL Editor** -# MAGIC * Paste the query above and click the blue **Run** in the top right -# MAGIC -# MAGIC **NOTE**: You will need to be connected to a DBSQL endpoint to execute these queries successfully. If you cannot connect to a DBSQL endpoint, you will need to contact your administrator to give you access. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Find Your Database -# MAGIC In the Data Explorer, find the database you created earlier (this should follow the pattern `dbacademy__acls_lab`). -# MAGIC -# MAGIC Clicking on the database name should display a list of the contained tables and views on the left hand side. On the right, you'll see some details about the database, including the **Owner** and **Location**. -# MAGIC -# MAGIC Click the **Permissions** tab to review who presently has permissions (depending on your workspace configuration, some permissions may have been inherited from settings on the catalog). - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Change Database Permissions -# MAGIC -# MAGIC Step: -# MAGIC 1. Make sure you have the **Permissions** tab selected for the database -# MAGIC 1. Click the blue **Grant** button -# MAGIC 1. Select the **USAGE**, **SELECT**, and **READ_METADATA** options -# MAGIC 1. Enter the username of your partner in the field at the top. -# MAGIC 1. Click **OK** -# MAGIC -# MAGIC Confirm with your partner that you can each see each others' databases and tables. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Run a Query to Confirm -# MAGIC -# MAGIC By granting `USAGE`, `SELECT`, and `READ_METADATA` on your database, your partner should now be able to freely query the tables and views in this database, but will not be able to create new tables OR modify your data. -# MAGIC -# MAGIC In the SQL Editor, each user should run a series of queries to confirm this behavior in the database they were just added to. -# MAGIC -# MAGIC **Make sure you specify your partner's database while running the queries below.** -# MAGIC -# MAGIC Queries to execute: -# MAGIC * `SELECT * FROM .beans` -# MAGIC * `SELECT * FROM .tasty_beans` -# MAGIC * `SELECT * FROM .beans MINUS SELECT * FROM .tasty_beans` -# MAGIC * ``` -# MAGIC UPDATE .beans -# MAGIC SET color = 'pink' -# MAGIC WHERE name = 'black' -# MAGIC ``` -# MAGIC -# MAGIC **NOTE**: These first 3 queries should succeed, but the last should fail. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Execute a Query to Generate the Union of Your Beans -# MAGIC -# MAGIC Modify the query below to specify the `beans` tables in each of your databases. -# MAGIC -# MAGIC ``` -# MAGIC SELECT * FROM .beans -# MAGIC UNION ALL TABLE .beans -# MAGIC ``` -# MAGIC -# MAGIC **NOTE**: Because random values were inserted for the `grams` and `delicious` columns, you should see 2 distinct rows for each `name`, `color` pair. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Register a Derivative View to Your Database -# MAGIC -# MAGIC Modify the query below to register the results of the previous query to your database. -# MAGIC -# MAGIC ``` -# MAGIC CREATE VIEW .our_beans AS -# MAGIC SELECT * FROM .beans -# MAGIC UNION ALL TABLE .beans -# MAGIC ``` - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Query Your Partner's View -# MAGIC -# MAGIC Once your partner has successfully completed the previous step, run the following query against each of your tables; you should get the same results: -# MAGIC -# MAGIC ``` -# MAGIC SELECT name, color, delicious, sum(grams) -# MAGIC FROM our_beans -# MAGIC GROUP BY name, color, delicious -# MAGIC ``` - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Add Modify Permissions -# MAGIC -# MAGIC Now try to drop each other's `beans` tables. At the moment, this shouldn't work. -# MAGIC -# MAGIC Using the Data Explorer, add the `MODIFY` permission for your `beans` table for your partner. -# MAGIC -# MAGIC Again, attempt to drop your partner's `beans` table. This time, it should succeed. -# MAGIC -# MAGIC Try to re-execute queries against any of the views of tables you'd previously queried in this lab. -# MAGIC -# MAGIC **NOTE**: If steps were completed successfully, none of your previous queries should work, as the data referenced by your views has been permanently deleted. This demonstrates the risks associated with providing `MODIFY` privileges to users on data that will be used in production applications and dashboards. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.1 - Last Mile ETL with DBSQL.py b/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.1 - Last Mile ETL with DBSQL.py deleted file mode 100644 index 453f786..0000000 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.1 - Last Mile ETL with DBSQL.py +++ /dev/null @@ -1,271 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC # Last Mile ETL with Databricks SQL -# MAGIC -# MAGIC Before we continue, let's do a recap of some of the things we've learned so far: -# MAGIC 1. The Databricks workspace contains a suite of tools to simplify the data engineering development lifecycle -# MAGIC 1. Databricks notebooks allow users to mix SQL with other programming languages to define ETL workloads -# MAGIC 1. Delta Lake provides ACID compliant transactions and makes incremental data processing easy in the Lakehouse -# MAGIC 1. Delta Live Tables extends the SQL syntax to support many design patterns in the Lakehouse, and simplifies infrastructure deployment -# MAGIC 1. Multi-task jobs allows for full task orchestration, adding dependencies while scheduling a mix of notebooks and DLT pipelines -# MAGIC 1. Databricks SQL allows users to edit and execute SQL queries, build visualizations, and define dashboards -# MAGIC 1. Data Explorer simplifies managing Table ACLs, making Lakehouse data available to SQL analysts (soon to be expanded greatly by Unity Catalog) -# MAGIC -# MAGIC In this section, we'll focus on exploring more DBSQL functionality to support production workloads. -# MAGIC -# MAGIC We'll start by focusing on leveraging Databricks SQL to configure queries that support last mile ETL for analytics. Note that while we'll be using the Databricks SQL UI for this demo, SQL Endpoints [integrate with a number of other tools to allow external query execution](https://docs.databricks.com/integrations/partners.html, as well as having [full API support for executing arbitrary queries programmatically](https://docs.databricks.com/sql/api/index.html). -# MAGIC -# MAGIC From these query results, we'll generate a series of visualizations, which we'll combine into a dashboard. -# MAGIC -# MAGIC Finally, we'll walk through scheduling updates for queries and dashboards, and demonstrate setting alerts to help monitor the state of production datasets over time. -# MAGIC -# MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, you will feel confident: -# MAGIC * Using Databricks SQL as a tool to support production ETL tasks backing analytic workloads -# MAGIC * Configuring SQL queries and visualizations with the Databricks SQL Editor -# MAGIC * Creating dashboards in Databricks SQL -# MAGIC * Scheduling updates for queries and dashboards -# MAGIC * Setting alerts for SQL queries - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Run Setup Script -# MAGIC The following cells runs a notebook that defines a class we'll use to generate SQL queries. - -# COMMAND ---------- - -# MAGIC %run ../Includes/query_generator - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Executing the following cell will reset the database set variables for later query formatting. You can remove the `mode="reset"` argument if you wish to print out the queries without resetting the target database. - -# COMMAND ---------- - -Gen = QueryGenerator(course="4_4", mode="reset") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Demo Database -# MAGIC Execute the following cell and copy the results into the Databricks SQL Editor. -# MAGIC -# MAGIC These queries: -# MAGIC * Create a new database -# MAGIC * Declare two tables (we'll use these for loading data) -# MAGIC * Declare two functions (we'll use these for generating data) -# MAGIC -# MAGIC Once copied, execute the query using the **Run** button. - -# COMMAND ---------- - -Gen.config() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC **NOTE**: The queries above are only designed to be run once after resetting the demo completely to reconfigure the environment. Users will need to have `CREATE` and `USAGE` permissions on the catalog to execute them. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Query to Load Data -# MAGIC Execute the cell below to print out a formatted SQL query for loading data in the `user_ping` table created in the previous step. -# MAGIC -# MAGIC Save this query with the name **Load Ping Data**. -# MAGIC -# MAGIC Run this query to load a batch of data. - -# COMMAND ---------- - -Gen.load() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Executing the query should load some data and return a preview of the data in the table. -# MAGIC -# MAGIC **NOTE**: Random numbers are being used to define and load data, so each user will have slightly different values present. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Set a Query Refresh Schedule -# MAGIC -# MAGIC Steps: -# MAGIC * Locate the **Refresh Schedule** field at the bottom right of the SQL query editor box; click the blue **Never** -# MAGIC * Use the drop down to change to Refresh every **1 minute** -# MAGIC * For **Ends**, click the **On** radio button -# MAGIC * Select tomorrow's date -# MAGIC * Click **OK** - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Query to Track Total Records -# MAGIC -# MAGIC Execute the cell below to print out a formatted SQL query to track total records in the `user_ping` table. -# MAGIC -# MAGIC Save this query with the name **User Counts**. -# MAGIC -# MAGIC Run the query to calculate the current results. - -# COMMAND ---------- - -Gen.user_counts() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Bar Graph Visualization -# MAGIC -# MAGIC Steps: -# MAGIC * Click the **Add Visualization** button -# MAGIC * Click on the name (should default to something like `Visualization 1`) and change the name to **Total User Records** -# MAGIC * Set `user_id` for the **X Column** -# MAGIC * Set `total_records` for the **Y Columns** -# MAGIC * Click **Save** - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a New Dashboard -# MAGIC -# MAGIC Steps: -# MAGIC * Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. -# MAGIC * Click the **Create new dashboard** option -# MAGIC * Name your dashboard **User Ping Summary ``** -# MAGIC * Click **Save** to create the new dashboard -# MAGIC * Your newly created dashboard should now be selected as the target; click **OK** to add your visualization - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Query to Calculate the Recent Average Ping -# MAGIC -# MAGIC Execute the cell below to print out a formatted SQL query to calculate the average ping observed per user over a 3 minute window. -# MAGIC -# MAGIC Save this query with the name **Avg Ping**. -# MAGIC -# MAGIC Run the query to calculate the current results. - -# COMMAND ---------- - -Gen.avg_ping() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Add a Line Plot Visualization to your Dashboard -# MAGIC -# MAGIC Steps: -# MAGIC * Click the **Add Visualization** button -# MAGIC * Click on the name (should default to something like `Visualization 1`) and change the name to **Avg User Ping** -# MAGIC * Select `Line` for the **Visualization Type** -# MAGIC * Set `end_time` for the **X Column** -# MAGIC * Set `avg_ping` for the **Y Columns** -# MAGIC * Set `user_id` for the **Group by** -# MAGIC * Click **Save** -# MAGIC * Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. -# MAGIC * Select the dashboard you created earlier -# MAGIC * Click **OK** to add your visualization - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create a Query to Report Summary Statistics -# MAGIC -# MAGIC Execute the cell below to print out a formatted SQL query that summarizes all records for a user. -# MAGIC -# MAGIC Save this query with the name **Ping Summary**. -# MAGIC -# MAGIC Run the query to calculate the current results. - -# COMMAND ---------- - -Gen.summary() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Add the Summary Table to your Dashboard -# MAGIC -# MAGIC Steps: -# MAGIC * Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. -# MAGIC * Select the dashboard you created earlier -# MAGIC * Click **OK** to add your visualization - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Review and Refresh your Dashboard -# MAGIC -# MAGIC Steps: -# MAGIC * Use the left side bar to navigate to **Dashboards** -# MAGIC * Find the dashboard you've added your queries to -# MAGIC * Click the blue **Refresh** button to update your dashboard -# MAGIC * Click the **Schedule** button to review dashboard scheduling options -# MAGIC * Note that scheduling a dashboard to update will execute all queries associated with that dashboard -# MAGIC * Do not schedule the dashboard at this time - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Share your Dashboard -# MAGIC -# MAGIC Steps: -# MAGIC * Click the blue **Share** button -# MAGIC * Select **All Users** from the top field -# MAGIC * Choose **Can Run** from the right field -# MAGIC * Click **Add** -# MAGIC * Change the **Credentials** to **Run as viewer** -# MAGIC -# MAGIC **NOTE**: At present, no other users should have any permissions to run your dashboard, as they have not been granted permissions to the underlying databases and tables using Table ACLs. If you wish other users to be able to trigger updates to your dashboard, you will either need to grant them permissions to **Run as owner** or add permissions for the tables referenced in your queries. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Set Up an Alert -# MAGIC -# MAGIC Steps: -# MAGIC * Use the left side bar to navigate to **Alerts** -# MAGIC * Click **Create Alert** in the top right -# MAGIC * Click the field at the top left of the screen to give the alert a name **` Count Check`** -# MAGIC * Select your **User Counts** query -# MAGIC * For the **Trigger when** options, configure: -# MAGIC * **Value column**: `total_records` -# MAGIC * **Condition**: `>` -# MAGIC * **Threshold**: 15 -# MAGIC * For **Refresh**, select **Never** -# MAGIC * Click **Create Alert** -# MAGIC * On the next screen, click the blue **Refresh** in the top right to evaluate the alert - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Review Alert Destination Options -# MAGIC -# MAGIC -# MAGIC -# MAGIC Steps: -# MAGIC * From the preview of your alert, click the blue **Add** button to the right of **Destinations** on the right side of the screen -# MAGIC * At the bottom of the window that pops up, locate the and click the blue text in the message **Create new destinations in Alert Destinations** -# MAGIC * Review the available alerting options - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/0 - Instructions and Configuration.py b/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/0 - Instructions and Configuration.py deleted file mode 100644 index fada765..0000000 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/0 - Instructions and Configuration.py +++ /dev/null @@ -1,280 +0,0 @@ -# Databricks notebook source -# MAGIC %md-sandbox -# MAGIC -# MAGIC
-# MAGIC Databricks Learning -# MAGIC
- -# COMMAND ---------- - -# MAGIC %md -# MAGIC -# MAGIC ## End-to-End ETL in the Lakehouse -# MAGIC -# MAGIC In this notebook, you will pull together concepts learned throughout the course to complete an example data pipeline. -# MAGIC -# MAGIC The following is a non-exhaustive list of skills and tasks necessary to successfully complete this exercise: -# MAGIC * Using Databricks notebooks to write queries in SQL and Python -# MAGIC * Creating and modifying databases, tables, and views -# MAGIC * Using Auto Loader and Spark Structured Streaming for incremental data processing in a multi-hop architecture -# MAGIC * Using Delta Live Table SQL syntax -# MAGIC * Configuring a Delta Live Table pipeline for continuous processing -# MAGIC * Using Databricks Jobs to orchestrate tasks from notebooks stored in Repos -# MAGIC * Setting chronological scheduling for Databricks Jobs -# MAGIC * Defining queries in Databricks SQL -# MAGIC * Creating visualizations in Databricks SQL -# MAGIC * Defining Databricks SQL dashboards to review metrics and results - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Run Setup -# MAGIC Run the following cell to reset all the databases and diretories associated with this lab. - -# COMMAND ---------- - -# MAGIC %run "../../Includes/dlt-setup" $mode="reset" - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Land Initial Data -# MAGIC Seed the landing zone with some data before proceeding. - -# COMMAND ---------- - -File.newData() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Create and Configure a DLT Pipeline -# MAGIC **NOTE**: The main difference between the instructions here and in previous labs with DLT is that in this instance, we will be setting up our pipeline for **Continuous** execution in **Production** mode. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Click the **Jobs** button on the sidebar, then select the **Delta Live Tables** tab. -# MAGIC 1. Click **Create Pipeline**. -# MAGIC 1. Fill in a **Pipeline Name** of your choosing. -# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the notebook `1 - DLT Task`. -# MAGIC 1. Run the cell below to generate values for **source**, **Target** and **Storage Location**. (All of these will include your current username). -# MAGIC * Click `Add configuration`; enter the word `source` in the **Key** field and the output printed next to `source` below in the value field. -# MAGIC * Enter the database name printed next to `Target` below in the **Target** field. -# MAGIC * Enter the location printed next to `Storage Location` below in the **Storage Location** field. -# MAGIC 1. Set **Pipeline Mode** to **Continuous**. -# MAGIC 1. Disable autoscaling. -# MAGIC 1. Set the number of wokers to 1. -# MAGIC 1. Click **Create**. -# MAGIC -# MAGIC In the UI that populates, change from **Development** to **Production** mode. This should begin the deployment of infrastructure. - -# COMMAND ---------- - -storage_location = userhome + "/output" -print(f"source : {dataLandingLocation.split(':')[1]}") -print(f"Target: {database}") -print(f"Storage Location: {storage_location.split(':')[1]}") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Schedule a Notebook Job -# MAGIC -# MAGIC Our DLT pipeline is setup to process data as soon as it arrives. We'll schedule a notebook to land a new batch of data each minute so we can see this functionality in action. -# MAGIC -# MAGIC Steps: -# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. -# MAGIC 1. Click the blue `Create Job` button -# MAGIC 1. Configure the task: -# MAGIC 1. Enter `Land-Data` for the task name -# MAGIC 1. Select the notebook `2 - Land New Data` using the notebook picker -# MAGIC 1. Select an Existing All Purpose Cluster from the **Cluster** dropdown -# MAGIC 1. Click **Create** -# MAGIC -# MAGIC **Note**: When selecting your all purpose cluster, you will get a warning about how this will be billed as all purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. -# MAGIC -# MAGIC ## Set a Chronological Schedule for your Job -# MAGIC Steps: -# MAGIC * On the right hand side of the Jobs UI, locate **Schedule** section. -# MAGIC * Click on the **Edit schedule** button to explore scheduling options. -# MAGIC * Change **Schedule type** field from **Manual** to **Scheduled** will bring up a chron scheduling UI. -# MAGIC * Set the schedule to update every **2 minutes** -# MAGIC * Click **Save** -# MAGIC -# MAGIC **NOTE**: If you wish, you can click **Run now** to trigger the first run, or wait until the top of the next minute to make sure your scheduling has worked successfully. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Register DLT Event Metrics for Querying with DBSQL -# MAGIC -# MAGIC The following cell prints out SQL statements to register the DLT event logs to your target database for querying in DBSQL. -# MAGIC -# MAGIC Execute the output code with the DBSQL Query Editor to register these tables and views. Explore each and make note of the logged event metrics. - -# COMMAND ---------- - -print(f""" -CREATE TABLE IF NOT EXISTS {database}.dlt_events -LOCATION '{storage_location}/system/events'; - -CREATE VIEW IF NOT EXISTS {database}.dlt_success AS -SELECT * FROM {database}.dlt_events -WHERE details:flow_progress:metrics IS NOT NULL; - -CREATE VIEW IF NOT EXISTS {database}.dlt_metrics AS -SELECT timestamp, origin.flow_name, details -FROM {database}.dlt_success -ORDER BY timestamp DESC; -""") - - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Define a Query on the Gold Table -# MAGIC -# MAGIC The `daily_patient_avg` table is automatically updated each time a new batch of data is processed through the DLT pipeline. Each time a query is executed against this table, DBSQL will confirm if there is a newer version and then materialize results from the newest available version. -# MAGIC -# MAGIC Run the following cell to print out a query with your database name. Save this as a DBSQL query. - -# COMMAND ---------- - -print(f"SELECT * FROM {database}.daily_patient_avg") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Add a Line Plot Visualization -# MAGIC -# MAGIC To track trends in patient averages over time, create a line plot and add it to a new dashboard. -# MAGIC -# MAGIC Create a line plot with the following settings: -# MAGIC * **X Column**: `date` -# MAGIC * **Y Columns**: `avg_heartrate` -# MAGIC * **Group By**: `name` -# MAGIC -# MAGIC Add this visualization to a dashboard. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Track Data Processing Progress -# MAGIC -# MAGIC The code below extracts the `flow_name`, `timestamp`, and `num_output_rows` from the DLT event logs. -# MAGIC -# MAGIC Save this query in DBSQL, then define a bar plot visualization that shows: -# MAGIC * **X Column**: `timestamp` -# MAGIC * **Y Columns**: `num_output_rows` -# MAGIC * **Group By**: `flow_name` -# MAGIC -# MAGIC Add your visualization to your dashboard. - -# COMMAND ---------- - -print(f""" -SELECT flow_name, timestamp, int(details:flow_progress:metrics:num_output_rows) num_output_rows -FROM {database}.dlt_metrics -ORDER BY timestamp DESC -""") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Refresh your Dashboard and Track Results -# MAGIC -# MAGIC The `Land-Data` notebook scheduled with Jobs above has 12 batches of data, each representing a month of recordings for our small sampling of patients. As configured per our instructions, it should take just over 20 minutes for all of these batches of data to be triggered and processed (we scheduled the Databricks Job to run every 2 minutes, and batches of data will process through our pipeline very quickly after initial ingestion). -# MAGIC -# MAGIC Refresh your dashboard and review your visualizations to see how many batches of data have been processed. (If you followed the instructions as outlined here, there should be 12 distinct flow updates tracked by your DLT metrics.) If all source data has not yet been processed, you can go back to the Databricks Jobs UI and manually trigger additional batches. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Execute a Query to Repair Broken Data -# MAGIC -# MAGIC Review the code that defined the `recordings_enriched` table to identify the filter applied for the quality check. -# MAGIC -# MAGIC In the cell below, write a query that returns all the records from the `recordings_bronze` table that were refused by this quality check. - -# COMMAND ---------- - -# ANSWER -display(spark.sql(f"SELECT * FROM {database}.recordings_bronze WHERE heartrate <= 0")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC For the purposes of our demo, let's assume that thorough manual review of our data and systems has demonstrated that occassionally otherwise valid heartrate recordings are returned as negative values. -# MAGIC -# MAGIC Run the following query to examine these same rows with the negative sign removed. - -# COMMAND ---------- - -display(spark.sql(f"SELECT abs(heartrate), * FROM {database}.recordings_bronze WHERE heartrate <= 0")) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC To complete our dataset, we wish to insert these fixed records into the silver `recordings_enriched` table. -# MAGIC -# MAGIC Use the cell below to update the query used in the DLT pipeline to execute this repair. -# MAGIC -# MAGIC **NOTE**: Make sure you update the code to only process those records that were previously rejected due to the quality check. - -# COMMAND ---------- - -# ANSWER -spark.sql(f""" - MERGE INTO {database}.recordings_enriched t - USING (SELECT - CAST(a.device_id AS INTEGER) device_id, - CAST(a.mrn AS LONG) mrn, - abs(CAST(a.heartrate AS DOUBLE)) heartrate, - CAST(from_unixtime(a.time, 'yyyy-MM-dd HH:mm:ss') AS TIMESTAMP) time, - b.name - FROM {database}.recordings_bronze a - INNER JOIN {database}.pii b - ON a.mrn = b.mrn - WHERE heartrate <= 0) v - ON t.mrn=v.mrn AND t.time=v.time - WHEN NOT MATCHED THEN INSERT * -""") - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Use the cell below to manually or programmatically confirm that this update has been successful. -# MAGIC -# MAGIC (The total number of records in the `recordings_bronze` should now be equal to the total records in `recordings_enriched`). - -# COMMAND ---------- - -# ANSWER -assert spark.table(f"{database}.recordings_bronze").count() == spark.table(f"{database}.recordings_enriched").count() - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Consider Production Data Permissions -# MAGIC -# MAGIC Note that while our manual repair of the data was successful, as the owner of these datasets, by default we have permissions to modify or delete these data from any location we're executing code. -# MAGIC -# MAGIC To put this another way: our current permissions would allow us to change or drop our production tables permanently if an errant SQL query is accidentally executed with the current user's permissions (or if other users are granted similar permissions). -# MAGIC -# MAGIC While for the purposes of this lab, we desired to have full permissions on our data, as we move code from development to production, it is safer to leverage [service principals](https://docs.databricks.com/administration-guide/users-groups/service-principals.html) when scheduling Jobs and DLT Pipelines to avoid accidental data modifications. - -# COMMAND ---------- - -# MAGIC %md -# MAGIC ## Shut Down Production Infrastructure -# MAGIC -# MAGIC Note that Databricks Jobs, DLT Pipelines, and scheduled DBSQL queries and dashboards are all designed to provide sustained execution of production code. In this end-to-end demo, you were instructed to configure a Job and Pipeline for continuous data processing. To prevent these workloads from continuing to execute, you should **Pause** your Databricks Job and **Stop** your DLT pipeline. Deleting these assets will also ensure that production infrastructure is terminated. -# MAGIC -# MAGIC **NOTE**: All instructions for DBSQL asset scheduling in previous lessons instructed users to set the update schedule to end tomorrow. You may choose to go back and also cancel these updates to prevent DBSQL endpoints from staying on until that time. - -# COMMAND ---------- - -# MAGIC %md-sandbox -# MAGIC © 2022 Databricks, Inc. All rights reserved.
-# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
-# MAGIC
-# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/2 - Land New Data.py b/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/2 - Land New Data.py deleted file mode 100644 index 466f683..0000000 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/2 - Land New Data.py +++ /dev/null @@ -1,8 +0,0 @@ -# Databricks notebook source -# MAGIC %run ../../Includes/dlt-setup - -# COMMAND ---------- - -File.newData() - - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/classic-setup.py b/Data-Engineering-With-Databricks/Solutions/Includes/classic-setup.py deleted file mode 100644 index 7f69e33..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/classic-setup.py +++ /dev/null @@ -1,71 +0,0 @@ -# Databricks notebook source -for stream in spark.streams.active: - stream.stop() - -# COMMAND ---------- - -import pyspark.sql.functions as F -import re - -course_name = "dewd" - -username = spark.sql("SELECT current_user()").first()[0] -clean_username = re.sub("[^a-zA-Z0-9]", "_", username) -database = f"dbacademy_{clean_username}_{course_name}" - -userhome = f"dbfs:/user/{username}/dbacademy/{course_name}" - -print(f""" -username: {username} -userhome: {userhome} -database: {database}""") - -dbutils.widgets.text("mode", "setup") -mode = dbutils.widgets.get("mode") - -if mode == "reset" or mode == "clean": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - -if mode != "clean": - spark.sql(f"CREATE DATABASE IF NOT EXISTS {database}") - spark.sql(f"USE {database}") - -# COMMAND ---------- - -# MAGIC %run ./mount-datasets - -# COMMAND ---------- - -outputPath = userhome + "/streaming-concepts" -checkpointPath = outputPath + "/checkpoint" - -# original dataset -dataSource = "/mnt/training/definitive-guide/data/activity-json/streaming" - -# data landing location; files will be copies from original dataset one at a time for incremental ingestion use case -dataLandingLocation = outputPath + "/landing-zone" - -outputTable = "bronze_table" - -spark.conf.set('c.outputTable', outputTable) - -# COMMAND ---------- - -class FileArrival: - def __init__(self, dataSource, landingZone): - self.sourceFiles = dbutils.fs.ls(dataSource) - dbutils.fs.mkdirs(landingZone) - self.landingZone = landingZone - self.fileID = 0 - - def newData(self, numFiles=1): - for i in range(numFiles): - dbutils.fs.cp(self.sourceFiles[self.fileID].path, self.landingZone) - self.fileID+=1 - -# COMMAND ---------- - -File = FileArrival(dataSource, dataLandingLocation) -File.newData() - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/dlt-setup.py b/Data-Engineering-With-Databricks/Solutions/Includes/dlt-setup.py deleted file mode 100644 index 2c76be0..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/dlt-setup.py +++ /dev/null @@ -1,80 +0,0 @@ -# Databricks notebook source -for stream in spark.streams.active: - stream.stop() - -# COMMAND ---------- - -import pyspark.sql.functions as F -import re - -dbutils.widgets.text("course", "dewd") -course_name = dbutils.widgets.get("course") - -username = spark.sql("SELECT current_user()").first()[0] -clean_username = re.sub("[^a-zA-Z0-9]", "_", username) -database = f"dbacademy_{clean_username}_{course_name}" - -userhome = f"dbfs:/user/{username}/dbacademy/{course_name}" - -print(f""" -username: {username} -userhome: {userhome} -database: {database}""") - -dbutils.widgets.text("mode", "setup") -mode = dbutils.widgets.get("mode") - -if mode == "reset" or mode == "clean": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - -# COMMAND ---------- - -# MAGIC %run ./mount-datasets - -# COMMAND ---------- - -sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) - -# COMMAND ---------- - -dataSource = "/mnt/training/healthcare" - -dataLandingLocation = userhome + "/source" -bronzePath = userhome + "/bronze" -recordingsParsedPath = userhome + "/silver/recordings_parsed" -recordingsEnrichedPath = userhome + "/silver/recordings_enriched" -dailyAvgPath = userhome + "/gold/dailyAvg" - -checkpointPath = userhome + "/checkpoints" -bronzeCheckpoint = userhome + "/checkpoints/bronze" -recordingsParsedCheckpoint = userhome + "/checkpoints/recordings_parsed" -recordingsEnrichedCheckpoint = userhome + "/checkpoints/recordings_enriched" -dailyAvgCheckpoint = userhome + "/checkpoints/dailyAvgPath" - -# COMMAND ---------- - -class FileArrival: - def __init__(self): - self.source = dataSource + "/tracker/streaming/" - self.userdir = dataLandingLocation + "/" - try: - self.curr_mo = 1 + int(max([x[1].split(".")[0] for x in dbutils.fs.ls(self.userdir)])) - except: - self.curr_mo = 1 - - def newData(self, continuous=False): - if self.curr_mo > 12: - print("Data source exhausted\n") - elif continuous == True: - while self.curr_mo <= 12: - curr_file = f"{self.curr_mo:02}.json" - dbutils.fs.cp(self.source + curr_file, self.userdir + curr_file) - self.curr_mo += 1 - else: - curr_file = f"{str(self.curr_mo).zfill(2)}.json" - dbutils.fs.cp(self.source + curr_file, self.userdir + curr_file) - self.curr_mo += 1 - -File = FileArrival() - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/multi-hop-setup.py b/Data-Engineering-With-Databricks/Solutions/Includes/multi-hop-setup.py deleted file mode 100644 index fdc0645..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/multi-hop-setup.py +++ /dev/null @@ -1,80 +0,0 @@ -# Databricks notebook source -for stream in spark.streams.active: - stream.stop() - -# COMMAND ---------- - -import pyspark.sql.functions as F -import re - -course_name = "dewd" - -username = spark.sql("SELECT current_user()").first()[0] -clean_username = re.sub("[^a-zA-Z0-9]", "_", username) -database = f"dbacademy_{clean_username}_{course_name}" - -userhome = f"dbfs:/user/{username}/dbacademy/{course_name}" - -print(f""" -username: {username} -userhome: {userhome} -database: {database}""") - -dbutils.widgets.text("mode", "setup") -mode = dbutils.widgets.get("mode") - -if mode == "reset" or mode == "clean": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - -if mode != "clean": - spark.sql(f"CREATE DATABASE IF NOT EXISTS {database}") - spark.sql(f"USE {database}") - -# COMMAND ---------- - -# MAGIC %run ./mount-datasets - -# COMMAND ---------- - -sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) - -# COMMAND ---------- - -dataSource = "/mnt/training/healthcare" - -dataLandingLocation = userhome + "/source" -bronzePath = userhome + "/bronze" -recordingsParsedPath = userhome + "/silver/recordings_parsed" -recordingsEnrichedPath = userhome + "/silver/recordings_enriched" -dailyAvgPath = userhome + "/gold/dailyAvg" - -checkpointPath = userhome + "/checkpoints" -bronzeCheckpoint = userhome + "/checkpoints/bronze" -recordingsParsedCheckpoint = userhome + "/checkpoints/recordings_parsed" -recordingsEnrichedCheckpoint = userhome + "/checkpoints/recordings_enriched" -dailyAvgCheckpoint = userhome + "/checkpoints/dailyAvgPath" - -# COMMAND ---------- - -class FileArrival: - def __init__(self): - self.source = dataSource + "/tracker/streaming/" - self.userdir = dataLandingLocation + "/" - self.curr_mo = 1 - - def newData(self, continuous=False): - if self.curr_mo > 12: - print("Data source exhausted\n") - elif continuous == True: - while self.curr_mo <= 12: - curr_file = f"{self.curr_mo:02}.json" - dbutils.fs.cp(self.source + curr_file, self.userdir + curr_file) - self.curr_mo += 1 - else: - curr_file = f"{str(self.curr_mo).zfill(2)}.json" - dbutils.fs.cp(self.source + curr_file, self.userdir + curr_file) - self.curr_mo += 1 - -File = FileArrival() - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/query_generator.py b/Data-Engineering-With-Databricks/Solutions/Includes/query_generator.py deleted file mode 100644 index 14b40b3..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/query_generator.py +++ /dev/null @@ -1,88 +0,0 @@ -# Databricks notebook source -class QueryGenerator: - def __init__(self, course, mode="normal"): - import re - import random - self.username = spark.sql("SELECT current_user()").first()[0] - self.userhome = f"dbfs:/user/{self.username}/{course}" - self.database = f"""dbacademy_{re.sub("[^a-zA-Z0-9]", "_", self.username)}_{course}""" - - if mode == "reset": - spark.sql(f"DROP DATABASE IF EXISTS {self.database} CASCADE") - dbutils.fs.rm(self.userhome, True) - - def config(self): - print(f""" -CREATE DATABASE {self.database} -LOCATION '{self.userhome}'; - -USE {self.database}; - -CREATE TABLE user_ping -(user_id STRING, ping INTEGER, time TIMESTAMP); - -CREATE TABLE user_ids (user_id STRING); - -INSERT INTO user_ids VALUES -("potato_luver"), -("beanbag_lyfe"), -("default_username"), -("the_king"), -("n00b"), -("frodo"), -("data_the_kid"), -("el_matador"), -("the_wiz"); - -CREATE FUNCTION get_ping() - RETURNS INT - RETURN int(rand() * 250); - -CREATE FUNCTION is_active() - RETURNS BOOLEAN - RETURN CASE - WHEN rand() > .25 THEN true - ELSE false - END; - """) - - def load(self): - print(f""" -INSERT INTO {self.database}.user_ping -SELECT *, - {self.database}.get_ping() ping, - current_timestamp() time -FROM {self.database}.user_ids -WHERE {self.database}.is_active()=true; - -SELECT * FROM {self.database}.user_ping; - """) - - def user_counts(self): - print(f""" -SELECT user_id, count(*) total_records -FROM {self.database}.user_ping -GROUP BY user_id -ORDER BY - total_records DESC, - user_id ASC;; - """) - - def avg_ping(self): - print(f""" -SELECT user_id, window.end end_time, mean(ping) avg_ping -FROM {self.database}.user_ping -GROUP BY user_id, window(time, '3 minutes') -ORDER BY - end_time DESC, - user_id ASC; - """) - - def summary(self): - print(f""" -SELECT user_id, min(time) first_seen, max(time) last_seen, count(*) total_records, avg(ping) total_avg_ping -FROM {self.database}.user_ping -GROUP BY user_id -ORDER BY user_id ASC - """) - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/setup-cleaned.py b/Data-Engineering-With-Databricks/Solutions/Includes/setup-cleaned.py deleted file mode 100644 index 09cb1e2..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/setup-cleaned.py +++ /dev/null @@ -1,56 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./setup-updates - -# COMMAND ---------- - -def merge_deduped_users(): - spark.sql(f""" - CREATE OR REPLACE TEMP VIEW deduped_users AS - SELECT user_id, user_first_touch_timestamp, max(email) email, max(updated) updated - FROM users_update - GROUP BY user_id, user_first_touch_timestamp - """) - - spark.sql(f""" - MERGE INTO users a - USING deduped_users b - ON a.user_id = b.user_id - WHEN MATCHED AND a.email IS NULL AND b.email IS NOT NULL THEN - UPDATE SET email = b.email, updated = b.updated - WHEN NOT MATCHED THEN INSERT * - """) - - -# COMMAND ---------- - -def merge_events_update(): - spark.sql(f""" - MERGE INTO events a - USING events_update b - ON a.user_id = b.user_id AND a.event_timestamp = b.event_timestamp - WHEN NOT MATCHED AND b.traffic_source = 'email' THEN - INSERT * - """) - - -# COMMAND ---------- - -def merge_sales_update(): - spark.sql(f""" - COPY INTO sales - FROM "{Paths.source}/sales/sales-30m.parquet" - FILEFORMAT = PARQUET - """) - - -# COMMAND ---------- - -merge_deduped_users() -merge_events_update() -merge_sales_update() - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC CREATE OR REPLACE TABLE item_lookup AS -# MAGIC SELECT * FROM parquet.`${c.source}/products/products.parquet` diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/setup-external.py b/Data-Engineering-With-Databricks/Solutions/Includes/setup-external.py deleted file mode 100644 index 6acbf65..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/setup-external.py +++ /dev/null @@ -1,21 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./setup - -# COMMAND ---------- - -dbutils.fs.rm(f"{Paths.source}/sales/sales.csv", True) -dbutils.fs.cp(f"{Paths.source_uri}/sales/sales.csv", f"{Paths.source}/sales/sales.csv", True) - -(spark - .read - .format("parquet") - .load(f"{Paths.source}/users/users.parquet") - .repartition(1) - .write - .format("org.apache.spark.sql.jdbc") - .option("url", f"jdbc:sqlite:/{username}_ecommerce.db") - .option("dbtable", "users") - .mode("overwrite") - .save()) - - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/setup-load.py b/Data-Engineering-With-Databricks/Solutions/Includes/setup-load.py deleted file mode 100644 index 8ed10a1..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/setup-load.py +++ /dev/null @@ -1,26 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./setup - -# COMMAND ---------- - -def load_historical(): - spark.sql(f""" - CREATE OR REPLACE TABLE events AS - SELECT * FROM parquet.`{Paths.source}/events/events.parquet` - """) - - spark.sql(f""" - CREATE OR REPLACE TABLE users AS - SELECT *, current_timestamp() updated FROM parquet.`{Paths.source}/users/users.parquet` - """) - - spark.sql(f""" - CREATE OR REPLACE TABLE sales AS - SELECT * FROM parquet.`{Paths.source}/sales/sales.parquet` - """) - - -# COMMAND ---------- - -load_historical() - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/setup-meta.py b/Data-Engineering-With-Databricks/Solutions/Includes/setup-meta.py deleted file mode 100644 index 05a5aa0..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/setup-meta.py +++ /dev/null @@ -1,11 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./sql-setup $course="meta" $mode="cleanup" - -# COMMAND ---------- - -URI = "wasbs://courseware@dbacademy.blob.core.windows.net/databases_tables_and_views_on_databricks/v02" - -# COMMAND ---------- - -dbutils.fs.cp(URI, f"{userhome}/datasets", True) - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/setup-transactions.py b/Data-Engineering-With-Databricks/Solutions/Includes/setup-transactions.py deleted file mode 100644 index 93da748..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/setup-transactions.py +++ /dev/null @@ -1,43 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./setup-cleaned - -# COMMAND ---------- - -def create_transactions(): - spark.sql(f""" - CREATE OR REPLACE TABLE transactions AS - SELECT * FROM ( - SELECT - user_id, - order_id, - transaction_timestamp, - total_item_quantity, - purchase_revenue_in_usd, - unique_items, - a.items_exploded.item_id item_id, - a.items_exploded.quantity quantity - FROM - ( SELECT *, explode(items) items_exploded FROM sales ) a - INNER JOIN users b - ON a.email = b.email - ) PIVOT ( - sum(quantity) FOR item_id in ( - 'P_FOAM_K', - 'M_STAN_Q', - 'P_FOAM_S', - 'M_PREM_Q', - 'M_STAN_F', - 'M_STAN_T', - 'M_PREM_K', - 'M_PREM_F', - 'M_STAN_K', - 'M_PREM_T', - 'P_DOWN_S', - 'P_DOWN_K' - ) - ) - """) - -create_transactions() - - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/setup-updates.py b/Data-Engineering-With-Databricks/Solutions/Includes/setup-updates.py deleted file mode 100644 index 9db7eaa..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/setup-updates.py +++ /dev/null @@ -1,62 +0,0 @@ -# Databricks notebook source -# MAGIC %run ./setup-load - -# COMMAND ---------- - -def load_events_raw(): - spark.sql(f""" - CREATE TABLE IF NOT EXISTS events_json - (key BINARY, offset INT, partition BIGINT, timestamp BIGINT, topic STRING, value BINARY) - USING JSON OPTIONS (path = "{Paths.source}/events/events-kafka.json"); - """) - - spark.sql(f""" - CREATE OR REPLACE TABLE events_raw - (key BINARY, offset BIGINT, partition BIGINT, timestamp BIGINT, topic STRING, value BINARY); - """) - - spark.sql(f""" - INSERT INTO events_raw - SELECT * FROM events_json - """) - - -# COMMAND ---------- - -# lesson: nested data & advanced transformations -# Last Lab & Writing to Delta -def create_events_update(): - spark.sql(f""" - CREATE OR REPLACE TEMP VIEW events_raw_json AS - SELECT from_json(cast(value as STRING), ("device STRING, ecommerce STRUCT< purchase_revenue_in_usd: DOUBLE, total_item_quantity: BIGINT, unique_items: BIGINT>, event_name STRING, event_previous_timestamp BIGINT, event_timestamp BIGINT, geo STRUCT< city: STRING, state: STRING>, items ARRAY< STRUCT< coupon: STRING, item_id: STRING, item_name: STRING, item_revenue_in_usd: DOUBLE, price_in_usd: DOUBLE, quantity: BIGINT>>, traffic_source STRING, user_first_touch_timestamp BIGINT, user_id STRING")) json - FROM events_raw - """) - - spark.sql(f""" - CREATE OR REPLACE TEMP VIEW events_update AS - WITH deduped_events_raw AS ( - SELECT max(json) json FROM events_raw_json - GROUP BY json.user_id, json.event_timestamp - ) - SELECT json.* FROM deduped_events_raw - """) - - -# COMMAND ---------- - -# lesson: Writing delta -def create_users_update(): - spark.sql(f""" - CREATE OR REPLACE TEMP VIEW users_update AS - SELECT *, current_timestamp() updated - FROM parquet.`{Paths.source}/users/users-30m.parquet` - """) - - -# COMMAND ---------- - -load_events_raw() -create_events_update() -create_users_update() - - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/setup.py b/Data-Engineering-With-Databricks/Solutions/Includes/setup.py deleted file mode 100644 index 32ef8d1..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/setup.py +++ /dev/null @@ -1,98 +0,0 @@ -# Databricks notebook source -import pyspark.sql.functions as F -import re - -class BuildEnvironmentVariables: - - def __init__(self, username): - self.course_name = "eltsql" - self.source_uri = "wasbs://courseware@dbacademy.blob.core.windows.net/elt-with-spark-sql/v01" - - self.username = username - self.working_dir = f"dbfs:/user/{self.username}/dbacademy/{self.course_name}" - self.userhome = self.working_dir # TEMPORARY BACKWARDS COMPATABILITY - - clean_username = re.sub("[^a-zA-Z0-9]", "_", self.username) - self.database_name = f"{clean_username}_dbacademy_{self.course_name}" - self.database_location = f"{self.working_dir}/db" - - self.source = f"{self.working_dir}/source_datasets" - self.base_path=f"{self.working_dir}/tables" - - self.sales_table_path = f"{self.base_path}/sales" - self.users_table_path = f"{self.base_path}/users" - self.events_raw_table_path = f"{self.base_path}/events_raw" - self.events_clean_table_path = f"{self.base_path}/events_clean" - self.transactions_table_path = f"{self.base_path}/transactions" - self.clickpaths_table_path = f"{self.base_path}/clickpaths" - - def set_hive_variables(self): - for (k, v) in self.__dict__.items(): - spark.sql(f"SET c.{k} = {v}") - - def __repr__(self): - return self.__dict__.__repr__().replace(", ", ",\n") - - -# COMMAND ---------- - -username = spark.sql("SELECT current_user()").first()[0] -dbacademy_env = BuildEnvironmentVariables(username) -Paths = dbacademy_env # Temporary backwards compatability - -# Hack for backwards compatability -username = dbacademy_env.username -database = dbacademy_env.database_name -userhome = dbacademy_env.working_dir - -print(f"username: {username}") -print(f"database: {database}") -print(f"userhome: {userhome}") - -# print(f"dbacademy_env: Databricks Academy configuration object") -# print(f"dbacademy_env.username: {dbacademy_env.username}") -# print(f"dbacademy_env.database_name: {dbacademy_env.database_name}") -# print(f"dbacademy_env.working_dir: {dbacademy_env.working_dir}") - - -# COMMAND ---------- - -def path_exists(path): - try: - return len(dbutils.fs.ls(path)) >= 0 - except Exception: - return False - -dbutils.widgets.text("mode", "default") -mode = dbutils.widgets.get("mode") - -if mode == "reset" or mode == "cleanup": - # Drop the database and remove all data for both reset and cleanup - print(f"Removing the database {database}") - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - - print(f"Removing previously generated datasets from\n{dbacademy_env.working_dir}") - dbutils.fs.rm(dbacademy_env.working_dir, True) - -if mode != "cleanup": - # We are not cleaning up so we want to setup the environment - - # RESET is in case we want to force a reset - # not-existing for net-new install - if mode == "reset" or not path_exists(dbacademy_env.source): - print(f"\nInstalling datasets to\n{dbacademy_env.source}") - print(f"""\nNOTE: The datasets that we are installing are located in Washington, USA - depending on the - region that your workspace is in, this operation can take as little as 3 minutes and - upwards to 6 minutes, but this is a one-time operation.""") - - dbutils.fs.cp(dbacademy_env.source_uri, dbacademy_env.source, True) - print(f"""\nThe install of the datasets completed successfully.""") - - # Create the database and use it. - spark.sql(f"CREATE DATABASE IF NOT EXISTS {dbacademy_env.database_name} LOCATION '{dbacademy_env.database_location}'") - spark.sql(f"USE {dbacademy_env.database_name}") - - # Once the database is created, init the hive variables - dbacademy_env.set_hive_variables() - - diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/sql-setup.py b/Data-Engineering-With-Databricks/Solutions/Includes/sql-setup.py deleted file mode 100644 index 6d1d83a..0000000 --- a/Data-Engineering-With-Databricks/Solutions/Includes/sql-setup.py +++ /dev/null @@ -1,32 +0,0 @@ -# Databricks notebook source -import pyspark.sql.functions as F -import re - -dbutils.widgets.text("course", "dewd") -course = dbutils.widgets.get("course") -username = spark.sql("SELECT current_user()").collect()[0][0] -userhome = f"dbfs:/user/{username}/{course}" -database = f"""{course}_{re.sub("[^a-zA-Z0-9]", "_", username)}_db""" -print(f""" -username: {username} -userhome: {userhome} -database: {database}""") - -spark.sql(f"SET c.username = {username}") -spark.sql(f"SET c.userhome = {userhome}") -spark.sql(f"SET c.database = {database}") - -dbutils.widgets.text("mode", "setup") -mode = dbutils.widgets.get("mode") - -if mode == "reset": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - spark.sql(f"CREATE DATABASE IF NOT EXISTS {database} LOCATION '{userhome}'") - spark.sql(f"USE {database}") - -if mode == "cleanup": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - - diff --git a/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/DE 1.1 - Create and Manage Interactive Clusters.py b/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/DE 1.1 - Create and Manage Interactive Clusters.py new file mode 100644 index 0000000..3a2dce1 --- /dev/null +++ b/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/DE 1.1 - Create and Manage Interactive Clusters.py @@ -0,0 +1,100 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC # Create and Manage Interactive Clusters +# MAGIC +# MAGIC A Databricks cluster is a set of computation resources and configurations on which you run data engineering, data science, and data analytics workloads, such as production ETL pipelines, streaming analytics, ad-hoc analytics, and machine learning. You run these workloads as a set of commands in a notebook or as an automated job. +# MAGIC +# MAGIC Databricks makes a distinction between all-purpose clusters and job clusters. +# MAGIC - You use all-purpose clusters to analyze data collaboratively using interactive notebooks. +# MAGIC - You use job clusters to run fast and robust automated jobs. +# MAGIC +# MAGIC This demo will cover creating and managing all-purpose Databricks clusters using the Databricks Data Science & Engineering Workspace. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Use the Clusters UI to configure and deploy a cluster +# MAGIC * Edit, terminate, restart, and delete clusters + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ## Create Cluster +# MAGIC +# MAGIC Depending on the workspace in which you're currently working, you may or may not have cluster creation privileges. +# MAGIC +# MAGIC Instructions in this section assume that you **do** have cluster creation privileges, and that you need to deploy a new cluster to execute the lessons in this course. +# MAGIC +# MAGIC **NOTE**: Check with your instructor or a platform admin to confirm whether or not you should create a new cluster or connect to a cluster that has already been deployed. Cluster policies may impact your options for cluster configuration. +# MAGIC +# MAGIC Steps: +# MAGIC 1. Use the left sidebar to navigate to the **Compute** page by clicking on the ![compute](https://files.training.databricks.com/images/clusters-icon.png) icon +# MAGIC 1. Click the blue **Create Cluster** button +# MAGIC 1. For the **Cluster name**, use your name so that you can find it easily and the instructor can easily identify it if you have problems +# MAGIC 1. Set the **Cluster mode** to **Single Node** (this mode is required to run this course) +# MAGIC 1. Use the recommended **Databricks runtime version** for this course +# MAGIC 1. Leave boxes checked for the default settings under the **Autopilot Options** +# MAGIC 1. Click the blue **Create Cluster** button +# MAGIC +# MAGIC **NOTE:** Clusters can take several minutes to deploy. Once you have finished deploying a cluster, feel free to continue to explore the cluster creation UI. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ### Single-Node Cluster Required for This Course +# MAGIC **IMPORTANT:** This course requires you to run notebooks on a single-node cluster. +# MAGIC +# MAGIC Follow the instructions above to create a cluster that has **Cluster mode** set to **`Single Node`**. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ## Manage Clusters +# MAGIC +# MAGIC Once the cluster is created, go back to the **Compute** page to view the cluster. +# MAGIC +# MAGIC Select a cluster to review the current configuration. +# MAGIC +# MAGIC Click the **Edit** button. Note that most settings can be modified (if you have sufficient permissions). Changing most settings will require running clusters to be restarted. +# MAGIC +# MAGIC **NOTE**: We'll be using our cluster in the following lesson. Restarting, terminating, or deleting your cluster may put you behind as you wait for new resources to be deployed. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ## Restart, Terminate, and Delete +# MAGIC +# MAGIC Note that while **Restart**, **Terminate**, and **Delete** have different effects, they all start with a cluster termination event. (Clusters will also terminate automatically due to inactivity assuming this setting is used.) +# MAGIC +# MAGIC When a cluster terminates, all cloud resources currently in use are deleted. This means: +# MAGIC * Associated VMs and operational memory will be purged +# MAGIC * Attached volume storage will be deleted +# MAGIC * Network connections between nodes will be removed +# MAGIC +# MAGIC In short, all resources previously associated with the compute environment will be completely removed. This means that **any results that need to be persisted should be saved to a permanent location**. Note that you will not lose your code, nor will you lose data files that you've saved out appropriately. +# MAGIC +# MAGIC The **Restart** button will allow us to manually restart our cluster. This can be useful if we need to completely clear out the cache on the cluster or wish to completely reset our compute environment. +# MAGIC +# MAGIC The **Terminate** button allows us to stop our cluster. We maintain our cluster configuration setting, and can use the **Restart** button to deploy a new set of cloud resources using the same configuration. +# MAGIC +# MAGIC The **Delete** button will stop our cluster and remove the cluster configuration. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/DE 1.2 - Notebook Basics.py b/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/DE 1.2 - Notebook Basics.py new file mode 100644 index 0000000..baf42d9 --- /dev/null +++ b/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/DE 1.2 - Notebook Basics.py @@ -0,0 +1,398 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Notebook Basics +# MAGIC +# MAGIC Notebooks are the primary means of developing and executing code interactively on Databricks. This lesson provides a basic introduction to working with Databricks notebooks. +# MAGIC +# MAGIC If you've previously used Databricks notebooks but this is your first time executing a notebook in Databricks Repos, you'll notice that basic functionality is the same. In the next lesson, we'll review some of the functionality that Databricks Repos adds to notebooks. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Attach a notebook to a cluster +# MAGIC * Execute a cell in a notebook +# MAGIC * Set the language for a notebook +# MAGIC * Describe and use magic commands +# MAGIC * Create and run a SQL cell +# MAGIC * Create and run a Python cell +# MAGIC * Create a markdown cell +# MAGIC * Export a Databricks notebook +# MAGIC * Export a collection of Databricks notebooks + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Attach to a Cluster +# MAGIC +# MAGIC In the previous lesson, you should have either deployed a cluster or identified a cluster that an admin has configured for you to use. +# MAGIC +# MAGIC Directly below the name of this notebook at the top of your screen, use the drop-down list to connect this notebook to your cluster. +# MAGIC +# MAGIC **NOTE**: Deploying a cluster can take several minutes. A green arrow will appear to the right of the cluster name once resources have been deployed. If your cluster has a solid gray circle to the left, you will need to follow instructions to start a cluster. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Notebooks Basics +# MAGIC +# MAGIC Notebooks provide cell-by-cell execution of code. Multiple languages can be mixed in a notebook. Users can add plots, images, and markdown text to enhance their code. +# MAGIC +# MAGIC Throughout this course, our notebooks are designed as learning instruments. Notebooks can be easily deployed as production code with Databricks, as well as providing a robust toolset for data exploration, reporting, and dashboarding. +# MAGIC +# MAGIC ### Running a Cell +# MAGIC * Run the cell below using one of the following options: +# MAGIC * **CTRL+ENTER** or **CTRL+RETURN** +# MAGIC * **SHIFT+ENTER** or **SHIFT+RETURN** to run the cell and move to the next one +# MAGIC * Using **Run Cell**, **Run All Above** or **Run All Below** as seen here
+ +# COMMAND ---------- + +print("I'm running Python!") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC **NOTE**: Cell-by-cell code execution means that cells can be executed multiple times or out of order. Unless explicitly instructed, you should always assume that the notebooks in this course are intended to be run one cell at a time from top to bottom. If you encounter an error, make sure you read the text before and after a cell to ensure that the error wasn't an intentional learning moment before you try to troubleshoot. Most errors can be resolved by either running earlier cells in a notebook that were missed or re-executing the entire notebook from the top. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ### Setting the Default Notebook Language +# MAGIC +# MAGIC The cell above executes a Python command, because our current default language for the notebook is set to Python. +# MAGIC +# MAGIC Databricks notebooks support Python, SQL, Scala, and R. A language can be selected when a notebook is created, but this can be changed at any time. +# MAGIC +# MAGIC The default language appears directly to the right of the notebook title at the top of the page. Throughout this course, we'll use a blend of SQL and Python notebooks. +# MAGIC +# MAGIC We'll change the default language for this notebook to SQL. +# MAGIC +# MAGIC Steps: +# MAGIC * Click on the **Python** next to the notebook title at the top of the screen +# MAGIC * In the UI that pops up, select **SQL** from the drop down list +# MAGIC +# MAGIC **NOTE**: In the cell just before this one, you should see a new line appear with %python. We'll discuss this in a moment. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ### Create and Run a SQL Cell +# MAGIC +# MAGIC * Highlight this cell and press the **B** button on the keyboard to create a new cell below +# MAGIC * Copy the following code into the cell below and then run the cell +# MAGIC +# MAGIC **`%sql`**
+# MAGIC **`SELECT "I'm running SQL!"`** +# MAGIC +# MAGIC **NOTE**: There are a number of different methods for adding, moving, and deleting cells including GUI options and keyboard shortcuts. Refer to the docs for details. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Magic Commands +# MAGIC * Magic commands are specific to the Databricks notebooks +# MAGIC * They are very similar to magic commands found in comparable notebook products +# MAGIC * These are built-in commands that provide the same outcome regardless of the notebook's language +# MAGIC * A single percent (%) symbol at the start of a cell identifies a magic command +# MAGIC * You can only have one magic command per cell +# MAGIC * A magic command must be the first thing in a cell + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ### Language Magics +# MAGIC Language magic commands allow for the execution of code in languages other than the notebook's default. In this course, we'll see the following language magics: +# MAGIC * %python +# MAGIC * %sql +# MAGIC +# MAGIC Adding the language magic for the currently set notebook type is not necessary. +# MAGIC +# MAGIC When we changed the notebook language from Python to SQL above, existing cells written in Python had the %python command added. +# MAGIC +# MAGIC **NOTE**: Rather than changing the default language of a notebook constantly, you should stick with a primary language as the default and only use language magics as necessary to execute code in another language. + +# COMMAND ---------- + +print("Hello Python!") + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC +# MAGIC select "Hello SQL!" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ### Markdown +# MAGIC +# MAGIC The magic command **%md** allows us to render Markdown in a cell: +# MAGIC * Double click this cell to begin editing it +# MAGIC * Then hit **`Esc`** to stop editing +# MAGIC +# MAGIC # Title One +# MAGIC ## Title Two +# MAGIC ### Title Three +# MAGIC +# MAGIC This is a test of the emergency broadcast system. This is only a test. +# MAGIC +# MAGIC This is text with a **bold** word in it. +# MAGIC +# MAGIC This is text with an *italicized* word in it. +# MAGIC +# MAGIC This is an ordered list +# MAGIC 0. once +# MAGIC 0. two +# MAGIC 0. three +# MAGIC +# MAGIC This is an unordered list +# MAGIC * apples +# MAGIC * peaches +# MAGIC * bananas +# MAGIC +# MAGIC Links/Embedded HTML: Markdown - Wikipedia +# MAGIC +# MAGIC Images: +# MAGIC ![Spark Engines](https://files.training.databricks.com/images/Apache-Spark-Logo_TM_200px.png) +# MAGIC +# MAGIC And of course, tables: +# MAGIC +# MAGIC | name | value | +# MAGIC |--------|-------| +# MAGIC | Yi | 1 | +# MAGIC | Ali | 2 | +# MAGIC | Selina | 3 | + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ### %run +# MAGIC * You can run a notebook from another notebook by using the magic command **%run** +# MAGIC * Notebooks to be run are specified with relative paths +# MAGIC * The referenced notebook executes as if it were part of the current notebook, so temporary views and other local declarations will be available from the calling notebook + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Uncommenting and executing the following cell will generate the following error:
+# MAGIC **`Error in SQL statement: AnalysisException: Table or view not found: demo_tmp_vw`** + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- SELECT * FROM demo_tmp_vw + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC But we can declare it and a handful of other variables and functions buy running this cell: + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-1.2 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The **`../Includes/Classroom-Setup-1.2`** notebook we referenced includes logic to create and **`USE`** a database, as well as creating the temp view **`demo_temp_vw`**. +# MAGIC +# MAGIC We can see this temp view is now available in our current notebook session with the following query. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM demo_tmp_vw + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC We'll use this pattern of "setup" notebooks throughout the course to help configure the environment for lessons and labs. +# MAGIC +# MAGIC These "provided" variables, functions and other objects should be easily identifiable in that they are part of the **`DA`** object which is an instance of **`DBAcademyHelper`**. +# MAGIC +# MAGIC With that in mind, most lessons will use variables derived from your username to organize files and databases. +# MAGIC +# MAGIC This pattern allows us to avoid collision with other users in shared a workspace. +# MAGIC +# MAGIC The cell below uses Python to print some of those variables previously defined in this notebook's setup script: + +# COMMAND ---------- + +print(f"DA: {DA}") +print(f"DA.username: {DA.username}") +print(f"DA.paths.working_dir: {DA.paths.working_dir}") +print(f"DA.db_name: {DA.db_name}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC In addition to this, these same variables are "injected" into the SQL context so that we can use them in SQL statements. +# MAGIC +# MAGIC We will talk more about this later, but you can see a quick example in the following cell. +# MAGIC +# MAGIC Note the subtle but important difference in the casing of the word **`da`** and **`DA`** in these two examples. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT '${da.username}' AS current_username, +# MAGIC '${da.paths.working_dir}' AS working_directory, +# MAGIC '${da.db_name}' as database_name + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Databricks Utilities +# MAGIC Databricks notebooks provide a number of utility commands for configuring and interacting with the environment: dbutils docs +# MAGIC +# MAGIC Throughout this course, we'll occasionally use **`dbutils.fs.ls()`** to list out directories of files from Python cells. + +# COMMAND ---------- + +dbutils.fs.ls("/databricks-datasets") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## display() +# MAGIC +# MAGIC When running SQL queries from cells, results will always be displayed in a rendered tabular format. +# MAGIC +# MAGIC When we have tabular data returned by a Python cell, we can call **`display`** to get the same type of preview. +# MAGIC +# MAGIC Here, we'll wrap the previous list command on our file system with **`display`**. + +# COMMAND ---------- + +display(dbutils.fs.ls("/databricks-datasets")) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The **`display()`** command has the following capabilities and limitations: +# MAGIC * Preview of results limited to 1000 records +# MAGIC * Provides button to download results data as CSV +# MAGIC * Allows rendering plots + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Downloading Notebooks +# MAGIC +# MAGIC There are a number of options for downloading either individual notebooks or collections of notebooks. +# MAGIC +# MAGIC Here, you'll go through the process to download this notebook as well as a collection of all the notebooks in this course. +# MAGIC +# MAGIC ### Download a Notebook +# MAGIC +# MAGIC Steps: +# MAGIC * Click the **File** option to the right of the cluster selection at the top of the notebook +# MAGIC * From the menu that appears, hover over **Export** and then select **Source File** +# MAGIC +# MAGIC The notebook will download to your personal laptop. It will be named with the current notebook name and have the file extension for the default language. You can open this notebook with any file editor and see the raw contents of Databricks notebooks. +# MAGIC +# MAGIC These source files can be uploaded into any Databricks workspace. +# MAGIC +# MAGIC ### Download a Collection of Notebooks +# MAGIC +# MAGIC **NOTE**: The following instructions assume you have imported these materials using **Repos**. +# MAGIC +# MAGIC Steps: +# MAGIC * Click the ![](https://files.training.databricks.com/images/repos-icon.png) **Repos** on the left sidebar +# MAGIC * This should give you a preview of the parent directories for this notebook +# MAGIC * On the left side of the directory preview around the middle of the screen, there should be a left arrow. Click this to move up in your file hierarchy. +# MAGIC * You should see a directory called **Data Engineering with Databricks**. Click the the down arrow/chevron to bring up a menu +# MAGIC * From the menu, hover over **Export** and select **DBC Archive** +# MAGIC +# MAGIC The DBC(Databricks Cloud) file that is downloaded contains a zipped collection of the directories and notebooks in this course. Users should not attempt to edit these DBC files locally, but they can be safely uploaded into any Databricks workspace to move or share notebook contents. +# MAGIC +# MAGIC **NOTE**: When downloading a collection of DBCs, result previews and plots will also be exported. When downloading source notebooks, only code will be saved. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Learning More +# MAGIC +# MAGIC We like to encourage you to explore the documentation to learn more about the various features of the Databricks platform and notebooks. +# MAGIC * User Guide +# MAGIC * Getting Started with Databricks +# MAGIC * User Guide / Notebooks +# MAGIC * Importing notebooks - Supported Formats +# MAGIC * Repos +# MAGIC * Administration Guide +# MAGIC * Cluster Configuration +# MAGIC * REST API +# MAGIC * Release Notes + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## One more note! +# MAGIC +# MAGIC At the end of each lesson you will see the following command, **`DA.cleanup()`**. +# MAGIC +# MAGIC This method drops lesson-specific databases and working directories in an attempt to keep your workspace clean and maintain the immutability of each lesson. +# MAGIC +# MAGIC Run the following cell to delete the tables and files associated with this lesson. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/DE 1.3L - Getting Started with the Databricks Platform Lab.py b/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/DE 1.3L - Getting Started with the Databricks Platform Lab.py new file mode 100644 index 0000000..5689ed6 --- /dev/null +++ b/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/DE 1.3L - Getting Started with the Databricks Platform Lab.py @@ -0,0 +1,222 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Getting Started with the Databricks Platform +# MAGIC +# MAGIC This notebook provides a hands-on review of some of the basic functionality of the Databricks Data Science and Engineering Workspace. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC - Rename a notebook and change the default language +# MAGIC - Attach a cluster +# MAGIC - Use the **`%run`** magic command +# MAGIC - Run Python and SQL cells +# MAGIC - Create a Markdown cell + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Renaming a Notebook +# MAGIC +# MAGIC Changing the name of a notebook is easy. Click on the name at the top of this page, then make changes to the name. To make it easier to navigate back to this notebook later in case you need to, append a short test string to the end of the existing name. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Attaching a cluster +# MAGIC +# MAGIC Executing cells in a notebook requires computing resources, which is provided by clusters. The first time you execute a cell in a notebook, you will be prompted to attach to a cluster if one is not already attached. +# MAGIC +# MAGIC Attach a cluster to this notebook now by clicking the dropdown near the top-left corner of this page. Select the cluster you created previously. This will clear the execution state of the notebook and connect the notebook to the selected cluster. +# MAGIC +# MAGIC Note that the dropdown menu provides the option of starting or restarting the cluster as needed. You can also detach and re-attach to a cluster in a single movement. This is useful for clearing the execution state when needed. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Using %run +# MAGIC +# MAGIC Complex projects of any type can benefit from the ability to break them down into simpler, reusable components. +# MAGIC +# MAGIC In the context of Databricks notebooks, this facility is provided through the **`%run`** magic command. +# MAGIC +# MAGIC When used this way, variables, functions and code blocks become part of the current programming context. +# MAGIC +# MAGIC Consider this example: +# MAGIC +# MAGIC **`Notebook_A`** has four commands: +# MAGIC 1. **`name = "John"`** +# MAGIC 2. **`print(f"Hello {name}")`** +# MAGIC 3. **`%run ./Notebook_B`** +# MAGIC 4. **`print(f"Welcome back {full_name}`** +# MAGIC +# MAGIC **`Notebook_B`** has only one commands: +# MAGIC 1. **`full_name = f"{name} Doe"`** +# MAGIC +# MAGIC If we run **`Notebook_B`** it will fail to execute becaues the variable **`name`** is not defined in **`Notebook_B`** +# MAGIC +# MAGIC Likewise, one might think that **`Notebook_A`** would fail becase it uses the variable **`full_name`** which is likewise not defined in **`Notebook_A`**, but it doesn't! +# MAGIC +# MAGIC What actually happens is that the two notebooks are merged together as we see below and **then** executed: +# MAGIC 1. **`name = "John"`** +# MAGIC 2. **`print(f"Hello {name}")`** +# MAGIC 3. **`full_name = f"{name} Doe"`** +# MAGIC 4. **`print(f"Welcome back {full_name}`** +# MAGIC +# MAGIC And thus providing the expected behavior: +# MAGIC * **`Hello John`** +# MAGIC * **`Welcome back John Doe`** + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC The folder that contains this notebook contains a subfolder named **`ExampleSetupFolder`**, which in turn contains a notebook called **`example-setup`**. +# MAGIC +# MAGIC This simple notebook declares the variable **`my_name`**, sets it to **`None`** and then creates a DataFrame called **`example_df`**. +# MAGIC +# MAGIC Open the example-setup notebook and modify it so that name is not **`None`** but rather your name (or anyone's name) enclosed in quotes, and so that the following two cells execute without throwing an **`AssertionError`**. + +# COMMAND ---------- + +# MAGIC %run ./ExampleSetupFolder/example-setup + +# COMMAND ---------- + +assert my_name is not None, "Name is still None" +print(my_name) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Run a Python cell +# MAGIC +# MAGIC Run the following cell to verify that the **`example-setup`** notebook was executed by displaying the **`example_df`** Dataframe. This table consists of 16 rows of increasing values. + +# COMMAND ---------- + +display(example_df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Detach and Reattach a Cluster +# MAGIC +# MAGIC While attaching to clusters is a fairly common task, sometimes it is useful to detach and re-attach in one single operation. The main side-effect this achieves is clearing the execution state. This can be useful when you want to test cells in isolation, or you simply want to reset the execution state. +# MAGIC +# MAGIC Revisit the cluster dropdown. In the menu item representing the currently attached cluster, select the **Detach & Re-attach** link. +# MAGIC +# MAGIC Notice that the output from the cell above remains since results and execution state are unrelated, but the execution state is cleared. This can be verified by attempting to re-run the cell above. This fails, since the **`example_df`** variable has been cleared, along with the rest of the state. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Change Language +# MAGIC +# MAGIC Notice that the default language for this notebook is set to Python. Change this by clicking the **Python** button to the right of the notebook name. Change the default language to SQL. +# MAGIC +# MAGIC Notice that the Python cells are automatically prepended with a %python magic command to maintain validity of those cells. Notice that this operation also clears the execution state. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Create a Markdown Cell +# MAGIC +# MAGIC Add a new cell below this one. Populate with some Markdown that includes at least the following elements: +# MAGIC * A header +# MAGIC * Bullet points +# MAGIC * A link (using your choice of HTML or Markdown conventions) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Run a SQL cell +# MAGIC +# MAGIC Run the following cell to query a Delta table using SQL. This executes a simple query against a table is backed by a Databricks-provided example dataset included in all DBFS installations. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM delta.`/databricks-datasets/nyctaxi-with-zipcodes/subsampled` + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC Execute the following cell to view the underlying files backing this table. + +# COMMAND ---------- + +files = dbutils.fs.ls("/databricks-datasets/nyctaxi-with-zipcodes/subsampled") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Review Changes +# MAGIC +# MAGIC Assuming you have imported this material into your workspace using a Databricks Repo, open the Repo dialog by clicking the **`published`** branch button at the top-left corner of this page. You should see three changes: +# MAGIC 1. **Removed** with the old notebook name +# MAGIC 1. **Added** with the new notebook name +# MAGIC 1. **Modified** for creating a markdown cell above +# MAGIC +# MAGIC Use the dialog to revert the changes and restore this notebook to its original state. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Wrapping Up +# MAGIC +# MAGIC By completing this lab, you should now feel comfortable manipulating notebooks, creating new cells, and running notebooks within notebooks. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/ExampleSetupFolder/example-setup.py b/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/ExampleSetupFolder/example-setup.py new file mode 100644 index 0000000..bf35c99 --- /dev/null +++ b/Data-Engineering-with-Databricks/01 - Databricks Workspace and Services/ExampleSetupFolder/example-setup.py @@ -0,0 +1,8 @@ +# Databricks notebook source +# TODO +my_name = None + +# COMMAND ---------- + +example_df = spark.range(16) + diff --git a/Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/DE 1.3.1 - Managing Delta Tables.sql b/Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.1 - Managing Delta Tables.sql similarity index 65% rename from Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/DE 1.3.1 - Managing Delta Tables.sql rename to Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.1 - Managing Delta Tables.sql index 3c225fd..0f7343d 100644 --- a/Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/DE 1.3.1 - Managing Delta Tables.sql +++ b/Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.1 - Managing Delta Tables.sql @@ -8,43 +8,53 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC # Managing Delta Tables -- MAGIC -- MAGIC If you know any flavor of SQL, you already have much of the knowledge you'll need to work effectively in the data lakehouse. -- MAGIC --- MAGIC In this notebook, we'll explore the basic of manipulating data and tables with SQL on Databricks. +-- MAGIC In this notebook, we'll explore basic manipulation of data and tables with SQL on Databricks. -- MAGIC -- MAGIC Note that Delta Lake is the default format for all tables created with Databricks; if you've been running SQL statements on Databricks, you're likely already working with Delta Lake. -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC By the end of this lesson, students should feel confident: --- MAGIC * Creating Delta Lake tables --- MAGIC * Querying data from Delta Lake tables --- MAGIC * Inserting, updating, and deleting records in Delta Lake tables --- MAGIC * Writing upsert statements with Delta Lake --- MAGIC * Dropping Delta Lake tables +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC * Create Delta Lake tables +-- MAGIC * Query data from Delta Lake tables +-- MAGIC * Insert, update, and delete records in Delta Lake tables +-- MAGIC * Write upsert statements with Delta Lake +-- MAGIC * Drop Delta Lake tables -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC The first thing we're going to do is run a setup script. It will define a username, userhome, and database that is scoped to each user. -- COMMAND ---------- --- MAGIC %run ../Includes/sql-setup $mode="reset" +-- MAGIC %run ../Includes/Classroom-Setup-2.1 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Creating a Delta Table -- MAGIC -- MAGIC There's not much code you need to write to create a table with Delta Lake. There are a number of ways to create Delta Lake tables that we'll see throughout the course. We'll begin with one of the easiest methods: registering an empty Delta Lake table. -- MAGIC -- MAGIC We need: --- MAGIC - A `CREATE` statement --- MAGIC - A table name (below we use `students`) +-- MAGIC - A **`CREATE TABLE`** statement +-- MAGIC - A table name (below we use **`students`**) -- MAGIC - A schema +-- MAGIC +-- MAGIC **NOTE:** In Databricks Runtime 8.0 and above, Delta Lake is the default format and you don’t need **`USING DELTA`**. -- COMMAND ---------- @@ -54,9 +64,12 @@ CREATE TABLE students -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC If we try to go back and run that cell again...it will error out! This is expected - because the table exists already, we receive an error. -- MAGIC --- MAGIC We can add in an additional argument, `IF NOT EXISTS` which checks if the table exists. This will overcome our error. +-- MAGIC We can add in an additional argument, **`IF NOT EXISTS`** which checks if the table exists. This will overcome our error. -- COMMAND ---------- @@ -65,7 +78,10 @@ CREATE TABLE IF NOT EXISTS students -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Inserting Data -- MAGIC Most often, data will be inserted to tables as the result of a query from another source. -- MAGIC @@ -80,7 +96,10 @@ INSERT INTO students VALUES (3, "Elia", 3.3); -- COMMAND ---------- -- MAGIC %md --- MAGIC In the cell above, we completed three separate `INSERT` statements. Each of these is processed as a separate transaction with its own ACID guarantees. Most frequently, we'll insert many records in a single transaction. +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC In the cell above, we completed three separate **`INSERT`** statements. Each of these is processed as a separate transaction with its own ACID guarantees. Most frequently, we'll insert many records in a single transaction. -- COMMAND ---------- @@ -93,14 +112,20 @@ VALUES -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that Databricks doesn't have a `COMMIT` keyword; transactions run as soon as they're executed, and commit as they succeed. +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Note that Databricks doesn't have a **`COMMIT`** keyword; transactions run as soon as they're executed, and commit as they succeed. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Querying a Delta Table -- MAGIC --- MAGIC You probably won't be suprised that querying a Delta Lake table is as easy as using a standard `SELECT` statement. +-- MAGIC You probably won't be surprised that querying a Delta Lake table is as easy as using a standard **`SELECT`** statement. -- COMMAND ---------- @@ -109,18 +134,24 @@ SELECT * FROM students -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC What may surprise you is that Delta Lake guarantees that any read against a table will **always** return the most recent version of the table, and that you'll never encounter a state of deadlock due to ongoing operations. -- MAGIC --- MAGIC To repeat: table reads can never conflict with other operations, and the newest version of your data is immediately available to all clients that can query your lakehouse. Because all transaction information is stored in cloud object storage alongside your data files, concurrency reads on Delta Lake tables is limited only by the hard limits of object storage on cloud vendors. (**NOTE**: It's not infinite, but it's at least thousands of reads per second.) +-- MAGIC To repeat: table reads can never conflict with other operations, and the newest version of your data is immediately available to all clients that can query your lakehouse. Because all transaction information is stored in cloud object storage alongside your data files, concurrent reads on Delta Lake tables is limited only by the hard limits of object storage on cloud vendors. (**NOTE**: It's not infinite, but it's at least thousands of reads per second.) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Updating Records -- MAGIC --- MAGIC Updating records provides atomic guarantees as well: we perform a snapshot read of the current version of our table, find all fields that match our `WHERE` clause, and then apply the changes as described. +-- MAGIC Updating records provides atomic guarantees as well: we perform a snapshot read of the current version of our table, find all fields that match our **`WHERE`** clause, and then apply the changes as described. -- MAGIC --- MAGIC Below, we find all students that have a name starting with the letter **T** and add 1 to the number in their `value` column. +-- MAGIC Below, we find all students that have a name starting with the letter **T** and add 1 to the number in their **`value`** column. -- COMMAND ---------- @@ -131,6 +162,9 @@ WHERE name LIKE "T%" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Query the table again to see these changes applied. -- COMMAND ---------- @@ -140,11 +174,14 @@ SELECT * FROM students -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Deleting Records -- MAGIC -- MAGIC Deletes are also atomic, so there's no risk of only partially succeeding when removing data from your data lakehouse. -- MAGIC --- MAGIC A delete statement can remove one or many records, but will always result in a single transaction. +-- MAGIC A **`DELETE`** statement can remove one or many records, but will always result in a single transaction. -- COMMAND ---------- @@ -154,11 +191,14 @@ WHERE value > 6 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Using Merge -- MAGIC -- MAGIC Some SQL systems have the concept of an upsert, which allows updates, inserts, and other data manipulations to be run as a single command. -- MAGIC --- MAGIC Databricks uses the `MERGE` keyword to perform this operation. +-- MAGIC Databricks uses the **`MERGE`** keyword to perform this operation. -- MAGIC -- MAGIC Consider the following temporary view, which contains 4 records that might be output by a Change Data Capture (CDC) feed. @@ -175,13 +215,16 @@ SELECT * FROM updates; -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Using the syntax we've seen so far, we could filter from this view by type to write 3 statements, one each to insert, update, and delete records. But this would result in 3 separate transactions; if any of these transactions were to fail, it might leave our data in an invalid state. -- MAGIC -- MAGIC Instead, we combine these actions into a single atomic transaction, applying all 3 types of changes together. -- MAGIC --- MAGIC `MERGE` statements must have at least one field to match on, and each `WHEN MATCHED` or `WHEN NOT MATCHED` clause can have any number of additional conditional statements. +-- MAGIC **`MERGE`** statements must have at least one field to match on, and each **`WHEN MATCHED`** or **`WHEN NOT MATCHED`** clause can have any number of additional conditional statements. -- MAGIC --- MAGIC Here, we match on our `id` field and then filter on the `type` field to appropriately update, delete, or insert our records. +-- MAGIC Here, we match on our **`id`** field and then filter on the **`type`** field to appropriately update, delete, or insert our records. -- COMMAND ---------- @@ -198,16 +241,22 @@ WHEN NOT MATCHED AND u.type = "insert" -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that only 3 records were impacted by our `MERGE` statement; one of the records in our updates table did not have a matching `id` in the students table but was marked as an `update`. Based on our custom logic, we ignored this record rather than inserting it. -- MAGIC --- MAGIC How would you modify the above statement to include unmatched records marked `update` in the final `INSERT` clause? +-- MAGIC +-- MAGIC +-- MAGIC Note that only 3 records were impacted by our **`MERGE`** statement; one of the records in our updates table did not have a matching **`id`** in the students table but was marked as an **`update`**. Based on our custom logic, we ignored this record rather than inserting it. +-- MAGIC +-- MAGIC How would you modify the above statement to include unmatched records marked **`update`** in the final **`INSERT`** clause? -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Dropping a Table -- MAGIC --- MAGIC Assuming that you have proper permissions on the target table, you can permanently delete data in the lakehouse using a `DROP TABLE` command. +-- MAGIC Assuming that you have proper permissions on the target table, you can permanently delete data in the lakehouse using a **`DROP TABLE`** command. -- MAGIC -- MAGIC **NOTE**: Later in the course, we'll discuss Table Access Control Lists (ACLs) and default permissions. In a properly configured lakehouse, users should **not** be able to delete production tables. @@ -217,6 +266,19 @@ DROP TABLE students -- COMMAND ---------- +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + -- MAGIC %md-sandbox -- MAGIC © 2022 Databricks, Inc. All rights reserved.
-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
diff --git a/Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/Labs/DE 1.3.2L - Manipulating Tables with Delta Lake.sql b/Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.2L - Manipulating Tables with Delta Lake Lab.sql similarity index 86% rename from Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/Labs/DE 1.3.2L - Manipulating Tables with Delta Lake.sql rename to Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.2L - Manipulating Tables with Delta Lake Lab.sql index b756f75..eafe491 100644 --- a/Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/Labs/DE 1.3.2L - Manipulating Tables with Delta Lake.sql +++ b/Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.2L - Manipulating Tables with Delta Lake Lab.sql @@ -8,39 +8,48 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC # Manipulating Tables with Delta Lake -- MAGIC -- MAGIC This notebook provides a hands-on review of some of the basic functionality of Delta Lake. -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC By the end of this lessons, student will be able to: +-- MAGIC By the end of this lab, you should be able to: -- MAGIC - Execute standard operations to create and manipulate Delta Lake tables, including: --- MAGIC - `CREATE TABLE` --- MAGIC - `INSERT INTO` --- MAGIC - `SELECT FROM` --- MAGIC - `UPDATE` --- MAGIC - `DELETE` --- MAGIC - `MERGE` --- MAGIC - `DROP TABLE` +-- MAGIC - **`CREATE TABLE`** +-- MAGIC - **`INSERT INTO`** +-- MAGIC - **`SELECT FROM`** +-- MAGIC - **`UPDATE`** +-- MAGIC - **`DELETE`** +-- MAGIC - **`MERGE`** +-- MAGIC - **`DROP TABLE`** -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Setup -- MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. -- COMMAND ---------- --- MAGIC %run ../../Includes/sql-setup $course="delta" $mode="reset" +-- MAGIC %run ../Includes/Classroom-Setup-2.2L -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Create a Table -- MAGIC -- MAGIC In this notebook, we'll be creating a table to track our bean collection. -- MAGIC --- MAGIC Use the cell below to create a managed Delta Lake table named `beans`. +-- MAGIC Use the cell below to create a managed Delta Lake table named **`beans`**. -- MAGIC -- MAGIC Provide the following schema: -- MAGIC @@ -59,6 +68,9 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC **NOTE**: We'll use Python to run checks occasionally throughout the lab. The following cell will return as error with a message on what needs to change if you have not followed instructions. No output from cell execution means that you have completed this step. -- COMMAND ---------- @@ -71,6 +83,8 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Insert Data -- MAGIC -- MAGIC Run the following cell to insert three rows into the table. @@ -85,6 +99,9 @@ INSERT INTO beans VALUES -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Manually review the table contents to ensure data was written as expected. -- COMMAND ---------- @@ -95,6 +112,9 @@ INSERT INTO beans VALUES -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Insert the additional records provided below. Make sure you execute this as a single transaction. -- COMMAND ---------- @@ -108,6 +128,9 @@ INSERT INTO beans VALUES -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to confirm the data is in the proper state. -- COMMAND ---------- @@ -120,6 +143,9 @@ INSERT INTO beans VALUES -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Update Records -- MAGIC -- MAGIC A friend is reviewing your inventory of beans. After much debate, you agree that jelly beans are delicious. @@ -135,9 +161,12 @@ WHERE name = "jelly" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC You realize that you've accidentally entered the weight of your pinto beans incorrectly. -- MAGIC --- MAGIC Update the `grams` column for this record to the correct weight of 1500. +-- MAGIC Update the **`grams`** column for this record to the correct weight of 1500. -- COMMAND ---------- @@ -147,6 +176,9 @@ WHERE name = "jelly" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to confirm this has completed properly. -- COMMAND ---------- @@ -161,6 +193,9 @@ WHERE name = "jelly" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Delete Records -- MAGIC -- MAGIC You've decided that you only want to keep track of delicious beans. @@ -175,6 +210,9 @@ WHERE name = "jelly" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the following cell to confirm this operation was successful. -- COMMAND ---------- @@ -186,6 +224,9 @@ WHERE name = "jelly" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Using Merge to Upsert Records -- MAGIC -- MAGIC Your friend gives you some new beans. The cell below registers these as a temporary view. @@ -203,7 +244,10 @@ SELECT * FROM new_beans -- COMMAND ---------- -- MAGIC %md --- MAGIC In the cell below, use the above view to write a merge statement to update and insert new records to your `beans` table as one transaction. +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC In the cell below, use the above view to write a merge statement to update and insert new records to your **`beans`** table as one transaction. -- MAGIC -- MAGIC Make sure your logic: -- MAGIC - Matches beans by name **and** color @@ -218,6 +262,9 @@ SELECT * FROM new_beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to check your work. -- COMMAND ---------- @@ -235,13 +282,16 @@ SELECT * FROM new_beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Dropping Tables -- MAGIC -- MAGIC When working with managed Delta Lake tables, dropping a table results in permanently deleting access to the table and all underlying data files. -- MAGIC -- MAGIC **NOTE**: Later in the course, we'll learn about external tables, which approach Delta Lake tables as a collection of files and have different persistence guarantees. -- MAGIC --- MAGIC In the cell below, write a query to drop the `beans` table. +-- MAGIC In the cell below, write a query to drop the **`beans`** table. -- COMMAND ---------- @@ -251,6 +301,9 @@ SELECT * FROM new_beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to assert that your table no longer exists. -- COMMAND ---------- @@ -261,16 +314,26 @@ SELECT * FROM new_beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Wrapping Up -- MAGIC -- MAGIC By completing this lab, you should now feel comfortable: -- MAGIC * Completing standard Delta Lake table creation and data manipulation commands + +-- COMMAND ---------- + +-- MAGIC %md -- MAGIC --- MAGIC Run the following cell to remove the database and all data associated with this lab. +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. -- COMMAND ---------- --- MAGIC %run ../../Includes/sql-setup $course="delta" $mode="cleanup" +-- MAGIC %python +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/DE 1.3.3 - Advanced Delta Lake Features.sql b/Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.3 - Advanced Delta Lake Features.sql similarity index 59% rename from Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/DE 1.3.3 - Advanced Delta Lake Features.sql rename to Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.3 - Advanced Delta Lake Features.sql index f0be390..3bded34 100644 --- a/Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/DE 1.3.3 - Advanced Delta Lake Features.sql +++ b/Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.3 - Advanced Delta Lake Features.sql @@ -8,6 +8,8 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Advanced Delta Lake Features -- MAGIC -- MAGIC Now that you feel comfortable performing basic data tasks with Delta Lake, we can discuss a few features unique to Delta Lake. @@ -15,30 +17,38 @@ -- MAGIC Note that while some of the keywords used here aren't part of standard ANSI SQL, all Delta Lake operations can be run on Databricks using SQL -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC By the end of this lesson, students should feel confident: --- MAGIC * Using `OPTIMIZE` to compact small files --- MAGIC * Using `ZORDER` to index tables --- MAGIC * Describing the directory structure of Delta Lake files --- MAGIC * Reviewing a history of table transactions --- MAGIC * Querying and rolling back to previous table version --- MAGIC * Cleaning up stale data files with `VACUUM` +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC * Use **`OPTIMIZE`** to compact small files +-- MAGIC * Use **`ZORDER`** to index tables +-- MAGIC * Describe the directory structure of Delta Lake files +-- MAGIC * Review a history of table transactions +-- MAGIC * Query and roll back to previous table version +-- MAGIC * Clean up stale data files with **`VACUUM`** +-- MAGIC +-- MAGIC **Resources** +-- MAGIC * Delta Optimize - Databricks Docs +-- MAGIC * Delta Vacuum - Databricks Docs -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC The first thing we're going to do is run a setup script. It will define a username, userhome, and database that is scoped to each user. -- COMMAND ---------- --- MAGIC %run ../Includes/sql-setup $mode="reset" +-- MAGIC %run ../Includes/Classroom-Setup-2.3 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Creating a Delta Table with History -- MAGIC --- MAGIC The cell below condenses all the transactions from the previous lesson into a single cell. (Except for the `DROP TABLE`!) +-- MAGIC The cell below condenses all the transactions from the previous lesson into a single cell. (Except for the **`DROP TABLE`**!) -- MAGIC -- MAGIC As you're waiting for this query to run, see if you can identify the total number of transactions being executed. @@ -83,11 +93,13 @@ WHEN NOT MATCHED AND u.type = "insert" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Examine Table Details -- MAGIC -- MAGIC Databricks uses a Hive metastore by default to register databases, tables, and views. -- MAGIC --- MAGIC Using `DESCRIBE EXTENDED` allows us to see important metadata about our table. +-- MAGIC Using **`DESCRIBE EXTENDED`** allows us to see important metadata about our table. -- COMMAND ---------- @@ -96,13 +108,28 @@ DESCRIBE EXTENDED students -- COMMAND ---------- -- MAGIC %md --- MAGIC Note the `Location` field. +-- MAGIC +-- MAGIC +-- MAGIC **`DESCRIBE DETAIL`** is another command that allows us to explore table metadata. + +-- COMMAND ---------- + +DESCRIBE DETAIL students + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note the **`Location`** field. -- MAGIC -- MAGIC While we've so far been thinking about our table as just a relational entity within a database, a Delta Lake table is actually backed by a collection of files stored in cloud object storage. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Explore Delta Lake Files -- MAGIC -- MAGIC We can see the files backing our Delta Lake table by using a Databricks Utilities function. @@ -112,37 +139,43 @@ DESCRIBE EXTENDED students -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{userhome}/students")) +-- MAGIC display(dbutils.fs.ls(f"{DA.paths.user_db}/students")) -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that our directory contains a number of Parquet data files and a directory named `_delta_log`. +-- MAGIC +-- MAGIC +-- MAGIC Note that our directory contains a number of Parquet data files and a directory named **`_delta_log`**. -- MAGIC -- MAGIC Records in Delta Lake tables are stored as data in Parquet files. -- MAGIC --- MAGIC Transactions to Delta Lake tables are recorded in the `_delta_log`. +-- MAGIC Transactions to Delta Lake tables are recorded in the **`_delta_log`**. -- MAGIC --- MAGIC We can peak inside the `_delta_log` to see more. +-- MAGIC We can peek inside the **`_delta_log`** to see more. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{userhome}/students/_delta_log")) +-- MAGIC display(dbutils.fs.ls(f"{DA.paths.user_db}/students/_delta_log")) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Each transaction results in a new JSON file being written to the Delta Lake transaction log. Here, we can see that there are 8 total transactions against this table (Delta Lake is 0 indexed). -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Reasoning about Data Files -- MAGIC -- MAGIC We just saw a lot of data files for what is obviously a very small table. -- MAGIC --- MAGIC `DESCRIBE DETAIL` allows us to see some other details about our Delta table, including the number of files. +-- MAGIC **`DESCRIBE DETAIL`** allows us to see some other details about our Delta table, including the number of files. -- COMMAND ---------- @@ -151,36 +184,42 @@ DESCRIBE DETAIL students -- COMMAND ---------- -- MAGIC %md --- MAGIC Here we see that our table currently contains 3 data files in its present version. So what are all those other Parquet files doing in our table directory? +-- MAGIC +-- MAGIC +-- MAGIC Here we see that our table currently contains 4 data files in its present version. So what are all those other Parquet files doing in our table directory? -- MAGIC -- MAGIC Rather than overwriting or immediately deleting files containing changed data, Delta Lake uses the transaction log to indicate whether or not files are valid in a current version of the table. -- MAGIC --- MAGIC Here, we'll look at the transaction log corresponding the `MERGE` statement above, where records were inserted, updated, and deleted. +-- MAGIC Here, we'll look at the transaction log corresponding the **`MERGE`** statement above, where records were inserted, updated, and deleted. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(spark.sql(f"SELECT * FROM json.`{userhome}/students/_delta_log/00000000000000000007.json`")) +-- MAGIC display(spark.sql(f"SELECT * FROM json.`{DA.paths.user_db}/students/_delta_log/00000000000000000007.json`")) -- COMMAND ---------- -- MAGIC %md --- MAGIC The `add` column contains a list of all the new files written to our table; the `remove` column indicates those files that no longer should be included in our table. +-- MAGIC +-- MAGIC +-- MAGIC The **`add`** column contains a list of all the new files written to our table; the **`remove`** column indicates those files that no longer should be included in our table. -- MAGIC -- MAGIC When we query a Delta Lake table, the query engine uses the transaction logs to resolve all the files that are valid in the current version, and ignores all other data files. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Compacting Small Files and Indexing -- MAGIC -- MAGIC Small files can occur for a variety of reasons; in our case, we performed a number of operations where only one or several records were inserted. -- MAGIC --- MAGIC Files will be combined toward an optimal size (scaled based on the size of the table) by using the `OPTIMIZE` command. +-- MAGIC Files will be combined toward an optimal size (scaled based on the size of the table) by using the **`OPTIMIZE`** command. -- MAGIC --- MAGIC `OPTIMIZE` will replace existing data files by combining records and rewriting the results. +-- MAGIC **`OPTIMIZE`** will replace existing data files by combining records and rewriting the results. -- MAGIC --- MAGIC When executing `OPTIMIZE`, users can optionally specify one or several fields for `ZORDER` indexing. While the specific math of Z-order is unimportant, it speeds up data retrieval when filtering on provided fields by collocating data with similar values within data files. +-- MAGIC When executing **`OPTIMIZE`**, users can optionally specify one or several fields for **`ZORDER`** indexing. While the specific math of Z-order is unimportant, it speeds up data retrieval when filtering on provided fields by colocating data with similar values within data files. -- COMMAND ---------- @@ -190,14 +229,18 @@ ZORDER BY id -- COMMAND ---------- -- MAGIC %md --- MAGIC Given how small our data is, `ZORDER` does not provide any benefit, but we can see all of the metrics that result from this operation. +-- MAGIC +-- MAGIC +-- MAGIC Given how small our data is, **`ZORDER`** does not provide any benefit, but we can see all of the metrics that result from this operation. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Reviewing Delta Lake Transactions -- MAGIC --- MAGIC Because all changes to the Delta Lake table are stored in the transaction log, we can easily review the history of our table. +-- MAGIC Because all changes to the Delta Lake table are stored in the transaction log, we can easily review the table history. -- COMMAND ---------- @@ -206,7 +249,9 @@ DESCRIBE HISTORY students -- COMMAND ---------- -- MAGIC %md --- MAGIC As expected, `OPTIMIZE` created another version of our table, meaning that version 8 is our most current version. +-- MAGIC +-- MAGIC +-- MAGIC As expected, **`OPTIMIZE`** created another version of our table, meaning that version 8 is our most current version. -- MAGIC -- MAGIC Remember all of those extra data files that had been marked as removed in our transaction log? These provide us with the ability to query previous versions of our table. -- MAGIC @@ -222,11 +267,15 @@ FROM students VERSION AS OF 3 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC What's important to note about time travel is that we're not recreating a previous state of the table by undoing transactions against our current version; rather, we're just querying all those data files that were indicated as valid as of the specified version. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Rollback Versions -- MAGIC -- MAGIC Suppose you're typing up query to manually delete some records from a table and you accidentally execute this query in the following state. @@ -238,7 +287,9 @@ DELETE FROM students -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that when we see a `-1` for number of rows affected by a delete, this means an entire directory of data has been removed. +-- MAGIC +-- MAGIC +-- MAGIC Note that when we see a **`-1`** for number of rows affected by a delete, this means an entire directory of data has been removed. -- MAGIC -- MAGIC Let's confirm this below. @@ -249,6 +300,8 @@ SELECT * FROM students -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Deleting all the records in your table is probably not a desired outcome. Luckily, we can simply rollback this commit. -- COMMAND ---------- @@ -258,38 +311,40 @@ RESTORE TABLE students TO VERSION AS OF 8 -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that a `RESTORE` command is recorded as a transaction; you won't be able to completely hide the fact that you accidentally deleted all the records in the table, but you will be able to undo the operation and bring your table back to a desired state. +-- MAGIC +-- MAGIC +-- MAGIC Note that a **`RESTORE`** command is recorded as a transaction; you won't be able to completely hide the fact that you accidentally deleted all the records in the table, but you will be able to undo the operation and bring your table back to a desired state. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Cleaning Up Stale Files -- MAGIC -- MAGIC Databricks will automatically clean up stale files in Delta Lake tables. -- MAGIC -- MAGIC While Delta Lake versioning and time travel are great for querying recent versions and rolling back queries, keeping the data files for all versions of large production tables around indefinitely is very expensive (and can lead to compliance issues if PII is present). -- MAGIC --- MAGIC If you wish to manually purge old data files, this can be performed with the `VACUUM` operation. +-- MAGIC If you wish to manually purge old data files, this can be performed with the **`VACUUM`** operation. -- MAGIC --- MAGIC Let's do this below with a retention of `0 HOURS` to only keep our current version. +-- MAGIC Uncomment the following cell and execute it with a retention of **`0 HOURS`** to keep only the current version: -- COMMAND ---------- -VACUUM students RETAIN 0 HOURS +-- VACUUM students RETAIN 0 HOURS -- COMMAND ---------- -- MAGIC %md --- MAGIC By default, `VACUUM` will prevent you from deleting files less than 7 days old, just to ensure that no long-running operations are still referencing any of the files to be deleted. We turned off the setting that prese -- MAGIC --- MAGIC In our demos, you may see Databricks executing code that specifies a retention of `0 HOURS`. This is almost exclusively for demo purposes. There are some instances where a power user of Databricks may need to perform this operation, **you should probably never do this on a production table.** -- MAGIC --- MAGIC That being said, let's do it here. +-- MAGIC By default, **`VACUUM`** will prevent you from deleting files less than 7 days old, just to ensure that no long-running operations are still referencing any of the files to be deleted. If you run **`VACUUM`** on a Delta table, you lose the ability time travel back to a version older than the specified data retention period. In our demos, you may see Databricks executing code that specifies a retention of **`0 HOURS`**. This is simply to demonstrate the feature and is not typically done in production. -- MAGIC -- MAGIC In the following cell, we: --- MAGIC * Turn off a check to prevent premature deletion of data files --- MAGIC * Make sure that logging of `VACUUM` commands is enabled --- MAGIC * Use the `DRY RUN` version of vacuum to print out all records to be deleted +-- MAGIC 1. Turn off a check to prevent premature deletion of data files +-- MAGIC 1. Make sure that logging of **`VACUUM`** commands is enabled +-- MAGIC 1. Use the **`DRY RUN`** version of vacuum to print out all records to be deleted -- COMMAND ---------- @@ -301,7 +356,9 @@ VACUUM students RETAIN 0 HOURS DRY RUN -- COMMAND ---------- -- MAGIC %md --- MAGIC By running `VACUUM` and deleting the 9 files above, we will permanently remove access to versions of the table that require these files to materialize. +-- MAGIC +-- MAGIC +-- MAGIC By running **`VACUUM`** and deleting the 10 files above, we will permanently remove access to versions of the table that require these files to materialize. -- COMMAND ---------- @@ -310,12 +367,26 @@ VACUUM students RETAIN 0 HOURS -- COMMAND ---------- -- MAGIC %md --- MAGIC Because of the way the Delta Cache works, we may still be able to query previous versions of the table on the currently active cluster until it restarts. However, listing out our table directory will show that files have been successfully deleted. +-- MAGIC +-- MAGIC +-- MAGIC Check the table directory to show that files have been successfully deleted. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC display(dbutils.fs.ls(f"{DA.paths.user_db}/students")) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{userhome}/students")) +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/Labs/DE 1.3.4L - Delta Lake Versioning, Optimization, and Vacuuming.sql b/Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.4L - Delta Lake Versioning, Optimization, and Vacuuming Lab.sql similarity index 74% rename from Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/Labs/DE 1.3.4L - Delta Lake Versioning, Optimization, and Vacuuming.sql rename to Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.4L - Delta Lake Versioning, Optimization, and Vacuuming Lab.sql index 9cfb56d..810b8c8 100644 --- a/Data-Engineering-With-Databricks/01 - Databricks Lakehouse Platform/Labs/DE 1.3.4L - Delta Lake Versioning, Optimization, and Vacuuming.sql +++ b/Data-Engineering-with-Databricks/02 - Delta Lake/DE 2.4L - Delta Lake Versioning, Optimization, and Vacuuming Lab.sql @@ -8,12 +8,14 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Delta Lake Versioning, Optimization, and Vacuuming -- MAGIC -- MAGIC This notebook provides a hands-on review of some of the more esoteric features Delta Lake brings to the data lakehouse. -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC By the end of this lessons, student will be able to: +-- MAGIC By the end of this lab, you should be able to: -- MAGIC - Review table history -- MAGIC - Query previous table versions and rollback a table to a specific version -- MAGIC - Perform file compaction and Z-order indexing @@ -22,21 +24,25 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Setup -- MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. -- COMMAND ---------- --- MAGIC %run ../../Includes/sql-setup $course="delta" $mode="reset" +-- MAGIC %run ../Includes/Classroom-Setup-2.4L -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Recreate the History of your Bean Collection -- MAGIC --- MAGIC This lab picks up where the last lab left off. The cell below condenses all the operations from the last lab into a single cell (other than the final `DROP TABLE` statement). +-- MAGIC This lab picks up where the last lab left off. The cell below condenses all the operations from the last lab into a single cell (other than the final **`DROP TABLE`** statement). -- MAGIC --- MAGIC For quick reference, the schema of the `beans` table created is: +-- MAGIC For quick reference, the schema of the **`beans`** table created is: -- MAGIC -- MAGIC | Field Name | Field type | -- MAGIC | --- | --- | @@ -88,11 +94,13 @@ WHEN NOT MATCHED AND b.delicious = true THEN -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Review the Table History -- MAGIC -- MAGIC Delta Lake's transaction log stores information about each transaction that modifies a table's contents or settings. -- MAGIC --- MAGIC Review the history of the `beans` table below. +-- MAGIC Review the history of the **`beans`** table below. -- COMMAND ---------- @@ -102,6 +110,8 @@ WHEN NOT MATCHED AND b.delicious = true THEN -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC If all the previous operations were completed as described you should see 7 versions of the table (**NOTE**: Delta Lake versioning starts with 0, so the max version number will be 6). -- MAGIC -- MAGIC The operations should be as follows: @@ -116,15 +126,17 @@ WHEN NOT MATCHED AND b.delicious = true THEN -- MAGIC | 5 | DELETE | -- MAGIC | 6 | MERGE | -- MAGIC --- MAGIC The `operationsParameters` column will let you review predicates used for updates, deletes, and merges. The `operationMetrics` column indicates how many rows and files are added in each operation. +-- MAGIC The **`operationsParameters`** column will let you review predicates used for updates, deletes, and merges. The **`operationMetrics`** column indicates how many rows and files are added in each operation. -- MAGIC -- MAGIC Spend some time reviewing the Delta Lake history to understand which table version matches with a given transaction. -- MAGIC --- MAGIC **NOTE**: The `version` column designates the state of a table once a given transaction completes. The `readVersion` column indicates the version of the table an operation executed against. In this simple demo (with no concurrent transactions), this relationship should always increment by 1. +-- MAGIC **NOTE**: The **`version`** column designates the state of a table once a given transaction completes. The **`readVersion`** column indicates the version of the table an operation executed against. In this simple demo (with no concurrent transactions), this relationship should always increment by 1. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Query a Specific Version -- MAGIC -- MAGIC After reviewing the table history, you decide you want to view the state of your table after your very first data was inserted. @@ -138,6 +150,8 @@ SELECT * FROM beans VERSION AS OF 1 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC And now review the current state of your data. -- COMMAND ---------- @@ -147,9 +161,11 @@ SELECT * FROM beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC You want to review the weights of your beans before you deleted any records. -- MAGIC --- MAGIC Fill in the query below to register and query a temporary view of the version just before data was deleted. +-- MAGIC Fill in the statement below to register a temporary view of the version just before data was deleted, then run the following cell to query the view. -- COMMAND ---------- @@ -157,11 +173,15 @@ SELECT * FROM beans CREATE OR REPLACE TEMP VIEW pre_delete_vw AS +-- COMMAND ---------- + SELECT * FROM pre_delete_vw -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to check that you have captured the correct version. -- COMMAND ---------- @@ -174,11 +194,13 @@ SELECT * FROM pre_delete_vw -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Restore a Previous Version -- MAGIC -- MAGIC Apparently there was a misunderstanding; the beans your friend gave you that you merged into your collection were not intended for you to keep. -- MAGIC --- MAGIC Revert your table to the version before this `MERGE` statement completed. +-- MAGIC Revert your table to the version before this **`MERGE`** statement completed. -- COMMAND ---------- @@ -188,6 +210,8 @@ SELECT * FROM pre_delete_vw -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Review the history of your table. Make note of the fact that restoring to a previous version adds another table version. -- COMMAND ---------- @@ -204,10 +228,12 @@ DESCRIBE HISTORY beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## File Compaction -- MAGIC Looking at the transaction metrics during your reversion, you are surprised you have some many files for such a small collection of data. -- MAGIC --- MAGIC While indexing on a table of this size is unlikely to improve performance, you decide to add a Z-order index on the `name` field in anticipation of your bean collection growing exponentially over time. +-- MAGIC While indexing on a table of this size is unlikely to improve performance, you decide to add a Z-order index on the **`name`** field in anticipation of your bean collection growing exponentially over time. -- MAGIC -- MAGIC Use the cell below to perform file compaction and Z-order indexing. @@ -219,6 +245,8 @@ DESCRIBE HISTORY beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Your data should have been compacted to a single file; confirm this manually by running the following cell. -- COMMAND ---------- @@ -228,6 +256,8 @@ DESCRIBE DETAIL beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to check that you've successfully optimized and indexed your table. -- COMMAND ---------- @@ -240,19 +270,21 @@ DESCRIBE DETAIL beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Cleaning Up Stale Data Files -- MAGIC --- MAGIC You know that while all your data now resides in 1 data file, the data files from previous versions of your table are still being stored alongside this. You wish to remove these files and remove access to previous versions of the table by running `VACUUM` on the table. +-- MAGIC You know that while all your data now resides in 1 data file, the data files from previous versions of your table are still being stored alongside this. You wish to remove these files and remove access to previous versions of the table by running **`VACUUM`** on the table. -- MAGIC --- MAGIC Executing `VACUUM` performs garbage cleanup on the table directory. By default, a retention threshold of 7 days will be enforced. +-- MAGIC Executing **`VACUUM`** performs garbage cleanup on the table directory. By default, a retention threshold of 7 days will be enforced. -- MAGIC -- MAGIC The cell below modifies some Spark configurations. The first command overrides the retention threshold check to allow us to demonstrate permanent removal of data. -- MAGIC -- MAGIC **NOTE**: Vacuuming a production table with a short retention can lead to data corruption and/or failure of long-running queries. This is for demonstration purposes only and extreme caution should be used when disabling this setting. -- MAGIC --- MAGIC The second command sets `spark.databricks.delta.vacuum.logging.enabled` to `true` to ensures that the `VACUUM` operation is recorded in the transaction log. +-- MAGIC The second command sets **`spark.databricks.delta.vacuum.logging.enabled`** to **`true`** to ensure that the **`VACUUM`** operation is recorded in the transaction log. -- MAGIC --- MAGIC **NOTE**: Because of slight differences in storage protocols on various clouds, logging `VACUUM` commands is not on by default for some clouds as of DBR 9.1. +-- MAGIC **NOTE**: Because of slight differences in storage protocols on various clouds, logging **`VACUUM`** commands is not on by default for some clouds as of DBR 9.1. -- COMMAND ---------- @@ -262,7 +294,9 @@ SET spark.databricks.delta.vacuum.logging.enabled = true; -- COMMAND ---------- -- MAGIC %md --- MAGIC Before permanently deleting data files, review them manually using the `DRY RUN` command. +-- MAGIC +-- MAGIC +-- MAGIC Before permanently deleting data files, review them manually using the **`DRY RUN`** option. -- COMMAND ---------- @@ -271,9 +305,11 @@ VACUUM beans RETAIN 0 HOURS DRY RUN -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC All data files not in the current version of the table will be shown in the preview above. -- MAGIC --- MAGIC Run the command again without `DRY RUN` to permanently delete these files. +-- MAGIC Run the command again without **`DRY RUN`** to permanently delete these files. -- MAGIC -- MAGIC **NOTE**: All previous versions of the table will no longer be accessible. @@ -284,7 +320,9 @@ VACUUM beans RETAIN 0 HOURS -- COMMAND ---------- -- MAGIC %md --- MAGIC Because `VACUUM` can be such a destructive act for important datasets, it's always a good idea to turn the retention duration check back on. Run the cell below to reactive this setting. +-- MAGIC +-- MAGIC +-- MAGIC Because **`VACUUM`** can be such a destructive act for important datasets, it's always a good idea to turn the retention duration check back on. Run the cell below to reactive this setting. -- COMMAND ---------- @@ -293,7 +331,9 @@ SET spark.databricks.delta.retentionDurationCheck.enabled = true -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that the table history will indicate the user that completed the `VACUUM` operation, the number of files deleted, and log that the retention check was disabled during this operation. +-- MAGIC +-- MAGIC +-- MAGIC Note that the table history will indicate the user that completed the **`VACUUM`** operation, the number of files deleted, and log that the retention check was disabled during this operation. -- COMMAND ---------- @@ -302,6 +342,8 @@ DESCRIBE HISTORY beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Query your table again to confirm you still have access to the current version. -- COMMAND ---------- @@ -311,32 +353,42 @@ SELECT * FROM beans -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that because Delta Cache stores copies of files queried in the current session on storage volumes deployed to your currently active cluster, you may still be able to temporarily access previous table versions (though systems should **not** be designed to expect this behavior). Restarting the cluster will ensure that these cached data files are permanently purged. +-- MAGIC +-- MAGIC +-- MAGIC Because Delta Cache stores copies of files queried in the current session on storage volumes deployed to your currently active cluster, you may still be able to temporarily access previous table versions (though systems should **not** be designed to expect this behavior). +-- MAGIC +-- MAGIC Restarting the cluster will ensure that these cached data files are permanently purged. +-- MAGIC +-- MAGIC You can see an example of this by uncommenting and running the following cell that may, or may not, fail +-- MAGIC (depending on the state of the cache). -- COMMAND ---------- -SELECT * FROM beans@v1 +-- SELECT * FROM beans@v1 -- COMMAND ---------- -- MAGIC %md --- MAGIC ## Wrapping Up -- MAGIC --- MAGIC Run the following cell to remove the database and all data associated with this lab. +-- MAGIC +-- MAGIC By completing this lab, you should now feel comfortable: +-- MAGIC * Completing standard Delta Lake table creation and data manipulation commands +-- MAGIC * Reviewing table metadata including table history +-- MAGIC * Leverage Delta Lake versioning for snapshot queries and rollbacks +-- MAGIC * Compacting small files and indexing tables +-- MAGIC * Using **`VACUUM`** to review files marked for deletion and committing these deletes -- COMMAND ---------- --- MAGIC %run ../../Includes/sql-setup $course="delta" $mode="cleanup" +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. -- COMMAND ---------- --- MAGIC %md --- MAGIC By completing this lab, you should now feel comfortable: --- MAGIC * Completing standard Delta Lake table creation and data manipulation commands --- MAGIC * Reviewing table metadata including table history --- MAGIC * Leverage Delta Lake versioning for snapshot queries and rollbacks --- MAGIC * Compacting small files and indexing tables --- MAGIC * Using `VACUUM` to review files marked for deletion and committing these deletes +-- MAGIC %python +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.1.1 - Databases and Tables on Databricks.sql b/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.1 - Databases and Tables on Databricks.sql similarity index 51% rename from Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.1.1 - Databases and Tables on Databricks.sql rename to Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.1 - Databases and Tables on Databricks.sql index 9401dd3..d600477 100644 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.1.1 - Databases and Tables on Databricks.sql +++ b/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.1 - Databases and Tables on Databricks.sql @@ -8,36 +8,42 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Databases and Tables on Databricks -- MAGIC In this demonstration, you will create and explore databases and tables. -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC By the end of this lesson, you will be able to: +-- MAGIC By the end of this lesson, you should be able to: -- MAGIC * Use Spark SQL DDL to define databases and tables --- MAGIC * Describe how the `LOCATION` keyword impacts the default storage directory +-- MAGIC * Describe how the **`LOCATION`** keyword impacts the default storage directory -- MAGIC -- MAGIC -- MAGIC -- MAGIC **Resources** --- MAGIC * [Databases and Tables - Databricks Docs](https://docs.databricks.com/user-guide/tables.html) --- MAGIC * [Managed and Unmanaged Tables](https://docs.databricks.com/user-guide/tables.html#managed-and-unmanaged-tables) --- MAGIC * [Creating a Table with the UI](https://docs.databricks.com/user-guide/tables.html#create-a-table-using-the-ui) --- MAGIC * [Create a Local Table](https://docs.databricks.com/user-guide/tables.html#create-a-local-table) --- MAGIC * [Saving to Persistent Tables](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html#saving-to-persistent-tables) +-- MAGIC * Databases and Tables - Databricks Docs +-- MAGIC * Managed and Unmanaged Tables +-- MAGIC * Creating a Table with the UI +-- MAGIC * Create a Local Table +-- MAGIC * Saving to Persistent Tables -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Lesson Setup -- MAGIC The following script clears out previous runs of this demo and configures some Hive variables that will be used in our SQL queries. -- COMMAND ---------- --- MAGIC %run ../Includes/setup-meta +-- MAGIC %run ../Includes/Classroom-Setup-3.1 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Using Hive Variables -- MAGIC -- MAGIC While not a pattern that is generally recommended in Spark SQL, this notebook will use some Hive variables to substitute in string values derived from the account email of the current user. @@ -46,59 +52,76 @@ -- COMMAND ---------- -SELECT "${c.database}"; +SELECT "${da.db_name}" AS db_name, + "${da.paths.working_dir}" AS working_dir -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Because you may be working in a shared workspace, this course uses variables derived from your username so the databases don't conflict with other users. Again, consider this use of Hive variables a hack for our lesson environment rather than a good practice for development. -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Databases -- MAGIC Let's start by creating two databases: --- MAGIC - One with no LOCATION specified --- MAGIC - One with LOCATION specified +-- MAGIC - One with no **`LOCATION`** specified +-- MAGIC - One with **`LOCATION`** specified -- COMMAND ---------- -CREATE DATABASE IF NOT EXISTS ${c.database}_default_location; -CREATE DATABASE IF NOT EXISTS ${c.database}_custom_location LOCATION '${c.userhome}'; +CREATE DATABASE IF NOT EXISTS ${da.db_name}_default_location; +CREATE DATABASE IF NOT EXISTS ${da.db_name}_custom_location LOCATION '${da.paths.working_dir}/_custom_location.db'; -- COMMAND ---------- --- MAGIC %md --- MAGIC Note that the location of the first database is in the default location under `dbfs:/user/hive/warehouse/` +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that the location of the first database is in the default location under **`dbfs:/user/hive/warehouse/`** and that the database directory is the name of the database with the **`.db`** extension -- COMMAND ---------- -DESCRIBE DATABASE EXTENDED ${c.database}_default_location; +DESCRIBE DATABASE EXTENDED ${da.db_name}_default_location; -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that the location of the second database is in the directory specified after the `LOCATION` keyword. +-- MAGIC +-- MAGIC +-- MAGIC Note that the location of the second database is in the directory specified after the **`LOCATION`** keyword. -- COMMAND ---------- -DESCRIBE DATABASE EXTENDED ${c.database}_custom_location; +DESCRIBE DATABASE EXTENDED ${da.db_name}_custom_location; -- COMMAND ---------- --- MAGIC %md --- MAGIC We will create a table in the database with default location and insert data. Note that the schema must be provided because there are no data from which to infer the schema. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We will create a table in the database with default location and insert data. +-- MAGIC +-- MAGIC Note that the schema must be provided because there is no data from which to infer the schema. -- COMMAND ---------- -USE ${c.database}_default_location; +USE ${da.db_name}_default_location; + CREATE OR REPLACE TABLE managed_table_in_db_with_default_location (width INT, length INT, height INT); -INSERT INTO managed_table_in_db_with_default_location VALUES (3, 2, 1); +INSERT INTO managed_table_in_db_with_default_location +VALUES (3, 2, 1); SELECT * FROM managed_table_in_db_with_default_location; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC We can look at the extended table description to find the location (you'll need to scroll down in the results). -- COMMAND ---------- @@ -108,18 +131,30 @@ DESCRIBE EXTENDED managed_table_in_db_with_default_location; -- COMMAND ---------- -- MAGIC %md --- MAGIC By default, managed tables in a database without the location specified will be created in the `dbfs:/user/hive/warehouse/.db/` directory. +-- MAGIC +-- MAGIC +-- MAGIC By default, managed tables in a database without the location specified will be created in the **`dbfs:/user/hive/warehouse/.db/`** directory. -- MAGIC -- MAGIC We can see that, as expected, the data and metadata for our Delta Table are stored in that location. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"dbfs:/user/hive/warehouse/{database}_default_location.db/managed_table_in_db_with_default_location")) +-- MAGIC hive_root = f"dbfs:/user/hive/warehouse" +-- MAGIC db_name = f"{DA.db_name}_default_location.db" +-- MAGIC table_name = f"managed_table_in_db_with_default_location" +-- MAGIC +-- MAGIC tbl_location = f"{hive_root}/{db_name}/{table_name}" +-- MAGIC print(tbl_location) +-- MAGIC +-- MAGIC files = dbutils.fs.ls(tbl_location) +-- MAGIC display(files) -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Drop the table. -- COMMAND ---------- @@ -128,29 +163,41 @@ DROP TABLE managed_table_in_db_with_default_location; -- COMMAND ---------- --- MAGIC %md --- MAGIC Note the table's folder and its log and data file are deleted. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note the table's directory and its log and data files are deleted. Only the database directory remains. -- COMMAND ---------- -- MAGIC %python --- MAGIC dbutils.fs.ls(f"dbfs:/user/hive/warehouse/{database}_default_location.db") +-- MAGIC +-- MAGIC db_location = f"{hive_root}/{db_name}" +-- MAGIC print(db_location) +-- MAGIC dbutils.fs.ls(db_location) -- COMMAND ---------- --- MAGIC %md --- MAGIC We now create a table in the database with custom location and insert data. Note that the schema must be provided because there are no data from which to infer the schema. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We now create a table in the database with custom location and insert data. +-- MAGIC +-- MAGIC Note that the schema must be provided because there is no data from which to infer the schema. -- COMMAND ---------- -USE ${c.database}_custom_location; +USE ${da.db_name}_custom_location; + CREATE OR REPLACE TABLE managed_table_in_db_with_custom_location (width INT, length INT, height INT); INSERT INTO managed_table_in_db_with_custom_location VALUES (3, 2, 1); SELECT * FROM managed_table_in_db_with_custom_location; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Again, we'll look at the description to find the table location. -- COMMAND ---------- @@ -159,17 +206,27 @@ DESCRIBE EXTENDED managed_table_in_db_with_custom_location; -- COMMAND ---------- --- MAGIC %md --- MAGIC As expected, this managed table is created in the path specified with the `LOCATION` keyword during database creation. As such, the data and metadata for the table are persisted in a directory here. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC As expected, this managed table is created in the path specified with the **`LOCATION`** keyword during database creation. As such, the data and metadata for the table are persisted in a directory here. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{userhome}/managed_table_in_db_with_custom_location")) +-- MAGIC +-- MAGIC table_name = f"managed_table_in_db_with_custom_location" +-- MAGIC tbl_location = f"{DA.paths.working_dir}/_custom_location.db/{table_name}" +-- MAGIC print(tbl_location) +-- MAGIC +-- MAGIC files = dbutils.fs.ls(tbl_location) +-- MAGIC display(files) -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Let's drop the table. -- COMMAND ---------- @@ -178,40 +235,51 @@ DROP TABLE managed_table_in_db_with_custom_location; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Note the table's folder and the log file and data file are deleted. -- MAGIC --- MAGIC Only the "datasets" folder remains. +-- MAGIC Only the database location remains -- COMMAND ---------- -- MAGIC %python --- MAGIC dbutils.fs.ls(userhome) +-- MAGIC +-- MAGIC db_location = f"{DA.paths.working_dir}/_custom_location.db" +-- MAGIC print(db_location) +-- MAGIC +-- MAGIC dbutils.fs.ls(db_location) -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Tables --- MAGIC We will create an external (unmanaged) table from sample data. The data we are going to use are in csv format. We want to create a Delta table with a LOCATION provided in the directory of our choice. +-- MAGIC We will create an external (unmanaged) table from sample data. +-- MAGIC +-- MAGIC The data we are going to use are in CSV format. We want to create a Delta table with a **`LOCATION`** provided in the directory of our choice. -- COMMAND ---------- -USE ${c.database}_default_location; +USE ${da.db_name}_default_location; --- mode "FAILFAST" will abort file parsing with a RuntimeException if any malformed lines are encountered CREATE OR REPLACE TEMPORARY VIEW temp_delays USING CSV OPTIONS ( - path '${c.userhome}/datasets/flights/departuredelays.csv', - header "true", - mode "FAILFAST" + path = '${da.paths.working_dir}/flights/departuredelays.csv', + header = "true", + mode = "FAILFAST" -- abort file parsing with a RuntimeException if any malformed lines are encountered ); -CREATE OR REPLACE TABLE external_table LOCATION '${c.userhome}/external_table' AS +CREATE OR REPLACE TABLE external_table LOCATION '${da.paths.working_dir}/external_table' AS SELECT * FROM temp_delays; SELECT * FROM external_table; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Let's note the location of the table's data in this lesson's working directory. -- COMMAND ---------- @@ -220,7 +288,9 @@ DESCRIBE TABLE EXTENDED external_table; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Now, we drop the table. -- COMMAND ---------- @@ -229,34 +299,42 @@ DROP TABLE external_table; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC The table definition no longer exists in the metastore, but the underlying data remain intact. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{userhome}/external_table")) +-- MAGIC tbl_path = f"{DA.paths.working_dir}/external_table" +-- MAGIC files = dbutils.fs.ls(tbl_path) +-- MAGIC display(files) -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Clean up -- MAGIC Drop both databases. -- COMMAND ---------- -DROP DATABASE ${c.database}_default_location CASCADE; -DROP DATABASE ${c.database}_custom_location CASCADE; +DROP DATABASE ${da.db_name}_default_location CASCADE; +DROP DATABASE ${da.db_name}_custom_location CASCADE; -- COMMAND ---------- -- MAGIC %md --- MAGIC Delete the working directory and its contents. +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. -- COMMAND ---------- -- MAGIC %python --- MAGIC dbutils.fs.rm(userhome, True) +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.2A - Views and CTEs on Databricks.sql b/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.2A - Views and CTEs on Databricks.sql new file mode 100644 index 0000000..10fd81d --- /dev/null +++ b/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.2A - Views and CTEs on Databricks.sql @@ -0,0 +1,210 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Views and CTEs on Databricks +-- MAGIC In this demonstration, you will create and explore views and common table expressions (CTEs). +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC * Use Spark SQL DDL to define views +-- MAGIC * Run queries that use common table expressions +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC **Resources** +-- MAGIC * Create View - Databricks Docs +-- MAGIC * Common Table Expressions - Databricks Docs + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Classroom Setup +-- MAGIC The following script clears out previous runs of this demo and configures some Hive variables that will be used in our SQL queries. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-3.2A + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We start by creating a table of data we can use for the demonstration. + +-- COMMAND ---------- + +-- mode "FAILFAST" will abort file parsing with a RuntimeException if any malformed lines are encountered +CREATE TABLE external_table +USING CSV OPTIONS ( + path = '${da.paths.working_dir}/flight_delays', + header = "true", + mode = "FAILFAST" +); + +SELECT * FROM external_table; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC To show a list of tables (and views), we use the **`SHOW TABLES`** command also demonstrated below. + +-- COMMAND ---------- + +SHOW TABLES; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Views, Temp Views & Global Temp Views +-- MAGIC +-- MAGIC To set this demonstration up, we are going to first create one of each type of view. +-- MAGIC +-- MAGIC Then in the next notebook, we will explore the differences between how each one behaves. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ### Views +-- MAGIC Let's create a view that contains only the data where the origin is "ABQ" and the destination is "LAX". + +-- COMMAND ---------- + +CREATE VIEW view_delays_abq_lax AS + SELECT * + FROM external_table + WHERE origin = 'ABQ' AND destination = 'LAX'; + +SELECT * FROM view_delays_abq_lax; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Note that the **`view_delays_abq_lax`** view has been added to the list below: + +-- COMMAND ---------- + +SHOW TABLES; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ### Temporary Views +-- MAGIC +-- MAGIC Next we'll create a temporary view. +-- MAGIC +-- MAGIC The syntax is very similar but adds **`TEMPORARY`** to the command. + +-- COMMAND ---------- + +CREATE TEMPORARY VIEW temp_view_delays_gt_120 +AS SELECT * FROM external_table WHERE delay > 120 ORDER BY delay ASC; + +SELECT * FROM temp_view_delays_gt_120; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Now if we show our tables again, we will see the one table and both views. +-- MAGIC +-- MAGIC Make note of the values in the **`isTemporary`** column. + +-- COMMAND ---------- + +SHOW TABLES; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ### Global Temp Views +-- MAGIC +-- MAGIC Lastly, we'll create a global temp view. +-- MAGIC +-- MAGIC Here we simply add **`GLOBAL`** to the command. +-- MAGIC +-- MAGIC Also note the **`global_temp`** database qualifer in the subsequent **`SELECT`** statement. + +-- COMMAND ---------- + +CREATE GLOBAL TEMPORARY VIEW global_temp_view_dist_gt_1000 +AS SELECT * FROM external_table WHERE distance > 1000; + +SELECT * FROM global_temp.global_temp_view_dist_gt_1000; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Before we move on, review one last time the database's tables and views... + +-- COMMAND ---------- + +SHOW TABLES; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ...and the tables and views in the **`global_temp`** database: + +-- COMMAND ---------- + +SHOW TABLES IN global_temp; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Next we are going to demonstrate how tables and views are persisted across multiple sessions and how temp views are not. +-- MAGIC +-- MAGIC To do this simply open the next notebook, [DE 3.2B - Views and CTEs on Databricks, Cont]($./DE 3.2B - Views and CTEs on Databricks, Cont), and continue with the lesson. +-- MAGIC +-- MAGIC Note: There are several scenarios in which a new session may be created: +-- MAGIC * Restarting a cluster +-- MAGIC * Detaching and reataching to a cluster +-- MAGIC * Installing a python package which in turn restarts the Python interpreter +-- MAGIC * Or simply opening a new notebook + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.2B - Views and CTEs on Databricks, Cont.sql b/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.2B - Views and CTEs on Databricks, Cont.sql new file mode 100644 index 0000000..b00468c --- /dev/null +++ b/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.2B - Views and CTEs on Databricks, Cont.sql @@ -0,0 +1,262 @@ +-- Databricks notebook source +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Views and CTEs on Databricks, Cont' +-- MAGIC +-- MAGIC We are picking up from notebook [DE 3.2A - Views and CTEs on Databricks]($./DE 3.2A - Views and CTEs on Databricks) where we just reviewed the following two lists of tables
+-- MAGIC and views with the special note that our global temp view **`global_temp_view_dist_gt_1000`** was not included in the first list. + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC With the Notebook's state reset, we need to re-initialize some of our lesson-specific configuration. +-- MAGIC +-- MAGIC Note: We will **NOT** be recreating the database for the second 1/2 of this lesson. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-3.2B + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC But, we do need to configure this session to use our database by default. + +-- COMMAND ---------- + +USE ${da.db_name}; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Finally, run the following two cells to confirm that: +-- MAGIC 1. The table **`external_table`** still exists. +-- MAGIC 2. The view **`view_delays_abq_lax`** still exists. +-- MAGIC 3. The temp view **`temp_view_delays_gt_120`** does **NOT** exist. +-- MAGIC 3. The global temp view **`global_temp_view_dist_gt_1000`** does exist in the special **`global_temp`** database. +-- MAGIC +-- MAGIC Hint: If you were to go back to the previous notebook and run **`SHOW TABLES`**
+-- MAGIC again, all three tables and views from the current database will still be shown. + +-- COMMAND ---------- + +SHOW TABLES; + +-- COMMAND ---------- + +SHOW TABLES IN global_temp; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC As mentioned previously, temp views are tied to a Spark session and as such are not accessible... +-- MAGIC * After restarting a cluster +-- MAGIC * After detaching and reataching to a cluster +-- MAGIC * After installing a python package which in turn restarts the Python interpreter +-- MAGIC * Or from another notebook +-- MAGIC +-- MAGIC ...with the special exception of global temporary views. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Global temp views behave much like other temporary views but differ in one important way. +-- MAGIC +-- MAGIC They are added to the **`global_temp`** database that exists on the **`cluster`**. +-- MAGIC +-- MAGIC As long as the cluster is running, this database persists and any notebooks attached to the cluster can access its global temporary views. +-- MAGIC +-- MAGIC We can see this in action by running the follow cells: + +-- COMMAND ---------- + +SELECT * FROM global_temp.global_temp_view_dist_gt_1000; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Global temp views are "lost" when the cluster is restarted. +-- MAGIC +-- MAGIC Take our word for it, don't do it now, but if you were to restart the cluster, the above select statement would fail because the table would no longer exist. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Common Table Expressions (CTEs) +-- MAGIC CTEs can be used in a variety of contexts. Below, are a few examples of the different ways a CTE can be used in a query. First, an example of making multiple column aliases using a CTE. + +-- COMMAND ---------- + +WITH flight_delays( + total_delay_time, + origin_airport, + destination_airport +) AS ( + SELECT + delay, + origin, + destination + FROM + external_table +) +SELECT + * +FROM + flight_delays +WHERE + total_delay_time > 120 + AND origin_airport = "ATL" + AND destination_airport = "DEN"; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Next, is an example of a CTE in a CTE definition. + +-- COMMAND ---------- + +WITH lax_bos AS ( + WITH origin_destination (origin_airport, destination_airport) AS ( + SELECT + origin, + destination + FROM + external_table + ) + SELECT + * + FROM + origin_destination + WHERE + origin_airport = 'LAX' + AND destination_airport = 'BOS' +) +SELECT + count(origin_airport) AS `Total Flights from LAX to BOS` +FROM + lax_bos; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Now, here is an example of a CTE in a subquery. + +-- COMMAND ---------- + +SELECT + max(total_delay) AS `Longest Delay (in minutes)` +FROM + ( + WITH delayed_flights(total_delay) AS ( + SELECT + delay + FROM + external_table + ) + SELECT + * + FROM + delayed_flights + ); + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We can also use a CTE in a subquery expression. + +-- COMMAND ---------- + +SELECT + ( + WITH distinct_origins AS ( + SELECT DISTINCT origin FROM external_table + ) + SELECT + count(origin) AS `Number of Distinct Origins` + FROM + distinct_origins + ) AS `Number of Different Origin Airports`; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Finally, here is a CTE in a **`CREATE VIEW`** statement. + +-- COMMAND ---------- + +CREATE OR REPLACE VIEW BOS_LAX +AS WITH origin_destination(origin_airport, destination_airport) +AS (SELECT origin, destination FROM external_table) +SELECT * FROM origin_destination +WHERE origin_airport = 'BOS' AND destination_airport = 'LAX'; + +SELECT count(origin_airport) AS `Number of Delayed Flights from BOS to LAX` FROM BOS_LAX; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Clean up +-- MAGIC We first drop the training database. + +-- COMMAND ---------- + +DROP DATABASE ${da.db_name} CASCADE; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.3L - Databases, Tables & Views Lab.sql b/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.3L - Databases, Tables & Views Lab.sql new file mode 100644 index 0000000..cd1966e --- /dev/null +++ b/Data-Engineering-with-Databricks/03 - Relational Entities on Databricks/DE 3.3L - Databases, Tables & Views Lab.sql @@ -0,0 +1,515 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Databases, Tables, and Views Lab +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lab, you should be able to: +-- MAGIC - Create and explore interactions between various relational entities, including: +-- MAGIC - Databases +-- MAGIC - Tables (managed and external) +-- MAGIC - Views (views, temp views, and global temp views) +-- MAGIC +-- MAGIC **Resources** +-- MAGIC * Databases and Tables - Databricks Docs +-- MAGIC * Managed and Unmanaged Tables +-- MAGIC * Creating a Table with the UI +-- MAGIC * Create a Local Table +-- MAGIC * Saving to Persistent Tables + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ### Getting Started +-- MAGIC +-- MAGIC Run the following cell to configure variables and datasets for this lesson. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-3.3L + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Overview of the Data +-- MAGIC +-- MAGIC The data include multiple entries from a selection of weather stations, including average temperatures recorded in either Fahrenheit or Celsius. The schema for the table: +-- MAGIC +-- MAGIC |ColumnName | DataType| Description| +-- MAGIC |------------|---------|------------| +-- MAGIC |NAME |string | Station name | +-- MAGIC |STATION |string | Unique ID | +-- MAGIC |LATITUDE |float | Latitude | +-- MAGIC |LONGITUDE |float | Longitude | +-- MAGIC |ELEVATION |float | Elevation | +-- MAGIC |DATE |date | YYYY-MM-DD | +-- MAGIC |UNIT |string | Temperature units | +-- MAGIC |TAVG |float | Average temperature | +-- MAGIC +-- MAGIC This data is stored in the Parquet format; preview the data with the query below. + +-- COMMAND ---------- + +SELECT * +FROM parquet.`${da.paths.working_dir}/weather` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Create a Database +-- MAGIC +-- MAGIC Create a database in the default location using the **`da.db_name`** variable defined in setup script. + +-- COMMAND ---------- + +-- TODO + + ${da.db_name} + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.sql(f"SHOW DATABASES").filter(f"databaseName == '{DA.db_name}'").count() == 1, "Database not present" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Change to Your New Database +-- MAGIC +-- MAGIC **`USE`** your newly created database. + +-- COMMAND ---------- + +-- TODO + + ${da.db_name} + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.sql(f"SHOW CURRENT DATABASE").first()["namespace"] == DA.db_name, "Not using the correct database" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Create a Managed Table +-- MAGIC Use a CTAS statement to create a managed table named **`weather_managed`**. + +-- COMMAND ---------- + +-- TODO + + +SELECT * +FROM parquet.`${da.paths.working_dir}/weather` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("weather_managed"), "Table named `weather_managed` does not exist" +-- MAGIC assert spark.table("weather_managed").count() == 2559, "Incorrect row count" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Create an External Table +-- MAGIC +-- MAGIC Recall that an external table differs from a managed table through specification of a location. Create an external table called **`weather_external`** below. + +-- COMMAND ---------- + +-- TODO + + +LOCATION "${da.paths.working_dir}/lab/external" +AS SELECT * +FROM parquet.`${da.paths.working_dir}/weather` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("weather_external"), "Table named `weather_external` does not exist" +-- MAGIC assert spark.table("weather_external").count() == 2559, "Incorrect row count" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Examine Table Details +-- MAGIC Use the SQL command **`DESCRIBE EXTENDED table_name`** to examine the two weather tables. + +-- COMMAND ---------- + +DESCRIBE EXTENDED weather_managed + +-- COMMAND ---------- + +DESCRIBE EXTENDED weather_external + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following helper code to extract and compare the table locations. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC def getTableLocation(tableName): +-- MAGIC return spark.sql(f"DESCRIBE DETAIL {tableName}").select("location").first()[0] + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC managedTablePath = getTableLocation("weather_managed") +-- MAGIC externalTablePath = getTableLocation("weather_external") +-- MAGIC +-- MAGIC print(f"""The weather_managed table is saved at: +-- MAGIC +-- MAGIC {managedTablePath} +-- MAGIC +-- MAGIC The weather_external table is saved at: +-- MAGIC +-- MAGIC {externalTablePath}""") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC List the contents of these directories to confirm that data exists in both locations. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC files = dbutils.fs.ls(managedTablePath) +-- MAGIC display(files) + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC files = dbutils.fs.ls(externalTablePath) +-- MAGIC display(files) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ### Check Directory Contents after Dropping Database and All Tables +-- MAGIC The **`CASCADE`** keyword will accomplish this. + +-- COMMAND ---------- + +-- TODO + + ${da.db_name} + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.sql(f"SHOW DATABASES").filter(f"databaseName == '{DA.db_name}'").count() == 0, "Database present" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC With the database dropped, the files will have been deleted as well. +-- MAGIC +-- MAGIC Uncomment and run the following cell, which will throw a **`FileNotFoundException`** as your confirmation. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC # files = dbutils.fs.ls(managedTablePath) +-- MAGIC # display(files) + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC files = dbutils.fs.ls(externalTablePath) +-- MAGIC display(files) + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC files = dbutils.fs.ls(DA.paths.working_dir) +-- MAGIC display(files) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC **This highlights the main differences between managed and external tables.** By default, the files associated with managed tables will be stored to this location on the root DBFS storage linked to the workspace, and will be deleted when a table is dropped. +-- MAGIC +-- MAGIC Files for external tables will be persisted in the location provided at table creation, preventing users from inadvertently deleting underlying files. **External tables can easily be migrated to other databases or renamed, but these operations with managed tables will require rewriting ALL underlying files.** + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Create a Database with a Specified Path +-- MAGIC +-- MAGIC Assuming you dropped your database in the last step, you can use the same **`database`** name. + +-- COMMAND ---------- + +CREATE DATABASE ${da.db_name} LOCATION '${da.paths.working_dir}/${da.db_name}'; +USE ${da.db_name}; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Recreate your **`weather_managed`** table in this new database and print out the location of this table. + +-- COMMAND ---------- + +-- TODO + + + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC getTableLocation("weather_managed") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("weather_managed"), "Table named `weather_managed` does not exist" +-- MAGIC assert spark.table("weather_managed").count() == 2559, "Incorrect row count" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC While here we're using the **`working_dir`** directory created on the DBFS root, _any_ object store can be used as the database directory. **Defining database directories for groups of users can greatly reduce the chances of accidental data exfiltration**. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Views and their Scoping +-- MAGIC +-- MAGIC Using the provided **`AS`** clause, register: +-- MAGIC - a view named **`celsius`** +-- MAGIC - a temporary view named **`celsius_temp`** +-- MAGIC - a global temp view named **`celsius_global`** + +-- COMMAND ---------- + +-- TODO + + +AS (SELECT * + FROM weather_managed + WHERE UNIT = "C") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("celsius"), "Table named `celsius` does not exist" +-- MAGIC assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'celsius'").first()["isTemporary"] == False, "Table is temporary" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Now create a temporary view. + +-- COMMAND ---------- + +-- TODO + + +AS (SELECT * + FROM weather_managed + WHERE UNIT = "C") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("celsius_temp"), "Table named `celsius_temp` does not exist" +-- MAGIC assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'celsius_temp'").first()["isTemporary"] == True, "Table is not temporary" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Now register a global temp view. + +-- COMMAND ---------- + +-- TODO + + +AS (SELECT * + FROM weather_managed + WHERE UNIT = "C") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("global_temp.celsius_global"), "Global temporary view named `celsius_global` does not exist" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Views will be displayed alongside tables when listing from the catalog. + +-- COMMAND ---------- + +SHOW TABLES + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note the following: +-- MAGIC - The view is associated with the current database. This view will be available to any user that can access this database and will persist between sessions. +-- MAGIC - The temp view is not associated with any database. The temp view is ephemeral and is only accessible in the current SparkSession. +-- MAGIC - The global temp view does not appear in our catalog. **Global temp views will always register to the **`global_temp`** database**. The **`global_temp`** database is ephemeral but tied to the lifetime of the cluster; however, it is only accessible by notebooks attached to the same cluster on which it was created. + +-- COMMAND ---------- + +SELECT * FROM global_temp.celsius_global + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC While no job was triggered when defining these views, a job is triggered _each time_ a query is executed against the view. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Clean Up +-- MAGIC Drop the database and all tables to clean up your workspace. + +-- COMMAND ---------- + +DROP DATABASE ${da.db_name} CASCADE + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Synopsis +-- MAGIC +-- MAGIC In this lab we: +-- MAGIC - Created and deleted databases +-- MAGIC - Explored behavior of managed and external tables +-- MAGIC - Learned about the scoping of views + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.1 - Querying Files Directly.sql b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.1 - Querying Files Directly.sql similarity index 57% rename from Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.1 - Querying Files Directly.sql rename to Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.1 - Querying Files Directly.sql index 16a2882..e5710a6 100644 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.1 - Querying Files Directly.sql +++ b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.1 - Querying Files Directly.sql @@ -8,40 +8,48 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Extracting Data Directly from Files -- MAGIC -- MAGIC In this notebook, you'll learn to extract data directly from files using Spark SQL on Databricks. -- MAGIC -- MAGIC A number of file formats support this option, but it is most useful for self-describing data formats (such as parquet and JSON). -- MAGIC --- MAGIC ##### Objectives +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: -- MAGIC - Use Spark SQL to directly query data files --- MAGIC - Layer views and CTEs to make referencing data files easier --- MAGIC - Discuss limitations and applications of this approach +-- MAGIC - Leverage **`text`** and **`binaryFile`** methods to review raw file contents -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC -- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. -- COMMAND ---------- --- MAGIC %run ../Includes/setup +-- MAGIC %run ../Includes/Classroom-Setup-4.1 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Data Overview -- MAGIC --- MAGIC In this example, we'll work with a sample of raw Kafka data written as JSON files. Each file contains all records consumed during a 5-second interval, stored with the full Kafka schema as a multiple-record JSON file. +-- MAGIC In this example, we'll work with a sample of raw Kafka data written as JSON files. +-- MAGIC +-- MAGIC Each file contains all records consumed during a 5-second interval, stored with the full Kafka schema as a multiple-record JSON file. -- MAGIC -- MAGIC | field | type | description | -- MAGIC | --- | --- | --- | --- MAGIC | key | BINARY | The `user_id` field is used as the key; this is a unique alphanumeric field that corresponds to session/cookie information | +-- MAGIC | key | BINARY | The **`user_id`** field is used as the key; this is a unique alphanumeric field that corresponds to session/cookie information | -- MAGIC | value | BINARY | This is the full data payload (to be discussed later), sent as JSON | --- MAGIC | topic | STRING | While the Kafka service hosts multiple topics, only those records from the `clickstream` topic are included here | +-- MAGIC | topic | STRING | While the Kafka service hosts multiple topics, only those records from the **`clickstream`** topic are included here | -- MAGIC | partition | INTEGER | Our current Kafka implementation uses only 2 partitions (0 and 1) | -- MAGIC | offset | LONG | This is a unique value, monotonically increasing for each partition | -- MAGIC | timestamp | LONG | This timestamp is recorded as milliseconds since epoch, and represents the time at which the producer appends a record to a partition | @@ -49,59 +57,81 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Note that our source directory contains many JSON files. -- COMMAND ---------- -- MAGIC %python --- MAGIC len(dbutils.fs.ls(f"{Paths.source}/events/events-kafka.json")) +-- MAGIC dataset_path = f"{DA.paths.datasets}/raw/events-kafka" +-- MAGIC print(dataset_path) +-- MAGIC +-- MAGIC files = dbutils.fs.ls(dataset_path) +-- MAGIC display(files) -- COMMAND ---------- -- MAGIC %md --- MAGIC Here, we'll be using relative file paths to data that's been written to the DBFS root. Most workflows will require users to access data from external cloud storage locations. In most companies, a workspace administrative will be responsible for configuring access to these storage locations. -- MAGIC --- MAGIC Instructions for configuring and accessing these locations can be found in the cloud-vendor specific courses titled "Cloud Architecture & Systems Integrations". +-- MAGIC +-- MAGIC Here, we'll be using relative file paths to data that's been written to the DBFS root. +-- MAGIC +-- MAGIC Most workflows will require users to access data from external cloud storage locations. +-- MAGIC +-- MAGIC In most companies, a workspace administrator will be responsible for configuring access to these storage locations. +-- MAGIC +-- MAGIC Instructions for configuring and accessing these locations can be found in the cloud-vendor specific self-paced courses titled "Cloud Architecture & Systems Integrations". -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Query a Single File -- MAGIC -- MAGIC To query the data contained in a single file, execute the query with the following pattern: -- MAGIC --- MAGIC ``` --- MAGIC SELECT * FROM file_format.`/path/to/file` --- MAGIC ``` +-- MAGIC SELECT * FROM file_format.`/path/to/file` +-- MAGIC +-- MAGIC Make special note of the use of back-ticks (not single quotes) around the path. -- COMMAND ---------- -SELECT * FROM json.`${c.source}/events/events-kafka.json/001.json` +SELECT * FROM json.`${da.paths.datasets}/raw/events-kafka/001.json` -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that our preview displays all 94 rows of our source file. +-- MAGIC +-- MAGIC +-- MAGIC Note that our preview displays all 321 rows of our source file. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Query a Directory of Files -- MAGIC --- MAGIC Assuming all of the files in a directory have the same format and schema, all files can be queried simultaneously by specifying the directory path rather than an individual file. +-- MAGIC Assuming all of the files in a directory have the same format and schema, all files can be queried simultaneously by specifying the directory path rather than an individual file. -- COMMAND ---------- -SELECT * FROM json.`${c.source}/events/events-kafka.json` +SELECT * FROM json.`${da.paths.datasets}/raw/events-kafka` -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC By default, this query will only show the first 1000 rows. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Create References to Files -- MAGIC This ability to directly query files and directories means that additional Spark logic can be chained to queries against files. -- MAGIC @@ -110,33 +140,49 @@ SELECT * FROM json.`${c.source}/events/events-kafka.json` -- COMMAND ---------- CREATE OR REPLACE TEMP VIEW events_temp_view -AS SELECT * FROM json.`${c.source}/events/events-kafka.json`; +AS SELECT * FROM json.`${da.paths.datasets}/raw/events-kafka/`; SELECT * FROM events_temp_view -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Extract Text Files as Raw Strings -- MAGIC --- MAGIC When working with text-based files (which include JSON, CSV, TSV, and TXT formats), you can use the `text` format to load each line of the file as a row with one string column named `value`. This can be useful when data sources are prone to corruption and custom text parsing functions will be used to extract value from text fields. +-- MAGIC When working with text-based files (which include JSON, CSV, TSV, and TXT formats), you can use the **`text`** format to load each line of the file as a row with one string column named **`value`**. This can be useful when data sources are prone to corruption and custom text parsing functions will be used to extract value from text fields. -- COMMAND ---------- -SELECT * FROM text.`${c.source}/events/events-kafka.json` +SELECT * FROM text.`${da.paths.datasets}/raw/events-kafka/` -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Extract the Raw Bytes and Metadata of a File -- MAGIC --- MAGIC Some workflows may require working with entire files, such as when dealing with images or unstructured data. Using `binaryFile` to query a directory will provide file metadata alongside the binary representation of the file contents. +-- MAGIC Some workflows may require working with entire files, such as when dealing with images or unstructured data. Using **`binaryFile`** to query a directory will provide file metadata alongside the binary representation of the file contents. +-- MAGIC +-- MAGIC Specifically, the fields created will indicate the **`path`**, **`modificationTime`**, **`length`**, and **`content`**. + +-- COMMAND ---------- + +SELECT * FROM binaryFile.`${da.paths.datasets}/raw/events-kafka/` + +-- COMMAND ---------- + +-- MAGIC %md -- MAGIC --- MAGIC Specifically, the fields created will indicate the `path`, `modificationTime`, `length`, and `content`. +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. -- COMMAND ---------- -SELECT * FROM binaryFile.`${c.source}/events/events-kafka.json` +-- MAGIC %python +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.2 - Providing Options for External Sources.sql b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.2 - Providing Options for External Sources.sql similarity index 68% rename from Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.2 - Providing Options for External Sources.sql rename to Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.2 - Providing Options for External Sources.sql index f6d44eb..cddc30a 100644 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.2 - Providing Options for External Sources.sql +++ b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.2 - Providing Options for External Sources.sql @@ -8,12 +8,15 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Providing Options for External Sources -- MAGIC While directly querying files works well for self-describing formats, many data sources require additional configurations or schema declaration to properly ingest records. -- MAGIC -- MAGIC In this lesson, we will create tables using external data sources. While these tables will not yet be stored in the Delta Lake format (and therefore not be optimized for the Lakehouse), this technique helps to facilitate extracting data from diverse external systems. -- MAGIC --- MAGIC ##### Objectives +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: -- MAGIC - Use Spark SQL to configure options for extracting data from external sources -- MAGIC - Create tables against external data sources for various file formats -- MAGIC - Describe default behavior when querying tables defined against external sources @@ -21,17 +24,21 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC -- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. -- COMMAND ---------- --- MAGIC %run ../Includes/setup-external +-- MAGIC %run ../Includes/Classroom-Setup-4.2 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## When Direct Queries Don't Work -- MAGIC -- MAGIC While views can be used to persist direct queries against files between sessions, this approach has limited utility. @@ -40,41 +47,46 @@ -- COMMAND ---------- -SELECT * FROM csv.`${c.source}/sales/sales.csv` +SELECT * FROM csv.`${da.paths.working_dir}/sales-csv` -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC We can see from the above that: -- MAGIC 1. The header row is being extracted as a table row -- MAGIC 1. All columns are being loaded as a single column --- MAGIC 1. The file is pipe-delimited (`|`) +-- MAGIC 1. The file is pipe-delimited (**`|`**) -- MAGIC 1. The final column appears to contain nested data that is being truncated -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Registering Tables on External Data with Read Options -- MAGIC -- MAGIC While Spark will extract some self-describing data sources efficiently using default settings, many formats will require declaration of schema or other options. -- MAGIC --- MAGIC While there are many [additional configurations](https://docs.databricks.com/spark/latest/spark-sql/language-manual/sql-ref-syntax-ddl-create-table-datasource.html) you can set while creating tables against external sources, the syntax below demonstrates the essentials required to extract data from most formats. +-- MAGIC While there are many additional configurations you can set while creating tables against external sources, the syntax below demonstrates the essentials required to extract data from most formats. -- MAGIC --- MAGIC ``` --- MAGIC CREATE TABLE table_identifier --- MAGIC (col_name1 col_type1, ...) --- MAGIC USING data_source --- MAGIC OPTIONS (key1 = val1, key2 = val2, ...) --- MAGIC LOCATION path --- MAGIC ``` +-- MAGIC +-- MAGIC CREATE TABLE table_identifier (col_name1 col_type1, ...)
+-- MAGIC USING data_source
+-- MAGIC OPTIONS (key1 = val1, key2 = val2, ...)
+-- MAGIC LOCATION = path
+-- MAGIC -- MAGIC --- MAGIC Note that options are passed with keys as unquoted text and values in quotes. Spark supports many [data sources](https://docs.databricks.com/data/data-sources/index.html) with custom options, and additional systems may have unofficial support through external [libraries](https://docs.databricks.com/libraries/index.html). +-- MAGIC Note that options are passed with keys as unquoted text and values in quotes. Spark supports many data sources with custom options, and additional systems may have unofficial support through external libraries. -- MAGIC --- MAGIC **NOTE**: Depending on your workspace settings, you may need adminstrator assistance to load libraries and configure the requisite security settings for some data sources. +-- MAGIC **NOTE**: Depending on your workspace settings, you may need administrator assistance to load libraries and configure the requisite security settings for some data sources. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC The cell below demonstrates using Spark SQL DDL to create a table against an external CSV source, specifying: -- MAGIC 1. The column names and types -- MAGIC 1. The file format @@ -84,8 +96,6 @@ SELECT * FROM csv.`${c.source}/sales/sales.csv` -- COMMAND ---------- -DROP TABLE IF EXISTS sales_csv; - CREATE TABLE sales_csv (order_id LONG, email STRING, transactions_timestamp LONG, total_item_quantity INTEGER, purchase_revenue_in_usd DOUBLE, unique_items INTEGER, items STRING) USING CSV @@ -93,11 +103,13 @@ OPTIONS ( header = "true", delimiter = "|" ) -LOCATION "${c.source}/sales/sales.csv" +LOCATION "${da.paths.working_dir}/sales-csv" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Note that no data has moved during table declaration. Similar to when we directly queried our files and created a view, we are still just pointing to files stored in an external location. -- MAGIC -- MAGIC Run the following cell to confirm that data is now being loaded correctly. @@ -113,11 +125,13 @@ SELECT COUNT(*) FROM sales_csv -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC All the metadata and options passed during table declaration will be persisted to the metastore, ensuring that data in the location will always be read with these options. -- MAGIC -- MAGIC **NOTE**: When working with CSVs as a data source, it's important to ensure that column order does not change if additional data files will be added to the source directory. Because the data format does not have strong schema enforcement, Spark will load columns and apply column names and data types in the order specified during table declaration. -- MAGIC --- MAGIC Running `DESCRIBE EXTENDED` on a table will show all of the metadata associated with the table definition. +-- MAGIC Running **`DESCRIBE EXTENDED`** on a table will show all of the metadata associated with the table definition. -- COMMAND ---------- @@ -126,6 +140,8 @@ DESCRIBE EXTENDED sales_csv -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Limits of Tables with External Data Sources -- MAGIC -- MAGIC If you've taken other courses on Databricks or reviewed any of our company literature, you may have heard about Delta Lake and the Lakehouse. Note that whenever we're defining tables or queries against external data sources, we **cannot** expect the performance guarantees associated with Delta Lake and Lakehouse. @@ -137,11 +153,16 @@ DESCRIBE EXTENDED sales_csv -- COMMAND ---------- -- MAGIC %python --- MAGIC spark.table("sales_csv").write.mode("append").format("csv").save(f"{Paths.source}/sales/sales.csv") +-- MAGIC (spark.table("sales_csv") +-- MAGIC .write.mode("append") +-- MAGIC .format("csv") +-- MAGIC .save(f"{DA.paths.working_dir}/sales-csv")) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC If we look at the current count of records in our table, the number we see will not reflect these newly inserted rows. -- COMMAND ---------- @@ -151,9 +172,13 @@ SELECT COUNT(*) FROM sales_csv -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC At the time we previously queried this data source, Spark automatically cached the underlying data in local storage. This ensures that on subsequent queries, Spark will provide the optimal performance by just querying this local cache. -- MAGIC --- MAGIC Our external data source is not configured to tell Spark is should refresh this data. We **can** manually refresh the cache of our data by running the `REFRESH TABLE` command. +-- MAGIC Our external data source is not configured to tell Spark that it should refresh this data. +-- MAGIC +-- MAGIC We **can** manually refresh the cache of our data by running the **`REFRESH TABLE`** command. -- COMMAND ---------- @@ -162,7 +187,11 @@ REFRESH TABLE sales_csv -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that refreshing our table will invalidate our cache, meaning that we'll need to rescan our original data source and pull all data back into memory. For very large datasets, this may take a significant amount of time. +-- MAGIC +-- MAGIC +-- MAGIC Note that refreshing our table will invalidate our cache, meaning that we'll need to rescan our original data source and pull all data back into memory. +-- MAGIC +-- MAGIC For very large datasets, this may take a significant amount of time. -- COMMAND ---------- @@ -171,37 +200,46 @@ SELECT COUNT(*) FROM sales_csv -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Extracting Data from SQL Databases -- MAGIC SQL databases are an extremely common data source, and Databricks has a standard JDBC driver for connecting with many flavors of SQL. -- MAGIC -- MAGIC The general syntax for creating these connections is: --- MAGIC ``` --- MAGIC CREATE TABLE --- MAGIC USING org.apache.spark.sql.jdbc --- MAGIC OPTIONS ( --- MAGIC url "jdbc:://:", --- MAGIC dbtable ".table", --- MAGIC user "", --- MAGIC password "" +-- MAGIC +-- MAGIC +-- MAGIC CREATE TABLE
+-- MAGIC USING JDBC
+-- MAGIC OPTIONS (
+-- MAGIC     url = "jdbc:{databaseServerType}://{jdbcHostname}:{jdbcPort}",
+-- MAGIC     dbtable = "{jdbcDatabase}.table",
+-- MAGIC     user = "{jdbcUsername}",
+-- MAGIC     password = "{jdbcPassword}"
-- MAGIC ) --- MAGIC ``` +-- MAGIC -- MAGIC --- MAGIC In the code sample below, we'll connect with SQLite (which uses a local file to store a database, and doesn't have users and passwords). +-- MAGIC In the code sample below, we'll connect with SQLite. +-- MAGIC +-- MAGIC **NOTE:** SQLite uses a local file to store a database, and doesn't require a port, username, or password. +-- MAGIC +-- MAGIC **WARNING**: The backend-configuration of the JDBC server assume you are running this notebook on a single-node cluster. If you are running on a cluster with multiple workers, the client running in the executors will not be able to connect to the driver. -- COMMAND ---------- DROP TABLE IF EXISTS users_jdbc; CREATE TABLE users_jdbc -USING org.apache.spark.sql.jdbc +USING JDBC OPTIONS ( - url "jdbc:sqlite:/${c.username}_ecommerce.db", - dbtable "users" + url = "jdbc:sqlite:/${da.username}_ecommerce.db", + dbtable = "users" ) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Now we can query this table as if it were defined locally. -- COMMAND ---------- @@ -211,6 +249,8 @@ SELECT * FROM users_jdbc -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Looking at the table metadata reveals that we have captured the schema information from the external system. Storage properties (which would include the username and password associated with the connection) are automatically redacted. -- COMMAND ---------- @@ -220,12 +260,14 @@ DESCRIBE EXTENDED users_jdbc -- COMMAND ---------- -- MAGIC %md --- MAGIC While the table is listed as `MANAGED`, listing the contents of the specified location confirms that no data is being persisted locally. +-- MAGIC +-- MAGIC +-- MAGIC While the table is listed as **`MANAGED`**, listing the contents of the specified location confirms that no data is being persisted locally. -- COMMAND ---------- -- MAGIC %python --- MAGIC jdbc_users_path = f"{Paths.database_location}/jdbc_users" +-- MAGIC jdbc_users_path = f"{DA.paths.user_db}/users_jdbc/" -- MAGIC print(jdbc_users_path) -- MAGIC -- MAGIC files = dbutils.fs.ls(jdbc_users_path) @@ -234,6 +276,8 @@ DESCRIBE EXTENDED users_jdbc -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Note that some SQL systems such as data warehouses will have custom drivers. Spark will interact with various external databases differently, but the two basic approaches can be summarized as either: -- MAGIC 1. Moving the entire source table(s) to Databricks and then executing logic on the currently active cluster -- MAGIC 1. Pushing down the query to the external SQL database and only transferring the results back to Databricks @@ -244,6 +288,18 @@ DESCRIBE EXTENDED users_jdbc -- COMMAND ---------- +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + -- MAGIC %md-sandbox -- MAGIC © 2022 Databricks, Inc. All rights reserved.
-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
diff --git a/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.3 - Creating Delta Tables.sql b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.3 - Creating Delta Tables.sql new file mode 100644 index 0000000..ff80a07 --- /dev/null +++ b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.3 - Creating Delta Tables.sql @@ -0,0 +1,380 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Creating Delta Tables +-- MAGIC +-- MAGIC After extracting data from external data sources, load data into the Lakehouse to ensure that all of the benefits of the Databricks platform can be fully leveraged. +-- MAGIC +-- MAGIC While different organizations may have varying policies for how data is initially loaded into Databricks, we typically recommend that early tables represent a mostly raw version of the data, and that validation and enrichment occur in later stages. This pattern ensures that even if data doesn't match expectations with regards to data types or column names, no data will be dropped, meaning that programmatic or manual intervention can still salvage data in a partially corrupted or invalid state. +-- MAGIC +-- MAGIC This lesson will focus primarily on the pattern used to create most tables, **`CREATE TABLE _ AS SELECT`** (CTAS) statements. +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC - Use CTAS statements to create Delta Lake tables +-- MAGIC - Create new tables from existing views or tables +-- MAGIC - Enrich loaded data with additional metadata +-- MAGIC - Declare table schema with generated columns and descriptive comments +-- MAGIC - Set advanced options to control data location, quality enforcement, and partitioning + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Run Setup +-- MAGIC +-- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-4.3 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Create Table as Select (CTAS) +-- MAGIC +-- MAGIC **`CREATE TABLE AS SELECT`** statements create and populate Delta tables using data retrieved from an input query. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE sales AS +SELECT * FROM parquet.`${da.paths.datasets}/raw/sales-historical/`; + +DESCRIBE EXTENDED sales; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC CTAS statements automatically infer schema information from query results and do **not** support manual schema declaration. +-- MAGIC +-- MAGIC This means that CTAS statements are useful for external data ingestion from sources with well-defined schema, such as Parquet files and tables. +-- MAGIC +-- MAGIC CTAS statements also do not support specifying additional file options. +-- MAGIC +-- MAGIC We can see how this would present significant limitations when trying to ingest data from CSV files. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE sales_unparsed AS +SELECT * FROM csv.`${da.paths.datasets}/raw/sales-csv/`; + +SELECT * FROM sales_unparsed; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC To correctly ingest this data to a Delta Lake table, we'll need to use a reference to the files that allows us to specify options. +-- MAGIC +-- MAGIC In the previous lesson, we showed doing this by registering an external table. Here, we'll slightly evolve this syntax to specify the options to a temporary view, and then use this temp view as the source for a CTAS statement to successfully register the Delta table. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW sales_tmp_vw + (order_id LONG, email STRING, transactions_timestamp LONG, total_item_quantity INTEGER, purchase_revenue_in_usd DOUBLE, unique_items INTEGER, items STRING) +USING CSV +OPTIONS ( + path = "${da.paths.datasets}/raw/sales-csv", + header = "true", + delimiter = "|" +); + +CREATE TABLE sales_delta AS + SELECT * FROM sales_tmp_vw; + +SELECT * FROM sales_delta + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Filtering and Renaming Columns from Existing Tables +-- MAGIC +-- MAGIC Simple transformations like changing column names or omitting columns from target tables can be easily accomplished during table creation. +-- MAGIC +-- MAGIC The following statement creates a new table containing a subset of columns from the **`sales`** table. +-- MAGIC +-- MAGIC Here, we'll presume that we're intentionally leaving out information that potentially identifies the user or that provides itemized purchase details. We'll also rename our fields with the assumption that a downstream system has different naming conventions than our source data. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE purchases AS +SELECT order_id AS id, transaction_timestamp, purchase_revenue_in_usd AS price +FROM sales; + +SELECT * FROM purchases + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that we could have accomplished this same goal with a view, as shown below. + +-- COMMAND ---------- + +CREATE OR REPLACE VIEW purchases_vw AS +SELECT order_id AS id, transaction_timestamp, purchase_revenue_in_usd AS price +FROM sales; + +SELECT * FROM purchases_vw + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Declare Schema with Generated Columns +-- MAGIC +-- MAGIC As noted previously, CTAS statements do not support schema declaration. We note above that the timestamp column appears to be some variant of a Unix timestamp, which may not be the most useful for our analysts to derive insights. This is a situation where generated columns would be beneficial. +-- MAGIC +-- MAGIC Generated columns are a special type of column whose values are automatically generated based on a user-specified function over other columns in the Delta table (introduced in DBR 8.3). +-- MAGIC +-- MAGIC The code below demonstrates creating a new table while: +-- MAGIC 1. Specifying column names and types +-- MAGIC 1. Adding a generated column to calculate the date +-- MAGIC 1. Providing a descriptive column comment for the generated column + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE purchase_dates ( + id STRING, + transaction_timestamp STRING, + price STRING, + date DATE GENERATED ALWAYS AS ( + cast(cast(transaction_timestamp/1e6 AS TIMESTAMP) AS DATE)) + COMMENT "generated based on `transactions_timestamp` column") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Because **`date`** is a generated column, if we write to **`purchase_dates`** without providing values for the **`date`** column, Delta Lake automatically computes them. +-- MAGIC +-- MAGIC **NOTE**: The cell below configures a setting to allow for generating columns when using a Delta Lake **`MERGE`** statement. We'll see more on this syntax later in the course. + +-- COMMAND ---------- + +SET spark.databricks.delta.schema.autoMerge.enabled=true; + +MERGE INTO purchase_dates a +USING purchases b +ON a.id = b.id +WHEN NOT MATCHED THEN + INSERT * + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We can see below that all dates were computed correctly as data was inserted, although neither our source data or insert query specified the values in this field. +-- MAGIC +-- MAGIC As with any Delta Lake source, the query automatically reads the most recent snapshot of the table for any query; you never need to run **`REFRESH TABLE`**. + +-- COMMAND ---------- + +SELECT * FROM purchase_dates + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC It's important to note that if a field that would otherwise be generated is included in an insert to a table, this insert will fail if the value provided does not exactly match the value that would be derived by the logic used to define the generated column. +-- MAGIC +-- MAGIC We can see this error by uncommenting and running the cell below: + +-- COMMAND ---------- + +-- INSERT INTO purchase_dates VALUES +-- (1, 600000000, 42.0, "2020-06-18") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Add a Table Constraint +-- MAGIC +-- MAGIC The error message above refers to a **`CHECK constraint`**. Generated columns are a special implementation of check constraints. +-- MAGIC +-- MAGIC Because Delta Lake enforces schema on write, Databricks can support standard SQL constraint management clauses to ensure the quality and integrity of data added to a table. +-- MAGIC +-- MAGIC Databricks currently support two types of constraints: +-- MAGIC * **`NOT NULL`** constraints +-- MAGIC * **`CHECK`** constraints +-- MAGIC +-- MAGIC In both cases, you must ensure that no data violating the constraint is already in the table prior to defining the constraint. Once a constraint has been added to a table, data violating the constraint will result in write failure. +-- MAGIC +-- MAGIC Below, we'll add a **`CHECK`** constraint to the **`date`** column of our table. Note that **`CHECK`** constraints look like standard **`WHERE`** clauses you might use to filter a dataset. + +-- COMMAND ---------- + +ALTER TABLE purchase_dates ADD CONSTRAINT valid_date CHECK (date > '2020-01-01'); + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Table constraints are shown in the **`TBLPROPERTIES`** field. + +-- COMMAND ---------- + +DESCRIBE EXTENDED purchase_dates + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Enrich Tables with Additional Options and Metadata +-- MAGIC +-- MAGIC So far we've only scratched the surface as far as the options for enriching Delta Lake tables. +-- MAGIC +-- MAGIC Below, we show evolving a CTAS statement to include a number of additional configurations and metadata. +-- MAGIC +-- MAGIC Our **`SELECT`** clause leverages two built-in Spark SQL commands useful for file ingestion: +-- MAGIC * **`current_timestamp()`** records the timestamp when the logic is executed +-- MAGIC * **`input_file_name()`** records the source data file for each record in the table +-- MAGIC +-- MAGIC We also include logic to create a new date column derived from timestamp data in the source. +-- MAGIC +-- MAGIC The **`CREATE TABLE`** clause contains several options: +-- MAGIC * A **`COMMENT`** is added to allow for easier discovery of table contents +-- MAGIC * A **`LOCATION`** is specified, which will result in an external (rather than managed) table +-- MAGIC * The table is **`PARTITIONED BY`** a date column; this means that the data from each data will exist within its own directory in the target storage location +-- MAGIC +-- MAGIC **NOTE**: Partitioning is shown here primarily to demonstrate syntax and impact. Most Delta Lake tables (especially small-to-medium sized data) will not benefit from partitioning. Because partitioning physically separates data files, this approach can result in a small files problem and prevent file compaction and efficient data skipping. The benefits observed in Hive or HDFS do not translate to Delta Lake, and you should consult with an experienced Delta Lake architect before partitioning tables. +-- MAGIC +-- MAGIC **As a best practice, you should default to non-partitioned tables for most use cases when working with Delta Lake.** + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE users_pii +COMMENT "Contains PII" +LOCATION "${da.paths.working_dir}/tmp/users_pii" +PARTITIONED BY (first_touch_date) +AS + SELECT *, + cast(cast(user_first_touch_timestamp/1e6 AS TIMESTAMP) AS DATE) first_touch_date, + current_timestamp() updated, + input_file_name() source_file + FROM parquet.`${da.paths.datasets}/raw/users-historical/`; + +SELECT * FROM users_pii; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC The metadata fields added to the table provide useful information to understand when records were inserted and from where. This can be especially helpful if troubleshooting problems in the source data becomes necessary. +-- MAGIC +-- MAGIC All of the comments and properties for a given table can be reviewed using **`DESCRIBE TABLE EXTENDED`**. +-- MAGIC +-- MAGIC **NOTE**: Delta Lake automatically adds several table properties on table creation. + +-- COMMAND ---------- + +DESCRIBE EXTENDED users_pii + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Listing the location used for the table reveals that the unique values in the partition column **`first_touch_date`** are used to create data directories. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC files = dbutils.fs.ls(f"{DA.paths.working_dir}/tmp/users_pii") +-- MAGIC display(files) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Cloning Delta Lake Tables +-- MAGIC Delta Lake has two options for efficiently copying Delta Lake tables. +-- MAGIC +-- MAGIC **`DEEP CLONE`** fully copies data and metadata from a source table to a target. This copy occurs incrementally, so executing this command again can sync changes from the source to the target location. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE purchases_clone +DEEP CLONE purchases + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Because all the data files must be copied over, this can take quite a while for large datasets. +-- MAGIC +-- MAGIC If you wish to create a copy of a table quickly to test out applying changes without the risk of modifying the current table, **`SHALLOW CLONE`** can be a good option. Shallow clones just copy the Delta transaction logs, meaning that the data doesn't move. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE purchases_shallow_clone +SHALLOW CLONE purchases + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC In either case, data modifications applied to the cloned version of the table will be tracked and stored separately from the source. Cloning is a great way to set up tables for testing SQL code while still in development. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Summary +-- MAGIC +-- MAGIC In this notebook, we focused primarily on DDL and syntax for creating Delta Lake tables. In the next notebook, we'll explore options for writing updates to tables. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.4 - Writing to Tables.sql b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.4 - Writing to Tables.sql new file mode 100644 index 0000000..80a61dc --- /dev/null +++ b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.4 - Writing to Tables.sql @@ -0,0 +1,263 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Writing to Delta Tables +-- MAGIC Delta Lake tables provide ACID compliant updates to tables backed by data files in cloud object storage. +-- MAGIC +-- MAGIC In this notebook, we'll explore SQL syntax to process updates with Delta Lake. While many operations are standard SQL, slight variations exist to accommodate Spark and Delta Lake execution. +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC - Overwrite data tables using **`INSERT OVERWRITE`** +-- MAGIC - Append to a table using **`INSERT INTO`** +-- MAGIC - Append, update, and delete from a table using **`MERGE INTO`** +-- MAGIC - Ingest data incrementally into tables using **`COPY INTO`** + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Run Setup +-- MAGIC +-- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-4.4 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Complete Overwrites +-- MAGIC +-- MAGIC We can use overwrites to atomically replace all of the data in a table. There are multiple benefits to overwriting tables instead of deleting and recreating tables: +-- MAGIC - Overwriting a table is much faster because it doesn’t need to list the directory recursively or delete any files. +-- MAGIC - The old version of the table still exists; can easily retrieve the old data using Time Travel. +-- MAGIC - It’s an atomic operation. Concurrent queries can still read the table while you are deleting the table. +-- MAGIC - Due to ACID transaction guarantees, if overwriting the table fails, the table will be in its previous state. +-- MAGIC +-- MAGIC Spark SQL provides two easy methods to accomplish complete overwrites. +-- MAGIC +-- MAGIC Some students may have noticed previous lesson on CTAS statements actually used CRAS statements (to avoid potential errors if a cell was run multiple times). +-- MAGIC +-- MAGIC **`CREATE OR REPLACE TABLE`** (CRAS) statements fully replace the contents of a table each time they execute. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE events AS +SELECT * FROM parquet.`${da.paths.datasets}/raw/events-historical` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Reviewing the table history shows a previous version of this table was replaced. + +-- COMMAND ---------- + +DESCRIBE HISTORY events + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC **`INSERT OVERWRITE`** provides a nearly identical outcome as above: data in the target table will be replaced by data from the query. +-- MAGIC +-- MAGIC **`INSERT OVERWRITE`**: +-- MAGIC +-- MAGIC - Can only overwrite an existing table, not create a new one like our CRAS statement +-- MAGIC - Can overwrite only with new records that match the current table schema -- and thus can be a "safer" technique for overwriting an existing table without disrupting downstream consumers +-- MAGIC - Can overwrite individual partitions + +-- COMMAND ---------- + +INSERT OVERWRITE sales +SELECT * FROM parquet.`${da.paths.datasets}/raw/sales-historical/` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that different metrics are displayed than a CRAS statement; the table history also records the operation differently. + +-- COMMAND ---------- + +DESCRIBE HISTORY sales + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC A primary difference here has to do with how Delta Lake enforces schema on write. +-- MAGIC +-- MAGIC Whereas a CRAS statement will allow us to completely redefine the contents of our target table, **`INSERT OVERWRITE`** will fail if we try to change our schema (unless we provide optional settings). +-- MAGIC +-- MAGIC Uncomment and run the cell below to generated an expected error message. + +-- COMMAND ---------- + +-- INSERT OVERWRITE sales +-- SELECT *, current_timestamp() FROM parquet.`${da.paths.datasets}/raw/sales-historical` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Append Rows +-- MAGIC +-- MAGIC We can use **`INSERT INTO`** to atomically append new rows to an existing Delta table. This allows for incremental updates to existing tables, which is much more efficient than overwriting each time. +-- MAGIC +-- MAGIC Append new sale records to the **`sales`** table using **`INSERT INTO`**. + +-- COMMAND ---------- + +INSERT INTO sales +SELECT * FROM parquet.`${da.paths.datasets}/raw/sales-30m` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Note that **`INSERT INTO`** does not have any built-in guarantees to prevent inserting the same records multiple times. Re-executing the above cell would write the same records to the target table, resulting in duplicate records. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Merge Updates +-- MAGIC +-- MAGIC You can upsert data from a source table, view, or DataFrame into a target Delta table using the **`MERGE`** SQL operation. Delta Lake supports inserts, updates and deletes in **`MERGE`**, and supports extended syntax beyond the SQL standards to facilitate advanced use cases. +-- MAGIC +-- MAGIC +-- MAGIC MERGE INTO target a
+-- MAGIC USING source b
+-- MAGIC ON {merge_condition}
+-- MAGIC WHEN MATCHED THEN {matched_action}
+-- MAGIC WHEN NOT MATCHED THEN {not_matched_action}
+-- MAGIC +-- MAGIC +-- MAGIC We will use the **`MERGE`** operation to update historic users data with updated emails and new users. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW users_update AS +SELECT *, current_timestamp() AS updated +FROM parquet.`${da.paths.datasets}/raw/users-30m` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC The main benefits of **`MERGE`**: +-- MAGIC * updates, inserts, and deletes are completed as a single transaction +-- MAGIC * multiple conditionals can be added in addition to matching fields +-- MAGIC * provides extensive options for implementing custom logic +-- MAGIC +-- MAGIC Below, we'll only update records if the current row has a **`NULL`** email and the new row does not. +-- MAGIC +-- MAGIC All unmatched records from the new batch will be inserted. + +-- COMMAND ---------- + +MERGE INTO users a +USING users_update b +ON a.user_id = b.user_id +WHEN MATCHED AND a.email IS NULL AND b.email IS NOT NULL THEN + UPDATE SET email = b.email, updated = b.updated +WHEN NOT MATCHED THEN INSERT * + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that we explicitly specify the behavior of this function for both the **`MATCHED`** and **`NOT MATCHED`** conditions; the example demonstrated here is just an example of logic that can be applied, rather than indicative of all **`MERGE`** behavior. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Insert-Only Merge for Deduplication +-- MAGIC +-- MAGIC A common ETL use case is to collect logs or other every-appending datasets into a Delta table through a series of append operations. +-- MAGIC +-- MAGIC Many source systems can generate duplicate records. With merge, you can avoid inserting the duplicate records by performing an insert-only merge. +-- MAGIC +-- MAGIC This optimized command uses the same **`MERGE`** syntax but only provided a **`WHEN NOT MATCHED`** clause. +-- MAGIC +-- MAGIC Below, we use this to confirm that records with the same **`user_id`** and **`event_timestamp`** aren't already in the **`events`** table. + +-- COMMAND ---------- + +MERGE INTO events a +USING events_update b +ON a.user_id = b.user_id AND a.event_timestamp = b.event_timestamp +WHEN NOT MATCHED AND b.traffic_source = 'email' THEN + INSERT * + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Load Incrementally +-- MAGIC +-- MAGIC **`COPY INTO`** provides SQL engineers an idempotent option to incrementally ingest data from external systems. +-- MAGIC +-- MAGIC Note that this operation does have some expectations: +-- MAGIC - Data schema should be consistent +-- MAGIC - Duplicate records should try to be excluded or handled downstream +-- MAGIC +-- MAGIC This operation is potentially much cheaper than full table scans for data that grows predictably. +-- MAGIC +-- MAGIC While here we'll show simple execution on a static directory, the real value is in multiple executions over time picking up new files in the source automatically. + +-- COMMAND ---------- + +COPY INTO sales +FROM "${da.paths.datasets}/raw/sales-30m" +FILEFORMAT = PARQUET + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.2.5L - Extract and Load Data Lab.sql b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.5L - Extract and Load Data Lab.sql similarity index 68% rename from Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.2.5L - Extract and Load Data Lab.sql rename to Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.5L - Extract and Load Data Lab.sql index 93818ac..35fb287 100644 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.2.5L - Extract and Load Data Lab.sql +++ b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.5L - Extract and Load Data Lab.sql @@ -8,11 +8,14 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Extract and Load Data Lab -- MAGIC -- MAGIC In this lab, you will extract and load raw data from JSON files into a Delta table. -- MAGIC --- MAGIC ##### Objectives +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lab, you should be able to: -- MAGIC - Create an external table to extract data from JSON files -- MAGIC - Create an empty Delta table with a provided schema -- MAGIC - Insert records from an existing table into a Delta table @@ -21,60 +24,78 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC -- MAGIC Run the following cell to configure variables and datasets for this lesson. -- COMMAND ---------- --- MAGIC %run ../../Includes/setup +-- MAGIC %run ../Includes/Classroom-Setup-4.5L -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Overview of the Data -- MAGIC --- MAGIC We will work with a sample of raw Kafka data written as JSON files. Each file contains all records consumed during a 5-second interval, stored with the full Kafka schema as a multiple-record JSON file. The schema for the table: +-- MAGIC We will work with a sample of raw Kafka data written as JSON files. +-- MAGIC +-- MAGIC Each file contains all records consumed during a 5-second interval, stored with the full Kafka schema as a multiple-record JSON file. +-- MAGIC +-- MAGIC The schema for the table: -- MAGIC -- MAGIC | field | type | description | -- MAGIC | ------ | ---- | ----------- | --- MAGIC | key | BINARY | The `user_id` field is used as the key; this is a unique alphanumeric field that corresponds to session/cookie information | +-- MAGIC | key | BINARY | The **`user_id`** field is used as the key; this is a unique alphanumeric field that corresponds to session/cookie information | -- MAGIC | offset | LONG | This is a unique value, monotonically increasing for each partition | -- MAGIC | partition | INTEGER | Our current Kafka implementation uses only 2 partitions (0 and 1) | -- MAGIC | timestamp | LONG | This timestamp is recorded as milliseconds since epoch, and represents the time at which the producer appends a record to a partition | --- MAGIC | topic | STRING | While the Kafka service hosts multiple topics, only those records from the `clickstream` topic are included here | +-- MAGIC | topic | STRING | While the Kafka service hosts multiple topics, only those records from the **`clickstream`** topic are included here | -- MAGIC | value | BINARY | This is the full data payload (to be discussed later), sent as JSON | -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Extract Raw Events From JSON Files -- MAGIC To load this data into Delta properly, we first need to extract the JSON data using the correct schema. -- MAGIC --- MAGIC Create an external table against JSON files located at the filepath provided below. Name this table `events_json` and declare the schema above. +-- MAGIC Create an external table against JSON files located at the filepath provided below. Name this table **`events_json`** and declare the schema above. -- COMMAND ---------- -- TODO - {c.source}/events/events-kafka.json + ${da.paths.datasets}/raw/events-kafka/ -- COMMAND ---------- -- MAGIC %md --- MAGIC **NOTE**: We'll use Python to run checks occasionally throughout the lab. The following cell will return as error with a message on what needs to change if you have not followed instructions. No output from cell execution means that you have completed this step. +-- MAGIC +-- MAGIC +-- MAGIC **NOTE**: We'll use Python to run checks occasionally throughout the lab. The following cell will return an error with a message on what needs to change if you have not followed instructions. No output from cell execution means that you have completed this step. -- COMMAND ---------- -- MAGIC %python -- MAGIC assert spark.table("events_json"), "Table named `events_json` does not exist" -- MAGIC assert spark.table("events_json").columns == ['key', 'offset', 'partition', 'timestamp', 'topic', 'value'], "Please name the columns in the order provided above" --- MAGIC assert spark.table("events_json").dtypes == [('key', 'binary'), ('offset', 'int'), ('partition', 'bigint'), ('timestamp', 'bigint'), ('topic', 'string'), ('value', 'binary')], "Please make sure the column types are identical to those provided above" --- MAGIC assert spark.table("events_json").count() == 45105, "The table should have 45105 records" +-- MAGIC assert spark.table("events_json").dtypes == [('key', 'binary'), ('offset', 'bigint'), ('partition', 'int'), ('timestamp', 'bigint'), ('topic', 'string'), ('value', 'binary')], "Please make sure the column types are identical to those provided above" +-- MAGIC +-- MAGIC total = spark.table("events_json").count() +-- MAGIC assert total == 2252, f"Expected 2252 records, found {total}" -- COMMAND ---------- --- MAGIC %md ## Insert Raw Events Into Delta Table --- MAGIC Create a managed Delta table named `events_raw` using the same schema - we'll load the data extracted above into this empty table. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Insert Raw Events Into Delta Table +-- MAGIC Create an empty managed Delta table named **`events_raw`** using the same schema. -- COMMAND ---------- @@ -83,19 +104,27 @@ -- COMMAND ---------- --- MAGIC %md Run the cell below to confirm the table was created correctly. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Run the cell below to confirm the table was created correctly. -- COMMAND ---------- -- MAGIC %python --- MAGIC assert spark.table("events_raw"), "Table named `events_json` does not exist" +-- MAGIC assert spark.table("events_raw"), "Table named `events_raw` does not exist" -- MAGIC assert spark.table("events_raw").columns == ['key', 'offset', 'partition', 'timestamp', 'topic', 'value'], "Please name the columns in the order provided above" -- MAGIC assert spark.table("events_raw").dtypes == [('key', 'binary'), ('offset', 'bigint'), ('partition', 'int'), ('timestamp', 'bigint'), ('topic', 'string'), ('value', 'binary')], "Please make sure the column types are identical to those provided above" -- MAGIC assert spark.table("events_raw").count() == 0, "The table should have 0 records" -- COMMAND ---------- --- MAGIC %md Once the extracted data and Delta table are ready, insert the JSON records from the `events_json` table into the new `events_raw` Delta table. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Once the extracted data and Delta table are ready, insert the JSON records from the **`events_json`** table into the new **`events_raw`** Delta table. -- COMMAND ---------- @@ -105,6 +134,8 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Manually review the table contents to ensure data was written as expected. -- COMMAND ---------- @@ -115,28 +146,38 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to confirm the data has been loaded correctly. -- COMMAND ---------- -- MAGIC %python --- MAGIC assert spark.table("events_raw").count() == 45105, "The table should have 45105 records" --- MAGIC assert set(row['timestamp'] for row in spark.table("events_raw").select("timestamp").limit(5).collect()) == {1593879300053, 1593879300372, 1593879300607, 1593879300739, 1593879300821}, "Make sure you have not modified the data provided" +-- MAGIC assert spark.table("events_raw").count() == 2252, "The table should have 2252 records" +-- MAGIC assert set(row['timestamp'] for row in spark.table("events_raw").select("timestamp").limit(5).collect()) == {1593880885085, 1593880892303, 1593880889174, 1593880886106, 1593880889725}, "Make sure you have not modified the data provided" -- COMMAND ---------- --- MAGIC %md ## Create Delta Table from a Query +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Create Delta Table from a Query -- MAGIC In addition to new events data, let's also load a small lookup table that provides product details that we'll use later in the course. --- MAGIC Use a CTAS statement to create a managed Delta table named `item_lookup` that extracts data from the parquet directory provided below. +-- MAGIC Use a CTAS statement to create a managed Delta table named **`item_lookup`** that extracts data from the parquet directory provided below. -- COMMAND ---------- -- TODO - {c.source}/products/products.parquet + ${da.paths.datasets}/raw/item-lookup -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to confirm the lookup table has been loaded correctly. -- COMMAND ---------- @@ -147,6 +188,18 @@ -- COMMAND ---------- +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + -- MAGIC %md-sandbox -- MAGIC © 2022 Databricks, Inc. All rights reserved.
-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
diff --git a/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.6 - Cleaning Data.sql b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.6 - Cleaning Data.sql new file mode 100644 index 0000000..56c1bb3 --- /dev/null +++ b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.6 - Cleaning Data.sql @@ -0,0 +1,326 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Cleaning Data +-- MAGIC +-- MAGIC Most transformations completed with Spark SQL will be familiar to SQL-savvy developers. +-- MAGIC +-- MAGIC As we inspect and clean our data, we'll need to construct various column expressions and queries to express transformations to apply on our dataset. +-- MAGIC +-- MAGIC Column expressions are constructed from existing columns, operators, and built-in Spark SQL functions. They can be used in **`SELECT`** statements to express transformations that create new columns from datasets. +-- MAGIC +-- MAGIC Along with **`SELECT`**, many additional query commands can be used to express transformations in Spark SQL, including **`WHERE`**, **`DISTINCT`**, **`ORDER BY`**, **`GROUP BY`**, etc. +-- MAGIC +-- MAGIC In this notebook, we'll review a few concepts that might differ from other systems you're used to, as well as calling out a few useful functions for common operations. +-- MAGIC +-- MAGIC We'll pay special attention to behaviors around **`NULL`** values, as well as formatting strings and datetime fields. +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC - Summarize datasets and describe null behaviors +-- MAGIC - Retrieve and removing duplicates +-- MAGIC - Validate datasets for expected counts, missing values, and duplicate records +-- MAGIC - Apply common transformations to clean and transform data + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Run Setup +-- MAGIC +-- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-4.6 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We'll work with new users records in **`users_dirty`** table for this lesson. + +-- COMMAND ---------- + +SELECT * FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Inspect Data +-- MAGIC +-- MAGIC Let's start by counting values in each field of our data. + +-- COMMAND ---------- + +SELECT count(user_id), count(user_first_touch_timestamp), count(email), count(updated), count(*) +FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that **`count(col)`** skips **`NULL`** values when counting specific columns or expressions. +-- MAGIC +-- MAGIC However, **`count(*)`** is a special case that counts the total number of rows (including rows that are only **`NULL`** values). +-- MAGIC +-- MAGIC To count null values, use the **`count_if`** function or **`WHERE`** clause to provide a condition that filters for records where the value **`IS NULL`**. + +-- COMMAND ---------- + +SELECT + count_if(user_id IS NULL) AS missing_user_ids, + count_if(user_first_touch_timestamp IS NULL) AS missing_timestamps, + count_if(email IS NULL) AS missing_emails, + count_if(updated IS NULL) AS missing_updates +FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Clearly there are at least a handful of null values in all of our fields. Let's try to discover what is causing this. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Distinct Records +-- MAGIC +-- MAGIC Start by looking for distinct rows. + +-- COMMAND ---------- + +SELECT count(DISTINCT(*)) +FROM users_dirty + +-- COMMAND ---------- + +SELECT count(DISTINCT(user_id)) +FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Because **`user_id`** is generated alongside the **`user_first_touch_timestamp`**, these fields should always be in parity for counts. + +-- COMMAND ---------- + +SELECT count(DISTINCT(user_first_touch_timestamp)) +FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Here we note that while there are some duplicate records relative to our total row count, we have a much higher number of distinct values. +-- MAGIC +-- MAGIC Let's go ahead and combine our distinct counts with columnar counts to see these values side-by-side. + +-- COMMAND ---------- + +SELECT + count(user_id) AS total_ids, + count(DISTINCT user_id) AS unique_ids, + count(email) AS total_emails, + count(DISTINCT email) AS unique_emails, + count(updated) AS total_updates, + count(DISTINCT(updated)) AS unique_updates, + count(*) AS total_rows, + count(DISTINCT(*)) AS unique_non_null_rows +FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Based on the above summary, we know: +-- MAGIC * All of our emails are unique +-- MAGIC * Our emails contain the largest number of null values +-- MAGIC * The **`updated`** column contains only 1 distinct value, but most are non-null + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Deduplicate Rows +-- MAGIC Based on the above behavior, what do you expect will happen if we use **`DISTINCT *`** to try to remove duplicate records? + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW users_deduped AS + SELECT DISTINCT(*) FROM users_dirty; + +SELECT * FROM users_deduped + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note in the preview above that there appears to be null values, even though our **`COUNT(DISTINCT(*))`** ignored these nulls. +-- MAGIC +-- MAGIC How many rows do you expect passed through this **`DISTINCT`** command? + +-- COMMAND ---------- + +SELECT COUNT(*) FROM users_deduped + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that we now have a completely new number. +-- MAGIC +-- MAGIC Spark skips null values while counting values in a column or counting distinct values for a field, but does not omit rows with nulls from a **`DISTINCT`** query. +-- MAGIC +-- MAGIC Indeed, the reason we're seeing a new number that is 1 higher than previous counts is because we have 3 rows that are all nulls (here included as a single distinct row). + +-- COMMAND ---------- + +SELECT * FROM users_dirty +WHERE + user_id IS NULL AND + user_first_touch_timestamp IS NULL AND + email IS NULL AND + updated IS NULL + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Deduplicate Based on Specific Columns +-- MAGIC +-- MAGIC Recall that **`user_id`** and **`user_first_touch_timestamp`** should form unique tuples, as they are both generated when a given user is first encountered. +-- MAGIC +-- MAGIC We can see that we have some null values in each of these fields; exclude nulls counting the distinct number of pairs for these fields will get us the correct count for distinct values in our table. + +-- COMMAND ---------- + +SELECT COUNT(DISTINCT(user_id, user_first_touch_timestamp)) +FROM users_dirty +WHERE user_id IS NOT NULL + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Here, we'll use these distinct pairs to remove unwanted rows from our data. +-- MAGIC +-- MAGIC The code below uses **`GROUP BY`** to remove duplicate records based on **`user_id`** and **`user_first_touch_timestamp`**. +-- MAGIC +-- MAGIC The **`max()`** aggregate function is used on the **`email`** column as a hack to capture non-null emails when multiple records are present; in this batch, all **`updated`** values were equivalent, but we need to use an aggregate function to keep this value in the result of our group by. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW deduped_users AS +SELECT user_id, user_first_touch_timestamp, max(email) AS email, max(updated) AS updated +FROM users_dirty +WHERE user_id IS NOT NULL +GROUP BY user_id, user_first_touch_timestamp; + +SELECT count(*) FROM deduped_users + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Validate Datasets +-- MAGIC We've visually confirmed that our counts are as expected, based our manual review. +-- MAGIC +-- MAGIC Below, we programmatically do some validation using simple filters and **`WHERE`** clauses. +-- MAGIC +-- MAGIC Validate that the **`user_id`** for each row is unique. + +-- COMMAND ---------- + +SELECT max(row_count) <= 1 no_duplicate_ids FROM ( + SELECT user_id, count(*) AS row_count + FROM deduped_users + GROUP BY user_id) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Confirm that each email is associated with at most one **`user_id`**. + +-- COMMAND ---------- + +SELECT max(user_id_count) <= 1 at_most_one_id FROM ( + SELECT email, count(user_id) AS user_id_count + FROM deduped_users + WHERE email IS NOT NULL + GROUP BY email) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Date Format and Regex +-- MAGIC Now that we've removed null fields and eliminated duplicates, we may wish to extract further value out of the data. +-- MAGIC +-- MAGIC The code below: +-- MAGIC - Correctly scales and casts the **`user_first_touch_timestamp`** to a valid timestamp +-- MAGIC - Extracts the calendar data and clock time for this timestamp in human readable format +-- MAGIC - Uses **`regexp_extract`** to extract the domains from the email column using regex + +-- COMMAND ---------- + +SELECT *, + date_format(first_touch, "MMM d, yyyy") AS first_touch_date, + date_format(first_touch, "HH:mm:ss") AS first_touch_time, + regexp_extract(email, "(?<=@).+", 0) AS email_domain +FROM ( + SELECT *, + CAST(user_first_touch_timestamp / 1e6 AS timestamp) AS first_touch + FROM deduped_users +) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.7 - Advanced SQL Transformations.sql b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.7 - Advanced SQL Transformations.sql new file mode 100644 index 0000000..e689ca3 --- /dev/null +++ b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.7 - Advanced SQL Transformations.sql @@ -0,0 +1,426 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Advanced SQL Transformations +-- MAGIC +-- MAGIC Querying tabular data stored in the data lakehouse with Spark SQL is easy, efficient, and fast. +-- MAGIC +-- MAGIC This gets more complicated as the data structure becomes less regular, when many tables need to be used in a single query, or when the shape of data needs to be changed dramatically. This notebook introduces a number of functions present in Spark SQL to help engineers complete even the most complicated transformations. +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC - Use **`.`** and **`:`** syntax to query nested data +-- MAGIC - Work with JSON +-- MAGIC - Flatten and unpacking arrays and structs +-- MAGIC - Combine datasets using joins and set operators +-- MAGIC - Reshape data using pivot tables +-- MAGIC - Use higher order functions for working with arrays + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Run Setup +-- MAGIC +-- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-4.7 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Interacting with JSON Data +-- MAGIC +-- MAGIC The **`events_raw`** table was registered against data representing a Kafka payload. +-- MAGIC +-- MAGIC In most cases, Kafka data will be binary-encoded JSON values. We'll cast the **`key`** and **`value`** as strings below to look at these in a human-readable format. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW events_strings AS + SELECT string(key), string(value) + FROM events_raw; + +SELECT * FROM events_strings + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Spark SQL has built-in functionality to directly interact with JSON data stored as strings. We can use the **`:`** syntax to traverse nested data structures. + +-- COMMAND ---------- + +SELECT value:device, value:geo:city +FROM events_strings + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Spark SQL also has the ability to parse JSON objects into struct types (a native Spark type with nested attributes). +-- MAGIC +-- MAGIC However, the **`from_json`** function requires a schema. To derive the schema of our current data, we'll start by executing a query we know will return a JSON value with no null fields. + +-- COMMAND ---------- + +SELECT value +FROM events_strings +WHERE value:event_name = "finalize" +ORDER BY key +LIMIT 1 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Spark SQL also has a **`schema_of_json`** function to derive the JSON schema from an example. Here, we copy and paste an example JSON to the function and chain it into the **`from_json`** function to cast our **`value`** field to a struct type. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW parsed_events AS + SELECT from_json(value, schema_of_json('{"device":"Linux","ecommerce":{"purchase_revenue_in_usd":1075.5,"total_item_quantity":1,"unique_items":1},"event_name":"finalize","event_previous_timestamp":1593879231210816,"event_timestamp":1593879335779563,"geo":{"city":"Houston","state":"TX"},"items":[{"coupon":"NEWBED10","item_id":"M_STAN_K","item_name":"Standard King Mattress","item_revenue_in_usd":1075.5,"price_in_usd":1195.0,"quantity":1}],"traffic_source":"email","user_first_touch_timestamp":1593454417513109,"user_id":"UA000000106116176"}')) AS json + FROM events_strings; + +SELECT * FROM parsed_events + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Once a JSON string is unpacked to a struct type, Spark supports **`*`** (star) unpacking to flatten fields into columns. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW new_events_final AS + SELECT json.* + FROM parsed_events; + +SELECT * FROM new_events_final + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Explore Data Structures +-- MAGIC +-- MAGIC Spark SQL has robust syntax for working with complex and nested data types. +-- MAGIC +-- MAGIC Start by looking at the fields in the **`events`** table. + +-- COMMAND ---------- + +DESCRIBE events + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC The **`ecommerce`** field is a struct that contains a double and 2 longs. +-- MAGIC +-- MAGIC We can interact with the subfields in this field using standard **`.`** syntax similar to how we might traverse nested data in JSON. + +-- COMMAND ---------- + +SELECT ecommerce.purchase_revenue_in_usd +FROM events +WHERE ecommerce.purchase_revenue_in_usd IS NOT NULL + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Explode Arrays +-- MAGIC The **`items`** field in the **`events`** table is an array of structs. +-- MAGIC +-- MAGIC Spark SQL has a number of functions specifically to deal with arrays. +-- MAGIC +-- MAGIC The **`explode`** function lets us put each element in an array on its own row. + +-- COMMAND ---------- + +SELECT user_id, event_timestamp, event_name, explode(items) AS item +FROM events + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Collect Arrays +-- MAGIC +-- MAGIC The **`collect_set`** function can collect unique values for a field, including fields within arrays. +-- MAGIC +-- MAGIC The **`flatten`** function allows multiple arrays to be combined into a single array. +-- MAGIC +-- MAGIC The **`array_distinct`** function removes duplicate elements from an array. +-- MAGIC +-- MAGIC Here, we combine these queries to create a simple table that shows the unique collection of actions and the items in a user's cart. + +-- COMMAND ---------- + +SELECT user_id, + collect_set(event_name) AS event_history, + array_distinct(flatten(collect_set(items.item_id))) AS cart_history +FROM events +GROUP BY user_id + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Join Tables +-- MAGIC +-- MAGIC Spark SQL supports standard join operations (inner, outer, left, right, anti, cross, semi). +-- MAGIC +-- MAGIC Here we chain a join with a lookup table to an **`explode`** operation to grab the standard printed item name. + +-- COMMAND ---------- + +CREATE OR REPLACE VIEW sales_enriched AS +SELECT * +FROM ( + SELECT *, explode(items) AS item + FROM sales) a +INNER JOIN item_lookup b +ON a.item.item_id = b.item_id; + +SELECT * FROM sales_enriched + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Set Operators +-- MAGIC Spark SQL supports **`UNION`**, **`MINUS`**, and **`INTERSECT`** set operators. +-- MAGIC +-- MAGIC **`UNION`** returns the collection of two queries. +-- MAGIC +-- MAGIC The query below returns the same results as if we inserted our **`new_events_final`** into the **`events`** table. + +-- COMMAND ---------- + +SELECT * FROM events +UNION +SELECT * FROM new_events_final + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC **`INTERSECT`** returns all rows found in both relations. + +-- COMMAND ---------- + +SELECT * FROM events +INTERSECT +SELECT * FROM new_events_final + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC The above query returns no results because our two datasets have no values in common. +-- MAGIC +-- MAGIC **`MINUS`** returns all the rows found in one dataset but not the other; we'll skip executing this here as our previous query demonstrates we have no values in common. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Pivot Tables +-- MAGIC The **`PIVOT`** clause is used for data perspective. We can get the aggregated values based on specific column values, which will be turned to multiple columns used in **`SELECT`** clause. The **`PIVOT`** clause can be specified after the table name or subquery. +-- MAGIC +-- MAGIC **`SELECT * FROM ()`**: The **`SELECT`** statement inside the parentheses is the input for this table. +-- MAGIC +-- MAGIC **`PIVOT`**: The first argument in the clause is an aggregate function and the column to be aggregated. Then, we specify the pivot column in the **`FOR`** subclause. The **`IN`** operator contains the pivot column values. +-- MAGIC +-- MAGIC Here we use **`PIVOT`** to create a new **`transactions`** table that flattens out the information contained in the **`sales`** table. +-- MAGIC +-- MAGIC This flattened data format can be useful for dashboarding, but also useful for applying machine learning algorithms for inference or prediction. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE transactions AS + +SELECT * FROM ( + SELECT + email, + order_id, + transaction_timestamp, + total_item_quantity, + purchase_revenue_in_usd, + unique_items, + item.item_id AS item_id, + item.quantity AS quantity + FROM sales_enriched +) PIVOT ( + sum(quantity) FOR item_id in ( + 'P_FOAM_K', + 'M_STAN_Q', + 'P_FOAM_S', + 'M_PREM_Q', + 'M_STAN_F', + 'M_STAN_T', + 'M_PREM_K', + 'M_PREM_F', + 'M_STAN_K', + 'M_PREM_T', + 'P_DOWN_S', + 'P_DOWN_K' + ) +); + +SELECT * FROM transactions + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Higher Order Functions +-- MAGIC Higher order functions in Spark SQL allow you to work directly with complex data types. When working with hierarchical data, records are frequently stored as array or map type objects. Higher-order functions allow you to transform data while preserving the original structure. +-- MAGIC +-- MAGIC Higher order functions include: +-- MAGIC - **`FILTER`** filters an array using the given lambda function. +-- MAGIC - **`EXIST`** tests whether a statement is true for one or more elements in an array. +-- MAGIC - **`TRANSFORM`** uses the given lambda function to transform all elements in an array. +-- MAGIC - **`REDUCE`** takes two lambda functions to reduce the elements of an array to a single value by merging the elements into a buffer, and the apply a finishing function on the final buffer. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Filter +-- MAGIC Remove items that are not king-sized from all records in our **`items`** column. We can use the **`FILTER`** function to create a new column that excludes that value from each array. +-- MAGIC +-- MAGIC **`FILTER (items, i -> i.item_id LIKE "%K") AS king_items`** +-- MAGIC +-- MAGIC In the statement above: +-- MAGIC - **`FILTER`** : the name of the higher-order function
+-- MAGIC - **`items`** : the name of our input array
+-- MAGIC - **`i`** : the name of the iterator variable. You choose this name and then use it in the lambda function. It iterates over the array, cycling each value into the function one at a time.
+-- MAGIC - **`->`** : Indicates the start of a function
+-- MAGIC - **`i.item_id LIKE "%K"`** : This is the function. Each value is checked to see if it ends with the capital letter K. If it is, it gets filtered into the new column, **`king_items`** + +-- COMMAND ---------- + +-- filter for sales of only king sized items +SELECT + order_id, + items, + FILTER (items, i -> i.item_id LIKE "%K") AS king_items +FROM sales + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC You may write a filter that produces a lot of empty arrays in the created column. When that happens, it can be useful to use a **`WHERE`** clause to show only non-empty array values in the returned column. +-- MAGIC +-- MAGIC In this example, we accomplish that by using a subquery (a query within a query). They are useful for performing an operation in multiple steps. In this case, we're using it to create the named column that we will use with a **`WHERE`** clause. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW king_size_sales AS + +SELECT order_id, king_items +FROM ( + SELECT + order_id, + FILTER (items, i -> i.item_id LIKE "%K") AS king_items + FROM sales) +WHERE size(king_items) > 0; + +SELECT * FROM king_size_sales + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Transform +-- MAGIC Built-in functions are designed to operate on a single, simple data type within a cell; they cannot process array values. **`TRANSFORM`** can be particularly useful when you want to apply an existing function to each element in an array. +-- MAGIC +-- MAGIC Compute the total revenue from king-sized items per order. +-- MAGIC +-- MAGIC **`TRANSFORM(king_items, k -> CAST(k.item_revenue_in_usd * 100 AS INT)) AS item_revenues`** +-- MAGIC +-- MAGIC In the statement above, for each value in the input array, we extract the item's revenue value, multiply it by 100, and cast the result to integer. Note that we're using the same kind as references as in the previous command, but we name the iterator with a new variable, **`k`**. + +-- COMMAND ---------- + +-- get total revenue from king items per order +CREATE OR REPLACE TEMP VIEW king_item_revenues AS + +SELECT + order_id, + king_items, + TRANSFORM ( + king_items, + k -> CAST(k.item_revenue_in_usd * 100 AS INT) + ) AS item_revenues +FROM king_size_sales; + +SELECT * FROM king_item_revenues + + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Summary +-- MAGIC Spark SQL offers a comprehensive set of native functionality for interacting with and manipulating highly nested data. +-- MAGIC +-- MAGIC While some syntax for this functionality may be unfamiliar to SQL users, leveraging built-in functions like higher order functions can prevent SQL engineers from needing to rely on custom logic when dealing with highly complex data structures. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.8 - SQL UDFs and Control Flow.sql b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.8 - SQL UDFs and Control Flow.sql new file mode 100644 index 0000000..3afadab --- /dev/null +++ b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.8 - SQL UDFs and Control Flow.sql @@ -0,0 +1,205 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # SQL UDFs and Control Flow +-- MAGIC +-- MAGIC Databricks added support for User Defined Functions (UDFs) registered natively in SQL starting in DBR 9.1. +-- MAGIC +-- MAGIC This feature allows users to register custom combinations of SQL logic as functions in a database, making these methods reusable anywhere SQL can be run on Databricks. These functions leverage Spark SQL directly, maintaining all of the optimizations of Spark when applying your custom logic to large datasets. +-- MAGIC +-- MAGIC In this notebook, we'll first have a simple introduction to these methods, and then explore how this logic can be combined with **`CASE`** / **`WHEN`** clauses to provide reusable custom control flow logic. +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC * Define and registering SQL UDFs +-- MAGIC * Describe the security model used for sharing SQL UDFs +-- MAGIC * Use **`CASE`** / **`WHEN`** statements in SQL code +-- MAGIC * Leverage **`CASE`** / **`WHEN`** statements in SQL UDFs for custom control flow + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Setup +-- MAGIC Run the following cell to setup your environment. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-4.8 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Create a Simple Dataset +-- MAGIC +-- MAGIC For this notebook, we'll consider the following dataset, registered here as a temporary view. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMPORARY VIEW foods(food) AS VALUES +("beef"), +("beans"), +("potatoes"), +("bread"); + +SELECT * FROM foods + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## SQL UDFs +-- MAGIC At minimum, a SQL UDF requires a function name, optional parameters, the type to be returned, and some custom logic. +-- MAGIC +-- MAGIC Below, a simple function named **`yelling`** takes one parameter named **`text`**. It returns a string that will be in all uppercase letters with three exclamation points added to the end. + +-- COMMAND ---------- + +CREATE OR REPLACE FUNCTION yelling(text STRING) +RETURNS STRING +RETURN concat(upper(text), "!!!") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that this function is applied to all values of the column in a parallel fashion within the Spark processing engine. SQL UDFs are an efficient way to define custom logic that is optimized for execution on Databricks. + +-- COMMAND ---------- + +SELECT yelling(food) FROM foods + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Scoping and Permissions of SQL UDFs +-- MAGIC +-- MAGIC Note that SQL UDFs will persist between execution environments (which can include notebooks, DBSQL queries, and jobs). +-- MAGIC +-- MAGIC We can describe the function to see where it was registered and basic information about expected inputs and what is returned. + +-- COMMAND ---------- + +DESCRIBE FUNCTION yelling + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC By describing extended, we can get even more information. +-- MAGIC +-- MAGIC Note that the **`Body`** field at the bottom of the function description shows the SQL logic used in the function itself. + +-- COMMAND ---------- + +DESCRIBE FUNCTION EXTENDED yelling + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC SQL UDFs exist as objects in the metastore and are governed by the same Table ACLs as databases, tables, or views. +-- MAGIC +-- MAGIC In order to use a SQL UDF, a user must have **`USAGE`** and **`SELECT`** permissions on the function. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## CASE/WHEN +-- MAGIC +-- MAGIC The standard SQL syntactic construct **`CASE`** / **`WHEN`** allows the evaluation of multiple conditional statements with alternative outcomes based on table contents. +-- MAGIC +-- MAGIC Again, everything is evaluated natively in Spark, and so is optimized for parallel execution. + +-- COMMAND ---------- + +SELECT *, + CASE + WHEN food = "beans" THEN "I love beans" + WHEN food = "potatoes" THEN "My favorite vegetable is potatoes" + WHEN food <> "beef" THEN concat("Do you have any good recipes for ", food ,"?") + ELSE concat("I don't eat ", food) + END +FROM foods + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Simple Control Flow Functions +-- MAGIC +-- MAGIC Combining SQL UDFs with control flow in the form of **`CASE`** / **`WHEN`** clauses provides optimized execution for control flows within SQL workloads. +-- MAGIC +-- MAGIC Here, we demonstrate wrapping the previous logic in a function that will be reusable anywhere we can execute SQL. + +-- COMMAND ---------- + +CREATE FUNCTION foods_i_like(food STRING) +RETURNS STRING +RETURN CASE + WHEN food = "beans" THEN "I love beans" + WHEN food = "potatoes" THEN "My favorite vegetable is potatoes" + WHEN food <> "beef" THEN concat("Do you have any good recipes for ", food ,"?") + ELSE concat("I don't eat ", food) +END; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Using this method on our data provides the desired outcome. + +-- COMMAND ---------- + +SELECT foods_i_like(food) FROM foods + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC While the example provided here are simple string methods, these same basic principles can be used to add custom computations and logic for native execution in Spark SQL. +-- MAGIC +-- MAGIC Especially for enterprises that might be migrating users from systems with many defined procedures or custom-defined formulas, SQL UDFs can allow a handful of users to define the complex logic needed for common reporting and analytic queries. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.2.9L - Reshaping Data.sql b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.9L - Reshaping Data Lab.sql similarity index 58% rename from Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.2.9L - Reshaping Data.sql rename to Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.9L - Reshaping Data Lab.sql index 68e8e09..a923c69 100644 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/Labs/DE 2.2.9L - Reshaping Data.sql +++ b/Data-Engineering-with-Databricks/04 - ETL with Spark SQL/DE 4.9L - Reshaping Data Lab.sql @@ -8,39 +8,50 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Reshaping Data Lab -- MAGIC --- MAGIC In this lab, you will create a `clickpaths` table that aggregates the number of times each user took a particular action in `events` and then join this information with the flattened view of `transactions` created in the previous notebook. +-- MAGIC In this lab, you will create a **`clickpaths`** table that aggregates the number of times each user took a particular action in **`events`** and then join this information with the flattened view of **`transactions`** created in the previous notebook. -- MAGIC --- MAGIC You'll also explore a new higher order function to flag items recorded in `sales` based on information extracted from item names. +-- MAGIC You'll also explore a new higher order function to flag items recorded in **`sales`** based on information extracted from item names. -- MAGIC --- MAGIC ##### Objectives +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lab, you should be able to: -- MAGIC - Pivot and join tables to create clickpaths for each user -- MAGIC - Apply higher order functions to flag types of products purchased -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC -- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. -- COMMAND ---------- --- MAGIC %run ../../Includes/setup-transactions +-- MAGIC %run ../Includes/Classroom-Setup-4.9L -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Reshape Datasets to Create Click Paths --- MAGIC This operation will join data from your `events` and `transactions` tables in order to create a record of all actions a user took on the site and what their final order looked like. +-- MAGIC This operation will join data from your **`events`** and **`transactions`** tables in order to create a record of all actions a user took on the site and what their final order looked like. -- MAGIC --- MAGIC The `clickpaths` table should contain all the fields from your `transactions` table, as well as a count of every `event_name` in its own column. Each user that completed a purchase should have a single row in the final table. Let's start by pivoting the `events` table to get counts for each `event_name`. +-- MAGIC The **`clickpaths`** table should contain all the fields from your **`transactions`** table, as well as a count of every **`event_name`** in its own column. Each user that completed a purchase should have a single row in the final table. Let's start by pivoting the **`events`** table to get counts for each **`event_name`**. -- COMMAND ---------- --- MAGIC %md ### 1. Pivot `events` to count actions for each user --- MAGIC We want to aggregate the number of times each user performed a specific event, specified in the `event_name` column. To do this, group by `user` and pivot on `event_name` to provide a count of every event type in its own column, resulting in the schema below. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ### 1. Pivot **`events`** to count actions for each user +-- MAGIC We want to aggregate the number of times each user performed a specific event, specified in the **`event_name`** column. To do this, group by **`user`** and pivot on **`event_name`** to provide a count of every event type in its own column, resulting in the schema below. -- MAGIC -- MAGIC | field | type | -- MAGIC | --- | --- | @@ -83,31 +94,42 @@ CREATE OR REPLACE VIEW events_pivot -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC **NOTE**: We'll use Python to run checks occasionally throughout the lab. The helper functions below will return an error with a message on what needs to change if you have not followed instructions. No output means that you have completed this step. -- COMMAND ---------- -- MAGIC %python -- MAGIC def check_table_results(table_name, column_names, num_rows): --- MAGIC assert spark.table(table_name), f"Table named `{table_name}` does not exist" +-- MAGIC assert spark.table(table_name), f"Table named **`{table_name}`** does not exist" -- MAGIC assert spark.table(table_name).columns == column_names, "Please name the columns in the order provided above" -- MAGIC assert spark.table(table_name).count() == num_rows, f"The table should have {num_rows} records" -- COMMAND ---------- --- MAGIC %md Run the cell below to confirm the view was created correctly. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Run the cell below to confirm the view was created correctly. -- COMMAND ---------- -- MAGIC %python -- MAGIC event_columns = ['user', 'cart', 'pillows', 'login', 'main', 'careers', 'guest', 'faq', 'down', 'warranty', 'finalize', 'register', 'shipping_info', 'checkout', 'mattresses', 'add_item', 'press', 'email_coupon', 'cc_info', 'foam', 'reviews', 'original', 'delivery', 'premium'] --- MAGIC check_table_results("events_pivot", event_columns, 4085296) +-- MAGIC check_table_results("events_pivot", event_columns, 204586) -- COMMAND ---------- --- MAGIC %md ### 2. Join event counts and transactions for all users +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC --- MAGIC Next, join `events_pivot` with `transactions` to create the table `clickpaths`. This table should have the same event name columns from the `events_pivot` table created above, followed by columns from the `transactions` table, as shown below. +-- MAGIC ### 2. Join event counts and transactions for all users +-- MAGIC +-- MAGIC Next, join **`events_pivot`** with **`transactions`** to create the table **`clickpaths`**. This table should have the same event name columns from the **`events_pivot`** table created above, followed by columns from the **`transactions`** table, as shown below. -- MAGIC -- MAGIC | field | type | -- MAGIC | --- | --- | @@ -141,30 +163,36 @@ CREATE OR REPLACE VIEW clickpaths AS -- COMMAND ---------- --- MAGIC %md Run the cell below to confirm the table was created correctly. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Run the cell below to confirm the table was created correctly. -- COMMAND ---------- -- MAGIC %python -- MAGIC clickpath_columns = event_columns + ['user_id', 'order_id', 'transaction_timestamp', 'total_item_quantity', 'purchase_revenue_in_usd', 'unique_items', 'P_FOAM_K', 'M_STAN_Q', 'P_FOAM_S', 'M_PREM_Q', 'M_STAN_F', 'M_STAN_T', 'M_PREM_K', 'M_PREM_F', 'M_STAN_K', 'M_PREM_T', 'P_DOWN_S', 'P_DOWN_K'] --- MAGIC check_table_results("clickpaths", clickpath_columns, 180680) +-- MAGIC check_table_results("clickpaths", clickpath_columns, 9085) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Flag Types of Products Purchased --- MAGIC Here, you'll use the higher order function `EXISTS` to create boolean columns `mattress` and `pillow` that indicate whether the item purchased was a mattress or pillow product. +-- MAGIC Here, you'll use the higher order function **`EXISTS`** to create boolean columns **`mattress`** and **`pillow`** that indicate whether the item purchased was a mattress or pillow product. -- MAGIC --- MAGIC For example, if `item_name` from the `items` column ends with the string `"Mattress"`, the column value for `mattress` should be `true` and the value for `pillow` should be `false`. Here are a few examples of items and the resulting values. +-- MAGIC For example, if **`item_name`** from the **`items`** column ends with the string **`"Mattress"`**, the column value for **`mattress`** should be **`true`** and the value for **`pillow`** should be **`false`**. Here are a few examples of items and the resulting values. -- MAGIC -- MAGIC | items | mattress | pillow | -- MAGIC | ------- | -------- | ------ | --- MAGIC | `[{..., "item_id": "M_PREM_K", "item_name": "Premium King Mattress", ...}]` | true | false | --- MAGIC | `[{..., "item_id": "P_FOAM_S", "item_name": "Standard Foam Pillow", ...}]` | false | true | --- MAGIC | `[{..., "item_id": "M_STAN_F", "item_name": "Standard Full Mattress", ...}]` | true | false | +-- MAGIC | **`[{..., "item_id": "M_PREM_K", "item_name": "Premium King Mattress", ...}]`** | true | false | +-- MAGIC | **`[{..., "item_id": "P_FOAM_S", "item_name": "Standard Foam Pillow", ...}]`** | false | true | +-- MAGIC | **`[{..., "item_id": "M_STAN_F", "item_name": "Standard Full Mattress", ...}]`** | true | false | -- MAGIC --- MAGIC See documentation for the [exists](https://docs.databricks.com/sql/language-manual/functions/exists.html) function. --- MAGIC You can use the condition expression `item_name LIKE "%Mattress"` to check whether the string `item_name` ends with the word "Mattress". +-- MAGIC See documentation for the exists function. +-- MAGIC You can use the condition expression **`item_name LIKE "%Mattress"`** to check whether the string **`item_name`** ends with the word "Mattress". -- COMMAND ---------- @@ -176,14 +204,30 @@ EXISTS .item_name LIKE "%Pillow" -- COMMAND ---------- --- MAGIC %md Run the cell below to confirm the table was created correctly. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Run the cell below to confirm the table was created correctly. -- COMMAND ---------- -- MAGIC %python --- MAGIC check_table_results("sales_product_flags", ['items', 'mattress', 'pillow'], 210370) +-- MAGIC check_table_results("sales_product_flags", ['items', 'mattress', 'pillow'], 10539) -- MAGIC product_counts = spark.sql("SELECT sum(CAST(mattress AS INT)) num_mattress, sum(CAST(pillow AS INT)) num_pillow FROM sales_product_flags").first().asDict() --- MAGIC assert product_counts == {'num_mattress': 199764, 'num_pillow': 27481}, "There should be 199764 rows where mattress is true, and 27481 where pillow is true" +-- MAGIC assert product_counts == {'num_mattress': 10015, 'num_pillow': 1386}, "There should be 10015 rows where mattress is true, and 1386 where pillow is true" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.3.1 - Python Basics.py b/Data-Engineering-with-Databricks/05 - OPTIONAL Python for Spark SQL/DE 5.1 - Python Basics.py similarity index 77% rename from Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.3.1 - Python Basics.py rename to Data-Engineering-with-Databricks/05 - OPTIONAL Python for Spark SQL/DE 5.1 - Python Basics.py index 5d8e634..8e97427 100644 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.3.1 - Python Basics.py +++ b/Data-Engineering-with-Databricks/05 - OPTIONAL Python for Spark SQL/DE 5.1 - Python Basics.py @@ -8,6 +8,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Just Enough Python for Databricks SQL # MAGIC # MAGIC While Databricks SQL provides an ANSI-compliant flavor of SQL with many additional custom methods (including the entire Delta Lake SQL syntax), users migrating from some systems may run into missing features, especially around control flow and error handling. @@ -17,7 +19,7 @@ # MAGIC Mastering just a handful of Python concepts will unlock powerful new design practices for engineers and analysts proficient in SQL. Rather than trying to teach the entire language, this lesson focuses on those features that can immediately be leveraged to write more extensible SQL programs on Databricks. # MAGIC # MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, students should be able to: +# MAGIC By the end of this lesson, you should be able to: # MAGIC * Print and manipulate multi-line Python strings # MAGIC * Define variables and functions # MAGIC * Use f-strings for variable substitution @@ -25,8 +27,10 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Strings -# MAGIC Characters enclosed in single (`'`) or double (`"`) quotes are considered strings. +# MAGIC Characters enclosed in single (**`'`**) or double (**`"`**) quotes are considered strings. # COMMAND ---------- @@ -35,7 +39,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC To preview how a string will render, we can call `print()`. +# MAGIC +# MAGIC +# MAGIC To preview how a string will render, we can call **`print()`**. # COMMAND ---------- @@ -44,7 +50,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC By wrapping a string in triple quotes (`"""`), it's possible to use multiple lines. +# MAGIC +# MAGIC +# MAGIC By wrapping a string in triple quotes (**`"""`**), it's possible to use multiple lines. # COMMAND ---------- @@ -59,6 +67,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC This makes it easy to turn SQL queries into Python strings. # COMMAND ---------- @@ -71,7 +81,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC When we execute SQL from a Python cell, we will pass a string as an argument to `spark.sql()`. +# MAGIC +# MAGIC +# MAGIC When we execute SQL from a Python cell, we will pass a string as an argument to **`spark.sql()`**. # COMMAND ---------- @@ -80,7 +92,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC To render a query the way it would appear in a normal SQL notebook, we call `display()` on this function. +# MAGIC +# MAGIC +# MAGIC To render a query the way it would appear in a normal SQL notebook, we call **`display()`** on this function. # COMMAND ---------- @@ -89,21 +103,25 @@ # COMMAND ---------- # MAGIC %md -# MAGIC **NOTE**: Executing a cell with only a Python string in it will just print the string. Using `print()` with a string just renders it back to the notebook. # MAGIC -# MAGIC To execute a string that contains SQL using Python, it must be passed within a call to `spark.sql()`. +# MAGIC +# MAGIC **NOTE**: Executing a cell with only a Python string in it will just print the string. Using **`print()`** with a string just renders it back to the notebook. +# MAGIC +# MAGIC To execute a string that contains SQL using Python, it must be passed within a call to **`spark.sql()`**. # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Variables -# MAGIC Python variables are assigned using the `=`. +# MAGIC Python variables are assigned using the **`=`**. # MAGIC -# MAGIC Python variable names need to start with a letter, and can only contain letters, numbers, hyphens, and underscores. +# MAGIC Python variable names need to start with a letter, and can only contain letters, numbers, and underscores. (Variable names starting with underscores are valid but typically reserved for special use cases.) # MAGIC # MAGIC Many Python programmers favor snake casing, which uses only lowercase letters and underscores for all variables. # MAGIC -# MAGIC The cell below creates the variable `my_string`. +# MAGIC The cell below creates the variable **`my_string`**. # COMMAND ---------- @@ -112,6 +130,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Executing a cell with this variable will return its value. # COMMAND ---------- @@ -121,7 +141,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC The output here is the same as if we typed `"This is a string"` into the cell and ran it. +# MAGIC +# MAGIC +# MAGIC The output here is the same as if we typed **`"This is a string"`** into the cell and ran it. # MAGIC # MAGIC Note that the quotation marks aren't part of the string, as shown when we print it. @@ -132,9 +154,11 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC This variable can be used the same way a string would be. # MAGIC -# MAGIC String concatenation (joining to strings together) can be performed with a `+`. +# MAGIC String concatenation (joining to strings together) can be performed with a **`+`**. # COMMAND ---------- @@ -143,6 +167,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC We can join string variables with other string variables. # COMMAND ---------- @@ -153,12 +179,14 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Functions -# MAGIC Functions allow you to specify local variables as arguments and then apply custom logic. We define a function using the keyword `def` followed by the function name and, enclosed in parentheses, any variable arguments we wish to pass into the function. Finally, the function header has a `:` at the end. +# MAGIC Functions allow you to specify local variables as arguments and then apply custom logic. We define a function using the keyword **`def`** followed by the function name and, enclosed in parentheses, any variable arguments we wish to pass into the function. Finally, the function header has a **`:`** at the end. # MAGIC # MAGIC Note: In Python, indentation matters. You can see in the cell below that the logic of the function is indented in from the left margin. Any code that is indented to this level is part of the function. # MAGIC -# MAGIC The function below takes one argument (`arg`) and then prints it. +# MAGIC The function below takes one argument (**`arg`**) and then prints it. # COMMAND ---------- @@ -168,6 +196,8 @@ def print_string(arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC When we pass a string as the argument, it will be printed. # COMMAND ---------- @@ -177,6 +207,8 @@ def print_string(arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC We can also pass a variable as an argument. # COMMAND ---------- @@ -186,7 +218,9 @@ def print_string(arg): # COMMAND ---------- # MAGIC %md -# MAGIC Oftentimes we want to return the results of our function for use elsewhere. For this we use the `return` keyword. +# MAGIC +# MAGIC +# MAGIC Oftentimes we want to return the results of our function for use elsewhere. For this we use the **`return`** keyword. # MAGIC # MAGIC The function below constructs a new string by concatenating our argument. Note that both functions and arguments can have arbitrary names, just like variables (and follow the same rules). @@ -198,6 +232,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Running this function returns the output. # COMMAND ---------- @@ -207,6 +243,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Assigning it to a variable captures the output for reuse elsewhere. # COMMAND ---------- @@ -216,6 +254,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC This variable doesn't contain our function, just the results of our function (a string). # COMMAND ---------- @@ -225,8 +265,10 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## F-strings -# MAGIC By adding the letter `f` before a Python string, you can inject variables or evaluated Python code by inserted them inside curly braces (`{}`). +# MAGIC By adding the letter **`f`** before a Python string, you can inject variables or evaluated Python code by inserted them inside curly braces (**`{}`**). # MAGIC # MAGIC Evaluate the cell below to see string variable substitution. @@ -237,6 +279,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC The following cell inserts the string returned by a function. # COMMAND ---------- @@ -246,6 +290,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Combine this with triple quotes and you can format a paragraph or list, like below. # COMMAND ---------- @@ -261,6 +307,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Or you could format a SQL query. # COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.3.2 - Python Control Flow.py b/Data-Engineering-with-Databricks/05 - OPTIONAL Python for Spark SQL/DE 5.2 - Python Control Flow.py similarity index 60% rename from Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.3.2 - Python Control Flow.py rename to Data-Engineering-with-Databricks/05 - OPTIONAL Python for Spark SQL/DE 5.2 - Python Control Flow.py index 3b73ffa..251c62f 100644 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.3.2 - Python Control Flow.py +++ b/Data-Engineering-with-Databricks/05 - OPTIONAL Python for Spark SQL/DE 5.2 - Python Control Flow.py @@ -8,27 +8,33 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Just Enough Python for Databricks SQL # MAGIC # MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, students should be able to: -# MAGIC * Leverage `if/else` +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Leverage **`if`** / **`else`** # MAGIC * Describe how errors impact notebook execution -# MAGIC * Write simple tests with `assert` -# MAGIC * Use `try/except` to handle errors +# MAGIC * Write simple tests with **`assert`** +# MAGIC * Use **`try`** / **`except`** to handle errors # COMMAND ---------- # MAGIC %md -# MAGIC ## `if/else` # MAGIC -# MAGIC `if/else` clauses are common in many programming languages. # MAGIC -# MAGIC Note that SQL has the `CASE WHEN ... ELSE` construct, which is similar. +# MAGIC ## if/else +# MAGIC +# MAGIC **`if`** / **`else`** clauses are common in many programming languages. +# MAGIC +# MAGIC Note that SQL has the **`CASE WHEN ... ELSE`** construct, which is similar. # MAGIC -# MAGIC **If you're seeking to evaluate conditions within your tables or queries, use `CASE WHEN`.** Python control flow should be reserved for evaluating conditions outside of your query. +# MAGIC If you're seeking to evaluate conditions within your tables or queries, use **`CASE WHEN`**. # MAGIC -# MAGIC More on this later. First, an example with `"beans"`. +# MAGIC Python control flow should be reserved for evaluating conditions outside of your query. +# MAGIC +# MAGIC More on this later. First, an example with **`"beans"`**. # COMMAND ---------- @@ -37,18 +43,20 @@ # COMMAND ---------- # MAGIC %md -# MAGIC Working with `if` and `else` is all about evaluating whether or not certain conditions are true in your execution environment. +# MAGIC +# MAGIC +# MAGIC Working with **`if`** and **`else`** is all about evaluating whether or not certain conditions are true in your execution environment. # MAGIC # MAGIC Note that in Python, we have the following comparison operators: # MAGIC # MAGIC | Syntax | Operation | # MAGIC | --- | --- | -# MAGIC | `==` | equals | -# MAGIC | `>` | greater than | -# MAGIC | `<` | less than | -# MAGIC | `>=` | greater than or equal | -# MAGIC | `<=` | less than or equal | -# MAGIC | `!=` | not equal | +# MAGIC | **`==`** | equals | +# MAGIC | **`>`** | greater than | +# MAGIC | **`<`** | less than | +# MAGIC | **`>=`** | greater than or equal | +# MAGIC | **`<=`** | less than or equal | +# MAGIC | **`!=`** | not equal | # MAGIC # MAGIC If you read the sentence below out loud, you will be describing the control flow of your program. @@ -62,7 +70,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC As expected, because the variable `food` is the string literal `"beans"`, the `if` statement evaluated to `True` and the first print statement evaluated. +# MAGIC +# MAGIC +# MAGIC As expected, because the variable **`food`** is the string literal **`"beans"`**, the **`if`** statement evaluated to **`True`** and the first print statement evaluated. # MAGIC # MAGIC Let's assign a different value to the variable. @@ -73,7 +83,11 @@ # COMMAND ---------- # MAGIC %md -# MAGIC Now the first condition will evaluate as `False`. What do you think will happen when you run the following cell? +# MAGIC +# MAGIC +# MAGIC Now the first condition will evaluate as **`False`**. +# MAGIC +# MAGIC What do you think will happen when you run the following cell? # COMMAND ---------- @@ -85,6 +99,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Note that each time we assign a new value to a variable, this completely erases the old variable. # COMMAND ---------- @@ -95,14 +111,16 @@ # COMMAND ---------- # MAGIC %md -# MAGIC The Python keyword `elif` (short for `else if`) allows us to evaluate multiple conditions. +# MAGIC +# MAGIC +# MAGIC The Python keyword **`elif`** (short for **`else`** + **`if`**) allows us to evaluate multiple conditions. # MAGIC # MAGIC Note that conditions are evaluated from top to bottom. Once a condition evaluates to true, no further conditions will be evaluated. # MAGIC -# MAGIC `if/else` control flow patterns: -# MAGIC 1. Must contain an `if` clause -# MAGIC 1. Can contain any number of `elif` clauses -# MAGIC 1. Can contain at most one `else` clause +# MAGIC **`if`** / **`else`** control flow patterns: +# MAGIC 1. Must contain an **`if`** clause +# MAGIC 1. Can contain any number of **`elif`** clauses +# MAGIC 1. Can contain at most one **`else`** clause # COMMAND ---------- @@ -118,6 +136,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC By encapsulating the above logic in a function, we can reuse this logic and formatting with arbitrary arguments rather than referencing globally-defined variables. # COMMAND ---------- @@ -135,7 +155,9 @@ def foods_i_like(food): # COMMAND ---------- # MAGIC %md -# MAGIC Here, we pass the string `"bread"` to the function. +# MAGIC +# MAGIC +# MAGIC Here, we pass the string **`"bread"`** to the function. # COMMAND ---------- @@ -144,9 +166,11 @@ def foods_i_like(food): # COMMAND ---------- # MAGIC %md -# MAGIC As we evaluate the function, we locally assign the string `"bread"` to the `food` variable, and the logic behaves as expected. # MAGIC -# MAGIC Note that we don't overwrite the value of the `food` variable as previously defined in the notebook. +# MAGIC +# MAGIC As we evaluate the function, we locally assign the string **`"bread"`** to the **`food`** variable, and the logic behaves as expected. +# MAGIC +# MAGIC Note that we don't overwrite the value of the **`food`** variable as previously defined in the notebook. # COMMAND ---------- @@ -155,9 +179,11 @@ def foods_i_like(food): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## try/except # MAGIC -# MAGIC While `if/else` clauses allow us to define conditional logic based on evaluating conditional statements, `try/except` focuses on providing robust error handling. +# MAGIC While **`if`** / **`else`** clauses allow us to define conditional logic based on evaluating conditional statements, **`try`** / **`except`** focuses on providing robust error handling. # MAGIC # MAGIC Let's begin by considering a simple function. @@ -169,6 +195,8 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Let's assume that the desired use of this function is to multiply an integer value by 3. # MAGIC # MAGIC The below cell demonstrates this behavior. @@ -180,6 +208,8 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Note what happens if a string is passed to the function. # COMMAND ---------- @@ -189,11 +219,15 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC In this case, we don't get an error, but we also do not get the desired outcome. # MAGIC -# MAGIC `assert` statements allow us to run simple tests of Python code. If an `assert` statement evaluates to true, nothing happens. If it evaluates to false, an error is raised. +# MAGIC **`assert`** statements allow us to run simple tests of Python code. If an **`assert`** statement evaluates to true, nothing happens. # MAGIC -# MAGIC Run the two cells below to assert the types of `2` and `"2"`. +# MAGIC If it evaluates to false, an error is raised. +# MAGIC +# MAGIC Run the following cell to assert that the number **`2`** is an integer # COMMAND ---------- @@ -201,14 +235,26 @@ def three_times(number): # COMMAND ---------- -assert type("2") == int +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC Uncomment the following cell and then run it to assert that the string **`"2"`"** is an integer. +# MAGIC +# MAGIC It should throw an **`AssertionError`**. + +# COMMAND ---------- + +# assert type("2") == int # COMMAND ---------- # MAGIC %md -# MAGIC As expected, the string `"2"` does not evaluate as an integer. # MAGIC -# MAGIC Python strings have a property to report whether or not they can be safely cast as numeric values. +# MAGIC +# MAGIC As expected, the string **`"2"`** is not an integer. +# MAGIC +# MAGIC Python strings have a property to report whether or not they can be safely cast as numeric value as seen below. # COMMAND ---------- @@ -217,9 +263,13 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC String numbers are common; you may see them as results from an API query, raw records in a JSON or CSV file, or returned by a SQL query. # MAGIC -# MAGIC `int()` and `float()` are two common methods for casting values to numeric types. An `int` will always be a whole number, while a `float` will always have a decimal. +# MAGIC **`int()`** and **`float()`** are two common methods for casting values to numeric types. +# MAGIC +# MAGIC An **`int`** will always be a whole number, while a **`float`** will always have a decimal. # COMMAND ---------- @@ -228,18 +278,24 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC While Python will gladly cast a string containing numeric characters to a numeric type, it will not allow you to change other strings to numbers. +# MAGIC +# MAGIC Uncomment the following cell and give it a try: # COMMAND ---------- -int("two") +# int("two") # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Note that errors will stop the execution of a notebook script; all cells after an error will be skipped when a notebook is scheduled as a production job. # MAGIC -# MAGIC If we enclose code that might throw an error in a `try` statement, we can define alternate logic when an error is encountered. +# MAGIC If we enclose code that might throw an error in a **`try`** statement, we can define alternate logic when an error is encountered. # MAGIC # MAGIC Below is a simple function that demonstrates this. @@ -247,13 +303,18 @@ def three_times(number): def try_int(num_string): try: - return int(num_string) + int(num_string) + result = f"{num_string} is a number." except: - print(f"{num_string} is not a number!") + result = f"{num_string} is not a number!" + + print(result) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC When a numeric string is passed, the function will return the result as an integer. # COMMAND ---------- @@ -263,6 +324,8 @@ def try_int(num_string): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC When a non-numeric string is passed, an informative message is printed out. # MAGIC # MAGIC **NOTE**: An error is **not** raised, even though an error occurred, and no value was returned. Implementing logic that suppresses errors can lead to logic silently failing. @@ -274,6 +337,8 @@ def try_int(num_string): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Below, our earlier function is updated to include logic for handling errors to return an informative message. # COMMAND ---------- @@ -282,15 +347,15 @@ def three_times(number): try: return int(number) * 3 except ValueError as e: - print(f""" - You passed the string variable '{number}'. - The result of using this function would be to return the string '{number * 3}'. - Try passing an integer instead. - """) + print(f"You passed the string variable '{number}'.\n") + print(f"Try passing an integer instead.") + return None # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Now our function can process numbers passed as strings. # COMMAND ---------- @@ -300,6 +365,8 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC And prints an informative message when a string is passed. # COMMAND ---------- @@ -309,11 +376,15 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Note that as implemented, this logic would only be useful for interactive execution of this logic (the message isn't currently being logged anywhere, and the code will not return the data in the desired format; human intervention would be required to act upon the printed message). # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Applying Python Control Flow for SQL Queries # MAGIC # MAGIC While the above examples demonstrate the basic principles of using these designs in Python, the goal of this lesson is to learn how to apply these concepts to executing SQL logic on Databricks. @@ -333,6 +404,8 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Run the SQL cell below to preview the contents of this temp view. # COMMAND ---------- @@ -343,7 +416,9 @@ def three_times(number): # COMMAND ---------- # MAGIC %md -# MAGIC Running SQL in a Python cell simply requires passing the string query to `spark.sql()`. +# MAGIC +# MAGIC +# MAGIC Running SQL in a Python cell simply requires passing the string query to **`spark.sql()`**. # COMMAND ---------- @@ -353,7 +428,9 @@ def three_times(number): # COMMAND ---------- # MAGIC %md -# MAGIC But recall that executing a query with `spark.sql()` returns the results as a DataFrame rather than displaying them; below, the code is augmented to capture the result and display it. +# MAGIC +# MAGIC +# MAGIC But recall that executing a query with **`spark.sql()`** returns the results as a DataFrame rather than displaying them; below, the code is augmented to capture the result and display it. # COMMAND ---------- @@ -364,7 +441,9 @@ def three_times(number): # COMMAND ---------- # MAGIC %md -# MAGIC Using a simple `if` clause with a function allows us to execute arbitrary SQL queries, optionally displaying the results, and always returning the resultant DataFrame. +# MAGIC +# MAGIC +# MAGIC Using a simple **`if`** clause with a function allows us to execute arbitrary SQL queries, optionally displaying the results, and always returning the resultant DataFrame. # COMMAND ---------- @@ -381,7 +460,9 @@ def simple_query_function(query, preview=True): # COMMAND ---------- # MAGIC %md -# MAGIC Below, we execute a different query and set preview to `False`, as the purpose of the query is to create a temp view rather than return a preview of data. +# MAGIC +# MAGIC +# MAGIC Below, we execute a different query and set preview to **`False`**, as the purpose of the query is to create a temp view rather than return a preview of data. # COMMAND ---------- @@ -392,6 +473,8 @@ def simple_query_function(query, preview=True): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC We now have a simple extensible function that could be further parameterized depending on the needs of our organization. # MAGIC # MAGIC For example, suppose we want to protect our company from malicious SQL, like the query below. @@ -403,30 +486,71 @@ def simple_query_function(query, preview=True): # COMMAND ---------- # MAGIC %md -# MAGIC Below, we define a simple search for a semi-colon in the text, then use an assert statement with `try/except` to raise a custom error message. +# MAGIC +# MAGIC +# MAGIC +# MAGIC We can use the **`find()`** method to test for multiple SQL statements by looking for a semicolon. + +# COMMAND ---------- + +injection_query.find(";") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC If it's not found it will return **`-1`** + +# COMMAND ---------- + +injection_query.find("x") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC With that knowledge, we can define a simple search for a semicolon in the query string and raise a custom error message if it was found (not **`-1`**) # COMMAND ---------- def injection_check(query): semicolon_index = query.find(";") - try: - assert semicolon_index < 0, f"Query contains semi-colon at index {semicolon_index}\nBlocking execution to avoid SQL injection attack" - except AssertionError as e: - print(query) - raise e + if semicolon_index >= 0: + raise ValueError(f"Query contains semi-colon at index {semicolon_index}\nBlocking execution to avoid SQL injection attack") # COMMAND ---------- # MAGIC %md -# MAGIC **NOTE**: The example shown here is not sophisticated, but seeks to demonstrate a general principle. Always be wary of allowing untrusted users to pass text that will be passed to SQL queries. Also note that only one query can be executed using `spark.sql()`, so text with a semi-colon will always throw an error. +# MAGIC +# MAGIC +# MAGIC +# MAGIC **NOTE**: The example shown here is not sophisticated, but seeks to demonstrate a general principle. +# MAGIC +# MAGIC Always be wary of allowing untrusted users to pass text that will be passed to SQL queries. +# MAGIC +# MAGIC Also note that only one query can be executed using **`spark.sql()`**, so text with a semi-colon will always throw an error. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC Uncomment the following cell and give it a try: # COMMAND ---------- -injection_check(injection_query) +# injection_check(injection_query) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC If we add this method to our earlier query function, we now have a more robust function that will assess each query for potential threats before execution. # COMMAND ---------- @@ -441,6 +565,8 @@ def secure_query_function(query, preview=True): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC As expected, we see normal performance with a safe query. # COMMAND ---------- @@ -450,6 +576,8 @@ def secure_query_function(query, preview=True): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC But prevent execution when when bad logic is run. # COMMAND ---------- diff --git a/Data-Engineering-with-Databricks/05 - OPTIONAL Python for Spark SQL/DE 5.3L - Python for SQL Lab.py b/Data-Engineering-with-Databricks/05 - OPTIONAL Python for Spark SQL/DE 5.3L - Python for SQL Lab.py new file mode 100644 index 0000000..78ff6f2 --- /dev/null +++ b/Data-Engineering-with-Databricks/05 - OPTIONAL Python for Spark SQL/DE 5.3L - Python for SQL Lab.py @@ -0,0 +1,287 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Just Enough Python for Databricks SQL Lab +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Review basic Python code and describe expected outcomes of code execution +# MAGIC * Reason through control flow statements in Python functions +# MAGIC * Add parameters to a SQL query by wrapping it in a Python function + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-5.3L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Reviewing Python Basics +# MAGIC +# MAGIC In the previous notebook, we briefly explored using **`spark.sql()`** to execute arbitrary SQL commands from Python. +# MAGIC +# MAGIC Look at the following 3 cells. Before executing each cell, identify: +# MAGIC 1. The expected output of cell execution +# MAGIC 1. What logic is being executed +# MAGIC 1. Changes to the resultant state of the environment +# MAGIC +# MAGIC Then execute the cells, compare the results to your expectations, and see the explanations below. + +# COMMAND ---------- + +course = "dewd" + +# COMMAND ---------- + +spark.sql(f"SELECT '{course}' AS course_name") + +# COMMAND ---------- + +df = spark.sql(f"SELECT '{course}' AS course_name") +display(df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC 1. **Cmd 5** assigns a string to a variable. When a variable assignment is successful, no output is displayed to the notebook. A new variable is added to the current execution environment. +# MAGIC 1. **Cmd 6** executes a SQL query and displays the schema for the DataFrame alongside the word **`DataFrame`**. In this case, the SQL query is just to select a string, so no changes to our environment occur. +# MAGIC 1. **Cmd 7** executes the same SQL query and displays the output of the DataFrame. This combination of **`display()`** and **`spark.sql()`** most closely mirrors executing logic in a **`%sql`** cell; the results will always be printed in a formatted table, assuming results are returned by the query; some queries will instead manipulate tables or databases, in which case the work **`OK`** will print to show successful execution. In this case, no changes to our environment occur from running this code. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Setting Up a Development Environment +# MAGIC +# MAGIC Throughout this course, we use logic similar to the follow cell to capture information about the user currently executing the notebook and create an isolated development database. +# MAGIC +# MAGIC The **`re`** library is the standard Python library for regex. +# MAGIC +# MAGIC Databricks SQL has a special method to capture the username of the **`current_user()`**; and the **`.first()[0]`** code is a quick hack to capture the first row of the first column of a query executed with **`spark.sql()`** (in this case, we do this safely knowing that there will only be 1 row and 1 column). +# MAGIC +# MAGIC All other logic below is just string formatting. + +# COMMAND ---------- + +import re + +username = spark.sql("SELECT current_user()").first()[0] +clean_username = re.sub("[^a-zA-Z0-9]", "_", username) +db_name = f"dbacademy_{clean_username}_{course}_5_3l" +working_dir = f"dbfs:/user/{username}/dbacademy/{course}/5.3l" + +print(f"username: {username}") +print(f"db_name: {db_name}") +print(f"working_dir: {working_dir}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Below, we add a simple control flow statement to this logic to create and use this user-specific database. +# MAGIC +# MAGIC Optionally, we will reset this database and drop all of the contents on repeat execution. (Note the the default value for the parameter **`reset`** is **`True`**). + +# COMMAND ---------- + +def create_database(course, reset=True): + import re + + username = spark.sql("SELECT current_user()").first()[0] + clean_username = re.sub("[^a-zA-Z0-9]", "_", username) + db_name = f"dbacademy_{clean_username}_{course}_5_3l" + working_dir = f"dbfs:/user/{username}/dbacademy/{course}/5.3l" + + print(f"username: {username}") + print(f"db_name: {db_name}") + print(f"working_dir: {working_dir}") + + if reset: + spark.sql(f"DROP DATABASE IF EXISTS {db_name} CASCADE") + dbutils.fs.rm(working_dir, True) + + spark.sql(f"CREATE DATABASE IF NOT EXISTS {db_name} LOCATION '{working_dir}/{db_name}.db'") + spark.sql(f"USE {db_name}") + +create_database(course) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC While this logic as defined is geared toward isolating students in shared workspaces for instructional purposes, the same basic design could be leveraged for testing new logic in an isolated environment before pushing to production. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Handling Errors Gracefully +# MAGIC +# MAGIC Review the logic in the function below. +# MAGIC +# MAGIC Note that we've just declared a new database that currently contains no tables. + +# COMMAND ---------- + +def query_or_make_demo_table(table_name): + try: + display(spark.sql(f"SELECT * FROM {table_name}")) + print(f"Displayed results for the table {table_name}") + + except: + spark.sql(f"CREATE TABLE {table_name} (id INT, name STRING, value DOUBLE, state STRING)") + spark.sql(f"""INSERT INTO {table_name} + VALUES (1, "Yve", 1.0, "CA"), + (2, "Omar", 2.5, "NY"), + (3, "Elia", 3.3, "OH"), + (4, "Rebecca", 4.7, "TX"), + (5, "Ameena", 5.3, "CA"), + (6, "Ling", 6.6, "NY"), + (7, "Pedro", 7.1, "KY")""") + + display(spark.sql(f"SELECT * FROM {table_name}")) + print(f"Created the table {table_name}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Try to identify the following before executing the next cell: +# MAGIC 1. The expected output of cell execution +# MAGIC 1. What logic is being executed +# MAGIC 1. Changes to the resultant state of the environment + +# COMMAND ---------- + +query_or_make_demo_table("demo_table") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Now answer the same three questions before running the same query below. + +# COMMAND ---------- + +query_or_make_demo_table("demo_table") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC - On the first execution, the table **`demo_table`** did not yet exist. As such, the attempt to return the contents of the table created an error, which resulted in our **`except`** block of logic executing. This block: +# MAGIC 1. Created the table +# MAGIC 1. Inserted values +# MAGIC 1. Printed or displayed the contents of the table +# MAGIC - On the second execution, the table **`demo_table`** already exists, and so the first query in the **`try`** block executes without error. As a result, we just display the results of the query without modifying anything in our environment. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Adapting SQL to Python +# MAGIC Let's consider the following SQL query against our demo table created above. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT id, value +# MAGIC FROM demo_table +# MAGIC WHERE state = "CA" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC which can also be expressed using the PySpark API and the **`display`** function as seen here: + +# COMMAND ---------- + +results = spark.sql("SELECT id, value FROM demo_table WHERE state = 'CA'") +display(results) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Let's use this simple example to practice creating a Python function that adds optional functionality. +# MAGIC +# MAGIC Our target function will: +# MAGIC * Be based upon a query that only includes the **`id`** and **`value`** columns from the a table named **`demo_table`** +# MAGIC * Will allow filtering of that query by **`state`** where the the default behavior is to include all states +# MAGIC * Will optionally render the results of the query using the **`display`** function where the default behavior is to not render +# MAGIC * Will return: +# MAGIC * The query result object (a PySpark DataFrame) if **`render_results`** is False +# MAGIC * The **`None`** value if **`render_results`** is True +# MAGIC +# MAGIC Stretch Goal: +# MAGIC * Add an assert statement to verify that the value passed for the **`state`** parameter contains two, uppercase letters +# MAGIC +# MAGIC Some starter logic has been provided below: + +# COMMAND ---------- + +# TODO +def preview_values(state=, render_results=): + query = + + if state is not None: + + + if render_results + + + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The assert statements below can be used to check whether or not your function works as intended. + +# COMMAND ---------- + +import pyspark.sql.dataframe + +assert type(preview_values()) == pyspark.sql.dataframe.DataFrame, "Function should return the results as a DataFrame" +assert preview_values().columns == ["id", "value"], "Query should only return **`id`** and **`value`** columns" + +assert preview_values(render_results=True) is None, "Function should not return None when rendering" +assert preview_values(render_results=False) is not None, "Function should return DataFrame when not rendering" + +assert preview_values(state=None).count() == 7, "Function should allow no state" +assert preview_values(state="NY").count() == 2, "Function should allow filtering by state" +assert preview_values(state="CA").count() == 2, "Function should allow filtering by state" +assert preview_values(state="OH").count() == 1, "Function should allow filtering by state" + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/06 - Incremental Data Processing/DE 6.1 - Incremental Data Ingestion with Auto Loader.py b/Data-Engineering-with-Databricks/06 - Incremental Data Processing/DE 6.1 - Incremental Data Ingestion with Auto Loader.py new file mode 100644 index 0000000..7904cf1 --- /dev/null +++ b/Data-Engineering-with-Databricks/06 - Incremental Data Processing/DE 6.1 - Incremental Data Ingestion with Auto Loader.py @@ -0,0 +1,279 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Incremental Data Ingestion with Auto Loader +# MAGIC +# MAGIC Incremental ETL is important since it allows us to deal solely with new data that has been encountered since the last ingestion. Reliably processing only the new data reduces redundant processing and helps enterprises reliably scale data pipelines. +# MAGIC +# MAGIC The first step for any successful data lakehouse implementation is ingesting into a Delta Lake table from cloud storage. +# MAGIC +# MAGIC Historically, ingesting files from a data lake into a database has been a complicated process. +# MAGIC +# MAGIC Databricks Auto Loader provides an easy-to-use mechanism for incrementally and efficiently processing new data files as they arrive in cloud file storage. In this notebook, you'll see Auto Loader in action. +# MAGIC +# MAGIC Due to the benefits and scalability that Auto Loader delivers, Databricks recommends its use as general **best practice** when ingesting data from cloud object storage. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Execute Auto Loader code to incrementally ingest data from cloud storage to Delta Lake +# MAGIC * Describe what happens when a new file arrives in a directory configured for Auto Loader +# MAGIC * Query a table fed by a streaming Auto Loader query +# MAGIC +# MAGIC ## Dataset Used +# MAGIC This demo uses simplified artificially generated medical data representing heart rate recordings delivered in the JSON format. +# MAGIC +# MAGIC | Field | Type | +# MAGIC | --- | --- | +# MAGIC | device_id | int | +# MAGIC | mrn | long | +# MAGIC | time | double | +# MAGIC | heartrate | double | + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Getting Started +# MAGIC +# MAGIC Run the following cell to reset the demo and configure required variables and help functions. + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-6.1 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Using Auto Loader +# MAGIC +# MAGIC In the cell below, a function is defined to demonstrate using Databricks Auto Loader with the PySpark API. This code includes both a Structured Streaming read and write. +# MAGIC +# MAGIC The following notebook will provide a more robust overview of Structured Streaming. If you wish to learn more about Auto Loader options, refer to the documentation. +# MAGIC +# MAGIC Note that when using Auto Loader with automatic schema inference and evolution, the 4 arguments shown here should allow ingestion of most datasets. These arguments are explained below. +# MAGIC +# MAGIC | argument | what it is | how it's used | +# MAGIC | --- | --- | --- | +# MAGIC | **`data_source`** | The directory of the source data | Auto Loader will detect new files as they arrive in this location and queue them for ingestion; passed to the **`.load()`** method | +# MAGIC | **`source_format`** | The format of the source data | While the format for all Auto Loader queries will be **`cloudFiles`**, the format of the source data should always be specified for the **`cloudFiles.format`** option | +# MAGIC | **`table_name`** | The name of the target table | Spark Structured Streaming supports writing directly to Delta Lake tables by passing a table name as a string to the **`.table()`** method. Note that you can either append to an existing table or create a new table | +# MAGIC | **`checkpoint_directory`** | The location for storing metadata about the stream | This argument is pass to the **`checkpointLocation`** and **`cloudFiles.schemaLocation`** options. Checkpoints keep track of streaming progress, while the schema location tracks updates to the fields in the source dataset | +# MAGIC +# MAGIC **NOTE**: The code below has been streamlined to demonstrate Auto Loader functionality. We'll see in later lessons that additional transformations can be applied to source data before saving them to Delta Lake. + +# COMMAND ---------- + +def autoload_to_table(data_source, source_format, table_name, checkpoint_directory): + query = (spark.readStream + .format("cloudFiles") + .option("cloudFiles.format", source_format) + .option("cloudFiles.schemaLocation", checkpoint_directory) + .load(data_source) + .writeStream + .option("checkpointLocation", checkpoint_directory) + .option("mergeSchema", "true") + .table(table_name)) + return query + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC In the following cell, we use the previously defined function and some path variables defined in the setup script to begin an Auto Loader stream. +# MAGIC +# MAGIC Here, we're reading from a source directory of JSON files. + +# COMMAND ---------- + +query = autoload_to_table(data_source = f"{DA.paths.working_dir}/tracker", + source_format = "json", + table_name = "target_table", + checkpoint_directory = f"{DA.paths.checkpoints}/target_table") + + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Because Auto Loader uses Spark Structured Streaming to load data incrementally, the code above doesn't appear to finish executing. +# MAGIC +# MAGIC We can think of this as a **continuously active query**. This means that as soon as new data arrives in our data source, it will be processed through our logic and loaded into our target table. We'll explore this in just a second. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Helper Function for Streaming Lessons +# MAGIC +# MAGIC Our notebook-based lessons combine streaming functions with batch and streaming queries against the results of those operations. These notebooks are for instructional purposes and intended for interactive, cell-by-cell execution. This pattern is not intended for production. +# MAGIC +# MAGIC Below, we define a helper function that prevents our notebook from executing the next cell just long enough to ensure data has been written out by a given streaming query. This code should not be necessary in a production job. + +# COMMAND ---------- + +def block_until_stream_is_ready(query, min_batches=2): + import time + while len(query.recentProgress) < min_batches: + time.sleep(5) # Give it a couple of seconds + + print(f"The stream has processed {len(query.recentProgress)} batchs") + +block_until_stream_is_ready(query) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Query Target Table +# MAGIC +# MAGIC Once data has been ingested to Delta Lake with Auto Loader, users can interact with it the same way they would any table. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM target_table + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Note that the **`_rescued_data`** column is added by Auto Loader automatically to capture any data that might be malformed and not fit into the table otherwise. +# MAGIC +# MAGIC While Auto Loader captured the field names for our data correctly, note that it encoded all fields as **`STRING`** type. Because JSON is a text-based format, this is the safest and most permissive type, ensuring that the least amount of data is dropped or ignored at ingestion due to type mismatch. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC DESCRIBE TABLE target_table + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Use the cell below to define a temporary view that summarizes the recordings in our target table. +# MAGIC +# MAGIC We'll use this view below to demonstrate how new data is automatically ingested with Auto Loader. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC CREATE OR REPLACE TEMP VIEW device_counts AS +# MAGIC SELECT device_id, count(*) total_recordings +# MAGIC FROM target_table +# MAGIC GROUP BY device_id; +# MAGIC +# MAGIC SELECT * FROM device_counts + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Land New Data +# MAGIC +# MAGIC As mentioned previously, Auto Loader is configured to incrementally process files from a directory in cloud object storage into a Delta Lake table. +# MAGIC +# MAGIC We have configured and are currently executing a query to process JSON files from the location specified by **`source_path`** into a table named **`target_table`**. Let's review the contents of the **`source_path`** directory. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.working_dir}/tracker") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC At present, you should see a single JSON file listed in this location. +# MAGIC +# MAGIC The method in the cell below was configured in our setup script to allow us to model an external system writing data to this directory. Each time you execute the cell below, a new file will land in the **`source_path`** directory. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC List the contents of the **`source_path`** again using the cell below. You should see an additional JSON file for each time you ran the previous cell. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.working_dir}/tracker") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Tracking Ingestion Progress +# MAGIC +# MAGIC Historically, many systems have been configured to either reprocess all records in a source directory to calculate current results or require data engineers to implement custom logic to identify new data that's arrived since the last time a table was updated. +# MAGIC +# MAGIC With Auto Loader, your table has already been updated. +# MAGIC +# MAGIC Run the query below to confirm that new data has been ingested. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM device_counts + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The Auto Loader query we configured earlier automatically detects and processes records from the source directory into the target table. There is a slight delay as records are ingested, but an Auto Loader query executing with default streaming configuration should update results in near real time. +# MAGIC +# MAGIC The query below shows the table history. A new table version should be indicated for each **`STREAMING UPDATE`**. These update events coincide with new batches of data arriving at the source. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC DESCRIBE HISTORY target_table + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Clean Up +# MAGIC Feel free to continue landing new data and exploring the table results with the cells above. +# MAGIC +# MAGIC When you're finished, run the following cell to stop all active streams and remove created resources before continuing. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/06 - Incremental Data Processing/DE 6.2 - Reasoning about Incremental Data.py b/Data-Engineering-with-Databricks/06 - Incremental Data Processing/DE 6.2 - Reasoning about Incremental Data.py new file mode 100644 index 0000000..fe8cc79 --- /dev/null +++ b/Data-Engineering-with-Databricks/06 - Incremental Data Processing/DE 6.2 - Reasoning about Incremental Data.py @@ -0,0 +1,397 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Reasoning about Incremental Data +# MAGIC +# MAGIC Spark Structured Streaming extends the functionality of Apache Spark to allow for simplified configuration and bookkeeping when processing incremental datasets. In the past, much of the emphasis for streaming with big data has focused on reducing latency to provide near real time analytic insights. While Structured Streaming provides exceptional performance in achieving these goals, this lesson will focus more on the applications of incremental data processing. +# MAGIC +# MAGIC While incremental processing is not absolutely necessary to work successfully in the data lakehouse, our experience helping some of the world's largest companies derive insights from the world's largest datasets has led to the conclusion that many workloads can benefit substantially from an incremental processing approach. Many of the core features at the heart of Databricks have been optimized specifically to handle these ever-growing datasets. +# MAGIC +# MAGIC Consider the following datasets and use cases: +# MAGIC * Data scientists need secure, de-identified, versioned access to frequently updated records in an operational database +# MAGIC * Credit card transactions need to be compared to past customer behavior to identify and flag fraud +# MAGIC * A multi-national retailer seeks to serve custom product recommendations using purchase history +# MAGIC * Log files from distributed systems need to be analayzed to detect and respond to instabilities +# MAGIC * Clickstream data from millions of online shoppers needs to be leveraged for A/B testing of UX +# MAGIC +# MAGIC The above are just a small sample of datasets that grow incrementally and infinitely over time. +# MAGIC +# MAGIC In this lesson, we'll explore the basics of working with Spark Structured Streaming to allow incremental processing of data. In the next lesson, we'll talk more about how this incremental processing model simplifies data processing in the data lakehouse. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Describe the programming model used by Spark Structured Streaming +# MAGIC * Configure required options to perform a streaming read on a source +# MAGIC * Describe the requirements for end-to-end fault tolerance +# MAGIC * Configure required options to perform a streaming write to a sink + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Getting Started +# MAGIC +# MAGIC Run the following cell to configure our "classroom." + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-6.2 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Treating Infinite Data as a Table +# MAGIC +# MAGIC The magic behind Spark Structured Streaming is that it allows users to interact with ever-growing data sources as if they were just a static table of records. +# MAGIC +# MAGIC +# MAGIC +# MAGIC In the graphic above, a **data stream** describes any data source that grows over time. New data in a data stream might correspond to: +# MAGIC * A new JSON log file landing in cloud storage +# MAGIC * Updates to a database captured in a CDC feed +# MAGIC * Events queued in a pub/sub messaging feed +# MAGIC * A CSV file of sales closed the previous day +# MAGIC +# MAGIC Many organizations have traditionally taken an approach of reprocessing the entire source dataset each time they want to update their results. Another approach would be to write custom logic to only capture those files or records that have been added since the last time an update was run. +# MAGIC +# MAGIC Structured Streaming lets us define a query against the data source and automatically detect new records and propagate them through previously defined logic. +# MAGIC +# MAGIC **Spark Structured Streaming is optimized on Databricks to integrate closely with Delta Lake and Auto Loader.** + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Basic Concepts +# MAGIC +# MAGIC - The developer defines an **input table** by configuring a streaming read against a **source**. The syntax for doing this is similar to working with static data. +# MAGIC - A **query** is defined against the input table. Both the DataFrames API and Spark SQL can be used to easily define transformations and actions against the input table. +# MAGIC - This logical query on the input table generates the **results table**. The results table contains the incremental state information of the stream. +# MAGIC - The **output** of a streaming pipeline will persist updates to the results table by writing to an external **sink**. Generally, a sink will be a durable system such as files or a pub/sub messaging bus. +# MAGIC - New rows are appended to the input table for each **trigger interval**. These new rows are essentially analogous to micro-batch transactions and will be automatically propagated through the results table to the sink. +# MAGIC +# MAGIC +# MAGIC +# MAGIC +# MAGIC For more information, see the analogous section in the Structured Streaming Programming Guide (from which several images have been borrowed). + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## End-to-end Fault Tolerance +# MAGIC +# MAGIC Structured Streaming ensures end-to-end exactly-once fault-tolerance guarantees through _checkpointing_ (discussed below) and Write Ahead Logs. +# MAGIC +# MAGIC Structured Streaming sources, sinks, and the underlying execution engine work together to track the progress of stream processing. If a failure occurs, the streaming engine attempts to restart and/or reprocess the data. +# MAGIC For best practices on recovering from a failed streaming query see docs. +# MAGIC +# MAGIC This approach _only_ works if the streaming source is replayable; replayable sources include cloud-based object storage and pub/sub messaging services. +# MAGIC +# MAGIC At a high level, the underlying streaming mechanism relies on a couple of approaches: +# MAGIC +# MAGIC * First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval. +# MAGIC * Next, the streaming sinks are designed to be _idempotent_ - that is, multiple writes of the same data (as identified by the offset) do _not_ result in duplicates being written to the sink. +# MAGIC +# MAGIC Taken together, replayable data sources and idempotent sinks allow Structured Streaming to ensure **end-to-end, exactly-once semantics** under any failure condition. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Reading a Stream +# MAGIC +# MAGIC The **`spark.readStream()`** method returns a **`DataStreamReader`** used to configure and query the stream. +# MAGIC +# MAGIC In the previous lesson, we saw code configured for incrementally reading with Auto Loader. Here, we'll show how easy it is to incrementally read a Delta Lake table. +# MAGIC +# MAGIC The code uses the PySpark API to incrementally read a Delta Lake table named **`bronze`** and register a streaming temp view named **`streaming_tmp_vw`**. +# MAGIC +# MAGIC **NOTE**: A number of optional configurations (not shown here) can be set when configuring incremental reads, the most important of which allows you to limit the input rate. + +# COMMAND ---------- + +(spark.readStream + .table("bronze") + .createOrReplaceTempView("streaming_tmp_vw")) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC When we execute a query on a streaming temporary view, we'll continue to update the results of the query as new data arrives in the source. +# MAGIC +# MAGIC Think of a query executed against a streaming temp view as an **always-on incremental query**. +# MAGIC +# MAGIC **NOTE**: Generally speaking, unless a human is actively monitoring the output of a query during development or live dashboarding, we won't return streaming results to a notebook. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM streaming_tmp_vw + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC You will recognize the data as being the same as the Delta table written out in our previous lesson. +# MAGIC +# MAGIC Before continuing, click **`Stop Execution`** at the top of the notebook, **`Cancel`** immediately under the cell, or run the following cell to stop all active streaming queries. + +# COMMAND ---------- + +for s in spark.streams.active: + print("Stopping " + s.id) + s.stop() + s.awaitTermination() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Working with Streaming Data +# MAGIC We can execute most transformation against streaming temp views the same way we would with static data. Here, we'll run a simple aggregation to get counts of records for each **`device_id`**. +# MAGIC +# MAGIC Because we are querying a streaming temp view, this becomes a streaming query that executes indefinitely, rather than completing after retrieving a single set of results. For streaming queries like this, Databricks Notebooks include interactive dashboards that allow users to monitor streaming performance. Explore this below. +# MAGIC +# MAGIC One important note regarding this example: this is merely displaying an aggregation of input as seen by the stream. **None of these records are being persisted anywhere at this point.** + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT device_id, count(device_id) AS total_recordings +# MAGIC FROM streaming_tmp_vw +# MAGIC GROUP BY device_id + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Before continuing, click **`Stop Execution`** at the top of the notebook, **`Cancel`** immediately under the cell, or run the following cell to stop all active streaming queries. + +# COMMAND ---------- + +for s in spark.streams.active: + print("Stopping " + s.id) + s.stop() + s.awaitTermination() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Unsupported Operations +# MAGIC +# MAGIC Most operations on a streaming DataFrame are identical to a static DataFrame. There are some exceptions to this. +# MAGIC +# MAGIC Consider the model of the data as a constantly appending table. Sorting is one of a handful of operations that is either too complex or logically not possible to do when working with streaming data. +# MAGIC +# MAGIC A full discussion of these exceptions is out of scope for this course. Note that advanced streaming methods like windowing and watermarking can be used to add additional functionality to incremental workloads. +# MAGIC +# MAGIC Uncomment and run the following cell how this failure may appear: + +# COMMAND ---------- + +# %sql +# SELECT * +# FROM streaming_tmp_vw +# ORDER BY time + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Persisting Streaming Results +# MAGIC +# MAGIC In order to persist incremental results, we need to pass our logic back to the PySpark Structured Streaming DataFrames API. +# MAGIC +# MAGIC Above, we created a temp view from a PySpark streaming DataFrame. If we create another temp view from the results of a query against a streaming temp view, we'll again have a streaming temp view. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC CREATE OR REPLACE TEMP VIEW device_counts_tmp_vw AS ( +# MAGIC SELECT device_id, COUNT(device_id) AS total_recordings +# MAGIC FROM streaming_tmp_vw +# MAGIC GROUP BY device_id +# MAGIC ) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Writing a Stream +# MAGIC +# MAGIC To persist the results of a streaming query, we must write them out to durable storage. The **`DataFrame.writeStream`** method returns a **`DataStreamWriter`** used to configure the output. +# MAGIC +# MAGIC When writing to Delta Lake tables, we typically will only need to worry about 3 settings, discussed here. +# MAGIC +# MAGIC ### Checkpointing +# MAGIC +# MAGIC Databricks creates checkpoints by storing the current state of your streaming job to cloud storage. +# MAGIC +# MAGIC Checkpointing combines with write ahead logs to allow a terminated stream to be restarted and continue from where it left off. +# MAGIC +# MAGIC Checkpoints cannot be shared between separate streams. A checkpoint is required for every streaming write to ensure processing guarantees. +# MAGIC +# MAGIC ### Output Modes +# MAGIC +# MAGIC Streaming jobs have output modes similar to static/batch workloads. More details here. +# MAGIC +# MAGIC | Mode | Example | Notes | +# MAGIC | ------------- | ----------- | --- | +# MAGIC | **Append** | **`.outputMode("append")`** | **This is the default.** Only newly appended rows are incrementally appended to the target table with each batch | +# MAGIC | **Complete** | **`.outputMode("complete")`** | The Results Table is recalculated each time a write is triggered; the target table is overwritten with each batch | +# MAGIC +# MAGIC +# MAGIC ### Trigger Intervals +# MAGIC +# MAGIC When defining a streaming write, the **`trigger`** method specifies when the system should process the next set of data.. +# MAGIC +# MAGIC +# MAGIC | Trigger Type | Example | Behavior | +# MAGIC |----------------------------------------|----------|----------| +# MAGIC | Unspecified | | **This is the default.** This is equivalent to using **`processingTime="500ms"`** | +# MAGIC | Fixed interval micro-batches | **`.trigger(processingTime="2 minutes")`** | The query will be executed in micro-batches and kicked off at the user-specified intervals | +# MAGIC | Triggered micro-batch | **`.trigger(once=True)`** | The query will execute a single micro-batch to process all the available data and then stop on its own | +# MAGIC | Triggered micro-batches | **`.trigger(availableNow=True)`** | The query will execute multiple micro-batches to process all the available data and then stop on its own | +# MAGIC +# MAGIC Triggers are specified when defining how data will be written to a sink and control the frequency of micro-batches. By default, Spark will automatically detect and process all data in the source that has been added since the last trigger. +# MAGIC +# MAGIC **NOTE:** **`Trigger.AvailableNow`** is a new trigger type that is available in DBR 10.1 for Scala only and available in DBR 10.2 and above for Python and Scala. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Pulling It All Together +# MAGIC +# MAGIC The code below demonstrates using **`spark.table()`** to load data from a streaming temp view back to a DataFrame. Note that Spark will always load streaming views as a streaming DataFrame and static views as static DataFrames (meaning that incremental processing must be defined with read logic to support incremental writing). +# MAGIC +# MAGIC In this first query, we'll demonstrate using **`trigger(availableNow=True)`** to perform incremental batch processing. + +# COMMAND ---------- + +(spark.table("device_counts_tmp_vw") + .writeStream + .option("checkpointLocation", f"{DA.paths.checkpoints}/silver") + .outputMode("complete") + .trigger(availableNow=True) + .table("device_counts") + .awaitTermination() # This optional method blocks execution of the next cell until the incremental batch write has succeeded +) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Below, we change our trigger method to change this query from a triggered incremental batch to an always-on query triggered every 4 seconds. +# MAGIC +# MAGIC **NOTE**: As we start this query, no new records exist in our source table. We'll add new data shortly. + +# COMMAND ---------- + +query = (spark.table("device_counts_tmp_vw") + .writeStream + .option("checkpointLocation", f"{DA.paths.checkpoints}/silver") + .outputMode("complete") + .trigger(processingTime='4 seconds') + .table("device_counts")) + +# Like before, wait until our stream has processed some data +DA.block_until_stream_is_ready(query) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Querying the Output +# MAGIC Now let's query the output we've written from SQL. Because the result is a table, we only need to deserialize the data to return the results. +# MAGIC +# MAGIC Because we are now querying a table (not a streaming DataFrame), the following will **not** be a streaming query. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * +# MAGIC FROM device_counts + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Land New Data +# MAGIC +# MAGIC As in our previous lesson, we have configured a helper function to process new records into our source table. +# MAGIC +# MAGIC Run the cell below to land another batch of data. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Query the target table again to see the updated counts for each **`device_id`**. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * +# MAGIC FROM device_counts + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Clean Up +# MAGIC Feel free to continue landing new data and exploring the table results with the cells above. +# MAGIC +# MAGIC When you're finished, run the following cell to stop all active streams and remove created resources before continuing. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/06 - Incremental Data Processing/DE 6.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py b/Data-Engineering-with-Databricks/06 - Incremental Data Processing/DE 6.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py new file mode 100644 index 0000000..71db9fb --- /dev/null +++ b/Data-Engineering-with-Databricks/06 - Incremental Data Processing/DE 6.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py @@ -0,0 +1,176 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Using Auto Loader and Structured Streaming with Spark SQL +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Ingest data using Auto Loader +# MAGIC * Aggregate streaming data +# MAGIC * Stream data to a Delta table + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Setup +# MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-6.3L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Configure Streaming Read +# MAGIC +# MAGIC This lab uses a collection of customer-related CSV data from DBFS found in */databricks-datasets/retail-org/customers/*. +# MAGIC +# MAGIC Read this data using Auto Loader using its schema inference (use **`customers_checkpoint_path`** to store the schema info). Create a streaming temporary view called **`customers_raw_temp`**. + +# COMMAND ---------- + +# TODO +customers_checkpoint_path = f"{DA.paths.checkpoints}/customers" + +(spark + .readStream + + .load("/databricks-datasets/retail-org/customers/") + .createOrReplaceTempView("customers_raw_temp")) + +# COMMAND ---------- + +from pyspark.sql import Row +assert Row(tableName="customers_raw_temp", isTemporary=True) in spark.sql("show tables").select("tableName", "isTemporary").collect(), "Table not present or not temporary" +assert spark.table("customers_raw_temp").dtypes == [('customer_id', 'string'), + ('tax_id', 'string'), + ('tax_code', 'string'), + ('customer_name', 'string'), + ('state', 'string'), + ('city', 'string'), + ('postcode', 'string'), + ('street', 'string'), + ('number', 'string'), + ('unit', 'string'), + ('region', 'string'), + ('district', 'string'), + ('lon', 'string'), + ('lat', 'string'), + ('ship_to_address', 'string'), + ('valid_from', 'string'), + ('valid_to', 'string'), + ('units_purchased', 'string'), + ('loyalty_segment', 'string'), + ('_rescued_data', 'string')], "Incorrect Schema" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Define a streaming aggregation +# MAGIC +# MAGIC Using CTAS syntax, define a new streaming view called **`customer_count_by_state_temp`** that counts the number of customers per **`state`**, in a field called **`customer_count`**. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- TODO +# MAGIC +# MAGIC CREATE OR REPLACE TEMPORARY VIEW customer_count_by_state_temp AS +# MAGIC SELECT +# MAGIC + +# COMMAND ---------- + +assert Row(tableName="customer_count_by_state_temp", isTemporary=True) in spark.sql("show tables").select("tableName", "isTemporary").collect(), "Table not present or not temporary" +assert spark.table("customer_count_by_state_temp").dtypes == [('state', 'string'), ('customer_count', 'bigint')], "Incorrect Schema" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Write aggregated data to a Delta table +# MAGIC +# MAGIC Stream data from the **`customer_count_by_state_temp`** view to a Delta table called **`customer_count_by_state`**. + +# COMMAND ---------- + +# TODO +customers_count_checkpoint_path = f"{DA.paths.checkpoints}/customers_count" + +query = (spark + + +# COMMAND ---------- + +DA.block_until_stream_is_ready(query) + +# COMMAND ---------- + +assert Row(tableName="customer_count_by_state", isTemporary=False) in spark.sql("show tables").select("tableName", "isTemporary").collect(), "Table not present or not temporary" +assert spark.table("customer_count_by_state").dtypes == [('state', 'string'), ('customer_count', 'bigint')], "Incorrect Schema" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Query the results +# MAGIC +# MAGIC Query the **`customer_count_by_state`** table (this will not be a streaming query). Plot the results as a bar graph and also using the map plot. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- TODO + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Wrapping Up +# MAGIC +# MAGIC Run the following cell to remove the database and all data associated with this lab. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC By completing this lab, you should now feel comfortable: +# MAGIC * Using PySpark to configure Auto Loader for incremental data ingestion +# MAGIC * Using Spark SQL to aggregate streaming data +# MAGIC * Streaming data to a Delta table + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.2.1 - Incremental Multi-Hop in the Lakehouse.py b/Data-Engineering-with-Databricks/07 - Multi-Hop Architecture/DE 7.1 - Incremental Multi-Hop in the Lakehouse.py similarity index 80% rename from Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.2.1 - Incremental Multi-Hop in the Lakehouse.py rename to Data-Engineering-with-Databricks/07 - Multi-Hop Architecture/DE 7.1 - Incremental Multi-Hop in the Lakehouse.py index 3a82aac..ff85bf7 100644 --- a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.2.1 - Incremental Multi-Hop in the Lakehouse.py +++ b/Data-Engineering-with-Databricks/07 - Multi-Hop Architecture/DE 7.1 - Incremental Multi-Hop in the Lakehouse.py @@ -8,6 +8,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Incremental Multi-Hop in the Lakehouse # MAGIC # MAGIC Now that we have a better understanding of how to work with incremental data processing by combining Structured Streaming APIs and Spark SQL, we can explore the tight integration between Structured Streaming and Delta Lake. @@ -22,6 +24,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Incremental Updates in the Lakehouse # MAGIC # MAGIC Delta Lake allows users to easily combine streaming and batch workloads in a unified multi-hop pipeline. Each stage of the pipeline represents a state of our data valuable to driving core use cases within the business. Because all data and metadata lives in object storage in the cloud, multiple users and applications can access data in near-real time, allowing analysts to access the freshest data as it's being processed. @@ -43,6 +47,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Datasets Used # MAGIC # MAGIC This demo uses simplified artificially generated medical data. The schema of our two datasets is represented below. Note that we will be manipulating these schema during various steps. @@ -68,34 +74,42 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Getting Started # MAGIC # MAGIC Run the following cell to configure the lab environment. # COMMAND ---------- -# MAGIC %run "../Includes/multi-hop-setup" $mode="reset" +# MAGIC %run ../Includes/Classroom-Setup-7.1 # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Data Simulator -# MAGIC Databricks Auto Loader can automatically process files as they land in your cloud object stores. To simulate this process, you will be asked to run the following operation several times throughout the course. +# MAGIC Databricks Auto Loader can automatically process files as they land in your cloud object stores. +# MAGIC +# MAGIC To simulate this process, you will be asked to run the following operation several times throughout the course. # COMMAND ---------- -File.newData() +DA.data_factory.load() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Bronze Table: Ingesting Raw JSON Recordings # MAGIC # MAGIC Below, we configure a read on a raw JSON source using Auto Loader with schema inference. # MAGIC # MAGIC Note that while you need to use the Spark DataFrame API to set up an incremental read, once configured you can immediately register a temp view to leverage Spark SQL for streaming transformations on your data. # MAGIC -# MAGIC **NOTE**: For a JSON data source, Auto Loader will default to inferring each column as a string. Here, we demonstrate specifying the data type for the `time` column using the `cloudFiles.schemaHints` option. Note that specifying improper types for a field will result in null values. +# MAGIC **NOTE**: For a JSON data source, Auto Loader will default to inferring each column as a string. Here, we demonstrate specifying the data type for the **`time`** column using the **`cloudFiles.schemaHints`** option. Note that specifying improper types for a field will result in null values. # COMMAND ---------- @@ -103,13 +117,15 @@ .format("cloudFiles") .option("cloudFiles.format", "json") .option("cloudFiles.schemaHints", "time DOUBLE") - .option("cloudFiles.schemaLocation", bronzeCheckpoint) - .load(dataLandingLocation) + .option("cloudFiles.schemaLocation", f"{DA.paths.checkpoints}/bronze") + .load(DA.paths.data_landing_location) .createOrReplaceTempView("recordings_raw_temp")) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Here, we'll enrich our raw data with additional metadata describing the source file and the time it was ingested. This additional metadata can be ignored during downstream processing while providing useful information for troubleshooting errors if corrupt data is encountered. # COMMAND ---------- @@ -123,29 +139,35 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC The code below passes our enriched raw data back to PySpark API to process an incremental write to a Delta Lake table. # COMMAND ---------- (spark.table("recordings_bronze_temp") - .writeStream - .format("delta") - .option("checkpointLocation", bronzeCheckpoint) - .outputMode("append") - .table("bronze")) + .writeStream + .format("delta") + .option("checkpointLocation", f"{DA.paths.checkpoints}/bronze") + .outputMode("append") + .table("bronze")) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Trigger another file arrival with the following cell and you'll see the changes immediately detected by the streaming query you've written. # COMMAND ---------- -File.newData() +DA.data_factory.load() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ### Load Static Lookup Table # MAGIC The ACID guarantees that Delta Lake brings to your data are managed at the table level, ensuring that only fully successfully commits are reflected in your tables. If you choose to merge these data with other data sources, be aware of how those sources version data and what sort of consistency guarantees they have. # MAGIC @@ -153,13 +175,12 @@ # COMMAND ---------- -(spark - .read - .format("csv") - .schema("mrn STRING, name STRING") - .option("header", True) - .load(f"{dataSource}/patient/patient_info.csv") - .createOrReplaceTempView("pii")) +(spark.read + .format("csv") + .schema("mrn STRING, name STRING") + .option("header", True) + .load(f"{DA.paths.data_source}/patient/patient_info.csv") + .createOrReplaceTempView("pii")) # COMMAND ---------- @@ -169,10 +190,12 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Silver Table: Enriched Recording Data # MAGIC As a second hop in our silver level, we will do the follow enrichments and checks: # MAGIC - Our recordings data will be joined with the PII to add patient names -# MAGIC - The time for our recordings will be parsed to the format `'yyyy-MM-dd HH:mm:ss'` to be human-readable +# MAGIC - The time for our recordings will be parsed to the format **`'yyyy-MM-dd HH:mm:ss'`** to be human-readable # MAGIC - We will exclude heart rates that are <= 0, as we know that these either represent the absence of the patient or an error in transmission # COMMAND ---------- @@ -194,15 +217,17 @@ # COMMAND ---------- (spark.table("recordings_w_pii") - .writeStream - .format("delta") - .option("checkpointLocation", recordingsEnrichedCheckpoint) - .outputMode("append") - .table("recordings_enriched")) + .writeStream + .format("delta") + .option("checkpointLocation", f"{DA.paths.checkpoints}/recordings_enriched") + .outputMode("append") + .table("recordings_enriched")) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Trigger another new file and wait for it propagate through both previous queries. # COMMAND ---------- @@ -212,14 +237,16 @@ # COMMAND ---------- -File.newData() +DA.data_factory.load() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Gold Table: Daily Averages # MAGIC -# MAGIC Here we read a stream of data from `recordingsEnrichedPath` and write another stream to create an aggregate gold table of daily averages for each patient. +# MAGIC Here we read a stream of data from **`recordings_enriched`** and write another stream to create an aggregate gold table of daily averages for each patient. # COMMAND ---------- @@ -238,7 +265,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC Note that we're using `.trigger(once=True)` below. This provides us the ability to continue to use the strengths of structured streaming while trigger this job as a single batch. To recap, these strengths include: +# MAGIC +# MAGIC +# MAGIC Note that we're using **`.trigger(availableNow=True)`** below. This provides us the ability to continue to use the strengths of structured streaming while trigger this job one-time to process all available data in micro-batches. To recap, these strengths include: # MAGIC - exactly once end-to-end fault tolerant processing # MAGIC - automatic detection of changes in upstream data sources # MAGIC @@ -249,22 +278,23 @@ # COMMAND ---------- (spark.table("patient_avg") - .writeStream - .format("delta") - .outputMode("complete") - .option("checkpointLocation", dailyAvgCheckpoint) - .trigger(once=True) - .table("daily_patient_avg") -) + .writeStream + .format("delta") + .outputMode("complete") + .option("checkpointLocation", f"{DA.paths.checkpoints}/daily_avg") + .trigger(availableNow=True) + .table("daily_patient_avg")) # COMMAND ---------- # MAGIC %md -# MAGIC #### Important Considerations for `complete` Output with Delta # MAGIC -# MAGIC When using `complete` output mode, we rewrite the entire state of our table each time our logic runs. While this is ideal for calculating aggregates, we **cannot** read a stream from this directory, as Structured Streaming assumes data is only being appended in the upstream logic. # MAGIC -# MAGIC **NOTE**: Certain options can be set to change this behavior, but have other limitations attached. For more details, refer to [Delta Streaming: Ignoring Updates and Deletes](https://docs.databricks.com/delta/delta-streaming.html#ignoring-updates-and-deletes). +# MAGIC #### Important Considerations for complete Output with Delta +# MAGIC +# MAGIC When using **`complete`** output mode, we rewrite the entire state of our table each time our logic runs. While this is ideal for calculating aggregates, we **cannot** read a stream from this directory, as Structured Streaming assumes data is only being appended in the upstream logic. +# MAGIC +# MAGIC **NOTE**: Certain options can be set to change this behavior, but have other limitations attached. For more details, refer to Delta Streaming: Ignoring Updates and Deletes. # MAGIC # MAGIC The gold Delta table we have just registered will perform a static read of the current state of the data each time we run the following query. @@ -276,6 +306,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Note the above table includes all days for all users. If the predicates for our ad hoc queries match the data encoded here, we can push down our predicates to files at the source and very quickly generate more limited aggregate views. # COMMAND ---------- @@ -288,27 +320,33 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Process Remaining Records -# MAGIC The following cell will land additional files for the rest of 2020 in your source directory. You'll be able to see these process through the first 3 tables in your Delta Lake, but will need to re-run your final query to update your `daily_patient_avg` table, since this query uses the trigger once syntax. +# MAGIC The following cell will land additional files for the rest of 2020 in your source directory. You'll be able to see these process through the first 3 tables in your Delta Lake, but will need to re-run your final query to update your **`daily_patient_avg`** table, since this query uses the trigger available now syntax. # COMMAND ---------- -File.newData(continuous=True) +DA.data_factory.load(continuous=True) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Wrapping Up # MAGIC # MAGIC Finally, make sure all streams are stopped. # COMMAND ---------- -# MAGIC %run "../Includes/multi-hop-setup" $mode="clean" +DA.cleanup() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Summary # MAGIC # MAGIC Delta Lake and Structured Streaming combine to provide near real-time analytic access to data in the lakehouse. @@ -316,6 +354,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Additional Topics & Resources # MAGIC # MAGIC * Table Streaming Reads and Writes diff --git a/Data-Engineering-with-Databricks/07 - Multi-Hop Architecture/DE 7.2L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py b/Data-Engineering-with-Databricks/07 - Multi-Hop Architecture/DE 7.2L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py new file mode 100644 index 0000000..2a2a8d0 --- /dev/null +++ b/Data-Engineering-with-Databricks/07 - Multi-Hop Architecture/DE 7.2L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py @@ -0,0 +1,280 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Propagating Incremental Updates with Structured Streaming and Delta Lake +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Apply your knowledge of structured streaming and Auto Loader to implement a simple multi-hop architecture + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Setup +# MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-7.2L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Ingest data +# MAGIC +# MAGIC This lab uses a collection of customer-related CSV data from DBFS found in */databricks-datasets/retail-org/customers/*. +# MAGIC +# MAGIC Read this data using Auto Loader using its schema inference (use **`customers_checkpoint_path`** to store the schema info). Stream the raw data to a Delta table called **`bronze`**. + +# COMMAND ---------- + +# TODO +customers_checkpoint_path = f"{DA.paths.checkpoints}/customers" + +query = (spark + .readStream + + .load("/databricks-datasets/retail-org/customers/") + .writeStream + + .table("bronze") +) + +# COMMAND ---------- + +DA.block_until_stream_is_ready(query) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC Run the cell below to check your work. + +# COMMAND ---------- + +assert spark.table("bronze"), "Table named `bronze` does not exist" +assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'bronze'").first()["isTemporary"] == False, "Table is temporary" +assert spark.table("bronze").dtypes == [('customer_id', 'string'), ('tax_id', 'string'), ('tax_code', 'string'), ('customer_name', 'string'), ('state', 'string'), ('city', 'string'), ('postcode', 'string'), ('street', 'string'), ('number', 'string'), ('unit', 'string'), ('region', 'string'), ('district', 'string'), ('lon', 'string'), ('lat', 'string'), ('ship_to_address', 'string'), ('valid_from', 'string'), ('valid_to', 'string'), ('units_purchased', 'string'), ('loyalty_segment', 'string'), ('_rescued_data', 'string')], "Incorrect Schema" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC Let's create a streaming temporary view into the bronze table, so that we can perform transforms using SQL. + +# COMMAND ---------- + +(spark + .readStream + .table("bronze") + .createOrReplaceTempView("bronze_temp")) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Clean and enhance data +# MAGIC +# MAGIC Using CTAS syntax, define a new streaming view called **`bronze_enhanced_temp`** that does the following: +# MAGIC * Skips records with a null **`postcode`** (set to zero) +# MAGIC * Inserts a column called **`receipt_time`** containing a current timestamp +# MAGIC * Inserts a column called **`source_file`** containing the input filename + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- TODO +# MAGIC CREATE OR REPLACE TEMPORARY VIEW bronze_enhanced_temp AS +# MAGIC SELECT +# MAGIC + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC Run the cell below to check your work. + +# COMMAND ---------- + +assert spark.table("bronze_enhanced_temp"), "Table named `bronze_enhanced_temp` does not exist" +assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'bronze_enhanced_temp'").first()["isTemporary"] == True, "Table is not temporary" +assert spark.table("bronze_enhanced_temp").dtypes == [('customer_id', 'string'), ('tax_id', 'string'), ('tax_code', 'string'), ('customer_name', 'string'), ('state', 'string'), ('city', 'string'), ('postcode', 'string'), ('street', 'string'), ('number', 'string'), ('unit', 'string'), ('region', 'string'), ('district', 'string'), ('lon', 'string'), ('lat', 'string'), ('ship_to_address', 'string'), ('valid_from', 'string'), ('valid_to', 'string'), ('units_purchased', 'string'), ('loyalty_segment', 'string'), ('_rescued_data', 'string'), ('receipt_time', 'timestamp'), ('source_file', 'string')], "Incorrect Schema" +assert spark.table("bronze_enhanced_temp").isStreaming, "Not a streaming table" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Silver table +# MAGIC +# MAGIC Stream the data from **`bronze_enhanced_temp`** to a table called **`silver`**. + +# COMMAND ---------- + +# TODO +silver_checkpoint_path = f"{DA.paths.checkpoints}/silver" + +query = (spark.table("bronze_enhanced_temp") + + .table("silver")) + +# COMMAND ---------- + +DA.block_until_stream_is_ready(query) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC Run the cell below to check your work. + +# COMMAND ---------- + +assert spark.table("silver"), "Table named `silver` does not exist" +assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'silver'").first()["isTemporary"] == False, "Table is temporary" +assert spark.table("silver").dtypes == [('customer_id', 'string'), ('tax_id', 'string'), ('tax_code', 'string'), ('customer_name', 'string'), ('state', 'string'), ('city', 'string'), ('postcode', 'string'), ('street', 'string'), ('number', 'string'), ('unit', 'string'), ('region', 'string'), ('district', 'string'), ('lon', 'string'), ('lat', 'string'), ('ship_to_address', 'string'), ('valid_from', 'string'), ('valid_to', 'string'), ('units_purchased', 'string'), ('loyalty_segment', 'string'), ('_rescued_data', 'string'), ('receipt_time', 'timestamp'), ('source_file', 'string')], "Incorrect Schema" +assert spark.table("silver").filter("postcode <= 0").count() == 0, "Null postcodes present" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC Let's create a streaming temporary view into the silver table, so that we can perform business-level using SQL. + +# COMMAND ---------- + +(spark + .readStream + .table("silver") + .createOrReplaceTempView("silver_temp")) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Gold tables +# MAGIC +# MAGIC Using CTAS syntax, define a new streaming view called **`customer_count_temp`** that counts customers per state. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- TODO +# MAGIC CREATE OR REPLACE TEMPORARY VIEW customer_count_temp AS +# MAGIC SELECT +# MAGIC + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC Run the cell below to check your work. + +# COMMAND ---------- + +assert spark.table("customer_count_temp"), "Table named `customer_count_temp` does not exist" +assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'customer_count_temp'").first()["isTemporary"] == True, "Table is not temporary" +assert spark.table("customer_count_temp").dtypes == [('state', 'string'), ('customer_count', 'bigint')], "Incorrect Schema" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Finally, stream the data from the **`customer_count_temp`** view to a Delta table called **`gold_customer_count_by_state`**. + +# COMMAND ---------- + +# TODO +customers_count_checkpoint_path = f"{DA.paths.checkpoints}/customers_counts" + +query = (spark + .table("customer_count_temp") + .writeStream + + .table("gold_customer_count_by_state")) + +# COMMAND ---------- + +DA.block_until_stream_is_ready(query) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC Run the cell below to check your work. + +# COMMAND ---------- + +assert spark.table("gold_customer_count_by_state"), "Table named `gold_customer_count_by_state` does not exist" +assert spark.sql(f"show tables").filter(f"tableName == 'gold_customer_count_by_state'").first()["isTemporary"] == False, "Table is temporary" +assert spark.table("gold_customer_count_by_state").dtypes == [('state', 'string'), ('customer_count', 'bigint')], "Incorrect Schema" +assert spark.table("gold_customer_count_by_state").count() == 51, "Incorrect number of rows" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Query the results +# MAGIC +# MAGIC Query the **`gold_customer_count_by_state`** table (this will not be a streaming query). Plot the results as a bar graph and also using the map plot. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM gold_customer_count_by_state + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Wrapping Up +# MAGIC +# MAGIC Run the following cell to remove the database and all data associated with this lab. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC By completing this lab, you should now feel comfortable: +# MAGIC * Using PySpark to configure Auto Loader for incremental data ingestion +# MAGIC * Using Spark SQL to aggregate streaming data +# MAGIC * Streaming data to a Delta table + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.3.1 - DLT UI Walkthrough.py b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.1 - DLT UI Walkthrough.py similarity index 57% rename from Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.3.1 - DLT UI Walkthrough.py rename to Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.1 - DLT UI Walkthrough.py index c35c33d..a3c7150 100644 --- a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.3.1 - DLT UI Walkthrough.py +++ b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.1 - DLT UI Walkthrough.py @@ -8,10 +8,15 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Using the Delta Live Tables UI # MAGIC -# MAGIC This demo will explore the DLT UI. By the end of this lesson you will be able to: +# MAGIC This demo will explore the DLT UI. # MAGIC +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: # MAGIC * Deploy a DLT pipeline # MAGIC * Explore the resultant DAG # MAGIC * Execute an update of the pipeline @@ -20,65 +25,87 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Run Setup # MAGIC # MAGIC The following cell is configured to reset this demo. # COMMAND ---------- -# MAGIC %run ../Includes/dlt-setup $course="dlt_demo" $mode="reset" +# MAGIC %run ../../Includes/Classroom-Setup-8.1.1 # COMMAND ---------- # MAGIC %md -# MAGIC Execute the following cell to print out two values that will be used during the following configuration steps. +# MAGIC +# MAGIC +# MAGIC Execute the following cell to print out values that will be used during the following configuration steps. # COMMAND ---------- -print(f"Target: {database}") -print(f"Storage location: {userhome.split(':')[1]}") +DA.print_pipeline_config() # COMMAND ---------- # MAGIC %md -# MAGIC ## Create and configure a pipeline +# MAGIC +# MAGIC +# MAGIC ## Create and Configure a Pipeline # MAGIC # MAGIC In this section you will create a pipeline using a notebook provided with the courseware. We'll explore the contents of the notebook in the following lesson. # MAGIC -# MAGIC 1. Click the **Jobs** button on the sidebar, +# MAGIC 1. Click the **Jobs** button on the sidebar. # MAGIC 1. Select the **Delta Live Tables** tab. # MAGIC 1. Click **Create Pipeline**. -# MAGIC 1. Fill in a **Pipeline Name** of your choosing. -# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the companion notebook called **3.3.2 - SQL for Delta Live Tables**. -# MAGIC * Though this document is a standard Databricks Notebook, the SQL syntax is specialized to DLT table declarations. We will be exploring the syntax in the exercise that follows. -# MAGIC 1. In the **Target** field, specify the database name printed out next to **Target** in the cell above. (This should follow the pattern `dbacademy__dlt_demo`) +# MAGIC 1. Leave **Product Edition** as **Advanced**. +# MAGIC 1. Fill in a **Pipeline Name** - because these names must be unique, we suggest using the **`Pipeline Name`** provided by the cell above. +# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the companion notebook called **DE 8.1.2 - SQL for Delta Live Tables**. +# MAGIC * Alternatively, you can copy the **`Notebook Path`** provided by the cell above and paste it into the provided field. +# MAGIC * Even though this document is a standard Databricks Notebook, the SQL syntax is specialized to DLT table declarations. +# MAGIC * We will be exploring the syntax in the exercise that follows. +# MAGIC 1. In the **Target** field, specify the database name printed out next to **`Target`** in the cell above.
+# MAGIC This should follow the pattern **`dbacademy__dewd_dlt_demo_81`** # MAGIC * This field is optional; if not specified, then tables will not be registered to a metastore, but will still be available in the DBFS. Refer to the documentation for more information on this option. -# MAGIC 1. In the **Storage location** field, copy the directory as printed above. -# MAGIC * This optional field allows the user to specify a location to store logs, tables, and other information related to pipeline execution. If not specified, DLT will automatically generate a directory. +# MAGIC 1. In the **Storage location** field, copy the **`Storage Location`** path printed by the cell above. +# MAGIC * This optional field allows the user to specify a location to store logs, tables, and other information related to pipeline execution. +# MAGIC * If not specified, DLT will automatically generate a directory. # MAGIC 1. For **Pipeline Mode**, select **Triggered** -# MAGIC * This field specifies how the pipeline will be run. **Triggered** pipelines run once and then shut down until the next manual or scheduled update. **Continuous** pipelines run continuously, ingesting new data as it arrives. Choose the mode based on latency and cost requirements. -# MAGIC 1. Uncheck the **Enable autoscaling** box, and set the number of workers to 1., +# MAGIC * This field specifies how the pipeline will be run. +# MAGIC * **Triggered** pipelines run once and then shut down until the next manual or scheduled update. +# MAGIC * **Continuous** pipelines run continuously, ingesting new data as it arrives. Choose the mode based on latency and cost requirements. +# MAGIC 1. Uncheck the **Enable autoscaling** box, and set the number of workers to **`1`** (one). # MAGIC * **Enable autoscaling**, **Min Workers** and **Max Workers** control the worker configuration for the underlying cluster processing the pipeline. Notice the DBU estimate provided, similar to that provided when configuring interactive clusters. # MAGIC 1. Click **Create**. # COMMAND ---------- # MAGIC %md -# MAGIC ## Run a pipeline +# MAGIC +# MAGIC +# MAGIC ## Run a Pipeline # MAGIC # MAGIC With a pipeline created, you will now run the pipeline. # MAGIC -# MAGIC 1. Select **Development** to run the pipeline in development mode. Development mode provides for more expeditious iterative development by reusing the cluster (as opposed to creating a new cluster for each run) and disabling retries so that you can readily identify and fix errors. Refer to the documentation for more information on this feature. +# MAGIC 1. Select **Development** to run the pipeline in development mode. +# MAGIC * Development mode provides for more expeditious iterative development by reusing the cluster (as opposed to creating a new cluster for each run) and disabling retries so that you can readily identify and fix errors. +# MAGIC * Refer to the documentation for more information on this feature. # MAGIC 2. Click **Start**. # MAGIC -# MAGIC The initial run will take several minutes while a cluster is provisioned. Subsequent runs will be appreciably quicker. +# MAGIC The initial run will take several minutes while a cluster is provisioned. +# MAGIC +# MAGIC Subsequent runs will be appreciably quicker. # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Exploring the DAG # MAGIC -# MAGIC As the pipeline completes, the execution flow is graphed. Select the tables review the details. +# MAGIC As the pipeline completes, the execution flow is graphed. +# MAGIC +# MAGIC Selecting the tables reviews the details. # MAGIC # MAGIC Select **sales_orders_cleaned**. Notice the results reported in the **Data Quality** section. Because this flow has data expectations declared, those metrics are tracked here. No records are dropped because the constraint is declared in a way that allows violating records to be included in the output. This will be covered in more details in the next exercise. diff --git a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.3.2 - SQL for Delta Live Tables.sql b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.2 - SQL for Delta Live Tables.sql similarity index 51% rename from Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.3.2 - SQL for Delta Live Tables.sql rename to Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.2 - SQL for Delta Live Tables.sql index aa76e29..aef0e6e 100644 --- a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.3.2 - SQL for Delta Live Tables.sql +++ b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.2 - SQL for Delta Live Tables.sql @@ -8,62 +8,66 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # SQL for Delta Live Tables -- MAGIC -- MAGIC In the last lesson, we walked through the process of scheduling this notebook as a Delta Live Table (DLT) pipeline. Now we'll explore the contents of this notebook to better understand the syntax used by Delta Live Tables. -- MAGIC -- MAGIC This notebook uses SQL to declare Delta Live Tables that together implement a simple multi-hop architecture based on a Databricks-provided example dataset loaded by default into Databricks workspaces. -- MAGIC --- MAGIC At its simplest, you can think of DLT SQL as a slight modification to traditional CTAS statements. DLT tables and views will always be preceded by the `LIVE` keyword. +-- MAGIC At its simplest, you can think of DLT SQL as a slight modification to traditional CTAS statements. DLT tables and views will always be preceded by the **`LIVE`** keyword. -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC --- MAGIC By the end of this lesson, students should feel confident: --- MAGIC * Defining tables and views with Delta Live Tables --- MAGIC * Using SQL to incrementally ingest raw data with Auto Loader --- MAGIC * Performing incremental reads on Delta tables with SQL --- MAGIC * Updating code and redeploying a pipeline +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC * Define tables and views with Delta Live Tables +-- MAGIC * Use SQL to incrementally ingest raw data with Auto Loader +-- MAGIC * Perform incremental reads on Delta tables with SQL +-- MAGIC * Update code and redeploy a pipeline -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Declare Bronze Layer Tables -- MAGIC --- MAGIC Below we declare a table and view implementing the bronze layer. This represents data in its rawest form, but captured in a format that can be retained indefinitely and queried with the performance and benefits that Delta Lake has to offer. +-- MAGIC Below we declare two tables implementing the bronze layer. This represents data in its rawest form, but captured in a format that can be retained indefinitely and queried with the performance and benefits that Delta Lake has to offer. -- COMMAND ---------- -- MAGIC %md --- MAGIC ### `sales_orders_raw` -- MAGIC --- MAGIC `sales_orders_raw` ingests JSON data incrementally from the example dataset found in */databricks-datasets/retail-org/sales_orders/*. -- MAGIC --- MAGIC Incremental processing via Auto Loader (which uses the same processing model as Structured Streaming), requires the addition of the `INCREMENTAL` keyword in the declaration as seen below. The `cloud_files()` method enables Auto Loader to be used natively with SQL. This method takes the following positional parameters: +-- MAGIC ### sales_orders_raw +-- MAGIC +-- MAGIC **`sales_orders_raw`** ingests JSON data incrementally from the example dataset found in */databricks-datasets/retail-org/sales_orders/*. +-- MAGIC +-- MAGIC Incremental processing via Auto Loader (which uses the same processing model as Structured Streaming), requires the addition of the **`STREAMING`** keyword in the declaration as seen below. The **`cloud_files()`** method enables Auto Loader to be used natively with SQL. This method takes the following positional parameters: -- MAGIC * The source location, as mentioned above -- MAGIC * The source data format, which is JSON in this case --- MAGIC * An arbitrarily sized array of optional reader options. In this case, we set `cloudFiles.inferColumnTypes` to `true` +-- MAGIC * An arbitrarily sized array of optional reader options. In this case, we set **`cloudFiles.inferColumnTypes`** to **`true`** -- MAGIC -- MAGIC The following declaration also demonstrates the declaration of additional table metadata (a comment and properties in this case) that would be visible to anyone exploring the data catalog. -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE sales_orders_raw +CREATE OR REFRESH STREAMING LIVE TABLE sales_orders_raw COMMENT "The raw sales orders, ingested from /databricks-datasets." -AS -SELECT * FROM cloud_files("/databricks-datasets/retail-org/sales_orders/", "json", map("cloudFiles.inferColumnTypes", "true")) +AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/sales_orders/", "json", map("cloudFiles.inferColumnTypes", "true")) -- COMMAND ---------- -- MAGIC %md --- MAGIC ### `customers` -- MAGIC --- MAGIC `customers` presents a **view** into CSV customer data found in */databricks-datasets/retail-org/customers/*. A view differs from a table in that there is no actual data bound to the view; it can be thought of as a stored query. -- MAGIC --- MAGIC This view will soon be used in a join operation to look up customer data based on sales records. +-- MAGIC ### customers +-- MAGIC +-- MAGIC **`customers`** presents CSV customer data found in */databricks-datasets/retail-org/customers/*. This table will soon be used in a join operation to look up customer data based on sales records. -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE customers +CREATE OR REFRESH STREAMING LIVE TABLE customers COMMENT "The customers buying finished products, ingested from /databricks-datasets." AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv"); @@ -71,6 +75,8 @@ AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv" -- MAGIC %md -- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Declare Silver Layer Tables -- MAGIC -- MAGIC Now we declare tables implementing the silver layer. This layer represents a refined copy of data from the bronze layer, with the intention of optimizing downstream applications. At this level we apply operations like data cleansing and enrichment. @@ -78,7 +84,9 @@ AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv" -- COMMAND ---------- -- MAGIC %md --- MAGIC ### `sales_orders_cleaned` +-- MAGIC +-- MAGIC +-- MAGIC ### sales_orders_cleaned -- MAGIC -- MAGIC Here we declare our first silver table, which enriches the sales transaction data with customer information in addition to implementing quality control by rejecting records with a null order number. -- MAGIC @@ -86,60 +94,65 @@ AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv" -- MAGIC -- MAGIC #### Quality Control -- MAGIC --- MAGIC The `CONSTRAINT` keyword introduces quality control. Similar in function to a traditional `WHERE` clause, `CONSTRAINT` integrates with DLT, enabling it to collect metrics on constraint violations. Constraints provide an optional `ON VIOLATION` clause, specifying an action to take on records that violate the constraint. The three modes currently supported by DLT include: +-- MAGIC The **`CONSTRAINT`** keyword introduces quality control. Similar in function to a traditional **`WHERE`** clause, **`CONSTRAINT`** integrates with DLT, enabling it to collect metrics on constraint violations. Constraints provide an optional **`ON VIOLATION`** clause, specifying an action to take on records that violate the constraint. The three modes currently supported by DLT include: -- MAGIC --- MAGIC | `ON VIOLATION` | Behavior | +-- MAGIC | **`ON VIOLATION`** | Behavior | -- MAGIC | --- | --- | --- MAGIC | `FAIL UPDATE` | Pipeline failure when constraint is violated | --- MAGIC | `DROP ROW` | Discard records that violate constraints | +-- MAGIC | **`FAIL UPDATE`** | Pipeline failure when constraint is violated | +-- MAGIC | **`DROP ROW`** | Discard records that violate constraints | -- MAGIC | Omitted | Records violating constraints will be included (but violations will be reported in metrics) | -- MAGIC -- MAGIC #### References to DLT Tables and Views --- MAGIC References to other DLT tables and views will always include the `live.` prefix. A target database name will automatically be substituted at runtime, allowing for easily migration of pipelines between DEV/QA/PROD environments. +-- MAGIC References to other DLT tables and views will always include the **`live.`** prefix. A target database name will automatically be substituted at runtime, allowing for easily migration of pipelines between DEV/QA/PROD environments. -- MAGIC -- MAGIC #### References to Streaming Tables -- MAGIC --- MAGIC References to streaming DLT tables use the `STREAM()`, supplying the table name as an argument. +-- MAGIC References to streaming DLT tables use the **`STREAM()`**, supplying the table name as an argument. -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE sales_orders_cleaned( +CREATE OR REFRESH STREAMING LIVE TABLE sales_orders_cleaned( CONSTRAINT valid_order_number EXPECT (order_number IS NOT NULL) ON VIOLATION DROP ROW ) COMMENT "The cleaned sales orders with valid order_number(s) and partitioned by order_datetime." AS -SELECT f.customer_id, f.customer_name, f.number_of_line_items, - TIMESTAMP(from_unixtime((cast(f.order_datetime as long)))) as order_datetime, - DATE(from_unixtime((cast(f.order_datetime as long)))) as order_date, - f.order_number, f.ordered_products, c.state, c.city, c.lon, c.lat, c.units_purchased, c.loyalty_segment + SELECT f.customer_id, f.customer_name, f.number_of_line_items, + timestamp(from_unixtime((cast(f.order_datetime as long)))) as order_datetime, + date(from_unixtime((cast(f.order_datetime as long)))) as order_date, + f.order_number, f.ordered_products, c.state, c.city, c.lon, c.lat, c.units_purchased, c.loyalty_segment FROM STREAM(LIVE.sales_orders_raw) f LEFT JOIN LIVE.customers c - ON c.customer_id = f.customer_id - AND c.customer_name = f.customer_name + ON c.customer_id = f.customer_id + AND c.customer_name = f.customer_name -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Declare Gold Table -- MAGIC -- MAGIC At the most refined level of the architecture, we declare a table delivering an aggregation with business value, in this case a collection of sales order data based in a specific region. In aggregating, the report generates counts and totals of orders by date and customer. -- COMMAND ---------- -CREATE LIVE TABLE sales_order_in_la +CREATE OR REFRESH LIVE TABLE sales_order_in_la COMMENT "Sales orders in LA." AS -SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, SUM(ordered_products_explode.price) as sales, SUM(ordered_products_explode.qty) as quantity, COUNT(ordered_products_explode.id) as product_count -FROM ( - SELECT city, order_date, customer_id, customer_name, EXPLODE(ordered_products) as ordered_products_explode - FROM LIVE.sales_orders_cleaned - WHERE city = 'Los Angeles' - ) -GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode.curr + SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, + sum(ordered_products_explode.price) as sales, + sum(ordered_products_explode.qty) as quantity, + count(ordered_products_explode.id) as product_count + FROM (SELECT city, order_date, customer_id, customer_name, explode(ordered_products) as ordered_products_explode + FROM LIVE.sales_orders_cleaned + WHERE city = 'Los Angeles') + GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode.curr -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Explore Results -- MAGIC -- MAGIC Explore the DAG (Directed Acyclic Graph) representing the entities involved in the pipeline and the relationships between them. Click on each to view a summary, which includes: @@ -148,27 +161,37 @@ GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode. -- MAGIC * Schema -- MAGIC * Data quality metrics -- MAGIC --- MAGIC Refer to this companion notebook to inspect tables and logs. +-- MAGIC Refer to this companion notebook to inspect tables and logs. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Update Pipeline -- MAGIC --- MAGIC Uncomment the following cell to declare another gold table. Similar to the previous gold table declaration, this filters for the `city` of Chicago. Re-run your pipeline to examine the updated results. Does it run as expected? Can you identify any issues? +-- MAGIC Uncomment the following cell to declare another gold table. Similar to the previous gold table declaration, this filters for the **`city`** of Chicago. +-- MAGIC +-- MAGIC Re-run your pipeline to examine the updated results. +-- MAGIC +-- MAGIC Does it run as expected? +-- MAGIC +-- MAGIC Can you identify any issues? -- COMMAND ---------- --- CREATE LIVE TABLE sales_order_in_chicago +-- TODO +-- CREATE OR REFRESH LIVE TABLE sales_order_in_chicago -- COMMENT "Sales orders in Chicago." -- AS --- SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, SUM(ordered_products_explode.price) as sales, SUM(ordered_products_explode.qty) as quantity, COUNT(ordered_products_explode.id) as product_count --- FROM ( --- SELECT city, order_date, customer_id, customer_name, EXPLODE(ordered_products) as ordered_products_explode --- FROM sales_orders_cleaned --- WHERE city = 'Chicago' --- ) --- GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode.curr +-- SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, +-- sum(ordered_products_explode.price) as sales, +-- sum(ordered_products_explode.qty) as quantity, +-- count(ordered_products_explode.id) as product_count +-- FROM (SELECT city, order_date, customer_id, customer_name, explode(ordered_products) as ordered_products_explode +-- FROM sales_orders_cleaned +-- WHERE city = 'Chicago') +-- GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode.curr -- COMMAND ---------- diff --git a/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.3 - Pipeline Results.py b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.3 - Pipeline Results.py new file mode 100644 index 0000000..d3b6cb8 --- /dev/null +++ b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.3 - Pipeline Results.py @@ -0,0 +1,76 @@ +# Databricks notebook source +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Exploring the Results of a DLT Pipeline +# MAGIC +# MAGIC This notebook explores the execution results of a DLT pipeline. + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-8.1.3 + +# COMMAND ---------- + +files = dbutils.fs.ls(DA.paths.storage_location) +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The **`system`** directory captures events associated with the pipeline. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.storage_location}/system/events") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC These event logs are stored as a Delta table. Let's query the table. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM delta.`${da.paths.storage_location}/system/events` + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Let's view the contents of the *tables* directory. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.storage_location}/tables") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Let's query the gold table. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM ${da.db_name}.sales_order_in_la + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Run the following cell to delete the tables and files associated with this lesson. + +# COMMAND ---------- + +DA.cleanup() + diff --git a/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.1L - Lab Instructions.py b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.1L - Lab Instructions.py new file mode 100644 index 0000000..c9a9986 --- /dev/null +++ b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.1L - Lab Instructions.py @@ -0,0 +1,164 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Lab: Migrating SQL Notebooks to Delta Live Tables +# MAGIC +# MAGIC This notebook describes the overall structure for the lab exercise, configures the environment for the lab, provides simulated data streaming, and performs cleanup once you are done. A notebook like this is not typically needed in a production pipeline scenario. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Convert existing data pipelines to Delta Live Tables + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Datasets Used +# MAGIC +# MAGIC This demo uses simplified artificially generated medical data. The schema of our two datasets is represented below. Note that we will be manipulating these schema during various steps. +# MAGIC +# MAGIC #### Recordings +# MAGIC The main dataset uses heart rate recordings from medical devices delivered in the JSON format. +# MAGIC +# MAGIC | Field | Type | +# MAGIC | --- | --- | +# MAGIC | device_id | int | +# MAGIC | mrn | long | +# MAGIC | time | double | +# MAGIC | heartrate | double | +# MAGIC +# MAGIC #### PII +# MAGIC These data will later be joined with a static table of patient information stored in an external system to identify patients by name. +# MAGIC +# MAGIC | Field | Type | +# MAGIC | --- | --- | +# MAGIC | mrn | long | +# MAGIC | name | string | + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Getting Started +# MAGIC +# MAGIC Begin by running the following cell to configure the lab environment. + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-8.2.1L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Land Initial Data +# MAGIC Seed the landing zone with some data before proceeding. You will re-run this command to land additional data later. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Execute the following cell to print out values that will be used during the following configuration steps. + +# COMMAND ---------- + +DA.print_pipeline_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create and Configure a Pipeline +# MAGIC +# MAGIC 1. Click the **Jobs** button on the sidebar, then select the **Delta Live Tables** tab. +# MAGIC 1. Click **Create Pipeline**. +# MAGIC 1. Leave **Product Edition** as **Advanced**. +# MAGIC 1. Fill in a **Pipeline Name** - because these names must be unique, we suggest using the **Pipline Name** provided in the cell above. +# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the notebook **`DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab`**. +# MAGIC 1. Configure the Source +# MAGIC * Click **`Add configuration`** +# MAGIC * Enter the word **`source`** in the **Key** field +# MAGIC * Enter the **Source** value specified above to the **`Value`** field +# MAGIC 1. Enter the database name printed next to **`Target`** below in the **Target** field. +# MAGIC 1. Enter the location printed next to **`Storage Location`** below in the **Storage Location** field. +# MAGIC 1. Set **Pipeline Mode** to **Triggered**. +# MAGIC 1. Disable autoscaling. +# MAGIC 1. Set the number of **`workers`** to **`1`** (one). +# MAGIC 1. Click **Create**. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Open and Complete DLT Pipeline Notebook +# MAGIC +# MAGIC You will perform your work in the companion notebook [DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab]($./DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab),
+# MAGIC which you will ultimately deploy as a pipeline. +# MAGIC +# MAGIC Open the Notebook and, following the guidelines provided therein, fill in the cells where prompted to
+# MAGIC implement a multi-hop architecture similar to the one we worked with in the previous section. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Run your Pipeline +# MAGIC +# MAGIC Select **Development** mode, which accelerates the development lifecycle by reusing the same cluster across runs.
+# MAGIC It will also turn off automatic retries when jobs fail. +# MAGIC +# MAGIC Click **Start** to begin the first update to your table. +# MAGIC +# MAGIC Delta Live Tables will automatically deploy all the necessary infrastructure and resolve the dependencies between all datasets. +# MAGIC +# MAGIC **NOTE**: The first table update may take several minutes as relationships are resolved and infrastructure deploys. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Troubleshooting Code in Development Mode +# MAGIC +# MAGIC Don't despair if your pipeline fails the first time. Delta Live Tables is in active development, and error messages are improving all the time. +# MAGIC +# MAGIC Because relationships between tables are mapped as a DAG, error messages will often indicate that a dataset isn't found. +# MAGIC +# MAGIC Let's consider our DAG below: +# MAGIC +# MAGIC +# MAGIC +# MAGIC If the error message **`Dataset not found: 'recordings_parsed'`** is raised, there may be several culprits: +# MAGIC 1. The logic defining **`recordings_parsed`** is invalid +# MAGIC 1. There is an error reading from **`recordings_bronze`** +# MAGIC 1. A typo exists in either **`recordings_parsed`** or **`recordings_bronze`** +# MAGIC +# MAGIC The safest way to identify the culprit is to iteratively add table/view definitions back into your DAG starting from your initial ingestion tables. You can simply comment out later table/view definitions and uncomment these between runs. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.5L - Migrating a SQL Pipeline to DLT Lab.sql b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab.sql similarity index 63% rename from Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.5L - Migrating a SQL Pipeline to DLT Lab.sql rename to Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab.sql index 4c1cc8d..21a6ff3 100644 --- a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.5L - Migrating a SQL Pipeline to DLT Lab.sql +++ b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab.sql @@ -8,26 +8,28 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Lab: Migrating a SQL Pipeline to Delta Live Tables -- MAGIC --- MAGIC This notebook will be completed by you to implement a DLT pipeline using SQL. It is **not intended** to be executed interactively, but rather to be deployed as a pipeline once you have completed your changes. +-- MAGIC This notebook will be completed by you to implement a DLT pipeline using SQL. +-- MAGIC +-- MAGIC It is **not intended** to be executed interactively, but rather to be deployed as a pipeline once you have completed your changes. -- MAGIC -- MAGIC To aid in completion of this Notebook, please refer to the DLT syntax documentation. -- COMMAND ---------- --- MAGIC %run ../../Includes/classic-setup $mode="reset" - --- COMMAND ---------- - -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Declare Bronze Table -- MAGIC --- MAGIC Declare a bronze table that ingests JSON data incrementally (using Auto Loader) from the simulated cloud source. Here you will need to substitute the value obtained from the companion setup Notebook. +-- MAGIC Declare a bronze table that ingests JSON data incrementally (using Auto Loader) from the simulated cloud source. The source location is already supplied as an argument; using this value is illustrated in the cell below. -- MAGIC -- MAGIC As we did previously, include two additional columns: --- MAGIC * `receipt_time` that records a timestamp as returned by `current_timestamp()` --- MAGIC * `source_file` that is obtained by `input_file_name()` +-- MAGIC * **`receipt_time`** that records a timestamp as returned by **`current_timestamp()`** +-- MAGIC * **`source_file`** that is obtained by **`input_file_name()`** -- COMMAND ---------- @@ -39,6 +41,8 @@ AS SELECT -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ### PII File -- MAGIC -- MAGIC Using a similar CTAS syntax, create a live **table** into the CSV data found at */mnt/training/healthcare/patient*. @@ -47,8 +51,8 @@ AS SELECT -- MAGIC -- MAGIC | option | value | -- MAGIC | --- | --- | --- MAGIC | `header` | `true` | --- MAGIC | `cloudFiles.inferColumnTypes` | `true` | +-- MAGIC | **`header`** | **`true`** | +-- MAGIC | **`cloudFiles.inferColumnTypes`** | **`true`** | -- MAGIC -- MAGIC Auto Loader configurations for CSV can be found here. @@ -62,26 +66,29 @@ AS SELECT * -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Declare Silver Tables -- MAGIC --- MAGIC Our silver tables, `recordings_parsed`, will subscribe to the `recordings` dataset and cast the fields as follows: +-- MAGIC Our silver table, **`recordings_parsed`**, will consist of the following fields: -- MAGIC -- MAGIC | Field | Type | -- MAGIC | --- | --- | --- MAGIC | `device_id` | `INTEGER` | --- MAGIC | `mrn` | `LONG` | --- MAGIC | `heartrate` | `DOUBLE` | --- MAGIC | `time` | `TIMESTAMP` (example provided below) | +-- MAGIC | **`device_id`** | **`INTEGER`** | +-- MAGIC | **`mrn`** | **`LONG`** | +-- MAGIC | **`heartrate`** | **`DOUBLE`** | +-- MAGIC | **`time`** | **`TIMESTAMP`** (example provided below) | +-- MAGIC | **`name`** | **`STRING`** | -- MAGIC --- MAGIC This query should also enrich the data through an inner join with the `pii` table on the common `mrn` field. +-- MAGIC This query should also enrich the data through an inner join with the **`pii`** table on the common **`mrn`** field to obtain the name. -- MAGIC --- MAGIC Implement quality control by applying a contraint to drop records with an invalid `heartrate` (that is, not greater than zero). +-- MAGIC Implement quality control by applying a constraint to drop records with an invalid **`heartrate`** (that is, not greater than zero). -- COMMAND ---------- -- TODO -CREATE INCREMENTAL LIVE TABLE recordings_enriched - ( 0>) +CREATE OR REFRESH STREAMING LIVE TABLE recordings_enriched + ( 0>) AS SELECT CAST() device_id, , @@ -93,16 +100,18 @@ AS SELECT -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Gold Table -- MAGIC --- MAGIC Create a gold table, `daily_patient_avg`, that aggregates `recordings_enriched` by `mrn`, `name`, and `date` and delivers the following columns: +-- MAGIC Create a gold table, **`daily_patient_avg`**, that aggregates **`recordings_enriched`** by **`mrn`**, **`name`**, and **`date`** and delivers the following columns: -- MAGIC -- MAGIC | Column name | Value | -- MAGIC | --- | --- | --- MAGIC | `mrn` | `mrn` from source | --- MAGIC | `name` | `name` from source | --- MAGIC | `avg_heartrate` | Average `heartrate` from the grouping | --- MAGIC | `date` | Date extracted from `time` | +-- MAGIC | **`mrn`** | **`mrn`** from source | +-- MAGIC | **`name`** | **`name`** from source | +-- MAGIC | **`avg_heartrate`** | Average **`heartrate`** from the grouping | +-- MAGIC | **`date`** | Date extracted from **`time`** | -- COMMAND ---------- diff --git a/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.3L - Lab Conclusion.py b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.3L - Lab Conclusion.py new file mode 100644 index 0000000..31a8de3 --- /dev/null +++ b/Data-Engineering-with-Databricks/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.3L - Lab Conclusion.py @@ -0,0 +1,91 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Lab: Conclusion +# MAGIC Running the following cell to configure the lab environment: + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-8.2.3L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Display Results +# MAGIC +# MAGIC Assuming your pipeline runs successfully, display the contents of the gold table. +# MAGIC +# MAGIC **NOTE**: Because we specified a value for **Target**, tables are published to the specified database. Without a **Target** specification, we would need to query the table based on its underlying location in DBFS (relative to the **Storage Location**). + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM ${da.db_name}.daily_patient_avg + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Trigger another file arrival with the following cell. +# MAGIC +# MAGIC Feel free to run it a couple more times if desired. +# MAGIC +# MAGIC Following this, run the pipeline again and view the results. +# MAGIC +# MAGIC Feel free to re-run the cell above to gain an updated view of the **`daily_patient_avg`** table. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Wrapping Up +# MAGIC +# MAGIC Ensure that you delete your pipeline from the DLT UI, and run the following cell to clean up the files and tables that were created as part of the lab setup and execution. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Summary +# MAGIC +# MAGIC In this lab, you learned to convert an existing data pipeline to a Delta Live Tables SQL pipeline, and deployed that pipeline using the DLT UI. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Additional Topics & Resources +# MAGIC +# MAGIC * Delta Live Tables Documentation +# MAGIC * Delta Live Tables Demo + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.1.1 - Task Orchestration with Databricks Jobs.py b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.1.1 - Task Orchestration with Databricks Jobs.py new file mode 100644 index 0000000..a325efd --- /dev/null +++ b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.1.1 - Task Orchestration with Databricks Jobs.py @@ -0,0 +1,196 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Orchestrating Jobs with Databricks +# MAGIC +# MAGIC New updates to the Databricks Jobs UI have added the ability to schedule multiple tasks as part of a job, allowing Databricks Jobs to fully handle orchestration for most production workloads. +# MAGIC +# MAGIC Here, we'll start by reviewing the steps for scheduling a notebook as a triggered standalone job, and then add a dependent job using a DLT pipeline. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Schedule a notebook as a Databricks Job +# MAGIC * Describe job scheduling options and differences between cluster types +# MAGIC * Review Job Runs to track progress and see results +# MAGIC * Schedule a DLT pipeline as a Databricks Job +# MAGIC * Configure linear dependencies between tasks using the Databricks Jobs UI + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-9.1.1 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create and configure a pipeline +# MAGIC The pipeline we create here is nearly identical to the one in the previous unit. +# MAGIC +# MAGIC We will use it as part of a scheduled job in this lesson. +# MAGIC +# MAGIC Execute the following cell to print out the values that will be used during the following configuration steps. + +# COMMAND ---------- + +print_pipeline_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create and configure a pipeline +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the **Jobs** button on the sidebar, +# MAGIC 1. Select the **Delta Live Tables** tab. +# MAGIC 1. Click **Create Pipeline**. +# MAGIC 1. Fill in a **Pipeline Name** - because these names must be unique, we suggest using the **Pipeline Name** provided in the cell above. +# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the companion notebook called **DE 9.1.3 - DLT Job**. Alternatively, you can copy the **Notebook Path** and paste it into the field provided. +# MAGIC 1. In the **Target** field, specify the database name printed out next to **Target** in the cell above.
+# MAGIC This should follow the pattern **`dbacademy__dewd_dlt_demo_91`** +# MAGIC 1. In the **Storage location** field, copy the directory as printed above. +# MAGIC 1. For **Pipeline Mode**, select **Triggered** +# MAGIC 1. Uncheck the **Enable autoscaling** box +# MAGIC 1. Set the number of workers to **`1`** (one) +# MAGIC 1. Click **Create**. +# MAGIC +# MAGIC **Note**: we won't be executing this pipline directly as it will be executed by our job later in this lesson,
+# MAGIC but if you want to test it real quick, you can click the **Start** button now. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Schedule a Notebook Job +# MAGIC +# MAGIC When using the Jobs UI to orchestrate a workload with multiple tasks, you'll always begin by scheduling a single task. +# MAGIC +# MAGIC Before we start run the following cell to get the values used in this step. + +# COMMAND ---------- + +print_job_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Here, we'll start by scheduling the next notebook +# MAGIC +# MAGIC Steps: +# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. +# MAGIC 1. Click the blue **`Create Job`** button +# MAGIC 1. Configure the task: +# MAGIC 1. Enter **`reset`** for the task name +# MAGIC 1. Select the notebook **`DE 9.1.2 - Reset`** using the notebook picker. +# MAGIC 1. From the **Cluster** dropdown, under **Existing All Purpose Clusters**, select your cluster +# MAGIC 1. Click **Create** +# MAGIC 1. In the top-left of the screen rename the job (not the task) from **`reset`** (the defaulted value) to the **Job Name** provided for you in the previous cell. +# MAGIC 1. Click the blue **Run now** button in the top right to start the job. +# MAGIC +# MAGIC **Note**: When selecting your all-purpose cluster, you will get a warning about how this will be billed as all-purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Chron Scheduling of Databricks Jobs +# MAGIC +# MAGIC Note that on the right hand side of the Jobs UI, directly under the **Job Details** section is a section labeled **Schedule**. +# MAGIC +# MAGIC Click on the **Edit schedule** button to explore scheduling options. +# MAGIC +# MAGIC Changing the **Schedule type** field from **Manual** to **Scheduled** will bring up a chron scheduling UI. +# MAGIC +# MAGIC This UI provides extensive options for setting up chronological scheduling of your Jobs. Settings configured with the UI can also be output in chron syntax, which can be edited if custom configuration not available with the UI is needed. +# MAGIC +# MAGIC At this time, we'll leave our job set with **Manual** scheduling. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Review Run +# MAGIC +# MAGIC As currently configured, our single notebook provides identical performance to the legacy Databricks Jobs UI, which only allowed a single notebook to be scheduled. +# MAGIC +# MAGIC To Review the Job Run +# MAGIC 1. Select the **Runs** tab in the top-left of the screen (you should currently be on the **Tasks** tab) +# MAGIC 1. Find your job. If **the job is still running**, it will be under the **Active runs** section. If **the job finished running**, it will be under the **Completed runs** section +# MAGIC 1. Open the Output details by click on the timestamp field under the **Start time** column +# MAGIC 1. If **the job is still running**, you will see the active state of the notebook with a **Status** of **`Pending`** or **`Running`** in the right side panel. If **the job has completed**, you will see the full execution of the notebook with a **Status** of **`Succeeded`** or **`Failed`** in the right side panel +# MAGIC +# MAGIC The notebook employs the magic command **`%run`** to call an additional notebook using a relative path. Note that while not covered in this course, new functionality added to Databricks Repos allows loading Python modules using relative paths. +# MAGIC +# MAGIC The actual outcome of the scheduled notebook is to reset the environment for our new job and pipeline. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Schedule a DLT Pipeline as a Task +# MAGIC +# MAGIC In this step, we'll add a DLT pipeline to execute after the success of the task we configured at the start of this lesson. +# MAGIC +# MAGIC Steps: +# MAGIC 1. At the top left of your screen, you'll see the **Runs** tab is currently selected; click the **Tasks** tab. +# MAGIC 1. Click the large blue circle with a **+** at the center bottom of the screen to add a new task +# MAGIC 1. Specify the **Task name** as **`dlt`** +# MAGIC 1. From **Type**, select **`Delta Live Tables pipeline`** +# MAGIC 1. Click the **Pipeline** field and select the DLT pipeline you configured previously
+# MAGIC Note: The pipeline will start with **Jobs-Demo-91** and will end with your email address. +# MAGIC 1. The **Depends on** field defaults to your previously defined task but may have renamed itself from the value **reset** that you specified previously to something like **Jobs-Demo-91-youremailaddress**. +# MAGIC 1. Click the blue **Create task** button +# MAGIC +# MAGIC You should now see a screen with 2 boxes and a downward arrow between them. +# MAGIC +# MAGIC Your **`reset`** task (possibly renamed to something like **Jobs-Demo-91-youremailaddress**) will be at the top, +# MAGIC leading into your **`dlt`** task. +# MAGIC +# MAGIC This visualization represents the dependencies between these tasks. +# MAGIC +# MAGIC Click **Run now** to execute your job. +# MAGIC +# MAGIC **NOTE**: You may need to wait a few minutes as infrastructure for your job and pipeline is deployed. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Review Multi-Task Run Results +# MAGIC +# MAGIC Select the **Runs** tab again and then the most recent run under **Active runs** or **Completed runs** depending on if the job has completed or not. +# MAGIC +# MAGIC The visualizations for tasks will update in real time to reflect which tasks are actively running, and will change colors if task failures occur. +# MAGIC +# MAGIC Clicking on a task box will render the scheduled notebook in the UI. +# MAGIC +# MAGIC You can think of this as just an additional layer of orchestration on top of the previous Databricks Jobs UI, if that helps; note that if you have workloads scheduling jobs with the CLI or REST API, the JSON structure used to configure and get results about jobs has seen similar updates to the UI. +# MAGIC +# MAGIC **NOTE**: At this time, DLT pipelines scheduled as tasks do not directly render results in the Runs GUI; instead, you will be directed back to the DLT Pipeline GUI for the scheduled Pipeline. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.1.2 - Reset.py b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.1.2 - Reset.py new file mode 100644 index 0000000..3090a9b --- /dev/null +++ b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.1.2 - Reset.py @@ -0,0 +1,18 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-9.1.1 + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/2 - DLT Job.sql b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.1.3 - DLT Job.sql similarity index 89% rename from Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/2 - DLT Job.sql rename to Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.1.3 - DLT Job.sql index 85abadd..a434b53 100644 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/2 - DLT Job.sql +++ b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.1.3 - DLT Job.sql @@ -1,18 +1,18 @@ -- Databricks notebook source -CREATE INCREMENTAL LIVE TABLE sales_orders_raw +CREATE OR REFRESH STREAMING LIVE TABLE sales_orders_raw COMMENT "The raw sales orders, ingested from /databricks-datasets." AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/sales_orders/", "json", map("cloudFiles.inferColumnTypes", "true")) -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE customers +CREATE OR REFRESH STREAMING LIVE TABLE customers COMMENT "The customers buying finished products, ingested from /databricks-datasets." AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv"); -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE sales_orders_cleaned( +CREATE OR REFRESH STREAMING LIVE TABLE sales_orders_cleaned( CONSTRAINT valid_order_number EXPECT (order_number IS NOT NULL) ON VIOLATION DROP ROW ) COMMENT "The cleaned sales orders with valid order_number(s) and partitioned by order_datetime." @@ -28,7 +28,7 @@ SELECT f.customer_id, f.customer_name, f.number_of_line_items, -- COMMAND ---------- -CREATE LIVE TABLE sales_order_in_la +CREATE OR REFRESH LIVE TABLE sales_order_in_la COMMENT "Sales orders in LA." AS SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, SUM(ordered_products_explode.price) as sales, SUM(ordered_products_explode.qty) as quantity, COUNT(ordered_products_explode.id) as product_count @@ -41,7 +41,7 @@ GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode. -- COMMAND ---------- -CREATE LIVE TABLE sales_order_in_chicago +CREATE OR REFRESH LIVE TABLE sales_order_in_chicago COMMENT "Sales orders in Chicago." AS SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, SUM(ordered_products_explode.price) as sales, SUM(ordered_products_explode.qty) as quantity, COUNT(ordered_products_explode.id) as product_count diff --git a/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.1L - Lab Instructions.py b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.1L - Lab Instructions.py new file mode 100644 index 0000000..dea121f --- /dev/null +++ b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.1L - Lab Instructions.py @@ -0,0 +1,179 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Lab: Orchestrating Jobs with Databricks +# MAGIC +# MAGIC In this lab, you'll be configuring a multi-task job comprising of: +# MAGIC * A notebook that lands a new batch of data in a storage directory +# MAGIC * A Delta Live Table pipeline that processes this data through a series of tables +# MAGIC * A notebook that queries the gold table produced by this pipeline as well as various metrics output by DLT +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Schedule a notebook as a Databricks Job +# MAGIC * Schedule a DLT pipeline as a Databricks Job +# MAGIC * Configure linear dependencies between tasks using the Databricks Jobs UI + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-9.2.1L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Land Initial Data +# MAGIC Seed the landing zone with some data before proceeding. You will re-run this command to land additional data later. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create and Configure a Pipeline +# MAGIC +# MAGIC The pipeline we create here is nearly identical to the one in the previous unit. +# MAGIC +# MAGIC We will use it as part of a scheduled job in this lesson. +# MAGIC +# MAGIC Execute the following cell to print out the values that will be used during the following configuration steps. + +# COMMAND ---------- + +print_pipeline_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the **Jobs** button on the sidebar. +# MAGIC 1. Select the **Delta Live Tables** tab. +# MAGIC 1. Click **Create Pipeline**. +# MAGIC 1. Fill in a **Pipeline Name** - because these names must be unique, we suggest using the **Pipeline Name** provided in the cell above. +# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the companion notebook called **DE 9.2.3L - DLT Job**. +# MAGIC * Alternatively, you can copy the **Notebook Path** specified above and paste it into the field provided. +# MAGIC 1. Configure the Source +# MAGIC * Click **`Add configuration`** +# MAGIC * Enter the word **`source`** in the **Key** field +# MAGIC * Enter the **Source** value specified above to the **`Value`** field +# MAGIC 1. In the **Target** field, specify the database name printed out next to **Target** in the cell above.
+# MAGIC This should follow the pattern **`dbacademy__dewd_jobs_lab_92`** +# MAGIC 1. In the **Storage location** field, copy the directory as printed above. +# MAGIC 1. For **Pipeline Mode**, select **Triggered** +# MAGIC 1. Uncheck the **Enable autoscaling** box +# MAGIC 1. Set the number of workers to **`1`** (one) +# MAGIC 1. Click **Create**. +# MAGIC +# MAGIC +# MAGIC **Note**: we won't be executing this pipline directly as it will be executed by our job later in this lesson,
+# MAGIC but if you want to test it real quick, you can click the **Start** button now. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Schedule a Notebook Job +# MAGIC +# MAGIC When using the Jobs UI to orchestrate a workload with multiple tasks, you'll always begin by scheduling a single task. +# MAGIC +# MAGIC Before we start run the following cell to get the values used in this step. + +# COMMAND ---------- + +print_job_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Here, we'll start by scheduling the notebook batch job. +# MAGIC +# MAGIC Steps: +# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. +# MAGIC 1. Click the blue **Create Job** button +# MAGIC 1. Configure the task: +# MAGIC 1. Enter **Batch-Job** for the task name +# MAGIC 1. Select the notebook **DE 9.2.2L - Batch Job** using the notebook picker +# MAGIC 1. From the **Cluster** dropdown, under **Existing All Purpose Cluster**, select your cluster +# MAGIC 1. Click **Create** +# MAGIC 1. In the top-left of the screen rename the job (not the task) from **`Batch-Job`** (the defaulted value) to the **Job Name** provided for you in the previous cell. +# MAGIC 1. Click the blue **Run now** button in the top right to start the job to test the job real quick. +# MAGIC +# MAGIC **Note**: When selecting your all purpose cluster, you will get a warning about how this will be billed as all purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Schedule a DLT Pipeline as a Task +# MAGIC +# MAGIC In this step, we'll add a DLT pipeline to execute after the success of the task we configured at the start of this lesson. +# MAGIC +# MAGIC Steps: +# MAGIC 1. At the top left of your screen, click the **Tasks** tab if it is not already selected. +# MAGIC 1. Click the large blue circle with a **+** at the center bottom of the screen to add a new task +# MAGIC 1. Specify the **Task name** as **DLT-Pipeline** +# MAGIC 1. From **Type**, select **`Delta Live Tables pipeline`** +# MAGIC 1. Click the **Pipeline** field and select the DLT pipeline you configured previously
+# MAGIC Note: The pipeline will start with **Jobs-Labs-92** and will end with your email address. +# MAGIC 1. The **Depends on** field defaults to your previously defined task but may have renamed itself from the value **reset** that you specified previously to something like **Jobs-Lab-92-youremailaddress**. +# MAGIC 1. Click the blue **Create task** button +# MAGIC +# MAGIC You should now see a screen with 2 boxes and a downward arrow between them. +# MAGIC +# MAGIC Your **`Batch-Job`** task (possibly renamed to something like **Jobs-Labs-92-youremailaddress**) will be at the top, +# MAGIC leading into your **`DLT-Pipeline`** task. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Schedule an Additional Notebook Task +# MAGIC +# MAGIC An additional notebook has been provided which queries some of the DLT metrics and the gold table defined in the DLT pipeline. +# MAGIC +# MAGIC We'll add this as a final task in our job. +# MAGIC +# MAGIC Steps: +# MAGIC 1. At the top left of your screen, click the **Tasks** tab if it is not already selected. +# MAGIC 1. Click the large blue circle with a **+** at the center bottom of the screen to add a new task +# MAGIC 1. Specify the **Task name** as **Query-Results** +# MAGIC 1. Leave the **Type** set to **Notebook** +# MAGIC 1. Select the notebook **DE 9.2.4L - Query Results Job** using the notebook picker +# MAGIC 1. Note that the **Depends on** field defaults to your previously defined task, **DLT-Pipeline** +# MAGIC 1. From the **Cluster** dropdown, under **Existing All Purpose Cluster**, select your cluster +# MAGIC 1. Click the blue **Create task** button +# MAGIC +# MAGIC Click the blue **Run now** button in the top right of the screen to run this job. +# MAGIC +# MAGIC From the **Runs** tab, you will be able to click on the start time for this run under the **Active runs** section and visually track task progress. +# MAGIC +# MAGIC Once all your tasks have succeeded, review the contents of each task to confirm expected behavior. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.2L - Batch Job.py b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.2L - Batch Job.py new file mode 100644 index 0000000..55656d3 --- /dev/null +++ b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.2L - Batch Job.py @@ -0,0 +1,7 @@ +# Databricks notebook source +# MAGIC %run ../../Includes/Classroom-Setup-9.2.2L + +# COMMAND ---------- + +DA.data_factory.load() + diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/2 - DLT Job.sql b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.3L - DLT Job.sql similarity index 81% rename from Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/2 - DLT Job.sql rename to Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.3L - DLT Job.sql index 55e4d4e..a812762 100644 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/2 - DLT Job.sql +++ b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.3L - DLT Job.sql @@ -1,17 +1,17 @@ -- Databricks notebook source -CREATE INCREMENTAL LIVE TABLE recordings_bronze +CREATE OR REFRESH STREAMING LIVE TABLE recordings_bronze AS SELECT current_timestamp() receipt_time, input_file_name() source_file, * FROM cloud_files("${source}", "json", map("cloudFiles.schemaHints", "time DOUBLE")) -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE pii +CREATE OR REFRESH STREAMING LIVE TABLE pii AS SELECT * FROM cloud_files("/mnt/training/healthcare/patient", "csv", map("header", "true", "cloudFiles.inferColumnTypes", "true")) -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE recordings_enriched +CREATE OR REFRESH STREAMING LIVE TABLE recordings_enriched (CONSTRAINT positive_heartrate EXPECT (heartrate > 0) ON VIOLATION DROP ROW) AS SELECT CAST(a.device_id AS INTEGER) device_id, @@ -25,7 +25,7 @@ AS SELECT -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE daily_patient_avg +CREATE OR REFRESH STREAMING LIVE TABLE daily_patient_avg COMMENT "Daily mean heartrates by patient" AS SELECT mrn, name, MEAN(heartrate) avg_heartrate, DATE(time) `date` FROM STREAM(live.recordings_enriched) diff --git a/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.4L - Query Results Job.py b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.4L - Query Results Job.py new file mode 100644 index 0000000..158cba1 --- /dev/null +++ b/Data-Engineering-with-Databricks/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.4L - Query Results Job.py @@ -0,0 +1,71 @@ +# Databricks notebook source +# MAGIC %run ../../Includes/Classroom-Setup-9.2.4L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Exploring the Results of a DLT Pipeline +# MAGIC +# MAGIC Run the following cell to enumerate the output of your storage location: + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.working_dir}/storage") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The **system** directory captures events associated with the pipeline. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.working_dir}/storage/system/events") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC These event logs are stored as a Delta table. +# MAGIC +# MAGIC Let's query the table. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM delta.`${da.paths.working_dir}/storage/system/events` + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Let's view the contents of the *tables* directory. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.working_dir}/storage/tables") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Let's query the gold table. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM ${da.db_name}.daily_patient_avg + +# COMMAND ---------- + +DA.cleanup() + diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.2.1 - Navigating Databricks SQL and Attaching to Endpoints.py b/Data-Engineering-with-Databricks/10 - Running a DBSQL Query/DE 10.1 - Navigating Databricks SQL and Attaching to Endpoints.py similarity index 65% rename from Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.2.1 - Navigating Databricks SQL and Attaching to Endpoints.py rename to Data-Engineering-with-Databricks/10 - Running a DBSQL Query/DE 10.1 - Navigating Databricks SQL and Attaching to Endpoints.py index 7916b00..1dfdf45 100644 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.2.1 - Navigating Databricks SQL and Attaching to Endpoints.py +++ b/Data-Engineering-with-Databricks/10 - Running a DBSQL Query/DE 10.1 - Navigating Databricks SQL and Attaching to Endpoints.py @@ -8,34 +8,36 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Navigating Databricks SQL and Attaching to Endpoints # MAGIC # MAGIC * Navigate to Databricks SQL # MAGIC * Make sure that SQL is selected from the workspace option in the sidebar (directly below the Databricks logo) # MAGIC * Make sure a SQL endpoint is on and accessible -# MAGIC * Navigate to SQL enpoints in the sidebar -# MAGIC * If a SQL endpoint exists and has the State `Running`, you'll use this endpoint -# MAGIC * If a SQL endpoint exists but is `Stopped`, click the `Start` button if you have this option (**NOTE**: Start the smallest endpoint you have available to you) -# MAGIC * If no endpoints exist and you have the option, click `Create SQL Endpoint`; name the endpoint something you'll recognize and set the cluster size to 2X-Small. Leave all other options as default. +# MAGIC * Navigate to SQL endpoints in the sidebar +# MAGIC * If a SQL endpoint exists and has the State **`Running`**, you'll use this endpoint +# MAGIC * If a SQL endpoint exists but is **`Stopped`**, click the **`Start`** button if you have this option (**NOTE**: Start the smallest endpoint you have available to you) +# MAGIC * If no endpoints exist and you have the option, click **`Create SQL Endpoint`**; name the endpoint something you'll recognize and set the cluster size to 2X-Small. Leave all other options as default. # MAGIC * If you have no way to create or attach to a SQL endpoint, you'll need to contact a workspace administrator and request access to compute resources in Databricks SQL to continue. # MAGIC * Navigate to home page in Databricks SQL # MAGIC * Click the Databricks logo at the top of the side nav bar -# MAGIC * Locate the **Sample dashboards** and click `Visit gallery` -# MAGIC * Click `Import` next to the **Retail Revenue & Supply Chain** option +# MAGIC * Locate the **Sample dashboards** and click **`Visit gallery`** +# MAGIC * Click **`Import`** next to the **Retail Revenue & Supply Chain** option # MAGIC * Assuming you have a SQL endpoint available, this should load a dashboard and immediately display results # MAGIC * Click **Refresh** in the top right (the underlying data has not changed, but this is the button that would be used to pick up changes) # MAGIC # MAGIC # Updating a DBSQL Dashboard # MAGIC # MAGIC * Use the sidebar navigator to find the **Dashboards** -# MAGIC * Locate the sample dashboard you just loaded; it should be called **Retail Revenue & Supply Chain** and have your username under the `Created By` field. **NOTE**: the **My Dashboards** option on the right hand side can serve as a shortcut to filter out other dashboards in the workspace +# MAGIC * Locate the sample dashboard you just loaded; it should be called **Retail Revenue & Supply Chain** and have your username under the **`Created By`** field. **NOTE**: the **My Dashboards** option on the right hand side can serve as a shortcut to filter out other dashboards in the workspace # MAGIC * Click on the dashboard name to view it # MAGIC * View the query behind the **Shifts in Pricing Priorities** plot # MAGIC * Hover over the plot; three vertical dots should appear. Click on these # MAGIC * Select **View Query** from the menu that appears # MAGIC * Review the SQL code used to populate this plot # MAGIC * Note that 3 tier namespacing is used to identify the source table; this is a preview of new functionality to be supported by Unity Catalog -# MAGIC * Click `Run` in the top right of the screen to preview the results of the query +# MAGIC * Click **`Run`** in the top right of the screen to preview the results of the query # MAGIC * Review the visualization # MAGIC * Under the query, a tab named **Table** should be selected; click **Price by Priority over Time** to switch to a preview of your plot # MAGIC * Click **Edit Visualization** at the bottom of the screen to review settings @@ -43,37 +45,37 @@ # MAGIC * If you wish to apply your changes, click **Save**; otherwise, click **Cancel** # MAGIC * Back in the query editor, click the **Add Visualization** button to the right of the visualization name # MAGIC * Create a bar graph -# MAGIC * Set the **X Column** as `Date` -# MAGIC * Set the **Y Column** as `Total Price` -# MAGIC * **Group by** `Priority` -# MAGIC * Set **Stacking** to `Stack` +# MAGIC * Set the **X Column** as **`Date`** +# MAGIC * Set the **Y Column** as **`Total Price`** +# MAGIC * **Group by** **`Priority`** +# MAGIC * Set **Stacking** to **`Stack`** # MAGIC * Leave all other settings as defaults # MAGIC * Click **Save** -# MAGIC * Back in the query editor, click the default name for this visualization to edit it; change the visualization name to `Stacked Price` -# MAGIC * Add the bottom of the screen, click the three vertical dots to the left of the `Edit Visualization` button +# MAGIC * Back in the query editor, click the default name for this visualization to edit it; change the visualization name to **`Stacked Price`** +# MAGIC * Add the bottom of the screen, click the three vertical dots to the left of the **`Edit Visualization`** button # MAGIC * Select **Add to Dashboard** from the menu -# MAGIC * Select your `Retail Revenue & Supply Chain` dashboard +# MAGIC * Select your **`Retail Revenue & Supply Chain`** dashboard # MAGIC * Navigate back to your dashboard to view this change # MAGIC # MAGIC # Create a New Query # MAGIC # MAGIC * Use the sidebar to navigate to **Queries** -# MAGIC * Click the `Create Query` button -# MAGIC * In the **Schema Browser**, click on the current metastore and select `samples` -# MAGIC * Select the `tpch` database -# MAGIC * Click on the `partsupp` table to get a preview of the schema -# MAGIC * While hovering over the `partsupp` table name, click the `>>` button to insert the table name into your query text +# MAGIC * Click the **`Create Query`** button +# MAGIC * Make sure you are connected to an endpoint. In the **Schema Browser**, click on the current metastore and select **`samples`**. +# MAGIC * Select the **`tpch`** database +# MAGIC * Click on the **`partsupp`** table to get a preview of the schema +# MAGIC * While hovering over the **`partsupp`** table name, click the **>>** button to insert the table name into your query text # MAGIC * Write your first query: -# MAGIC * `SELECT * FROM` the `partsupp` table using the full name imported in the last step; click **Run** to preview results -# MAGIC * Modify this query to `GROUP BY ps_partkey` and return the `ps_partkey` and `sum(ps_availqty)`; click **Run** to preview results -# MAGIC * Update your query to alias the 2nd column to be named `total_availqty` and re-execute the query +# MAGIC * **`SELECT * FROM`** the **`partsupp`** table using the full name imported in the last step; click **Run** to preview results +# MAGIC * Modify this query to **`GROUP BY ps_partkey`** and return the **`ps_partkey`** and **`sum(ps_availqty)`**; click **Run** to preview results +# MAGIC * Update your query to alias the 2nd column to be named **`total_availqty`** and re-execute the query # MAGIC * Save your query # MAGIC * Click the **Save** button next to **Run** near the top right of the screen # MAGIC * Give the query a name you'll remember # MAGIC * Add the query to your dashboard # MAGIC * Click the three vertical buttons at the bottom of the screen # MAGIC * Click **Add to Dashboard** -# MAGIC * Select your `Retail Revenue & Supply Chain` dashboard +# MAGIC * Select your **`Retail Revenue & Supply Chain`** dashboard # MAGIC * Navigate back to your dashboard to view this change # MAGIC * If you wish to change the organization of visualizations, click the three vertical buttons in the top right of the screen; click **Edit** in the menu that appears and you'll be able to drag and resize visualizations diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.3.1 - Managing Permissions for Databases, Tables, and Views.py b/Data-Engineering-with-Databricks/11 - Managing Permissions/DE 11.1 - Managing Permissions for Databases, Tables, and Views.py similarity index 79% rename from Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.3.1 - Managing Permissions for Databases, Tables, and Views.py rename to Data-Engineering-with-Databricks/11 - Managing Permissions/DE 11.1 - Managing Permissions for Databases, Tables, and Views.py index ac3fca8..d8f2d04 100644 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.3.1 - Managing Permissions for Databases, Tables, and Views.py +++ b/Data-Engineering-with-Databricks/11 - Managing Permissions/DE 11.1 - Managing Permissions for Databases, Tables, and Views.py @@ -8,12 +8,15 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Managing Permissions for Databases, Tables, and Views # MAGIC # MAGIC The instructions as detailed below are provided for groups of users to explore how Table ACLs on Databricks work. It leverages Databricks SQL and the Data Explorer to accomplish these tasks, and assumes that at least one user in the group has administrator status (or that an admin has previously configured permissions to allow proper permissions for users to create databases, tables, and views). # MAGIC # MAGIC As written, these instructions are for the admin user to complete. The following notebook will have a similar exercise for users to complete in pairs. # MAGIC +# MAGIC ## Learning Objectives # MAGIC By the end of this lesson, you should be able to: # MAGIC * Describe the default permissions for users and admins in DBSQL # MAGIC * Identify the default owner for databases, tables, and views created in DBSQL and change ownership @@ -23,65 +26,43 @@ # COMMAND ---------- +# MAGIC %run ../Includes/Classroom-Setup-11.1 + +# COMMAND ---------- + # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Generate Setup Statements # MAGIC -# MAGIC The following cell uses Python to extract username of the present user and format this into several statements used to create databases, tables, and views. +# MAGIC The following cell uses Python to extract username of the current user and format this into several statements used to create databases, tables, and views. # MAGIC # MAGIC Only the admin needs to execute the following cell. Successful execution will print out a series of formatted SQL queries, which can be copied into the DBSQL query editor and executed. # COMMAND ---------- -def generate_query(course, mode="reset"): - import re - - username = spark.sql("SELECT current_user()").first()[0] - userhome = f"dbfs:/user/{username}/{course}" - database = f"""dbacademy_{re.sub("[^a-zA-Z0-9]", "_", username)}_{course}""" - - if mode == "reset": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - - print(f""" -CREATE DATABASE IF NOT EXISTS {database} -LOCATION '{userhome}'; - -USE {database}; - -CREATE TABLE users -(id INT, name STRING, value DOUBLE, state STRING); - -INSERT INTO users -VALUES (1, "Yve", 1.0, "CA"), - (2, "Omar", 2.5, "NY"), - (3, "Elia", 3.3, "OH"), - (4, "Rebecca", 4.7, "TX"), - (5, "Ameena", 5.3, "CA"), - (6, "Ling", 6.6, "NY"), - (7, "Pedro", 7.1, "KY"); - -CREATE VIEW ny_users_vw -AS SELECT * FROM users WHERE state = 'NY'; - """) -generate_query("acls_demo") +DA.generate_users_table() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Steps: -# MAGIC * Run the cell above -# MAGIC * Copy the entire output to your clipboard -# MAGIC * Navigate to the Databricks SQL workspace -# MAGIC * Make sure that a DBSQL endpoint is running -# MAGIC * Use the left sidebar to select the **SQL Editor** -# MAGIC * Paste the query above and click the blue **Run** in the top right +# MAGIC 1. Run the cell above +# MAGIC 1. Copy the entire output to your clipboard +# MAGIC 1. Navigate to the Databricks SQL workspace +# MAGIC 1. Make sure that a DBSQL endpoint is running +# MAGIC 1. Use the left sidebar to select the **SQL Editor** +# MAGIC 1. Paste the query above and click the blue **Run** in the top right # MAGIC # MAGIC **NOTE**: You will need to be connected to a DBSQL endpoint to execute these queries successfully. If you cannot connect to a DBSQL endpoint, you will need to contact your administrator to give you access. # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Using Data Explorer # MAGIC # MAGIC * Use the left sidebar navigator to select the **Data** tab; this places you in the **Data Explorer** @@ -93,7 +74,7 @@ def generate_query(course, mode="reset"): # MAGIC * Explore data schema, metadata, and history # MAGIC * Set and modify permissions of relational entities # MAGIC -# MAGIC Note that at the moment these instructions are being written, Unity Catalog is not yet generally available. The 3 tier namespacing functionality it adds can be previewed to an extent by switching between the default `hive_metastore` and the `sample` catalog used for example dashboards and queries. Expect the Data Explorer UI and functionality to evolve as Unity Catalog is added to workspaces. +# MAGIC Note that at the moment these instructions are being written, Unity Catalog is not yet generally available. The 3 tier namespacing functionality it adds can be previewed to an extent by switching between the default **`hive_metastore`** and the **`sample`** catalog used for example dashboards and queries. Expect the Data Explorer UI and functionality to evolve as Unity Catalog is added to workspaces. # MAGIC # MAGIC ## Configuring Permissions # MAGIC @@ -114,7 +95,7 @@ def generate_query(course, mode="reset"): # MAGIC | FUNCTION | controls access to a named function. | # MAGIC | ANY FILE | controls access to the underlying filesystem. Users granted access to ANY FILE can bypass the restrictions put on the catalog, databases, tables, and views by reading from the file system directly. | # MAGIC -# MAGIC **NOTE**: At present, the `ANY FILE` object cannot be set from Data Explorer. +# MAGIC **NOTE**: At present, the **`ANY FILE`** object cannot be set from Data Explorer. # MAGIC # MAGIC ## Granting Privileges # MAGIC @@ -145,8 +126,10 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Review the Default Permissions -# MAGIC In the Data Explorer, find the database you created earlier (this should follow the pattern `dbacademy__acls_demo`). +# MAGIC In the Data Explorer, find the database you created earlier (this should follow the pattern **`dbacademy__acls_demo`**). # MAGIC # MAGIC Clicking on the database name should display a list of the contained tables and views on the left hand side. On the right, you'll see some details about the database, including the **Owner** and **Location**. # MAGIC @@ -155,6 +138,8 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Assigning Ownership # MAGIC # MAGIC Click the blue pencil next to the **Owner** field. Note that an owner can be set as an individual OR a group. For most implementations, having one or several small groups of trusted power users as owners will limit admin access to important datasets while ensuring that a single user does not create a choke point in productivity. @@ -164,11 +149,13 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Change Database Permissions # MAGIC # MAGIC Begin by allowing all users to review metadata about the database. # MAGIC -# MAGIC Step: +# MAGIC Steps: # MAGIC 1. Make sure you have the **Permissions** tab selected for the database # MAGIC 1. Click the blue **Grant** button # MAGIC 1. Select the **USAGE** and **READ_METADATA** options @@ -180,14 +167,16 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Change View Permissions # MAGIC # MAGIC While users can now see information about this database, they won't be able to interact with the table of view declared above. # MAGIC # MAGIC Let's start by giving users the ability to query our view. # MAGIC -# MAGIC Step: -# MAGIC 1. Select the `ny_users_vw` +# MAGIC Steps: +# MAGIC 1. Select the **`ny_users_vw`** # MAGIC 1. Select the **Permissions** tab # MAGIC * Users should have inherited the permissions granted at the database level; you'll be able to see which permissions users currently have on an asset, as well as where that permission is inherited from # MAGIC 1. Click the blue **Grant** button @@ -199,25 +188,29 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Run a Query to Confirm # MAGIC # MAGIC In the **SQL Editor**, all users should use the **Schema Browser** on the lefthand side to navigate to the database being controlled by the admin. # MAGIC -# MAGIC Users should start a query by typing `SELECT * FROM ` and then click the **>>** that appears while hovering over the view name to insert it into their query. +# MAGIC Users should start a query by typing **`SELECT * FROM`** and then click the **>>** that appears while hovering over the view name to insert it into their query. # MAGIC # MAGIC This query should return 2 results. # MAGIC -# MAGIC **NOTE**: This view is defined against the `users` table, which has not had any permissions set yet. Note that users have access only to that portion of the data that passes through the filters defined on the view; this pattern demonstrates how a single underlying table can be used to drive controlled access to data for relevant stakeholders. +# MAGIC **NOTE**: This view is defined against the **`users`** table, which has not had any permissions set yet. Note that users have access only to that portion of the data that passes through the filters defined on the view; this pattern demonstrates how a single underlying table can be used to drive controlled access to data for relevant stakeholders. # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Change Table Permissions # MAGIC -# MAGIC Perform the same steps as above, but now for the `users` table. +# MAGIC Perform the same steps as above, but now for the **`users`** table. # MAGIC -# MAGIC Step: -# MAGIC 1. Select the `users` table +# MAGIC Steps: +# MAGIC 1. Select the **`users`** table # MAGIC 1. Select the **Permissions** tab # MAGIC 1. Click the blue **Grant** button # MAGIC 1. Select the **SELECT** and **READ_METADATA** options @@ -227,7 +220,9 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md -# MAGIC ## Have Users Attempt to `DROP TABLE` +# MAGIC +# MAGIC +# MAGIC ## Have Users Attempt to **`DROP TABLE`** # MAGIC # MAGIC In the **SQL Editor**, encourage users to explore the data in this table. # MAGIC @@ -236,6 +231,8 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Create a Database for Derivative Datasets # MAGIC # MAGIC In most cases users will need a location to save out derivative datasets. At present, users may not have the ability to create new tables in any location (depending on existing ACLs in the workspace and databases created during previous lessons students have completed). @@ -246,22 +243,13 @@ def generate_query(course, mode="reset"): # COMMAND ---------- -import re - -username = spark.sql("SELECT current_user()").first()[0] -database = f"""dbacademy_{re.sub("[^a-zA-Z0-9]", "_", username)}_derivative""" - -print(f""" -CREATE DATABASE {database}; - -GRANT USAGE, READ_METADATA, CREATE, MODIFY, SELECT ON DATABASE `{database}` TO `users`; - -SHOW GRANT ON DATABASE `{database}` -""") +DA.generate_create_database_with_grants() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Have Users Create New Tables or Views # MAGIC # MAGIC Give users a moment to test that they can create tables and views in this new database. @@ -271,21 +259,19 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Admin Configuration # MAGIC -# MAGIC At present, users do not have any Table ACL permissions granted on the default catalog `hive_metastore` by default. The next lab assumes that users will be able to create databases. +# MAGIC At present, users do not have any Table ACL permissions granted on the default catalog **`hive_metastore`** by default. The next lab assumes that users will be able to create databases. # MAGIC # MAGIC To enable the ability to create databases and tables in the default catalog using Databricks SQL, have a workspace admin run the following command in the DBSQL query editor: # MAGIC -# MAGIC ``` -# MAGIC GRANT usage, create ON CATALOG `hive_metastore` TO `users` -# MAGIC ``` +# MAGIC GRANT usage, create ON CATALOG `hive_metastore` TO `users` # MAGIC # MAGIC To confirm this has run successfully, execute the following query: # MAGIC -# MAGIC ``` -# MAGIC SHOW GRANT ON CATALOG `hive_metastore` -# MAGIC ``` +# MAGIC SHOW GRANT ON CATALOG `hive_metastore` # COMMAND ---------- diff --git a/Data-Engineering-with-Databricks/11 - Managing Permissions/DE 11.2L - Configuring Privileges for Production Data and Derived Tables Lab.py b/Data-Engineering-with-Databricks/11 - Managing Permissions/DE 11.2L - Configuring Privileges for Production Data and Derived Tables Lab.py new file mode 100644 index 0000000..c4328ac --- /dev/null +++ b/Data-Engineering-with-Databricks/11 - Managing Permissions/DE 11.2L - Configuring Privileges for Production Data and Derived Tables Lab.py @@ -0,0 +1,215 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Configuring Privileges for Production Data and Derived Tables +# MAGIC +# MAGIC The instructions as detailed below are provided for pairs of users to explore how Table ACLs on Databricks work. It leverages Databricks SQL and the Data Explorer to accomplish these tasks, and assumes that neither user has admin privileges for the workspace. An admin will need to have previously granted **`CREATE`** and **`USAGE`** privileges on a catalog for users to be able to create databases in Databricks SQL. +# MAGIC +# MAGIC ##Learning Objectives +# MAGIC +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Use Data Explorer to navigate relational entities +# MAGIC * Configure permissions for tables and views with Data Explorer +# MAGIC * Configure minimal permissions to allow for table discovery and querying +# MAGIC * Change ownership for databases, tables, and views created in DBSQL + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-11.2L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Exchange User Names with your Partner +# MAGIC If you are not in a workspace where your usernames correspond with your email address, make sure your partner has your username. +# MAGIC +# MAGIC They will need this when assigning privileges and searching for your database at later steps. +# MAGIC +# MAGIC The following cell will print your username. + +# COMMAND ---------- + +print(f"Your username: {DA.username}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Generate Setup Statements +# MAGIC +# MAGIC The following cell uses Python to extract the username of the current user and format this into several statements used to create databases, tables, and views. +# MAGIC +# MAGIC Both students should execute the following cell. +# MAGIC +# MAGIC Successful execution will print out a series of formatted SQL queries, which can be copied into the DBSQL query editor and executed. + +# COMMAND ---------- + +DA.generate_query() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Steps: +# MAGIC 1. Run the cell above +# MAGIC 1. Copy the entire output to your clipboard +# MAGIC 1. Navigate to the Databricks SQL workspace +# MAGIC 1. Make sure that a DBSQL endpoint is running +# MAGIC 1. Use the left sidebar to select the **SQL Editor** +# MAGIC 1. Paste the query above and click the blue **Run** in the top right +# MAGIC +# MAGIC **NOTE**: You will need to be connected to a DBSQL endpoint to execute these queries successfully. If you cannot connect to a DBSQL endpoint, you will need to contact your administrator to give you access. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Find Your Database +# MAGIC In the Data Explorer, find the database you created earlier (this should follow the pattern **`dbacademy__dewd_acls_lab`**). +# MAGIC +# MAGIC Clicking on the database name should display a list of the contained tables and views on the left hand side. +# MAGIC +# MAGIC On the right, you'll see some details about the database, including the **Owner** and **Location**. +# MAGIC +# MAGIC Click the **Permissions** tab to review who presently has permissions (depending on your workspace configuration, some permissions may have been inherited from settings on the catalog). + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Change Database Permissions +# MAGIC +# MAGIC Steps: +# MAGIC 1. Make sure you have the **Permissions** tab selected for the database +# MAGIC 1. Click the blue **Grant** button +# MAGIC 1. Select the **USAGE**, **SELECT**, and **READ_METADATA** options +# MAGIC 1. Enter the username of your partner in the field at the top. +# MAGIC 1. Click **OK** +# MAGIC +# MAGIC Confirm with your partner that you can each see each others' databases and tables. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Run a Query to Confirm +# MAGIC +# MAGIC By granting **`USAGE`**, **`SELECT`**, and **`READ_METADATA`** on your database, your partner should now be able to freely query the tables and views in this database, but will not be able to create new tables OR modify your data. +# MAGIC +# MAGIC In the SQL Editor, each user should run a series of queries to confirm this behavior in the database they were just added to. +# MAGIC +# MAGIC **Make sure you specify your partner's database while running the queries below.** +# MAGIC +# MAGIC **NOTE**: These first 3 queries should succeed, but the last should fail. + +# COMMAND ---------- + +# Replace FILL_IN with your partner's username +DA.generate_confirmation_query("FILL_IN") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Execute a Query to Generate the Union of Your Beans +# MAGIC +# MAGIC Execute the query below against your own databases. +# MAGIC +# MAGIC **NOTE**: Because random values were inserted for the **`grams`** and **`delicious`** columns, you should see 2 distinct rows for each **`name`**, **`color`** pair. + +# COMMAND ---------- + +DA.generate_union_query() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Register a Derivative View to Your Database +# MAGIC +# MAGIC Execute the query below to register the results of the previous query to your database. + +# COMMAND ---------- + +DA.generate_derivative_view() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Query Your Partner's View +# MAGIC +# MAGIC Once your partner has successfully completed the previous step, run the following query against each of your tables; you should get the same results: + +# COMMAND ---------- + +# Replace FILL_IN with your partner's username +DA.generate_partner_view("FILL_IN") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Add Modify Permissions +# MAGIC +# MAGIC Now try to drop each other's **`beans`** tables. +# MAGIC +# MAGIC At the moment, this shouldn't work. +# MAGIC +# MAGIC Using the Data Explorer, add the **`MODIFY`** permission for your **`beans`** table for your partner. +# MAGIC +# MAGIC Again, attempt to drop your partner's **`beans`** table. +# MAGIC +# MAGIC It should again fail. +# MAGIC +# MAGIC **Only the owner of a table should be able to issue this statement**.
+# MAGIC (Note that ownership can be transferred from an individual to a group, if desired). +# MAGIC +# MAGIC Instead, execute a query to delete records from your partner's table: + +# COMMAND ---------- + +# Replace FILL_IN with your partner's username +DA.generate_delete_query("FILL_IN") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC This query should successfully drop all records from the target table. +# MAGIC +# MAGIC Try to re-execute queries against any of the views of tables you'd previously queried in this lab. +# MAGIC +# MAGIC **NOTE**: If steps were completed successfully, none of your previous queries should return results, as the data referenced by your views has been deleted. This demonstrates the risks associated with providing **`MODIFY`** privileges to users on data that will be used in production applications and dashboards. +# MAGIC +# MAGIC If you have additional time, see if you can use the Delta methods **`DESCRIBE HISTORY`** and **`RESTORE`** to revert the records in your table. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.1 - Last Mile ETL with DBSQL.py b/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.1 - Last Mile ETL with DBSQL.py new file mode 100644 index 0000000..171a1e1 --- /dev/null +++ b/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.1 - Last Mile ETL with DBSQL.py @@ -0,0 +1,304 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Last Mile ETL with Databricks SQL +# MAGIC +# MAGIC Before we continue, let's do a recap of some of the things we've learned so far: +# MAGIC 1. The Databricks workspace contains a suite of tools to simplify the data engineering development lifecycle +# MAGIC 1. Databricks notebooks allow users to mix SQL with other programming languages to define ETL workloads +# MAGIC 1. Delta Lake provides ACID compliant transactions and makes incremental data processing easy in the Lakehouse +# MAGIC 1. Delta Live Tables extends the SQL syntax to support many design patterns in the Lakehouse, and simplifies infrastructure deployment +# MAGIC 1. Multi-task jobs allows for full task orchestration, adding dependencies while scheduling a mix of notebooks and DLT pipelines +# MAGIC 1. Databricks SQL allows users to edit and execute SQL queries, build visualizations, and define dashboards +# MAGIC 1. Data Explorer simplifies managing Table ACLs, making Lakehouse data available to SQL analysts (soon to be expanded greatly by Unity Catalog) +# MAGIC +# MAGIC In this section, we'll focus on exploring more DBSQL functionality to support production workloads. +# MAGIC +# MAGIC We'll start by focusing on leveraging Databricks SQL to configure queries that support last mile ETL for analytics. Note that while we'll be using the Databricks SQL UI for this demo, SQL Endpoints integrate with a number of other tools to allow external query execution, as well as having full API support for executing arbitrary queries programmatically. +# MAGIC +# MAGIC From these query results, we'll generate a series of visualizations, which we'll combine into a dashboard. +# MAGIC +# MAGIC Finally, we'll walk through scheduling updates for queries and dashboards, and demonstrate setting alerts to help monitor the state of production datasets over time. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Use Databricks SQL as a tool to support production ETL tasks backing analytic workloads +# MAGIC * Configure SQL queries and visualizations with the Databricks SQL Editor +# MAGIC * Create dashboards in Databricks SQL +# MAGIC * Schedule updates for queries and dashboards +# MAGIC * Set alerts for SQL queries + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Run Setup Script +# MAGIC The following cells runs a notebook that defines a class we'll use to generate SQL queries. + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-12.1 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Demo Database +# MAGIC Execute the following cell and copy the results into the Databricks SQL Editor. +# MAGIC +# MAGIC These queries: +# MAGIC * Create a new database +# MAGIC * Declare two tables (we'll use these for loading data) +# MAGIC * Declare two functions (we'll use these for generating data) +# MAGIC +# MAGIC Once copied, execute the query using the **Run** button. + +# COMMAND ---------- + +DA.generate_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC **NOTE**: The queries above are only designed to be run once after resetting the demo completely to reconfigure the environment. Users will need to have **`CREATE`** and **`USAGE`** permissions on the catalog to execute them. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC +# MAGIC **WARNING:** Make sure to select your database before proceeding as the **`USE`** statement
doesn't yet change the database against which your queries will execute + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Query to Load Data +# MAGIC Steps: +# MAGIC 1. Execute the cell below to print out a formatted SQL query for loading data in the **`user_ping`** table created in the previous step. +# MAGIC 1. Save this query with the name **Load Ping Data**. +# MAGIC 1. Run this query to load a batch of data. + +# COMMAND ---------- + +DA.generate_load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Executing the query should load some data and return a preview of the data in the table. +# MAGIC +# MAGIC **NOTE**: Random numbers are being used to define and load data, so each user will have slightly different values present. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Set a Query Refresh Schedule +# MAGIC +# MAGIC Steps: +# MAGIC 1. Locate the **Refresh Schedule** field at the bottom right of the SQL query editor box; click the blue **Never** +# MAGIC 1. Use the drop down to change to Refresh every **1 minute** +# MAGIC 1. For **Ends**, click the **On** radio button +# MAGIC 1. Select tomorrow's date +# MAGIC 1. Click **OK** + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Query to Track Total Records +# MAGIC Steps: +# MAGIC 1. Execute the cell below. +# MAGIC 1. Save this query with the name **User Counts**. +# MAGIC 1. Run the query to calculate the current results. + +# COMMAND ---------- + +DA.generate_user_counts() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Bar Graph Visualization +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the **Add Visualization** button, located beneath the Refresh Schedule button in the bottom right-hand corner of the query window +# MAGIC 1. Click on the name (should default to something like **`Visualization 1`**) and change the name to **Total User Records** +# MAGIC 1. Set **`user_id`** for the **X Column** +# MAGIC 1. Set **`total_records`** for the **Y Columns** +# MAGIC 1. Click **Save** + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a New Dashboard +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. +# MAGIC 1. Click the **Create new dashboard** option +# MAGIC 1. Name your dashboard User Ping Summary **``** +# MAGIC 1. Click **Save** to create the new dashboard +# MAGIC 1. Your newly created dashboard should now be selected as the target; click **OK** to add your visualization + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Query to Calculate the Recent Average Ping +# MAGIC Steps: +# MAGIC 1. Execute the cell below to print out the formatted SQL query. +# MAGIC 1. Save this query with the name **Avg Ping**. +# MAGIC 1. Run the query to calculate the current results. + +# COMMAND ---------- + +DA.generate_avg_ping() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Add a Line Plot Visualization to your Dashboard +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the **Add Visualization** button +# MAGIC 1. Click on the name (should default to something like **`Visualization 1`**) and change the name to **Avg User Ping** +# MAGIC 1. Select **`Line`** for the **Visualization Type** +# MAGIC 1. Set **`end_time`** for the **X Column** +# MAGIC 1. Set **`avg_ping`** for the **Y Columns** +# MAGIC 1. Set **`user_id`** for the **Group by** +# MAGIC 1. Click **Save** +# MAGIC 1. Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. +# MAGIC 1. Select the dashboard you created earlier +# MAGIC 1. Click **OK** to add your visualization + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Query to Report Summary Statistics +# MAGIC Steps: +# MAGIC 1. Execute the cell below. +# MAGIC 1. Save this query with the name **Ping Summary**. +# MAGIC 1. Run the query to calculate the current results. + +# COMMAND ---------- + +DA.generate_summary() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Add the Summary Table to your Dashboard +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. +# MAGIC 1. Select the dashboard you created earlier +# MAGIC 1. Click **OK** to add your visualization + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Review and Refresh your Dashboard +# MAGIC +# MAGIC Steps: +# MAGIC 1. Use the left side bar to navigate to **Dashboards** +# MAGIC 1. Find the dashboard you've added your queries to +# MAGIC 1. Click the blue **Refresh** button to update your dashboard +# MAGIC 1. Click the **Schedule** button to review dashboard scheduling options +# MAGIC * Note that scheduling a dashboard to update will execute all queries associated with that dashboard +# MAGIC * Do not schedule the dashboard at this time + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Share your Dashboard +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the blue **Share** button +# MAGIC 1. Select **All Users** from the top field +# MAGIC 1. Choose **Can Run** from the right field +# MAGIC 1. Click **Add** +# MAGIC 1. Change the **Credentials** to **Run as viewer** +# MAGIC +# MAGIC **NOTE**: At present, no other users should have any permissions to run your dashboard, as they have not been granted permissions to the underlying databases and tables using Table ACLs. If you wish other users to be able to trigger updates to your dashboard, you will either need to grant them permissions to **Run as owner** or add permissions for the tables referenced in your queries. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Set Up an Alert +# MAGIC +# MAGIC Steps: +# MAGIC 1. Use the left side bar to navigate to **Alerts** +# MAGIC 1. Click **Create Alert** in the top right +# MAGIC 1. Click the field at the top left of the screen to give the alert a name **` Count Check`** +# MAGIC 1. Select your **User Counts** query +# MAGIC 1. For the **Trigger when** options, configure: +# MAGIC * **Value column**: **`total_records`** +# MAGIC * **Condition**: **`>`** +# MAGIC * **Threshold**: **`15`** +# MAGIC 1. For **Refresh**, select **Never** +# MAGIC 1. Click **Create Alert** +# MAGIC 1. On the next screen, click the blue **Refresh** in the top right to evaluate the alert + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Review Alert Destination Options +# MAGIC +# MAGIC +# MAGIC +# MAGIC Steps: +# MAGIC 1. From the preview of your alert, click the blue **Add** button to the right of **Destinations** on the right side of the screen +# MAGIC 1. At the bottom of the window that pops up, locate the and click the blue text in the message **Create new destinations in Alert Destinations** +# MAGIC 1. Review the available alerting options + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.1L - Instructions and Configuration.py b/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.1L - Instructions and Configuration.py new file mode 100644 index 0000000..41f73f6 --- /dev/null +++ b/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.1L - Instructions and Configuration.py @@ -0,0 +1,236 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## End-to-End ETL in the Lakehouse +# MAGIC +# MAGIC In this notebook, you will pull together concepts learned throughout the course to complete an example data pipeline. +# MAGIC +# MAGIC The following is a non-exhaustive list of skills and tasks necessary to successfully complete this exercise: +# MAGIC * Using Databricks notebooks to write queries in SQL and Python +# MAGIC * Creating and modifying databases, tables, and views +# MAGIC * Using Auto Loader and Spark Structured Streaming for incremental data processing in a multi-hop architecture +# MAGIC * Using Delta Live Table SQL syntax +# MAGIC * Configuring a Delta Live Table pipeline for continuous processing +# MAGIC * Using Databricks Jobs to orchestrate tasks from notebooks stored in Repos +# MAGIC * Setting chronological scheduling for Databricks Jobs +# MAGIC * Defining queries in Databricks SQL +# MAGIC * Creating visualizations in Databricks SQL +# MAGIC * Defining Databricks SQL dashboards to review metrics and results + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Run Setup +# MAGIC Run the following cell to reset all the databases and directories associated with this lab. + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-12.2.1L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Land Initial Data +# MAGIC Seed the landing zone with some data before proceeding. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create and Configure a DLT Pipeline +# MAGIC **NOTE**: The main difference between the instructions here and in previous labs with DLT is that in this instance, we will be setting up our pipeline for **Continuous** execution in **Production** mode. + +# COMMAND ---------- + +print_pipeline_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the **Jobs** button on the sidebar. +# MAGIC 1. Select the **Delta Live Tables** tab. +# MAGIC 1. Click **Create Pipeline**. +# MAGIC 1. Fill in a **Pipeline Name** - because these names must be unique, we suggest using the **Pipline Name** provided in the cell above. +# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the notebook **DE 12.2.2L - DLT Task**. +# MAGIC * Alternatively, you can copy the **Notebook Path** specified above and paste it into the field provided. +# MAGIC 1. Configure the Source +# MAGIC * Click **`Add configuration`** +# MAGIC * Enter the word **`source`** in the **Key** field +# MAGIC * Enter the **Source** value specified above to the **`Value`** field +# MAGIC 1. In the **Target** field, specify the database name printed out next to **Target** in the cell above.
+# MAGIC This should follow the pattern **`dbacademy__dewd_cap_12`** +# MAGIC 1. In the **Storage location** field, copy the directory as printed above. +# MAGIC 1. For **Pipeline Mode**, select **Continuous** +# MAGIC 1. Uncheck the **Enable autoscaling** box +# MAGIC 1. Set the number of workers to **`1`** (one) +# MAGIC 1. Click **Create**. +# MAGIC 1. After the UI updates, change from **Development** to **Production** mode +# MAGIC +# MAGIC This should begin the deployment of infrastructure. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Schedule a Notebook Job +# MAGIC +# MAGIC Our DLT pipeline is setup to process data as soon as it arrives. +# MAGIC +# MAGIC We'll schedule a notebook to land a new batch of data each minute so we can see this functionality in action. +# MAGIC +# MAGIC Before we start run the following cell to get the values used in this step. + +# COMMAND ---------- + +print_job_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Steps: +# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. +# MAGIC 1. Click the blue **Create Job** button +# MAGIC 1. Configure the task: +# MAGIC 1. Enter **Land-Data** for the task name +# MAGIC 1. Select the notebook **DE 12.2.3L - Land New Data** using the notebook picker +# MAGIC 1. From the **Cluster** dropdown, under **Existing All Purpose Cluster**, select your cluster +# MAGIC 1. Click **Create** +# MAGIC 1. In the top-left of the screen rename the job (not the task) from **`Land-Data`** (the defaulted value) to the **Job Name** provided for you in the previous cell. +# MAGIC +# MAGIC **Note**: When selecting your all purpose cluster, you will get a warning about how this will be billed as all purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Set a Chronological Schedule for your Job +# MAGIC Steps: +# MAGIC 1. Navigate to the **Jobs UI** and click on the job you just created. +# MAGIC 1. Locate the **Schedule** section in the side panel on the right. +# MAGIC 1. Click on the **Edit schedule** button to explore scheduling options. +# MAGIC 1. Change the **Schedule type** field from **Manual** to **Scheduled**, which will bring up a chron scheduling UI. +# MAGIC 1. Set the schedule to update **Every 2**, **Minutes** from **00** +# MAGIC 1. Click **Save** +# MAGIC +# MAGIC **NOTE**: If you wish, you can click **Run now** to trigger the first run, or wait until the top of the next minute to make sure your scheduling has worked successfully. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Register DLT Event Metrics for Querying with DBSQL +# MAGIC +# MAGIC The following cell prints out SQL statements to register the DLT event logs to your target database for querying in DBSQL. +# MAGIC +# MAGIC Execute the output code with the DBSQL Query Editor to register these tables and views. +# MAGIC +# MAGIC Explore each and make note of the logged event metrics. + +# COMMAND ---------- + +DA.generate_register_dlt_event_metrics_sql() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Define a Query on the Gold Table +# MAGIC +# MAGIC The **daily_patient_avg** table is automatically updated each time a new batch of data is processed through the DLT pipeline. Each time a query is executed against this table, DBSQL will confirm if there is a newer version and then materialize results from the newest available version. +# MAGIC +# MAGIC Run the following cell to print out a query with your database name. Save this as a DBSQL query. + +# COMMAND ---------- + +DA.generate_daily_patient_avg() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Add a Line Plot Visualization +# MAGIC +# MAGIC To track trends in patient averages over time, create a line plot and add it to a new dashboard. +# MAGIC +# MAGIC Create a line plot with the following settings: +# MAGIC * **X Column**: **`date`** +# MAGIC * **Y Column**: **`avg_heartrate`** +# MAGIC * **Group By**: **`name`** +# MAGIC +# MAGIC Add this visualization to a dashboard. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Track Data Processing Progress +# MAGIC +# MAGIC The code below extracts the **`flow_name`**, **`timestamp`**, and **`num_output_rows`** from the DLT event logs. +# MAGIC +# MAGIC Save this query in DBSQL, then define a bar plot visualization that shows: +# MAGIC * **X Column**: **`timestamp`** +# MAGIC * **Y Column**: **`num_output_rows`** +# MAGIC * **Group By**: **`flow_name`** +# MAGIC +# MAGIC Add your visualization to your dashboard. + +# COMMAND ---------- + +DA.generate_visualization_query() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Refresh your Dashboard and Track Results +# MAGIC +# MAGIC The **Land-Data** notebook scheduled with Jobs above has 12 batches of data, each representing a month of recordings for our small sampling of patients. As configured per our instructions, it should take just over 20 minutes for all of these batches of data to be triggered and processed (we scheduled the Databricks Job to run every 2 minutes, and batches of data will process through our pipeline very quickly after initial ingestion). +# MAGIC +# MAGIC Refresh your dashboard and review your visualizations to see how many batches of data have been processed. (If you followed the instructions as outlined here, there should be 12 distinct flow updates tracked by your DLT metrics.) If all source data has not yet been processed, you can go back to the Databricks Jobs UI and manually trigger additional batches. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC With everything configured, you can now continue to the final part of your lab in the notebook [DE 12.2.4L - Final Steps]($./DE 12.2.4L - Final Steps) + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/1 - DLT Task.sql b/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.2L - DLT Task.sql similarity index 81% rename from Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/1 - DLT Task.sql rename to Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.2L - DLT Task.sql index 55e4d4e..a812762 100644 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/1 - DLT Task.sql +++ b/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.2L - DLT Task.sql @@ -1,17 +1,17 @@ -- Databricks notebook source -CREATE INCREMENTAL LIVE TABLE recordings_bronze +CREATE OR REFRESH STREAMING LIVE TABLE recordings_bronze AS SELECT current_timestamp() receipt_time, input_file_name() source_file, * FROM cloud_files("${source}", "json", map("cloudFiles.schemaHints", "time DOUBLE")) -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE pii +CREATE OR REFRESH STREAMING LIVE TABLE pii AS SELECT * FROM cloud_files("/mnt/training/healthcare/patient", "csv", map("header", "true", "cloudFiles.inferColumnTypes", "true")) -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE recordings_enriched +CREATE OR REFRESH STREAMING LIVE TABLE recordings_enriched (CONSTRAINT positive_heartrate EXPECT (heartrate > 0) ON VIOLATION DROP ROW) AS SELECT CAST(a.device_id AS INTEGER) device_id, @@ -25,7 +25,7 @@ AS SELECT -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE daily_patient_avg +CREATE OR REFRESH STREAMING LIVE TABLE daily_patient_avg COMMENT "Daily mean heartrates by patient" AS SELECT mrn, name, MEAN(heartrate) avg_heartrate, DATE(time) `date` FROM STREAM(live.recordings_enriched) diff --git a/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.3L - Land New Data.py b/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.3L - Land New Data.py new file mode 100644 index 0000000..01adf27 --- /dev/null +++ b/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.3L - Land New Data.py @@ -0,0 +1,7 @@ +# Databricks notebook source +# MAGIC %run ../../Includes/Classroom-Setup-12.2.3L + +# COMMAND ---------- + +DA.data_factory.load() + diff --git a/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.4L - Final Steps.py b/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.4L - Final Steps.py new file mode 100644 index 0000000..b8dcfc2 --- /dev/null +++ b/Data-Engineering-with-Databricks/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.4L - Final Steps.py @@ -0,0 +1,133 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # End-to-End ETL in the Lakehouse +# MAGIC ## Final Steps +# MAGIC +# MAGIC We are picking up from the first notebook in this lab, [DE 12.2.1L - Instructions and Configuration]($./DE 12.2.1L - Instructions and Configuration) +# MAGIC +# MAGIC If everything is setup correctly, you should have: +# MAGIC * A DLT Pipeline running in **Continuous** mode +# MAGIC * A job that is feeding that pipline new data every 2 minutes +# MAGIC * A series of Databricks SQL Queries analysing the outputs of that pipeline + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-12.2.4L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Execute a Query to Repair Broken Data +# MAGIC +# MAGIC Review the code that defined the **`recordings_enriched`** table to identify the filter applied for the quality check. +# MAGIC +# MAGIC In the cell below, write a query that returns all the records from the **`recordings_bronze`** table that were refused by this quality check. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- TODO +# MAGIC + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC For the purposes of our demo, let's assume that thorough manual review of our data and systems has demonstrated that occasionally otherwise valid heartrate recordings are returned as negative values. +# MAGIC +# MAGIC Run the following query to examine these same rows with the negative sign removed. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT abs(heartrate), * FROM ${da.db_name}.recordings_bronze WHERE heartrate <= 0 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC To complete our dataset, we wish to insert these fixed records into the silver **`recordings_enriched`** table. +# MAGIC +# MAGIC Use the cell below to update the query used in the DLT pipeline to execute this repair. +# MAGIC +# MAGIC **NOTE**: Make sure you update the code to only process those records that were previously rejected due to the quality check. + +# COMMAND ---------- + +# TODO +# CREATE OR REFRESH STREAMING LIVE TABLE recordings_enriched +# (CONSTRAINT positive_heartrate EXPECT (heartrate > 0) ON VIOLATION DROP ROW) +# AS SELECT +# CAST(a.device_id AS INTEGER) device_id, +# CAST(a.mrn AS LONG) mrn, +# CAST(a.heartrate AS DOUBLE) heartrate, +# CAST(from_unixtime(a.time, 'yyyy-MM-dd HH:mm:ss') AS TIMESTAMP) time, +# b.name +# FROM STREAM(live.recordings_bronze) a +# INNER JOIN STREAM(live.pii) b +# ON a.mrn = b.mrn + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Use the cell below to manually or programmatically confirm that this update has been successful. +# MAGIC +# MAGIC (The total number of records in the **`recordings_bronze`** should now be equal to the total records in **`recordings_enriched`**). + +# COMMAND ---------- + +# TODO + + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Consider Production Data Permissions +# MAGIC +# MAGIC Note that while our manual repair of the data was successful, as the owner of these datasets, by default we have permissions to modify or delete these data from any location we're executing code. +# MAGIC +# MAGIC To put this another way: our current permissions would allow us to change or drop our production tables permanently if an errant SQL query is accidentally executed with the current user's permissions (or if other users are granted similar permissions). +# MAGIC +# MAGIC While for the purposes of this lab, we desired to have full permissions on our data, as we move code from development to production, it is safer to leverage service principals when scheduling Jobs and DLT Pipelines to avoid accidental data modifications. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Shut Down Production Infrastructure +# MAGIC +# MAGIC Note that Databricks Jobs, DLT Pipelines, and scheduled DBSQL queries and dashboards are all designed to provide sustained execution of production code. In this end-to-end demo, you were instructed to configure a Job and Pipeline for continuous data processing. To prevent these workloads from continuing to execute, you should **Pause** your Databricks Job and **Stop** your DLT pipeline. Deleting these assets will also ensure that production infrastructure is terminated. +# MAGIC +# MAGIC **NOTE**: All instructions for DBSQL asset scheduling in previous lessons instructed users to set the update schedule to end tomorrow. You may choose to go back and also cancel these updates to prevent DBSQL endpoints from staying on until that time. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-1.2.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-1.2.py new file mode 100644 index 0000000..d0762d6 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-1.2.py @@ -0,0 +1,21 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="1.2" + +# COMMAND ---------- + +def create_demo_tmp_vw(): + print("Creating the temp view demo_tmp_vw") + + spark.sql(""" + CREATE OR REPLACE TEMP VIEW demo_tmp_vw(name, value) AS VALUES + ("Yi", 1), + ("Ali", 2), + ("Selina", 3) + """) + +# COMMAND ---------- + +DA.init(create_db=False) +create_demo_tmp_vw() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-11.1.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-11.1.py new file mode 100644 index 0000000..43c5046 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-11.1.py @@ -0,0 +1,53 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="11.1" + +# COMMAND ---------- + +def print_sql(rows, sql): + html = f"""""" + displayHTML(html) + +# COMMAND ---------- + +def _generate_users_table(): + print_sql(20, f""" +CREATE DATABASE IF NOT EXISTS {DA.db_name} +LOCATION '{DA.paths.user_db}'; + +USE {DA.db_name}; + +CREATE TABLE users (id INT, name STRING, value DOUBLE, state STRING); + +INSERT INTO users +VALUES (1, "Yve", 1.0, "CA"), + (2, "Omar", 2.5, "NY"), + (3, "Elia", 3.3, "OH"), + (4, "Rebecca", 4.7, "TX"), + (5, "Ameena", 5.3, "CA"), + (6, "Ling", 6.6, "NY"), + (7, "Pedro", 7.1, "KY"); + +CREATE VIEW ny_users_vw +AS SELECT * FROM users WHERE state = 'NY'; +""") + +DA.generate_users_table = _generate_users_table + +# COMMAND ---------- + +def _generate_create_database_with_grants(): + print_sql(7, f""" +CREATE DATABASE {DA.db_name}_derivative; + +GRANT USAGE, READ_METADATA, CREATE, MODIFY, SELECT ON DATABASE `{DA.db_name}_derivative` TO `users`; + +SHOW GRANT ON DATABASE `{DA.db_name}_derivative`;""") + +DA.generate_create_database_with_grants = _generate_create_database_with_grants + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-11.2L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-11.2L.py new file mode 100644 index 0000000..af1f1d3 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-11.2L.py @@ -0,0 +1,122 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="acls_lab" + +# COMMAND ---------- + +def print_sql(rows, sql): + displayHTML(f"""""") + +# COMMAND ---------- + +def _generate_query(): + import re + import random + + print_sql(23, f""" +CREATE DATABASE IF NOT EXISTS {DA.db_name} +LOCATION '{DA.paths.user_db}'; + +USE {DA.db_name}; + +CREATE TABLE beans +(name STRING, color STRING, grams FLOAT, delicious BOOLEAN); + +INSERT INTO beans +VALUES ('black', 'black', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('lentils', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('jelly', 'rainbow', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('pinto', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('green', 'green', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('beanbag chair', 'white', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('lentils', 'green', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('kidney', 'red', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('castor', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}); + +CREATE VIEW tasty_beans +AS SELECT * FROM beans WHERE delicious = true; + """) + +DA.generate_query = _generate_query + +# COMMAND ---------- + +def _generate_confirmation_query(username): + import re + clean_username = re.sub("[^a-zA-Z0-9]", "_", username) + database = DA.db_name.replace(DA.clean_username, clean_username) + + print_sql(11, f""" +USE {database}; + +SELECT * FROM beans; +SELECT * FROM tasty_beans; +SELECT * FROM beans MINUS SELECT * FROM tasty_beans; + +UPDATE beans +SET color = 'pink' +WHERE name = 'black' +""") + +DA.generate_confirmation_query = _generate_confirmation_query + +# COMMAND ---------- + +def _generate_union_query(): + print_sql(6, f""" +USE {DA.db_name}; + +SELECT * FROM beans +UNION ALL TABLE beans;""") + +DA.generate_union_query = _generate_union_query + +# COMMAND ---------- + +def _generate_derivative_view(): + print_sql(7, f""" +USE {DA.db_name}; + +CREATE VIEW our_beans +AS SELECT * FROM beans +UNION ALL TABLE beans; +""") + +DA.generate_derivative_view = _generate_derivative_view + +# COMMAND ---------- + +def _generate_partner_view(username): + import re + clean_username = re.sub("[^a-zA-Z0-9]", "_", username) + database = DA.db_name.replace(DA.clean_username, clean_username) + + print_sql(7, f""" +USE {database}; + +SELECT name, color, delicious, sum(grams) +FROM our_beans +GROUP BY name, color, delicious;""") + +DA.generate_partner_view = _generate_partner_view + +# COMMAND ---------- + +def _generate_delete_query(username): + import re + clean_username = re.sub("[^a-zA-Z0-9]", "_", username) + database = DA.db_name.replace(DA.clean_username, clean_username) + + print_sql(5, f""" +USE {database}; + +DELETE FROM beans + """) + +DA.generate_delete_query = _generate_delete_query + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.1.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.1.py new file mode 100644 index 0000000..d2f0100 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.1.py @@ -0,0 +1,118 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="12.1" + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + +# COMMAND ---------- + +def print_sql(rows, sql): + html = f"" + displayHTML(html) + +# COMMAND ---------- + +def _generate_config(): + print_sql(33, f""" +CREATE DATABASE IF NOT EXISTS {DA.db_name} +LOCATION '{DA.paths.working_dir}'; + +USE {DA.db_name}; + +CREATE TABLE user_ping +(user_id STRING, ping INTEGER, time TIMESTAMP); + +CREATE TABLE user_ids (user_id STRING); + +INSERT INTO user_ids VALUES +("potato_luver"), +("beanbag_lyfe"), +("default_username"), +("the_king"), +("n00b"), +("frodo"), +("data_the_kid"), +("el_matador"), +("the_wiz"); + +CREATE FUNCTION get_ping() + RETURNS INT + RETURN int(rand() * 250); + +CREATE FUNCTION is_active() + RETURNS BOOLEAN + RETURN CASE + WHEN rand() > .25 THEN true + ELSE false + END; +""") + +DA.generate_config = _generate_config + +# COMMAND ---------- + +def _generate_load(): + print_sql(12, f""" +USE {DA.db_name}; + +INSERT INTO user_ping +SELECT *, + get_ping() ping, + current_timestamp() time +FROM user_ids +WHERE is_active()=true; + +SELECT * FROM user_ping; +""") + +DA.generate_load = _generate_load + +# COMMAND ---------- + +def _generate_user_counts(): + print_sql(10, f""" +USE {DA.db_name}; + +SELECT user_id, count(*) total_records +FROM user_ping +GROUP BY user_id +ORDER BY + total_records DESC, + user_id ASC; +""") + +DA.generate_user_counts = _generate_user_counts + +# COMMAND ---------- + +def _generate_avg_ping(): + print_sql(10, f""" +USE {DA.db_name}; + +SELECT user_id, window.end end_time, mean(ping) avg_ping +FROM user_ping +GROUP BY user_id, window(time, '3 minutes') +ORDER BY + end_time DESC, + user_id ASC; +""") + +DA.generate_avg_ping = _generate_avg_ping + +# COMMAND ---------- + +def _generate_summary(): + print_sql(8, f""" +USE {DA.db_name}; + +SELECT user_id, min(time) first_seen, max(time) last_seen, count(*) total_records, avg(ping) total_avg_ping +FROM user_ping +GROUP BY user_id +ORDER BY user_id ASC; +""") + +DA.generate_summary = _generate_summary + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.2.1L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.2.1L.py new file mode 100644 index 0000000..4b906bf --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.2.1L.py @@ -0,0 +1,83 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="cap_12" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def print_sql(rows, sql): + displayHTML(f"""""") + + +# COMMAND ---------- + +def _generate_daily_patient_avg(): + sql = f"SELECT * FROM {DA.db_name}.daily_patient_avg" + print_sql(3, sql) + +DA.generate_daily_patient_avg = _generate_daily_patient_avg + +# COMMAND ---------- + +def _generate_visualization_query(): + sql = f""" +SELECT flow_name, timestamp, int(details:flow_progress:metrics:num_output_rows) num_output_rows +FROM {DA.db_name}.dlt_metrics +ORDER BY timestamp DESC;""" + + print_sql(5, sql) + +DA.generate_visualization_query = _generate_visualization_query + +# COMMAND ---------- + +generate_register_dlt_event_metrics_sql_string = "" + +def _generate_register_dlt_event_metrics_sql(): + global generate_register_dlt_event_metrics_sql_string + + generate_register_dlt_event_metrics_sql_string = f""" +CREATE TABLE IF NOT EXISTS {DA.db_name}.dlt_events +LOCATION '{DA.paths.working_dir}/storage/system/events'; + +CREATE VIEW IF NOT EXISTS {DA.db_name}.dlt_success AS +SELECT * FROM {DA.db_name}.dlt_events +WHERE details:flow_progress:metrics IS NOT NULL; + +CREATE VIEW IF NOT EXISTS {DA.db_name}.dlt_metrics AS +SELECT timestamp, origin.flow_name, details +FROM {DA.db_name}.dlt_success +ORDER BY timestamp DESC;""".strip() + + print_sql(13, generate_register_dlt_event_metrics_sql_string) + +DA.generate_register_dlt_event_metrics_sql = _generate_register_dlt_event_metrics_sql + +# COMMAND ---------- + +def print_pipeline_config(): + path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) + path = "/".join(path.split("/")[:-1]) + "/DE 12.2.2L - DLT Task" + + displayHTML(f""" + + + + + +
Pipeline Name:Cap-12-{DA.username}
Source:{DA.paths.working_dir}/source/tracker
Target:{DA.db_name}
Storage Location:{DA.paths.working_dir}/storage
Notebook Path:{path}
""") + +def print_job_config(): + displayHTML(f""" + +
Job Name:Cap-12-{DA.username}
""") + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.2.3L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.2.3L.py new file mode 100644 index 0000000..9777fb2 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.2.3L.py @@ -0,0 +1,15 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="cap_12" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Don't clean up, continue where we left off. +# DA.cleanup() +DA.init(create_db=False) +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.2.4L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.2.4L.py new file mode 100644 index 0000000..d9f6771 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-12.2.4L.py @@ -0,0 +1,14 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="cap_12" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Don't clean up, continue where we left off. +# DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.1.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.1.py new file mode 100644 index 0000000..a4038dd --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.1.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="2.1" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.2L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.2L.py new file mode 100644 index 0000000..16ed498 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.2L.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="2.2L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.3.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.3.py new file mode 100644 index 0000000..60734f0 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.3.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="2.3" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.4L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.4L.py new file mode 100644 index 0000000..b59dd06 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-2.4L.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="2.4L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.1.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.1.py new file mode 100644 index 0000000..4562868 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.1.py @@ -0,0 +1,12 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="3.1" + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +install_dtavod_datasets(reinstall=False) +print() +copy_source_dataset(f"{DA.working_dir_prefix}/source/dtavod/flights/departuredelays.csv", f"{DA.paths.working_dir}/flights/departuredelays.csv", "csv", "flights") +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.2A.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.2A.py new file mode 100644 index 0000000..0d2d298 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.2A.py @@ -0,0 +1,17 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="3.2" + +# COMMAND ---------- + +DA.cleanup() +DA.init() + +# Clean out the global_temp database. +for row in spark.sql("SHOW TABLES IN global_temp").select("tableName").collect(): + table_name = row[0] + spark.sql(f"DROP TABLE global_temp.{table_name}") + +print() +install_dtavod_datasets(reinstall=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.2B.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.2B.py new file mode 100644 index 0000000..397a759 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.2B.py @@ -0,0 +1,22 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="3.2" + +# COMMAND ---------- + +# We want the state from the previous lesson +# DA.cleanup() # DO NOT EXECUTE + +tags = sc._jvm.scala.collection.JavaConversions.mapAsJavaMap(dbutils.entry_point.getDbutils().notebook().getContext().tags()) +is_job = "jobId" in tags +if is_job: print("Mocking global temp view") + +DA.init(create_db=is_job) + +if is_job: + spark.sql(f"""USE {DA.db_name}""") + spark.sql("""CREATE TABLE IF NOT EXISTS external_table USING CSV OPTIONS (path '${da.paths.working_dir}/flight_delays', header "true", mode "FAILFAST");""") + spark.sql("""CREATE OR REPLACE GLOBAL TEMPORARY VIEW global_temp_view_dist_gt_1000 AS SELECT * FROM external_table WHERE distance > 1000;""") + +print() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.3L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.3L.py new file mode 100644 index 0000000..7f5c2c6 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-3.3L.py @@ -0,0 +1,12 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="3.3L" + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +install_dtavod_datasets(reinstall=False) +print() +copy_source_dataset(f"{DA.working_dir_prefix}/source/dtavod/weather", f"{DA.paths.working_dir}/weather", "parquet", "weather") +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.1.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.1.py new file mode 100644 index 0000000..63ef9c5 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.1.py @@ -0,0 +1,10 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.1" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.2.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.2.py new file mode 100644 index 0000000..7d52166 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.2.py @@ -0,0 +1,11 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.2" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) +load_eltwss_external_tables() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.3.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.3.py new file mode 100644 index 0000000..a03307e --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.3.py @@ -0,0 +1,11 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.3" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) +load_eltwss_external_tables() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.4.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.4.py new file mode 100644 index 0000000..d5c8a52 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.4.py @@ -0,0 +1,23 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.4" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) + +print() + +clone_source_table("sales", f"{DA.paths.datasets}/delta", "sales_hist") +clone_source_table("users", f"{DA.paths.datasets}/delta", "users_hist") +clone_source_table("events", f"{DA.paths.datasets}/delta", "events_hist") + +clone_source_table("users_update", f"{DA.paths.datasets}/delta") +clone_source_table("events_update", f"{DA.paths.datasets}/delta") + +# clone_source_table("events_raw", f"{DA.paths.datasets}/delta") +# clone_source_table("item_lookup", f"{DA.paths.datasets}/delta") + +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.5L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.5L.py new file mode 100644 index 0000000..19f59c7 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.5L.py @@ -0,0 +1,10 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.5L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.6.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.6.py new file mode 100644 index 0000000..86ba4b9 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.6.py @@ -0,0 +1,14 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.6" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) + +print() +create_eltwss_users_update() + +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.7.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.7.py new file mode 100644 index 0000000..c4b714f --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.7.py @@ -0,0 +1,23 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.7" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) + +print() + +clone_source_table("sales", f"{DA.paths.datasets}/delta", "sales_hist") +# clone_source_table("users", f"{DA.paths.datasets}/delta", "users_hist") +clone_source_table("events", f"{DA.paths.datasets}/delta", "events_hist") + +# clone_source_table("users_update", f"{DA.paths.datasets}/delta") +clone_source_table("events_update", f"{DA.paths.datasets}/delta") + +clone_source_table("events_raw", f"{DA.paths.datasets}/delta") +clone_source_table("item_lookup", f"{DA.paths.datasets}/delta") + +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.8.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.8.py new file mode 100644 index 0000000..1cdde71 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.8.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.8" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.9L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.9L.py new file mode 100644 index 0000000..a938f41 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-4.9L.py @@ -0,0 +1,12 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.9L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) +print() +load_eltwss_tables() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-5.3L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-5.3L.py new file mode 100644 index 0000000..399dba2 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-5.3L.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="5.3L" + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-6.1.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-6.1.py new file mode 100644 index 0000000..bc915b1 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-6.1.py @@ -0,0 +1,43 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="6.1" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +class DataFactory: + def __init__(self, ): + self.source = "/mnt/training/healthcare/tracker/streaming/" + self.userdir = f"{DA.paths.working_dir}/tracker" + self.curr_mo = 1 + + def load(self, continuous=False): + if self.curr_mo > 12: + print("Data source exhausted\n") + + elif continuous == True: + while self.curr_mo <= 12: + curr_file = f"{self.curr_mo:02}.json" + dbutils.fs.cp(self.source + curr_file, self.userdir + curr_file) + self.curr_mo += 1 + else: + curr_file = f"{str(self.curr_mo).zfill(2)}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the tracker dataset") + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + +# COMMAND ---------- + +DA.init() +DA.paths.checkpoints = f"{DA.paths.working_dir}/checkpoints" +DA.data_factory = DataFactory() + +print() +DA.data_factory.load() +DA.conclude_setup() + +sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-6.2.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-6.2.py new file mode 100644 index 0000000..20eed0d --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-6.2.py @@ -0,0 +1,63 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="6.2" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def autoload_to_table(data_source, source_format, table_name, checkpoint_directory): + (spark.readStream + .format("cloudFiles") + .option("cloudFiles.format", source_format) + .option("cloudFiles.schemaLocation", checkpoint_directory) + .load(data_source) + .writeStream + .option("checkpointLocation", checkpoint_directory) + .option("mergeSchema", "true") + .trigger(availableNow=True) + .table(table_name) + .awaitTermination() + ) + +# COMMAND ---------- + +class DataFactory: + def __init__(self): + self.source = "/mnt/training/healthcare/tracker/streaming/" + self.userdir = f"{DA.paths.working_dir}/tracker" + self.curr_mo = 1 + + def load(self, continuous=False): + if self.curr_mo > 12: + print("Data source exhausted\n") + + elif continuous == True: + while self.curr_mo <= 12: + curr_file = f"{self.curr_mo:02}.json" + dbutils.fs.cp(f"{self.source}/{curr_file}", f"{self.userdir}/{curr_file}") + self.curr_mo += 1 + autoload_to_table(self.userdir, "json", "bronze", f"{DA.paths.checkpoints}/bronze") + else: + curr_file = f"{str(self.curr_mo).zfill(2)}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + + dbutils.fs.cp(f"{self.source}/{curr_file}", f"{self.userdir}/{curr_file}") + self.curr_mo += 1 + autoload_to_table(self.userdir, "json", "bronze", f"{DA.paths.checkpoints}/bronze") + + +# COMMAND ---------- + +DA.init() +DA.paths.checkpoints = f"{DA.paths.working_dir}/_checkpoints" +DA.data_factory = DataFactory() + +print() +DA.data_factory.load() +DA.conclude_setup() + +sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-6.3L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-6.3L.py new file mode 100644 index 0000000..c35fd86 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-6.3L.py @@ -0,0 +1,12 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="6.3L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.paths.checkpoints = f"{DA.paths.working_dir}/_checkpoints" +DA.conclude_setup() + +sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-7.1.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-7.1.py new file mode 100644 index 0000000..c581e45 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-7.1.py @@ -0,0 +1,57 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="7.1" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +class DataFactory: + def __init__(self): + self.source = f"{DA.paths.data_source}/tracker/streaming/" + self.userdir = DA.paths.data_landing_location + self.curr_mo = 1 + + def load(self, continuous=False): + if self.curr_mo > 12: + print("Data source exhausted\n") + elif continuous == True: + while self.curr_mo <= 12: + curr_file = f"{self.curr_mo:02}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + else: + curr_file = f"{str(self.curr_mo).zfill(2)}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + + +# COMMAND ---------- + +DA.init() +DA.paths.checkpoints = f"{DA.paths.working_dir}/_checkpoints" +DA.paths.data_source = "/mnt/training/healthcare" +DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +# bronzePath = f"{DA.paths.wokring_dir}/bronze" +# recordingsParsedPath = f"{DA.paths.wokring_dir}/silver/recordings_parsed" +# recordingsEnrichedPath = f"{DA.paths.wokring_dir}/silver/recordings_enriched" +# dailyAvgPath = f"{DA.paths.wokring_dir}/gold/dailyAvg" + +# checkpointPath = f"{DA.paths.wokring_dir}/checkpoints" +#bronzeCheckpoint = f"{DA.paths.checkpoints}/bronze" +# recordingsParsedCheckpoint = f"{DA.paths.checkpoints}/recordings_parsed" +# recordingsEnrichedCheckpoint = f"{DA.paths.checkpoints}/recordings_enriched" +# dailyAvgCheckpoint = f"{DA.paths.checkpoints}/dailyAvgPath" + +DA.data_factory = DataFactory() +DA.conclude_setup() + +sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-7.2L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-7.2L.py new file mode 100644 index 0000000..e12f6b5 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-7.2L.py @@ -0,0 +1,12 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="7.2L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.paths.checkpoints = f"{DA.paths.working_dir}/_checkpoints" +DA.conclude_setup() + +sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.1.1.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.1.1.py new file mode 100644 index 0000000..736ff02 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.1.1.py @@ -0,0 +1,102 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="dlt_demo_81" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def get_pipeline_config(): + path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) + path = "/".join(path.split("/")[:-1]) + "/DE 8.1.2 - SQL for Delta Live Tables" + + return f"DLT-Demo-81-{DA.username}", path + +def _print_pipeline_config(): + "Provided by DBAcademy, this function renders the configuration of the pipeline as HTML" + pipeline_name, path = get_pipeline_config() + + displayHTML(f""" + + + + + + + + + + + + +
Pipeline Name:
Target:
Storage Location:
Notebook Path:
""") + +DA.print_pipeline_config = _print_pipeline_config + +# COMMAND ---------- + +def _create_pipeline(): + "Provided by DBAcademy, this function creates the prescribed pipline" + + from dbacademy.dbrest import DBAcademyRestClient + client = DBAcademyRestClient() + + pipeline_name, path = get_pipeline_config() + + # Delete the existing pipeline if it exists + client.pipelines().delete_by_name(pipeline_name) + + # Create the new pipeline + pipeline = client.pipelines().create( + name = pipeline_name, + storage = DA.paths.storage_location, + target = DA.db_name, + notebooks = [path]) + + DA.pipeline_id = pipeline.get("pipeline_id") + +DA.create_pipeline = _create_pipeline + +# COMMAND ---------- + +def _start_pipeline(): + "Provided by DBAcademy, this function starts the pipeline and then blocks until it has completed, failed or was canceled" + + import time + from dbacademy.dbrest import DBAcademyRestClient + client = DBAcademyRestClient() + + # Start the pipeline + start = client.pipelines().start_by_id(DA.pipeline_id) + update_id = start.get("update_id") + + # Get the status and block until it is done + update = client.pipelines().get_update_by_id(DA.pipeline_id, update_id) + state = update.get("update").get("state") + + done = ["COMPLETED", "FAILED", "CANCELED"] + while state not in done: + duration = 15 + time.sleep(duration) + print(f"Current state is {state}, sleeping {duration} seconds.") + update = client.pipelines().get_update_by_id(DA.pipeline_id, update_id) + state = update.get("update").get("state") + + print(f"The final state is {state}.") + assert state == "COMPLETED", f"Expected the state to be COMPLETED, found {state}" + +DA.start_pipeline = _start_pipeline + +# COMMAND ---------- + +DA.cleanup() +DA.init() + +DA.paths.data_source = "/mnt/training/healthcare" +DA.paths.storage_location = f"{DA.paths.working_dir}/storage" +DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.1.3.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.1.3.py new file mode 100644 index 0000000..d6ca2c5 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.1.3.py @@ -0,0 +1,20 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="dlt_demo_81" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Continues where 8.1.1 picks up, don't remove assets +# DA.cleanup() +DA.init() + +DA.paths.data_source = "/mnt/training/healthcare" +DA.paths.storage_location = f"{DA.paths.working_dir}/storage" +DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.2.1L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.2.1L.py new file mode 100644 index 0000000..88f4cb2 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.2.1L.py @@ -0,0 +1,110 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="dlt_lab_82" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def get_pipeline_config(): + path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) + path = "/".join(path.split("/")[:-1]) + "/DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab" + + pipeline_name = f"DLT-Lab-82L-{DA.username}" + source = f"{DA.paths.working_dir}/source/tracker" + return pipeline_name, path, source + +def _print_pipeline_config(): + "Provided by DBAcademy, this function renders the configuration of the pipeline as HTML" + pipeline_name, path, source = get_pipeline_config() + + displayHTML(f""" + + + + + + + + + + + + + + + + + +
Pipeline Name:
Target:
Storage Location:
Notebook Path:
Source:
""") + +DA.print_pipeline_config = _print_pipeline_config + +# COMMAND ---------- + +def _create_pipeline(): + "Provided by DBAcademy, this function creates the prescribed pipline" + + from dbacademy.dbrest import DBAcademyRestClient + client = DBAcademyRestClient() + + pipeline_name, path, source = get_pipeline_config() + + # Delete the existing pipeline if it exists + client.pipelines().delete_by_name(pipeline_name) + + # Create the new pipeline + pipeline = client.pipelines().create( + name = pipeline_name, + storage = f"{DA.paths.working_dir}/storage", + target = DA.db_name, + notebooks = [path], + configuration = {"source": source}) + + DA.pipline_id = pipeline.get("pipeline_id") + +DA.create_pipeline = _create_pipeline + +# COMMAND ---------- + +def _start_pipeline(): + "Provided by DBAcademy, this function starts the pipline and then blocks until it has completed, failed or was canceled" + + import time + from dbacademy.dbrest import DBAcademyRestClient + client = DBAcademyRestClient() + + # Start the pipeline + start = client.pipelines().start_by_id(DA.pipline_id) + update_id = start.get("update_id") + + # Get the status and block until it is done + update = client.pipelines().get_update_by_id(DA.pipline_id, update_id) + state = update.get("update").get("state") + + done = ["COMPLETED", "FAILED", "CANCELED"] + while state not in done: + duration = 15 + time.sleep(duration) + print(f"Current state is {state}, sleeping {duration} seconds.") + update = client.pipelines().get_update_by_id(DA.pipline_id, update_id) + state = update.get("update").get("state") + + print(f"The final state is {state}.") + assert state == "COMPLETED", f"Expected the state to be COMPLETED, found {state}" + +DA.start_pipeline = _start_pipeline + +# COMMAND ---------- + +DA.cleanup() +DA.init() + +# DA.paths.data_source = "/mnt/training/healthcare" +# DA.paths.storage_location = f"{DA.paths.working_dir}/storage" +# DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.2.3L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.2.3L.py new file mode 100644 index 0000000..8358dc3 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-8.2.3L.py @@ -0,0 +1,21 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="dlt_lab_82" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Continuing from 8.2.3L, do not remove assets +# DA.cleanup() +DA.init() + +DA.paths.data_source = "/mnt/training/healthcare" +DA.paths.storage_location = f"{DA.paths.working_dir}/storage" + +DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.1.1.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.1.1.py new file mode 100644 index 0000000..bb17c2b --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.1.1.py @@ -0,0 +1,37 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="jobs_demo_91" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def print_pipeline_config(): + path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) + path = "/".join(path.split("/")[:-1]) + "/DE 9.1.3 - DLT Job" + + displayHTML(f""" + + + + +
Pipeline Name:Jobs-Demo-91-{DA.username}
Target:{DA.db_name}
Storage Location:{DA.paths.working_dir}/storage
Notebook Path:{path}
""") + +def print_job_config(): +# path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) +# path = "/".join(path.split("/")[:-1]) + "/DE 9.1.2 - Reset" +# Notebook Path:{path} + + displayHTML(f""" + + +
Job Name:Jobs-Demo-91-{DA.username}
""") + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.1.2.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.1.2.py new file mode 100644 index 0000000..2aa9973 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.1.2.py @@ -0,0 +1,14 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="jobs_demo_91" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.2.1L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.2.1L.py new file mode 100644 index 0000000..ebf54cb --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.2.1L.py @@ -0,0 +1,33 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="jobs_lab_92" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def print_pipeline_config(): + path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) + path = "/".join(path.split("/")[:-1]) + "/DE 9.2.3L - DLT Job" + + displayHTML(f""" + + + + + +
Pipeline Name:Jobs-Lab-92-{DA.username}
Source:{DA.paths.working_dir}/source/tracker
Target:{DA.db_name}
Storage Location:{DA.paths.working_dir}/storage
Notebook Path:{path}
""") + +def print_job_config(): + displayHTML(f""" + +
Job Name:Jobs-Lab-92-{DA.username}
""") + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.2.2L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.2.2L.py new file mode 100644 index 0000000..a82d3ee --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.2.2L.py @@ -0,0 +1,15 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="jobs_lab_92" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Don't reset our database or other assets +# DA.cleanup() +DA.init(create_db=False) +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.2.4L.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.2.4L.py new file mode 100644 index 0000000..f250172 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-9.2.4L.py @@ -0,0 +1,14 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="jobs_lab_92" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Don't reset our database or other assets +# DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Includes/Classroom-Setup-DLT-Lab.py b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-DLT-Lab.py new file mode 100644 index 0000000..f8e0da8 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Classroom-Setup-DLT-Lab.py @@ -0,0 +1,110 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="dlt_lab" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def print_sql(rows, sql): + displayHTML(f"""""") + + +# COMMAND ---------- + +generate_register_dlt_event_metrics_sql_string = "" + +def _generate_register_dlt_event_metrics_sql(): + global generate_register_dlt_event_metrics_sql_string + + generate_register_dlt_event_metrics_sql_string = f""" +CREATE TABLE IF NOT EXISTS {DA.db_name}.dlt_events +LOCATION '{DA.paths.storage_location}/system/events'; + +CREATE VIEW IF NOT EXISTS {DA.db_name}.dlt_success AS +SELECT * FROM {DA.db_name}.dlt_events +WHERE details:flow_progress:metrics IS NOT NULL; + +CREATE VIEW IF NOT EXISTS {DA.db_name}.dlt_metrics AS +SELECT timestamp, origin.flow_name, details +FROM {DA.db_name}.dlt_success +ORDER BY timestamp DESC;""".strip() + + print_sql(13, generate_register_dlt_event_metrics_sql_string) +DA.generate_register_dlt_event_metrics_sql = _generate_register_dlt_event_metrics_sql + +# COMMAND ---------- + +def _generate_daily_patient_avg(): + sql = f"SELECT * FROM {DA.db_name}.daily_patient_avg" + print_sql(3, sql) + +DA.generate_daily_patient_avg = _generate_daily_patient_avg + +# COMMAND ---------- + +def _generate_visualization_query(): + sql = f""" +SELECT flow_name, timestamp, int(details:flow_progress:metrics:num_output_rows) num_output_rows +FROM {DA.db_name}.dlt_metrics +ORDER BY timestamp DESC;""" + + print_sql(5, sql) + +DA.generate_visualization_query = _generate_visualization_query + +# COMMAND ---------- + +class DataFactory: + def __init__(self): + self.source = f"{DA.paths.data_source}/tracker/streaming/" + self.userdir = DA.paths.data_landing_location + try: + self.curr_mo = 1 + int(max([x[1].split(".")[0] for x in dbutils.fs.ls(self.userdir)])) + except: + self.curr_mo = 1 + + def load(self, continuous=False): + if self.curr_mo > 12: + print("Data source exhausted\n") + elif continuous == True: + while self.curr_mo <= 12: + curr_file = f"{self.curr_mo:02}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + else: + curr_file = f"{str(self.curr_mo).zfill(2)}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + +# COMMAND ---------- + +DA.init() + +DA.paths.data_source = "/mnt/training/healthcare" +DA.paths.storage_location = f"{DA.paths.working_dir}/storage" + +DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +# bronzePath = f"{DA.paths.wokring_dir}/bronze" +# recordingsParsedPath = f"{DA.paths.wokring_dir}/silver/recordings_parsed" +# recordingsEnrichedPath = f"{DA.paths.wokring_dir}/silver/recordings_enriched" +# dailyAvgPath = f"{DA.paths.wokring_dir}/gold/daily_avg" + +# checkpointPath = f"{DA.paths.wokring_dir}/checkpoints" +#bronzeCheckpoint = f"{DA.paths.checkpoints}/bronze" +# recordingsParsedCheckpoint = f"{DA.paths.checkpoints}/recordings_parsed" +# recordingsEnrichedCheckpoint = f"{DA.paths.checkpoints}/recordings_enriched" +# dailyAvgCheckpoint = f"{DA.paths.checkpoints}/dailyAvgPath" + +DA.data_factory = DataFactory() +DA.conclude_setup() + +# sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Includes/Reset.py b/Data-Engineering-with-Databricks/Includes/Reset.py new file mode 100644 index 0000000..f070f72 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/Reset.py @@ -0,0 +1,35 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="reset" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +DA.init() + +# COMMAND ---------- + +rows = spark.sql(f"show databases").collect() +for row in rows: + db_name = row[0] + if db_name.startswith(DA.db_name_prefix): + print(db_name) + spark.sql(f"DROP DATABASE {db_name} CASCADE") + +# COMMAND ---------- + +if DA.paths.exists(DA.working_dir_prefix): + print(DA.working_dir_prefix) + dbutils.fs.rm(DA.working_dir_prefix, True) + +# COMMAND ---------- + +# Create the source database, install the datasets, whatever, so that we can run the other notebooks asyncronously +install_dtavod_datasets(reinstall=True) +install_eltwss_datasets(reinstall=True) + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets diff --git a/Data-Engineering-with-Databricks/Includes/_utility-methods.py b/Data-Engineering-with-Databricks/Includes/_utility-methods.py new file mode 100644 index 0000000..b27e619 --- /dev/null +++ b/Data-Engineering-with-Databricks/Includes/_utility-methods.py @@ -0,0 +1,267 @@ +# Databricks notebook source +# MAGIC %pip install \ +# MAGIC git+https://github.com/databricks-academy/dbacademy-gems \ +# MAGIC git+https://github.com/databricks-academy/dbacademy-rest \ +# MAGIC --quiet --disable-pip-version-check + +# COMMAND ---------- + +class Paths(): + def __init__(self, working_dir, clean_lesson): + self.working_dir = working_dir + + if clean_lesson: self.user_db = f"{working_dir}/{clean_lesson}.db" + else: self.user_db = f"{working_dir}/database.db" + + def exists(self, path): + try: return len(dbutils.fs.ls(path)) >= 0 + except Exception:return False + + def print(self, padding=" "): + max_key_len = 0 + for key in self.__dict__: max_key_len = len(key) if len(key) > max_key_len else max_key_len + for key in self.__dict__: + label = f"{padding}DA.paths.{key}:" + print(label.ljust(max_key_len+13) + DA.paths.__dict__[key]) + +class DBAcademyHelper(): + def __init__(self, lesson=None): + import re, time + + self.start = int(time.time()) + + self.course_name = "dewd" + self.lesson = lesson.lower() + self.data_source_uri = "wasbs://courseware@dbacademy.blob.core.windows.net/data-engineering-with-databricks/v02" + + # Define username + self.username = spark.sql("SELECT current_user()").first()[0] + self.clean_username = re.sub("[^a-zA-Z0-9]", "_", self.username) + + self.db_name_prefix = f"dbacademy_{self.clean_username}_{self.course_name}" + self.source_db_name = None + + self.working_dir_prefix = f"dbfs:/user/{self.username}/dbacademy/{self.course_name}" + + if self.lesson: + clean_lesson = re.sub("[^a-zA-Z0-9]", "_", self.lesson) + working_dir = f"{self.working_dir_prefix}/{self.lesson}" + self.paths = Paths(working_dir, clean_lesson) + self.hidden = Paths(working_dir, clean_lesson) + self.db_name = f"{self.db_name_prefix}_{clean_lesson}" + else: + working_dir = self.working_dir_prefix + self.paths = Paths(working_dir, None) + self.hidden = Paths(working_dir, None) + self.db_name = self.db_name_prefix + + def init(self, create_db=True): + spark.catalog.clearCache() + self.create_db = create_db + + if create_db: + print(f"\nCreating the database \"{self.db_name}\"") + spark.sql(f"CREATE DATABASE IF NOT EXISTS {self.db_name} LOCATION '{self.paths.user_db}'") + spark.sql(f"USE {self.db_name}") + + def cleanup(self): + for stream in spark.streams.active: + print(f"Stopping the stream \"{stream.name}\"") + stream.stop() + try: stream.awaitTermination() + except: pass # Bury any exceptions + + if spark.sql(f"SHOW DATABASES").filter(f"databaseName == '{self.db_name}'").count() == 1: + print(f"Dropping the database \"{self.db_name}\"") + spark.sql(f"DROP DATABASE {self.db_name} CASCADE") + + if self.paths.exists(self.paths.working_dir): + print(f"Removing the working directory \"{self.paths.working_dir}\"") + dbutils.fs.rm(self.paths.working_dir, True) + + def conclude_setup(self): + import time + + spark.conf.set("da.username", self.username) + spark.conf.set("da.db_name", self.db_name) + for key in self.paths.__dict__: + spark.conf.set(f"da.paths.{key.lower()}", self.paths.__dict__[key]) + + print("\nPredefined Paths:") + DA.paths.print() + + if self.source_db_name: + print(f"\nPredefined tables in {self.source_db_name}:") + tables = spark.sql(f"SHOW TABLES IN {self.source_db_name}").filter("isTemporary == false").select("tableName").collect() + if len(tables) == 0: print(" -none-") + for row in tables: print(f" {row[0]}") + + if self.create_db: + print(f"\nPredefined tables in {self.db_name}:") + tables = spark.sql(f"SHOW TABLES IN {self.db_name}").filter("isTemporary == false").select("tableName").collect() + if len(tables) == 0: print(" -none-") + for row in tables: print(f" {row[0]}") + + print(f"\nSetup completed in {int(time.time())-self.start} seconds") + + def block_until_stream_is_ready(self, query, min_batches=2): + import time + while len(query.recentProgress) < min_batches: + time.sleep(5) # Give it a couple of seconds + + print(f"The stream has processed {len(query.recentProgress)} batchs") + +dbutils.widgets.text("lesson", "missing") +lesson = dbutils.widgets.get("lesson") +if lesson == "none": lesson = None +assert lesson != "missing", f"The lesson must be passed to the DBAcademyHelper" + +DA = DBAcademyHelper(lesson) + +# COMMAND ---------- + +def install_source_dataset(source_uri, reinstall, subdir): + target_dir = f"{DA.working_dir_prefix}/source/{subdir}" + +# if reinstall and DA.paths.exists(target_dir): +# print(f"Removing existing dataset at {target_dir}") +# dbutils.fs.rm(target_dir, True) + + if DA.paths.exists(target_dir): + print(f"Skipping install to \"{target_dir}\", dataset already exists") + else: + print(f"Installing datasets to \"{target_dir}\"") + dbutils.fs.cp(source_uri, target_dir, True) + + return target_dir + +# COMMAND ---------- + +def install_dtavod_datasets(reinstall): + source_uri = "wasbs://courseware@dbacademy.blob.core.windows.net/databases_tables_and_views_on_databricks/v02" + DA.paths.datasets = install_source_dataset(source_uri, reinstall, "dtavod") + + copy_source_dataset(f"{DA.paths.datasets}/flights/departuredelays.csv", + f"{DA.paths.working_dir}/flight_delays", + format="csv", name="flight_delays") + +# COMMAND ---------- + +def install_eltwss_datasets(reinstall): + source_uri = "wasbs://courseware@dbacademy.blob.core.windows.net/elt-with-spark-sql/v02/small-datasets" + DA.paths.datasets = install_source_dataset(source_uri, reinstall, "eltwss") + +# COMMAND ---------- + +def clone_source_table(table_name, source_path, source_name=None): + import time + start = int(time.time()) + + source_name = table_name if source_name is None else source_name + print(f"Cloning the {table_name} table from {source_path}/{source_name}", end="...") + + spark.sql(f""" + CREATE OR REPLACE TABLE {table_name} + SHALLOW CLONE delta.`{source_path}/{source_name}` + """) + + total = spark.read.table(table_name).count() + print(f"({int(time.time())-start} seconds / {total:,} records)") + +def load_eltwss_tables(): + clone_source_table("events", f"{DA.paths.datasets}/delta") + clone_source_table("sales", f"{DA.paths.datasets}/delta") + clone_source_table("users", f"{DA.paths.datasets}/delta") + clone_source_table("transactions", f"{DA.paths.datasets}/delta") + +# COMMAND ---------- + +def copy_source_dataset(src_path, dst_path, format, name): + import time + start = int(time.time()) + print(f"Creating the {name} dataset", end="...") + + dbutils.fs.cp(src_path, dst_path, True) + + total = spark.read.format(format).load(dst_path).count() + print(f"({int(time.time())-start} seconds / {total:,} records)") + +def load_eltwss_external_tables(): + copy_source_dataset(f"{DA.paths.datasets}/raw/sales-csv", + f"{DA.paths.working_dir}/sales-csv", "csv", "sales-csv") + + import time + start = int(time.time()) + print(f"Creating the users table", end="...") + + # REFACTORING - Making lesson-specific copy + dbutils.fs.cp(f"{DA.paths.datasets}/raw/users-historical", + f"{DA.paths.working_dir}/users-historical", True) + + # https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html + (spark.read + .format("parquet") + .load(f"{DA.paths.working_dir}/users-historical") + .repartition(1) + .write + .format("org.apache.spark.sql.jdbc") + .option("url", f"jdbc:sqlite:/{DA.username}_ecommerce.db") + .option("dbtable", "users") # The table name in sqllight + .mode("overwrite") + .save()) + + total = spark.read.parquet(f"{DA.paths.working_dir}/users-historical").count() + print(f"({int(time.time())-start} seconds / {total:,} records)") + +# COMMAND ---------- + +# lesson: Writing delta +def create_eltwss_users_update(): + import time + start = int(time.time()) + print(f"Creating the users_dirty table", end="...") + + # REFACTORING - Making lesson-specific copy + dbutils.fs.cp(f"{DA.paths.datasets}/raw/users-30m", + f"{DA.paths.working_dir}/users-30m", True) + + spark.sql(f""" + CREATE OR REPLACE TABLE users_dirty AS + SELECT *, current_timestamp() updated + FROM parquet.`{DA.paths.working_dir}/users-30m` + """) + + spark.sql("INSERT INTO users_dirty VALUES (NULL, NULL, NULL, NULL), (NULL, NULL, NULL, NULL), (NULL, NULL, NULL, NULL)") + + total = spark.read.table("users_dirty").count() + print(f"({int(time.time())-start} seconds / {total:,} records)") + +# COMMAND ---------- + +class DltDataFactory: + def __init__(self): + self.source = f"/mnt/training/healthcare/tracker/streaming" + self.userdir = f"{DA.paths.working_dir}/source/tracker" + try: + self.curr_mo = 1 + int(max([x[1].split(".")[0] for x in dbutils.fs.ls(self.userdir)])) + except: + self.curr_mo = 1 + + def load(self, continuous=False): + if self.curr_mo > 12: + print("Data source exhausted\n") + elif continuous == True: + while self.curr_mo <= 12: + curr_file = f"{self.curr_mo:02}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + else: + curr_file = f"{str(self.curr_mo).zfill(2)}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + diff --git a/Data-Engineering-With-Databricks/Includes/mount-datasets.py b/Data-Engineering-with-Databricks/Includes/mount-datasets.py similarity index 100% rename from Data-Engineering-With-Databricks/Includes/mount-datasets.py rename to Data-Engineering-with-Databricks/Includes/mount-datasets.py diff --git a/Data-Engineering-With-Databricks/Includes/setup-entities.py b/Data-Engineering-with-Databricks/Includes/setup-entities.py similarity index 100% rename from Data-Engineering-With-Databricks/Includes/setup-entities.py rename to Data-Engineering-with-Databricks/Includes/setup-entities.py diff --git a/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/DE 1.1 - Create and Manage Interactive Clusters.py b/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/DE 1.1 - Create and Manage Interactive Clusters.py new file mode 100644 index 0000000..3a2dce1 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/DE 1.1 - Create and Manage Interactive Clusters.py @@ -0,0 +1,100 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC # Create and Manage Interactive Clusters +# MAGIC +# MAGIC A Databricks cluster is a set of computation resources and configurations on which you run data engineering, data science, and data analytics workloads, such as production ETL pipelines, streaming analytics, ad-hoc analytics, and machine learning. You run these workloads as a set of commands in a notebook or as an automated job. +# MAGIC +# MAGIC Databricks makes a distinction between all-purpose clusters and job clusters. +# MAGIC - You use all-purpose clusters to analyze data collaboratively using interactive notebooks. +# MAGIC - You use job clusters to run fast and robust automated jobs. +# MAGIC +# MAGIC This demo will cover creating and managing all-purpose Databricks clusters using the Databricks Data Science & Engineering Workspace. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Use the Clusters UI to configure and deploy a cluster +# MAGIC * Edit, terminate, restart, and delete clusters + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ## Create Cluster +# MAGIC +# MAGIC Depending on the workspace in which you're currently working, you may or may not have cluster creation privileges. +# MAGIC +# MAGIC Instructions in this section assume that you **do** have cluster creation privileges, and that you need to deploy a new cluster to execute the lessons in this course. +# MAGIC +# MAGIC **NOTE**: Check with your instructor or a platform admin to confirm whether or not you should create a new cluster or connect to a cluster that has already been deployed. Cluster policies may impact your options for cluster configuration. +# MAGIC +# MAGIC Steps: +# MAGIC 1. Use the left sidebar to navigate to the **Compute** page by clicking on the ![compute](https://files.training.databricks.com/images/clusters-icon.png) icon +# MAGIC 1. Click the blue **Create Cluster** button +# MAGIC 1. For the **Cluster name**, use your name so that you can find it easily and the instructor can easily identify it if you have problems +# MAGIC 1. Set the **Cluster mode** to **Single Node** (this mode is required to run this course) +# MAGIC 1. Use the recommended **Databricks runtime version** for this course +# MAGIC 1. Leave boxes checked for the default settings under the **Autopilot Options** +# MAGIC 1. Click the blue **Create Cluster** button +# MAGIC +# MAGIC **NOTE:** Clusters can take several minutes to deploy. Once you have finished deploying a cluster, feel free to continue to explore the cluster creation UI. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ### Single-Node Cluster Required for This Course +# MAGIC **IMPORTANT:** This course requires you to run notebooks on a single-node cluster. +# MAGIC +# MAGIC Follow the instructions above to create a cluster that has **Cluster mode** set to **`Single Node`**. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ## Manage Clusters +# MAGIC +# MAGIC Once the cluster is created, go back to the **Compute** page to view the cluster. +# MAGIC +# MAGIC Select a cluster to review the current configuration. +# MAGIC +# MAGIC Click the **Edit** button. Note that most settings can be modified (if you have sufficient permissions). Changing most settings will require running clusters to be restarted. +# MAGIC +# MAGIC **NOTE**: We'll be using our cluster in the following lesson. Restarting, terminating, or deleting your cluster may put you behind as you wait for new resources to be deployed. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC ## Restart, Terminate, and Delete +# MAGIC +# MAGIC Note that while **Restart**, **Terminate**, and **Delete** have different effects, they all start with a cluster termination event. (Clusters will also terminate automatically due to inactivity assuming this setting is used.) +# MAGIC +# MAGIC When a cluster terminates, all cloud resources currently in use are deleted. This means: +# MAGIC * Associated VMs and operational memory will be purged +# MAGIC * Attached volume storage will be deleted +# MAGIC * Network connections between nodes will be removed +# MAGIC +# MAGIC In short, all resources previously associated with the compute environment will be completely removed. This means that **any results that need to be persisted should be saved to a permanent location**. Note that you will not lose your code, nor will you lose data files that you've saved out appropriately. +# MAGIC +# MAGIC The **Restart** button will allow us to manually restart our cluster. This can be useful if we need to completely clear out the cache on the cluster or wish to completely reset our compute environment. +# MAGIC +# MAGIC The **Terminate** button allows us to stop our cluster. We maintain our cluster configuration setting, and can use the **Restart** button to deploy a new set of cloud resources using the same configuration. +# MAGIC +# MAGIC The **Delete** button will stop our cluster and remove the cluster configuration. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/DE 1.2 - Notebook Basics.py b/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/DE 1.2 - Notebook Basics.py new file mode 100644 index 0000000..baf42d9 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/DE 1.2 - Notebook Basics.py @@ -0,0 +1,398 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Notebook Basics +# MAGIC +# MAGIC Notebooks are the primary means of developing and executing code interactively on Databricks. This lesson provides a basic introduction to working with Databricks notebooks. +# MAGIC +# MAGIC If you've previously used Databricks notebooks but this is your first time executing a notebook in Databricks Repos, you'll notice that basic functionality is the same. In the next lesson, we'll review some of the functionality that Databricks Repos adds to notebooks. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Attach a notebook to a cluster +# MAGIC * Execute a cell in a notebook +# MAGIC * Set the language for a notebook +# MAGIC * Describe and use magic commands +# MAGIC * Create and run a SQL cell +# MAGIC * Create and run a Python cell +# MAGIC * Create a markdown cell +# MAGIC * Export a Databricks notebook +# MAGIC * Export a collection of Databricks notebooks + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Attach to a Cluster +# MAGIC +# MAGIC In the previous lesson, you should have either deployed a cluster or identified a cluster that an admin has configured for you to use. +# MAGIC +# MAGIC Directly below the name of this notebook at the top of your screen, use the drop-down list to connect this notebook to your cluster. +# MAGIC +# MAGIC **NOTE**: Deploying a cluster can take several minutes. A green arrow will appear to the right of the cluster name once resources have been deployed. If your cluster has a solid gray circle to the left, you will need to follow instructions to start a cluster. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Notebooks Basics +# MAGIC +# MAGIC Notebooks provide cell-by-cell execution of code. Multiple languages can be mixed in a notebook. Users can add plots, images, and markdown text to enhance their code. +# MAGIC +# MAGIC Throughout this course, our notebooks are designed as learning instruments. Notebooks can be easily deployed as production code with Databricks, as well as providing a robust toolset for data exploration, reporting, and dashboarding. +# MAGIC +# MAGIC ### Running a Cell +# MAGIC * Run the cell below using one of the following options: +# MAGIC * **CTRL+ENTER** or **CTRL+RETURN** +# MAGIC * **SHIFT+ENTER** or **SHIFT+RETURN** to run the cell and move to the next one +# MAGIC * Using **Run Cell**, **Run All Above** or **Run All Below** as seen here
+ +# COMMAND ---------- + +print("I'm running Python!") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC **NOTE**: Cell-by-cell code execution means that cells can be executed multiple times or out of order. Unless explicitly instructed, you should always assume that the notebooks in this course are intended to be run one cell at a time from top to bottom. If you encounter an error, make sure you read the text before and after a cell to ensure that the error wasn't an intentional learning moment before you try to troubleshoot. Most errors can be resolved by either running earlier cells in a notebook that were missed or re-executing the entire notebook from the top. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ### Setting the Default Notebook Language +# MAGIC +# MAGIC The cell above executes a Python command, because our current default language for the notebook is set to Python. +# MAGIC +# MAGIC Databricks notebooks support Python, SQL, Scala, and R. A language can be selected when a notebook is created, but this can be changed at any time. +# MAGIC +# MAGIC The default language appears directly to the right of the notebook title at the top of the page. Throughout this course, we'll use a blend of SQL and Python notebooks. +# MAGIC +# MAGIC We'll change the default language for this notebook to SQL. +# MAGIC +# MAGIC Steps: +# MAGIC * Click on the **Python** next to the notebook title at the top of the screen +# MAGIC * In the UI that pops up, select **SQL** from the drop down list +# MAGIC +# MAGIC **NOTE**: In the cell just before this one, you should see a new line appear with %python. We'll discuss this in a moment. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ### Create and Run a SQL Cell +# MAGIC +# MAGIC * Highlight this cell and press the **B** button on the keyboard to create a new cell below +# MAGIC * Copy the following code into the cell below and then run the cell +# MAGIC +# MAGIC **`%sql`**
+# MAGIC **`SELECT "I'm running SQL!"`** +# MAGIC +# MAGIC **NOTE**: There are a number of different methods for adding, moving, and deleting cells including GUI options and keyboard shortcuts. Refer to the docs for details. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Magic Commands +# MAGIC * Magic commands are specific to the Databricks notebooks +# MAGIC * They are very similar to magic commands found in comparable notebook products +# MAGIC * These are built-in commands that provide the same outcome regardless of the notebook's language +# MAGIC * A single percent (%) symbol at the start of a cell identifies a magic command +# MAGIC * You can only have one magic command per cell +# MAGIC * A magic command must be the first thing in a cell + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ### Language Magics +# MAGIC Language magic commands allow for the execution of code in languages other than the notebook's default. In this course, we'll see the following language magics: +# MAGIC * %python +# MAGIC * %sql +# MAGIC +# MAGIC Adding the language magic for the currently set notebook type is not necessary. +# MAGIC +# MAGIC When we changed the notebook language from Python to SQL above, existing cells written in Python had the %python command added. +# MAGIC +# MAGIC **NOTE**: Rather than changing the default language of a notebook constantly, you should stick with a primary language as the default and only use language magics as necessary to execute code in another language. + +# COMMAND ---------- + +print("Hello Python!") + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC +# MAGIC select "Hello SQL!" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ### Markdown +# MAGIC +# MAGIC The magic command **%md** allows us to render Markdown in a cell: +# MAGIC * Double click this cell to begin editing it +# MAGIC * Then hit **`Esc`** to stop editing +# MAGIC +# MAGIC # Title One +# MAGIC ## Title Two +# MAGIC ### Title Three +# MAGIC +# MAGIC This is a test of the emergency broadcast system. This is only a test. +# MAGIC +# MAGIC This is text with a **bold** word in it. +# MAGIC +# MAGIC This is text with an *italicized* word in it. +# MAGIC +# MAGIC This is an ordered list +# MAGIC 0. once +# MAGIC 0. two +# MAGIC 0. three +# MAGIC +# MAGIC This is an unordered list +# MAGIC * apples +# MAGIC * peaches +# MAGIC * bananas +# MAGIC +# MAGIC Links/Embedded HTML: Markdown - Wikipedia +# MAGIC +# MAGIC Images: +# MAGIC ![Spark Engines](https://files.training.databricks.com/images/Apache-Spark-Logo_TM_200px.png) +# MAGIC +# MAGIC And of course, tables: +# MAGIC +# MAGIC | name | value | +# MAGIC |--------|-------| +# MAGIC | Yi | 1 | +# MAGIC | Ali | 2 | +# MAGIC | Selina | 3 | + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ### %run +# MAGIC * You can run a notebook from another notebook by using the magic command **%run** +# MAGIC * Notebooks to be run are specified with relative paths +# MAGIC * The referenced notebook executes as if it were part of the current notebook, so temporary views and other local declarations will be available from the calling notebook + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Uncommenting and executing the following cell will generate the following error:
+# MAGIC **`Error in SQL statement: AnalysisException: Table or view not found: demo_tmp_vw`** + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- SELECT * FROM demo_tmp_vw + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC But we can declare it and a handful of other variables and functions buy running this cell: + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-1.2 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The **`../Includes/Classroom-Setup-1.2`** notebook we referenced includes logic to create and **`USE`** a database, as well as creating the temp view **`demo_temp_vw`**. +# MAGIC +# MAGIC We can see this temp view is now available in our current notebook session with the following query. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM demo_tmp_vw + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC We'll use this pattern of "setup" notebooks throughout the course to help configure the environment for lessons and labs. +# MAGIC +# MAGIC These "provided" variables, functions and other objects should be easily identifiable in that they are part of the **`DA`** object which is an instance of **`DBAcademyHelper`**. +# MAGIC +# MAGIC With that in mind, most lessons will use variables derived from your username to organize files and databases. +# MAGIC +# MAGIC This pattern allows us to avoid collision with other users in shared a workspace. +# MAGIC +# MAGIC The cell below uses Python to print some of those variables previously defined in this notebook's setup script: + +# COMMAND ---------- + +print(f"DA: {DA}") +print(f"DA.username: {DA.username}") +print(f"DA.paths.working_dir: {DA.paths.working_dir}") +print(f"DA.db_name: {DA.db_name}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC In addition to this, these same variables are "injected" into the SQL context so that we can use them in SQL statements. +# MAGIC +# MAGIC We will talk more about this later, but you can see a quick example in the following cell. +# MAGIC +# MAGIC Note the subtle but important difference in the casing of the word **`da`** and **`DA`** in these two examples. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT '${da.username}' AS current_username, +# MAGIC '${da.paths.working_dir}' AS working_directory, +# MAGIC '${da.db_name}' as database_name + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Databricks Utilities +# MAGIC Databricks notebooks provide a number of utility commands for configuring and interacting with the environment: dbutils docs +# MAGIC +# MAGIC Throughout this course, we'll occasionally use **`dbutils.fs.ls()`** to list out directories of files from Python cells. + +# COMMAND ---------- + +dbutils.fs.ls("/databricks-datasets") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## display() +# MAGIC +# MAGIC When running SQL queries from cells, results will always be displayed in a rendered tabular format. +# MAGIC +# MAGIC When we have tabular data returned by a Python cell, we can call **`display`** to get the same type of preview. +# MAGIC +# MAGIC Here, we'll wrap the previous list command on our file system with **`display`**. + +# COMMAND ---------- + +display(dbutils.fs.ls("/databricks-datasets")) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The **`display()`** command has the following capabilities and limitations: +# MAGIC * Preview of results limited to 1000 records +# MAGIC * Provides button to download results data as CSV +# MAGIC * Allows rendering plots + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Downloading Notebooks +# MAGIC +# MAGIC There are a number of options for downloading either individual notebooks or collections of notebooks. +# MAGIC +# MAGIC Here, you'll go through the process to download this notebook as well as a collection of all the notebooks in this course. +# MAGIC +# MAGIC ### Download a Notebook +# MAGIC +# MAGIC Steps: +# MAGIC * Click the **File** option to the right of the cluster selection at the top of the notebook +# MAGIC * From the menu that appears, hover over **Export** and then select **Source File** +# MAGIC +# MAGIC The notebook will download to your personal laptop. It will be named with the current notebook name and have the file extension for the default language. You can open this notebook with any file editor and see the raw contents of Databricks notebooks. +# MAGIC +# MAGIC These source files can be uploaded into any Databricks workspace. +# MAGIC +# MAGIC ### Download a Collection of Notebooks +# MAGIC +# MAGIC **NOTE**: The following instructions assume you have imported these materials using **Repos**. +# MAGIC +# MAGIC Steps: +# MAGIC * Click the ![](https://files.training.databricks.com/images/repos-icon.png) **Repos** on the left sidebar +# MAGIC * This should give you a preview of the parent directories for this notebook +# MAGIC * On the left side of the directory preview around the middle of the screen, there should be a left arrow. Click this to move up in your file hierarchy. +# MAGIC * You should see a directory called **Data Engineering with Databricks**. Click the the down arrow/chevron to bring up a menu +# MAGIC * From the menu, hover over **Export** and select **DBC Archive** +# MAGIC +# MAGIC The DBC(Databricks Cloud) file that is downloaded contains a zipped collection of the directories and notebooks in this course. Users should not attempt to edit these DBC files locally, but they can be safely uploaded into any Databricks workspace to move or share notebook contents. +# MAGIC +# MAGIC **NOTE**: When downloading a collection of DBCs, result previews and plots will also be exported. When downloading source notebooks, only code will be saved. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Learning More +# MAGIC +# MAGIC We like to encourage you to explore the documentation to learn more about the various features of the Databricks platform and notebooks. +# MAGIC * User Guide +# MAGIC * Getting Started with Databricks +# MAGIC * User Guide / Notebooks +# MAGIC * Importing notebooks - Supported Formats +# MAGIC * Repos +# MAGIC * Administration Guide +# MAGIC * Cluster Configuration +# MAGIC * REST API +# MAGIC * Release Notes + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## One more note! +# MAGIC +# MAGIC At the end of each lesson you will see the following command, **`DA.cleanup()`**. +# MAGIC +# MAGIC This method drops lesson-specific databases and working directories in an attempt to keep your workspace clean and maintain the immutability of each lesson. +# MAGIC +# MAGIC Run the following cell to delete the tables and files associated with this lesson. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/DE 1.3L - Getting Started with the Databricks Platform Lab.py b/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/DE 1.3L - Getting Started with the Databricks Platform Lab.py new file mode 100644 index 0000000..5689ed6 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/DE 1.3L - Getting Started with the Databricks Platform Lab.py @@ -0,0 +1,222 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Getting Started with the Databricks Platform +# MAGIC +# MAGIC This notebook provides a hands-on review of some of the basic functionality of the Databricks Data Science and Engineering Workspace. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC - Rename a notebook and change the default language +# MAGIC - Attach a cluster +# MAGIC - Use the **`%run`** magic command +# MAGIC - Run Python and SQL cells +# MAGIC - Create a Markdown cell + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Renaming a Notebook +# MAGIC +# MAGIC Changing the name of a notebook is easy. Click on the name at the top of this page, then make changes to the name. To make it easier to navigate back to this notebook later in case you need to, append a short test string to the end of the existing name. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Attaching a cluster +# MAGIC +# MAGIC Executing cells in a notebook requires computing resources, which is provided by clusters. The first time you execute a cell in a notebook, you will be prompted to attach to a cluster if one is not already attached. +# MAGIC +# MAGIC Attach a cluster to this notebook now by clicking the dropdown near the top-left corner of this page. Select the cluster you created previously. This will clear the execution state of the notebook and connect the notebook to the selected cluster. +# MAGIC +# MAGIC Note that the dropdown menu provides the option of starting or restarting the cluster as needed. You can also detach and re-attach to a cluster in a single movement. This is useful for clearing the execution state when needed. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Using %run +# MAGIC +# MAGIC Complex projects of any type can benefit from the ability to break them down into simpler, reusable components. +# MAGIC +# MAGIC In the context of Databricks notebooks, this facility is provided through the **`%run`** magic command. +# MAGIC +# MAGIC When used this way, variables, functions and code blocks become part of the current programming context. +# MAGIC +# MAGIC Consider this example: +# MAGIC +# MAGIC **`Notebook_A`** has four commands: +# MAGIC 1. **`name = "John"`** +# MAGIC 2. **`print(f"Hello {name}")`** +# MAGIC 3. **`%run ./Notebook_B`** +# MAGIC 4. **`print(f"Welcome back {full_name}`** +# MAGIC +# MAGIC **`Notebook_B`** has only one commands: +# MAGIC 1. **`full_name = f"{name} Doe"`** +# MAGIC +# MAGIC If we run **`Notebook_B`** it will fail to execute becaues the variable **`name`** is not defined in **`Notebook_B`** +# MAGIC +# MAGIC Likewise, one might think that **`Notebook_A`** would fail becase it uses the variable **`full_name`** which is likewise not defined in **`Notebook_A`**, but it doesn't! +# MAGIC +# MAGIC What actually happens is that the two notebooks are merged together as we see below and **then** executed: +# MAGIC 1. **`name = "John"`** +# MAGIC 2. **`print(f"Hello {name}")`** +# MAGIC 3. **`full_name = f"{name} Doe"`** +# MAGIC 4. **`print(f"Welcome back {full_name}`** +# MAGIC +# MAGIC And thus providing the expected behavior: +# MAGIC * **`Hello John`** +# MAGIC * **`Welcome back John Doe`** + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC The folder that contains this notebook contains a subfolder named **`ExampleSetupFolder`**, which in turn contains a notebook called **`example-setup`**. +# MAGIC +# MAGIC This simple notebook declares the variable **`my_name`**, sets it to **`None`** and then creates a DataFrame called **`example_df`**. +# MAGIC +# MAGIC Open the example-setup notebook and modify it so that name is not **`None`** but rather your name (or anyone's name) enclosed in quotes, and so that the following two cells execute without throwing an **`AssertionError`**. + +# COMMAND ---------- + +# MAGIC %run ./ExampleSetupFolder/example-setup + +# COMMAND ---------- + +assert my_name is not None, "Name is still None" +print(my_name) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Run a Python cell +# MAGIC +# MAGIC Run the following cell to verify that the **`example-setup`** notebook was executed by displaying the **`example_df`** Dataframe. This table consists of 16 rows of increasing values. + +# COMMAND ---------- + +display(example_df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Detach and Reattach a Cluster +# MAGIC +# MAGIC While attaching to clusters is a fairly common task, sometimes it is useful to detach and re-attach in one single operation. The main side-effect this achieves is clearing the execution state. This can be useful when you want to test cells in isolation, or you simply want to reset the execution state. +# MAGIC +# MAGIC Revisit the cluster dropdown. In the menu item representing the currently attached cluster, select the **Detach & Re-attach** link. +# MAGIC +# MAGIC Notice that the output from the cell above remains since results and execution state are unrelated, but the execution state is cleared. This can be verified by attempting to re-run the cell above. This fails, since the **`example_df`** variable has been cleared, along with the rest of the state. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Change Language +# MAGIC +# MAGIC Notice that the default language for this notebook is set to Python. Change this by clicking the **Python** button to the right of the notebook name. Change the default language to SQL. +# MAGIC +# MAGIC Notice that the Python cells are automatically prepended with a %python magic command to maintain validity of those cells. Notice that this operation also clears the execution state. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Create a Markdown Cell +# MAGIC +# MAGIC Add a new cell below this one. Populate with some Markdown that includes at least the following elements: +# MAGIC * A header +# MAGIC * Bullet points +# MAGIC * A link (using your choice of HTML or Markdown conventions) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Run a SQL cell +# MAGIC +# MAGIC Run the following cell to query a Delta table using SQL. This executes a simple query against a table is backed by a Databricks-provided example dataset included in all DBFS installations. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM delta.`/databricks-datasets/nyctaxi-with-zipcodes/subsampled` + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC Execute the following cell to view the underlying files backing this table. + +# COMMAND ---------- + +files = dbutils.fs.ls("/databricks-datasets/nyctaxi-with-zipcodes/subsampled") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Review Changes +# MAGIC +# MAGIC Assuming you have imported this material into your workspace using a Databricks Repo, open the Repo dialog by clicking the **`published`** branch button at the top-left corner of this page. You should see three changes: +# MAGIC 1. **Removed** with the old notebook name +# MAGIC 1. **Added** with the new notebook name +# MAGIC 1. **Modified** for creating a markdown cell above +# MAGIC +# MAGIC Use the dialog to revert the changes and restore this notebook to its original state. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Wrapping Up +# MAGIC +# MAGIC By completing this lab, you should now feel comfortable manipulating notebooks, creating new cells, and running notebooks within notebooks. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/ExampleSetupFolder/example-setup.py b/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/ExampleSetupFolder/example-setup.py new file mode 100644 index 0000000..a6603bb --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/01 - Databricks Workspace and Services/ExampleSetupFolder/example-setup.py @@ -0,0 +1,8 @@ +# Databricks notebook source +# ANSWER +my_name = "Donald Duck" + +# COMMAND ---------- + +example_df = spark.range(16) + diff --git a/Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/DE 1.3.1 - Managing Delta Tables.sql b/Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.1 - Managing Delta Tables.sql similarity index 65% rename from Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/DE 1.3.1 - Managing Delta Tables.sql rename to Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.1 - Managing Delta Tables.sql index 3c225fd..0f7343d 100644 --- a/Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/DE 1.3.1 - Managing Delta Tables.sql +++ b/Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.1 - Managing Delta Tables.sql @@ -8,43 +8,53 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC # Managing Delta Tables -- MAGIC -- MAGIC If you know any flavor of SQL, you already have much of the knowledge you'll need to work effectively in the data lakehouse. -- MAGIC --- MAGIC In this notebook, we'll explore the basic of manipulating data and tables with SQL on Databricks. +-- MAGIC In this notebook, we'll explore basic manipulation of data and tables with SQL on Databricks. -- MAGIC -- MAGIC Note that Delta Lake is the default format for all tables created with Databricks; if you've been running SQL statements on Databricks, you're likely already working with Delta Lake. -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC By the end of this lesson, students should feel confident: --- MAGIC * Creating Delta Lake tables --- MAGIC * Querying data from Delta Lake tables --- MAGIC * Inserting, updating, and deleting records in Delta Lake tables --- MAGIC * Writing upsert statements with Delta Lake --- MAGIC * Dropping Delta Lake tables +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC * Create Delta Lake tables +-- MAGIC * Query data from Delta Lake tables +-- MAGIC * Insert, update, and delete records in Delta Lake tables +-- MAGIC * Write upsert statements with Delta Lake +-- MAGIC * Drop Delta Lake tables -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC The first thing we're going to do is run a setup script. It will define a username, userhome, and database that is scoped to each user. -- COMMAND ---------- --- MAGIC %run ../Includes/sql-setup $mode="reset" +-- MAGIC %run ../Includes/Classroom-Setup-2.1 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Creating a Delta Table -- MAGIC -- MAGIC There's not much code you need to write to create a table with Delta Lake. There are a number of ways to create Delta Lake tables that we'll see throughout the course. We'll begin with one of the easiest methods: registering an empty Delta Lake table. -- MAGIC -- MAGIC We need: --- MAGIC - A `CREATE` statement --- MAGIC - A table name (below we use `students`) +-- MAGIC - A **`CREATE TABLE`** statement +-- MAGIC - A table name (below we use **`students`**) -- MAGIC - A schema +-- MAGIC +-- MAGIC **NOTE:** In Databricks Runtime 8.0 and above, Delta Lake is the default format and you don’t need **`USING DELTA`**. -- COMMAND ---------- @@ -54,9 +64,12 @@ CREATE TABLE students -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC If we try to go back and run that cell again...it will error out! This is expected - because the table exists already, we receive an error. -- MAGIC --- MAGIC We can add in an additional argument, `IF NOT EXISTS` which checks if the table exists. This will overcome our error. +-- MAGIC We can add in an additional argument, **`IF NOT EXISTS`** which checks if the table exists. This will overcome our error. -- COMMAND ---------- @@ -65,7 +78,10 @@ CREATE TABLE IF NOT EXISTS students -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Inserting Data -- MAGIC Most often, data will be inserted to tables as the result of a query from another source. -- MAGIC @@ -80,7 +96,10 @@ INSERT INTO students VALUES (3, "Elia", 3.3); -- COMMAND ---------- -- MAGIC %md --- MAGIC In the cell above, we completed three separate `INSERT` statements. Each of these is processed as a separate transaction with its own ACID guarantees. Most frequently, we'll insert many records in a single transaction. +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC In the cell above, we completed three separate **`INSERT`** statements. Each of these is processed as a separate transaction with its own ACID guarantees. Most frequently, we'll insert many records in a single transaction. -- COMMAND ---------- @@ -93,14 +112,20 @@ VALUES -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that Databricks doesn't have a `COMMIT` keyword; transactions run as soon as they're executed, and commit as they succeed. +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Note that Databricks doesn't have a **`COMMIT`** keyword; transactions run as soon as they're executed, and commit as they succeed. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Querying a Delta Table -- MAGIC --- MAGIC You probably won't be suprised that querying a Delta Lake table is as easy as using a standard `SELECT` statement. +-- MAGIC You probably won't be surprised that querying a Delta Lake table is as easy as using a standard **`SELECT`** statement. -- COMMAND ---------- @@ -109,18 +134,24 @@ SELECT * FROM students -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC What may surprise you is that Delta Lake guarantees that any read against a table will **always** return the most recent version of the table, and that you'll never encounter a state of deadlock due to ongoing operations. -- MAGIC --- MAGIC To repeat: table reads can never conflict with other operations, and the newest version of your data is immediately available to all clients that can query your lakehouse. Because all transaction information is stored in cloud object storage alongside your data files, concurrency reads on Delta Lake tables is limited only by the hard limits of object storage on cloud vendors. (**NOTE**: It's not infinite, but it's at least thousands of reads per second.) +-- MAGIC To repeat: table reads can never conflict with other operations, and the newest version of your data is immediately available to all clients that can query your lakehouse. Because all transaction information is stored in cloud object storage alongside your data files, concurrent reads on Delta Lake tables is limited only by the hard limits of object storage on cloud vendors. (**NOTE**: It's not infinite, but it's at least thousands of reads per second.) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Updating Records -- MAGIC --- MAGIC Updating records provides atomic guarantees as well: we perform a snapshot read of the current version of our table, find all fields that match our `WHERE` clause, and then apply the changes as described. +-- MAGIC Updating records provides atomic guarantees as well: we perform a snapshot read of the current version of our table, find all fields that match our **`WHERE`** clause, and then apply the changes as described. -- MAGIC --- MAGIC Below, we find all students that have a name starting with the letter **T** and add 1 to the number in their `value` column. +-- MAGIC Below, we find all students that have a name starting with the letter **T** and add 1 to the number in their **`value`** column. -- COMMAND ---------- @@ -131,6 +162,9 @@ WHERE name LIKE "T%" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Query the table again to see these changes applied. -- COMMAND ---------- @@ -140,11 +174,14 @@ SELECT * FROM students -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Deleting Records -- MAGIC -- MAGIC Deletes are also atomic, so there's no risk of only partially succeeding when removing data from your data lakehouse. -- MAGIC --- MAGIC A delete statement can remove one or many records, but will always result in a single transaction. +-- MAGIC A **`DELETE`** statement can remove one or many records, but will always result in a single transaction. -- COMMAND ---------- @@ -154,11 +191,14 @@ WHERE value > 6 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Using Merge -- MAGIC -- MAGIC Some SQL systems have the concept of an upsert, which allows updates, inserts, and other data manipulations to be run as a single command. -- MAGIC --- MAGIC Databricks uses the `MERGE` keyword to perform this operation. +-- MAGIC Databricks uses the **`MERGE`** keyword to perform this operation. -- MAGIC -- MAGIC Consider the following temporary view, which contains 4 records that might be output by a Change Data Capture (CDC) feed. @@ -175,13 +215,16 @@ SELECT * FROM updates; -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Using the syntax we've seen so far, we could filter from this view by type to write 3 statements, one each to insert, update, and delete records. But this would result in 3 separate transactions; if any of these transactions were to fail, it might leave our data in an invalid state. -- MAGIC -- MAGIC Instead, we combine these actions into a single atomic transaction, applying all 3 types of changes together. -- MAGIC --- MAGIC `MERGE` statements must have at least one field to match on, and each `WHEN MATCHED` or `WHEN NOT MATCHED` clause can have any number of additional conditional statements. +-- MAGIC **`MERGE`** statements must have at least one field to match on, and each **`WHEN MATCHED`** or **`WHEN NOT MATCHED`** clause can have any number of additional conditional statements. -- MAGIC --- MAGIC Here, we match on our `id` field and then filter on the `type` field to appropriately update, delete, or insert our records. +-- MAGIC Here, we match on our **`id`** field and then filter on the **`type`** field to appropriately update, delete, or insert our records. -- COMMAND ---------- @@ -198,16 +241,22 @@ WHEN NOT MATCHED AND u.type = "insert" -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that only 3 records were impacted by our `MERGE` statement; one of the records in our updates table did not have a matching `id` in the students table but was marked as an `update`. Based on our custom logic, we ignored this record rather than inserting it. -- MAGIC --- MAGIC How would you modify the above statement to include unmatched records marked `update` in the final `INSERT` clause? +-- MAGIC +-- MAGIC +-- MAGIC Note that only 3 records were impacted by our **`MERGE`** statement; one of the records in our updates table did not have a matching **`id`** in the students table but was marked as an **`update`**. Based on our custom logic, we ignored this record rather than inserting it. +-- MAGIC +-- MAGIC How would you modify the above statement to include unmatched records marked **`update`** in the final **`INSERT`** clause? -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Dropping a Table -- MAGIC --- MAGIC Assuming that you have proper permissions on the target table, you can permanently delete data in the lakehouse using a `DROP TABLE` command. +-- MAGIC Assuming that you have proper permissions on the target table, you can permanently delete data in the lakehouse using a **`DROP TABLE`** command. -- MAGIC -- MAGIC **NOTE**: Later in the course, we'll discuss Table Access Control Lists (ACLs) and default permissions. In a properly configured lakehouse, users should **not** be able to delete production tables. @@ -217,6 +266,19 @@ DROP TABLE students -- COMMAND ---------- +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + -- MAGIC %md-sandbox -- MAGIC © 2022 Databricks, Inc. All rights reserved.
-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
diff --git a/Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/Labs/DE 1.3.2L - Manipulating Tables with Delta Lake.sql b/Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.2L - Manipulating Tables with Delta Lake Lab.sql similarity index 87% rename from Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/Labs/DE 1.3.2L - Manipulating Tables with Delta Lake.sql rename to Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.2L - Manipulating Tables with Delta Lake Lab.sql index ddedbe6..8abd21e 100644 --- a/Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/Labs/DE 1.3.2L - Manipulating Tables with Delta Lake.sql +++ b/Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.2L - Manipulating Tables with Delta Lake Lab.sql @@ -8,39 +8,48 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC # Manipulating Tables with Delta Lake -- MAGIC -- MAGIC This notebook provides a hands-on review of some of the basic functionality of Delta Lake. -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC By the end of this lessons, student will be able to: +-- MAGIC By the end of this lab, you should be able to: -- MAGIC - Execute standard operations to create and manipulate Delta Lake tables, including: --- MAGIC - `CREATE TABLE` --- MAGIC - `INSERT INTO` --- MAGIC - `SELECT FROM` --- MAGIC - `UPDATE` --- MAGIC - `DELETE` --- MAGIC - `MERGE` --- MAGIC - `DROP TABLE` +-- MAGIC - **`CREATE TABLE`** +-- MAGIC - **`INSERT INTO`** +-- MAGIC - **`SELECT FROM`** +-- MAGIC - **`UPDATE`** +-- MAGIC - **`DELETE`** +-- MAGIC - **`MERGE`** +-- MAGIC - **`DROP TABLE`** -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Setup -- MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. -- COMMAND ---------- --- MAGIC %run ../../Includes/sql-setup $course="delta" $mode="reset" +-- MAGIC %run ../Includes/Classroom-Setup-2.2L -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Create a Table -- MAGIC -- MAGIC In this notebook, we'll be creating a table to track our bean collection. -- MAGIC --- MAGIC Use the cell below to create a managed Delta Lake table named `beans`. +-- MAGIC Use the cell below to create a managed Delta Lake table named **`beans`**. -- MAGIC -- MAGIC Provide the following schema: -- MAGIC @@ -60,6 +69,9 @@ CREATE TABLE beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC **NOTE**: We'll use Python to run checks occasionally throughout the lab. The following cell will return as error with a message on what needs to change if you have not followed instructions. No output from cell execution means that you have completed this step. -- COMMAND ---------- @@ -72,6 +84,8 @@ CREATE TABLE beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Insert Data -- MAGIC -- MAGIC Run the following cell to insert three rows into the table. @@ -86,6 +100,9 @@ INSERT INTO beans VALUES -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Manually review the table contents to ensure data was written as expected. -- COMMAND ---------- @@ -96,6 +113,9 @@ SELECT * FROM beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Insert the additional records provided below. Make sure you execute this as a single transaction. -- COMMAND ---------- @@ -109,6 +129,9 @@ INSERT INTO beans VALUES -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to confirm the data is in the proper state. -- COMMAND ---------- @@ -121,6 +144,9 @@ INSERT INTO beans VALUES -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Update Records -- MAGIC -- MAGIC A friend is reviewing your inventory of beans. After much debate, you agree that jelly beans are delicious. @@ -136,9 +162,12 @@ WHERE name = "jelly" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC You realize that you've accidentally entered the weight of your pinto beans incorrectly. -- MAGIC --- MAGIC Update the `grams` column for this record to the correct weight of 1500. +-- MAGIC Update the **`grams`** column for this record to the correct weight of 1500. -- COMMAND ---------- @@ -150,6 +179,9 @@ WHERE name = 'pinto' -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to confirm this has completed properly. -- COMMAND ---------- @@ -164,6 +196,9 @@ WHERE name = 'pinto' -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Delete Records -- MAGIC -- MAGIC You've decided that you only want to keep track of delicious beans. @@ -179,6 +214,9 @@ WHERE delicious = false -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the following cell to confirm this operation was successful. -- COMMAND ---------- @@ -190,6 +228,9 @@ WHERE delicious = false -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Using Merge to Upsert Records -- MAGIC -- MAGIC Your friend gives you some new beans. The cell below registers these as a temporary view. @@ -207,7 +248,10 @@ SELECT * FROM new_beans -- COMMAND ---------- -- MAGIC %md --- MAGIC In the cell below, use the above view to write a merge statement to update and insert new records to your `beans` table as one transaction. +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC In the cell below, use the above view to write a merge statement to update and insert new records to your **`beans`** table as one transaction. -- MAGIC -- MAGIC Make sure your logic: -- MAGIC - Matches beans by name **and** color @@ -228,6 +272,9 @@ WHEN NOT MATCHED AND b.delicious = true THEN -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to check your work. -- COMMAND ---------- @@ -245,13 +292,16 @@ WHEN NOT MATCHED AND b.delicious = true THEN -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Dropping Tables -- MAGIC -- MAGIC When working with managed Delta Lake tables, dropping a table results in permanently deleting access to the table and all underlying data files. -- MAGIC -- MAGIC **NOTE**: Later in the course, we'll learn about external tables, which approach Delta Lake tables as a collection of files and have different persistence guarantees. -- MAGIC --- MAGIC In the cell below, write a query to drop the `beans` table. +-- MAGIC In the cell below, write a query to drop the **`beans`** table. -- COMMAND ---------- @@ -261,6 +311,9 @@ DROP TABLE beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to assert that your table no longer exists. -- COMMAND ---------- @@ -271,16 +324,26 @@ DROP TABLE beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Wrapping Up -- MAGIC -- MAGIC By completing this lab, you should now feel comfortable: -- MAGIC * Completing standard Delta Lake table creation and data manipulation commands + +-- COMMAND ---------- + +-- MAGIC %md -- MAGIC --- MAGIC Run the following cell to remove the database and all data associated with this lab. +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. -- COMMAND ---------- --- MAGIC %run ../../Includes/sql-setup $course="delta" $mode="cleanup" +-- MAGIC %python +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/DE 1.3.3 - Advanced Delta Lake Features.sql b/Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.3 - Advanced Delta Lake Features.sql similarity index 59% rename from Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/DE 1.3.3 - Advanced Delta Lake Features.sql rename to Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.3 - Advanced Delta Lake Features.sql index f0be390..3bded34 100644 --- a/Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/DE 1.3.3 - Advanced Delta Lake Features.sql +++ b/Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.3 - Advanced Delta Lake Features.sql @@ -8,6 +8,8 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Advanced Delta Lake Features -- MAGIC -- MAGIC Now that you feel comfortable performing basic data tasks with Delta Lake, we can discuss a few features unique to Delta Lake. @@ -15,30 +17,38 @@ -- MAGIC Note that while some of the keywords used here aren't part of standard ANSI SQL, all Delta Lake operations can be run on Databricks using SQL -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC By the end of this lesson, students should feel confident: --- MAGIC * Using `OPTIMIZE` to compact small files --- MAGIC * Using `ZORDER` to index tables --- MAGIC * Describing the directory structure of Delta Lake files --- MAGIC * Reviewing a history of table transactions --- MAGIC * Querying and rolling back to previous table version --- MAGIC * Cleaning up stale data files with `VACUUM` +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC * Use **`OPTIMIZE`** to compact small files +-- MAGIC * Use **`ZORDER`** to index tables +-- MAGIC * Describe the directory structure of Delta Lake files +-- MAGIC * Review a history of table transactions +-- MAGIC * Query and roll back to previous table version +-- MAGIC * Clean up stale data files with **`VACUUM`** +-- MAGIC +-- MAGIC **Resources** +-- MAGIC * Delta Optimize - Databricks Docs +-- MAGIC * Delta Vacuum - Databricks Docs -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC The first thing we're going to do is run a setup script. It will define a username, userhome, and database that is scoped to each user. -- COMMAND ---------- --- MAGIC %run ../Includes/sql-setup $mode="reset" +-- MAGIC %run ../Includes/Classroom-Setup-2.3 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Creating a Delta Table with History -- MAGIC --- MAGIC The cell below condenses all the transactions from the previous lesson into a single cell. (Except for the `DROP TABLE`!) +-- MAGIC The cell below condenses all the transactions from the previous lesson into a single cell. (Except for the **`DROP TABLE`**!) -- MAGIC -- MAGIC As you're waiting for this query to run, see if you can identify the total number of transactions being executed. @@ -83,11 +93,13 @@ WHEN NOT MATCHED AND u.type = "insert" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Examine Table Details -- MAGIC -- MAGIC Databricks uses a Hive metastore by default to register databases, tables, and views. -- MAGIC --- MAGIC Using `DESCRIBE EXTENDED` allows us to see important metadata about our table. +-- MAGIC Using **`DESCRIBE EXTENDED`** allows us to see important metadata about our table. -- COMMAND ---------- @@ -96,13 +108,28 @@ DESCRIBE EXTENDED students -- COMMAND ---------- -- MAGIC %md --- MAGIC Note the `Location` field. +-- MAGIC +-- MAGIC +-- MAGIC **`DESCRIBE DETAIL`** is another command that allows us to explore table metadata. + +-- COMMAND ---------- + +DESCRIBE DETAIL students + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note the **`Location`** field. -- MAGIC -- MAGIC While we've so far been thinking about our table as just a relational entity within a database, a Delta Lake table is actually backed by a collection of files stored in cloud object storage. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Explore Delta Lake Files -- MAGIC -- MAGIC We can see the files backing our Delta Lake table by using a Databricks Utilities function. @@ -112,37 +139,43 @@ DESCRIBE EXTENDED students -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{userhome}/students")) +-- MAGIC display(dbutils.fs.ls(f"{DA.paths.user_db}/students")) -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that our directory contains a number of Parquet data files and a directory named `_delta_log`. +-- MAGIC +-- MAGIC +-- MAGIC Note that our directory contains a number of Parquet data files and a directory named **`_delta_log`**. -- MAGIC -- MAGIC Records in Delta Lake tables are stored as data in Parquet files. -- MAGIC --- MAGIC Transactions to Delta Lake tables are recorded in the `_delta_log`. +-- MAGIC Transactions to Delta Lake tables are recorded in the **`_delta_log`**. -- MAGIC --- MAGIC We can peak inside the `_delta_log` to see more. +-- MAGIC We can peek inside the **`_delta_log`** to see more. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{userhome}/students/_delta_log")) +-- MAGIC display(dbutils.fs.ls(f"{DA.paths.user_db}/students/_delta_log")) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Each transaction results in a new JSON file being written to the Delta Lake transaction log. Here, we can see that there are 8 total transactions against this table (Delta Lake is 0 indexed). -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Reasoning about Data Files -- MAGIC -- MAGIC We just saw a lot of data files for what is obviously a very small table. -- MAGIC --- MAGIC `DESCRIBE DETAIL` allows us to see some other details about our Delta table, including the number of files. +-- MAGIC **`DESCRIBE DETAIL`** allows us to see some other details about our Delta table, including the number of files. -- COMMAND ---------- @@ -151,36 +184,42 @@ DESCRIBE DETAIL students -- COMMAND ---------- -- MAGIC %md --- MAGIC Here we see that our table currently contains 3 data files in its present version. So what are all those other Parquet files doing in our table directory? +-- MAGIC +-- MAGIC +-- MAGIC Here we see that our table currently contains 4 data files in its present version. So what are all those other Parquet files doing in our table directory? -- MAGIC -- MAGIC Rather than overwriting or immediately deleting files containing changed data, Delta Lake uses the transaction log to indicate whether or not files are valid in a current version of the table. -- MAGIC --- MAGIC Here, we'll look at the transaction log corresponding the `MERGE` statement above, where records were inserted, updated, and deleted. +-- MAGIC Here, we'll look at the transaction log corresponding the **`MERGE`** statement above, where records were inserted, updated, and deleted. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(spark.sql(f"SELECT * FROM json.`{userhome}/students/_delta_log/00000000000000000007.json`")) +-- MAGIC display(spark.sql(f"SELECT * FROM json.`{DA.paths.user_db}/students/_delta_log/00000000000000000007.json`")) -- COMMAND ---------- -- MAGIC %md --- MAGIC The `add` column contains a list of all the new files written to our table; the `remove` column indicates those files that no longer should be included in our table. +-- MAGIC +-- MAGIC +-- MAGIC The **`add`** column contains a list of all the new files written to our table; the **`remove`** column indicates those files that no longer should be included in our table. -- MAGIC -- MAGIC When we query a Delta Lake table, the query engine uses the transaction logs to resolve all the files that are valid in the current version, and ignores all other data files. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Compacting Small Files and Indexing -- MAGIC -- MAGIC Small files can occur for a variety of reasons; in our case, we performed a number of operations where only one or several records were inserted. -- MAGIC --- MAGIC Files will be combined toward an optimal size (scaled based on the size of the table) by using the `OPTIMIZE` command. +-- MAGIC Files will be combined toward an optimal size (scaled based on the size of the table) by using the **`OPTIMIZE`** command. -- MAGIC --- MAGIC `OPTIMIZE` will replace existing data files by combining records and rewriting the results. +-- MAGIC **`OPTIMIZE`** will replace existing data files by combining records and rewriting the results. -- MAGIC --- MAGIC When executing `OPTIMIZE`, users can optionally specify one or several fields for `ZORDER` indexing. While the specific math of Z-order is unimportant, it speeds up data retrieval when filtering on provided fields by collocating data with similar values within data files. +-- MAGIC When executing **`OPTIMIZE`**, users can optionally specify one or several fields for **`ZORDER`** indexing. While the specific math of Z-order is unimportant, it speeds up data retrieval when filtering on provided fields by colocating data with similar values within data files. -- COMMAND ---------- @@ -190,14 +229,18 @@ ZORDER BY id -- COMMAND ---------- -- MAGIC %md --- MAGIC Given how small our data is, `ZORDER` does not provide any benefit, but we can see all of the metrics that result from this operation. +-- MAGIC +-- MAGIC +-- MAGIC Given how small our data is, **`ZORDER`** does not provide any benefit, but we can see all of the metrics that result from this operation. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Reviewing Delta Lake Transactions -- MAGIC --- MAGIC Because all changes to the Delta Lake table are stored in the transaction log, we can easily review the history of our table. +-- MAGIC Because all changes to the Delta Lake table are stored in the transaction log, we can easily review the table history. -- COMMAND ---------- @@ -206,7 +249,9 @@ DESCRIBE HISTORY students -- COMMAND ---------- -- MAGIC %md --- MAGIC As expected, `OPTIMIZE` created another version of our table, meaning that version 8 is our most current version. +-- MAGIC +-- MAGIC +-- MAGIC As expected, **`OPTIMIZE`** created another version of our table, meaning that version 8 is our most current version. -- MAGIC -- MAGIC Remember all of those extra data files that had been marked as removed in our transaction log? These provide us with the ability to query previous versions of our table. -- MAGIC @@ -222,11 +267,15 @@ FROM students VERSION AS OF 3 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC What's important to note about time travel is that we're not recreating a previous state of the table by undoing transactions against our current version; rather, we're just querying all those data files that were indicated as valid as of the specified version. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Rollback Versions -- MAGIC -- MAGIC Suppose you're typing up query to manually delete some records from a table and you accidentally execute this query in the following state. @@ -238,7 +287,9 @@ DELETE FROM students -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that when we see a `-1` for number of rows affected by a delete, this means an entire directory of data has been removed. +-- MAGIC +-- MAGIC +-- MAGIC Note that when we see a **`-1`** for number of rows affected by a delete, this means an entire directory of data has been removed. -- MAGIC -- MAGIC Let's confirm this below. @@ -249,6 +300,8 @@ SELECT * FROM students -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Deleting all the records in your table is probably not a desired outcome. Luckily, we can simply rollback this commit. -- COMMAND ---------- @@ -258,38 +311,40 @@ RESTORE TABLE students TO VERSION AS OF 8 -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that a `RESTORE` command is recorded as a transaction; you won't be able to completely hide the fact that you accidentally deleted all the records in the table, but you will be able to undo the operation and bring your table back to a desired state. +-- MAGIC +-- MAGIC +-- MAGIC Note that a **`RESTORE`** command is recorded as a transaction; you won't be able to completely hide the fact that you accidentally deleted all the records in the table, but you will be able to undo the operation and bring your table back to a desired state. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Cleaning Up Stale Files -- MAGIC -- MAGIC Databricks will automatically clean up stale files in Delta Lake tables. -- MAGIC -- MAGIC While Delta Lake versioning and time travel are great for querying recent versions and rolling back queries, keeping the data files for all versions of large production tables around indefinitely is very expensive (and can lead to compliance issues if PII is present). -- MAGIC --- MAGIC If you wish to manually purge old data files, this can be performed with the `VACUUM` operation. +-- MAGIC If you wish to manually purge old data files, this can be performed with the **`VACUUM`** operation. -- MAGIC --- MAGIC Let's do this below with a retention of `0 HOURS` to only keep our current version. +-- MAGIC Uncomment the following cell and execute it with a retention of **`0 HOURS`** to keep only the current version: -- COMMAND ---------- -VACUUM students RETAIN 0 HOURS +-- VACUUM students RETAIN 0 HOURS -- COMMAND ---------- -- MAGIC %md --- MAGIC By default, `VACUUM` will prevent you from deleting files less than 7 days old, just to ensure that no long-running operations are still referencing any of the files to be deleted. We turned off the setting that prese -- MAGIC --- MAGIC In our demos, you may see Databricks executing code that specifies a retention of `0 HOURS`. This is almost exclusively for demo purposes. There are some instances where a power user of Databricks may need to perform this operation, **you should probably never do this on a production table.** -- MAGIC --- MAGIC That being said, let's do it here. +-- MAGIC By default, **`VACUUM`** will prevent you from deleting files less than 7 days old, just to ensure that no long-running operations are still referencing any of the files to be deleted. If you run **`VACUUM`** on a Delta table, you lose the ability time travel back to a version older than the specified data retention period. In our demos, you may see Databricks executing code that specifies a retention of **`0 HOURS`**. This is simply to demonstrate the feature and is not typically done in production. -- MAGIC -- MAGIC In the following cell, we: --- MAGIC * Turn off a check to prevent premature deletion of data files --- MAGIC * Make sure that logging of `VACUUM` commands is enabled --- MAGIC * Use the `DRY RUN` version of vacuum to print out all records to be deleted +-- MAGIC 1. Turn off a check to prevent premature deletion of data files +-- MAGIC 1. Make sure that logging of **`VACUUM`** commands is enabled +-- MAGIC 1. Use the **`DRY RUN`** version of vacuum to print out all records to be deleted -- COMMAND ---------- @@ -301,7 +356,9 @@ VACUUM students RETAIN 0 HOURS DRY RUN -- COMMAND ---------- -- MAGIC %md --- MAGIC By running `VACUUM` and deleting the 9 files above, we will permanently remove access to versions of the table that require these files to materialize. +-- MAGIC +-- MAGIC +-- MAGIC By running **`VACUUM`** and deleting the 10 files above, we will permanently remove access to versions of the table that require these files to materialize. -- COMMAND ---------- @@ -310,12 +367,26 @@ VACUUM students RETAIN 0 HOURS -- COMMAND ---------- -- MAGIC %md --- MAGIC Because of the way the Delta Cache works, we may still be able to query previous versions of the table on the currently active cluster until it restarts. However, listing out our table directory will show that files have been successfully deleted. +-- MAGIC +-- MAGIC +-- MAGIC Check the table directory to show that files have been successfully deleted. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC display(dbutils.fs.ls(f"{DA.paths.user_db}/students")) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{userhome}/students")) +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/Labs/DE 1.3.4L - Delta Lake Versioning, Optimization, and Vacuuming.sql b/Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.4L - Delta Lake Versioning, Optimization, and Vacuuming Lab.sql similarity index 74% rename from Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/Labs/DE 1.3.4L - Delta Lake Versioning, Optimization, and Vacuuming.sql rename to Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.4L - Delta Lake Versioning, Optimization, and Vacuuming Lab.sql index 56b7e33..35c502c 100644 --- a/Data-Engineering-With-Databricks/Solutions/01 - Databricks Lakehouse Platform/Labs/DE 1.3.4L - Delta Lake Versioning, Optimization, and Vacuuming.sql +++ b/Data-Engineering-with-Databricks/Solutions/02 - Delta Lake/DE 2.4L - Delta Lake Versioning, Optimization, and Vacuuming Lab.sql @@ -8,12 +8,14 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Delta Lake Versioning, Optimization, and Vacuuming -- MAGIC -- MAGIC This notebook provides a hands-on review of some of the more esoteric features Delta Lake brings to the data lakehouse. -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC By the end of this lessons, student will be able to: +-- MAGIC By the end of this lab, you should be able to: -- MAGIC - Review table history -- MAGIC - Query previous table versions and rollback a table to a specific version -- MAGIC - Perform file compaction and Z-order indexing @@ -22,21 +24,25 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Setup -- MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. -- COMMAND ---------- --- MAGIC %run ../../Includes/sql-setup $course="delta" $mode="reset" +-- MAGIC %run ../Includes/Classroom-Setup-2.4L -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Recreate the History of your Bean Collection -- MAGIC --- MAGIC This lab picks up where the last lab left off. The cell below condenses all the operations from the last lab into a single cell (other than the final `DROP TABLE` statement). +-- MAGIC This lab picks up where the last lab left off. The cell below condenses all the operations from the last lab into a single cell (other than the final **`DROP TABLE`** statement). -- MAGIC --- MAGIC For quick reference, the schema of the `beans` table created is: +-- MAGIC For quick reference, the schema of the **`beans`** table created is: -- MAGIC -- MAGIC | Field Name | Field type | -- MAGIC | --- | --- | @@ -88,11 +94,13 @@ WHEN NOT MATCHED AND b.delicious = true THEN -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Review the Table History -- MAGIC -- MAGIC Delta Lake's transaction log stores information about each transaction that modifies a table's contents or settings. -- MAGIC --- MAGIC Review the history of the `beans` table below. +-- MAGIC Review the history of the **`beans`** table below. -- COMMAND ---------- @@ -102,6 +110,8 @@ DESCRIBE HISTORY beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC If all the previous operations were completed as described you should see 7 versions of the table (**NOTE**: Delta Lake versioning starts with 0, so the max version number will be 6). -- MAGIC -- MAGIC The operations should be as follows: @@ -116,15 +126,17 @@ DESCRIBE HISTORY beans -- MAGIC | 5 | DELETE | -- MAGIC | 6 | MERGE | -- MAGIC --- MAGIC The `operationsParameters` column will let you review predicates used for updates, deletes, and merges. The `operationMetrics` column indicates how many rows and files are added in each operation. +-- MAGIC The **`operationsParameters`** column will let you review predicates used for updates, deletes, and merges. The **`operationMetrics`** column indicates how many rows and files are added in each operation. -- MAGIC -- MAGIC Spend some time reviewing the Delta Lake history to understand which table version matches with a given transaction. -- MAGIC --- MAGIC **NOTE**: The `version` column designates the state of a table once a given transaction completes. The `readVersion` column indicates the version of the table an operation executed against. In this simple demo (with no concurrent transactions), this relationship should always increment by 1. +-- MAGIC **NOTE**: The **`version`** column designates the state of a table once a given transaction completes. The **`readVersion`** column indicates the version of the table an operation executed against. In this simple demo (with no concurrent transactions), this relationship should always increment by 1. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Query a Specific Version -- MAGIC -- MAGIC After reviewing the table history, you decide you want to view the state of your table after your very first data was inserted. @@ -138,6 +150,8 @@ SELECT * FROM beans VERSION AS OF 1 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC And now review the current state of your data. -- COMMAND ---------- @@ -147,9 +161,11 @@ SELECT * FROM beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC You want to review the weights of your beans before you deleted any records. -- MAGIC --- MAGIC Fill in the query below to register and query a temporary view of the version just before data was deleted. +-- MAGIC Fill in the statement below to register a temporary view of the version just before data was deleted, then run the following cell to query the view. -- COMMAND ---------- @@ -157,11 +173,15 @@ SELECT * FROM beans CREATE OR REPLACE TEMP VIEW pre_delete_vw AS SELECT * FROM beans VERSION AS OF 4; +-- COMMAND ---------- + SELECT * FROM pre_delete_vw -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to check that you have captured the correct version. -- COMMAND ---------- @@ -174,11 +194,13 @@ SELECT * FROM pre_delete_vw -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Restore a Previous Version -- MAGIC -- MAGIC Apparently there was a misunderstanding; the beans your friend gave you that you merged into your collection were not intended for you to keep. -- MAGIC --- MAGIC Revert your table to the version before this `MERGE` statement completed. +-- MAGIC Revert your table to the version before this **`MERGE`** statement completed. -- COMMAND ---------- @@ -188,6 +210,8 @@ RESTORE TABLE beans TO VERSION AS OF 5 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Review the history of your table. Make note of the fact that restoring to a previous version adds another table version. -- COMMAND ---------- @@ -204,10 +228,12 @@ DESCRIBE HISTORY beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## File Compaction -- MAGIC Looking at the transaction metrics during your reversion, you are surprised you have some many files for such a small collection of data. -- MAGIC --- MAGIC While indexing on a table of this size is unlikely to improve performance, you decide to add a Z-order index on the `name` field in anticipation of your bean collection growing exponentially over time. +-- MAGIC While indexing on a table of this size is unlikely to improve performance, you decide to add a Z-order index on the **`name`** field in anticipation of your bean collection growing exponentially over time. -- MAGIC -- MAGIC Use the cell below to perform file compaction and Z-order indexing. @@ -220,6 +246,8 @@ ZORDER BY name -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Your data should have been compacted to a single file; confirm this manually by running the following cell. -- COMMAND ---------- @@ -229,6 +257,8 @@ DESCRIBE DETAIL beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to check that you've successfully optimized and indexed your table. -- COMMAND ---------- @@ -241,19 +271,21 @@ DESCRIBE DETAIL beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Cleaning Up Stale Data Files -- MAGIC --- MAGIC You know that while all your data now resides in 1 data file, the data files from previous versions of your table are still being stored alongside this. You wish to remove these files and remove access to previous versions of the table by running `VACUUM` on the table. +-- MAGIC You know that while all your data now resides in 1 data file, the data files from previous versions of your table are still being stored alongside this. You wish to remove these files and remove access to previous versions of the table by running **`VACUUM`** on the table. -- MAGIC --- MAGIC Executing `VACUUM` performs garbage cleanup on the table directory. By default, a retention threshold of 7 days will be enforced. +-- MAGIC Executing **`VACUUM`** performs garbage cleanup on the table directory. By default, a retention threshold of 7 days will be enforced. -- MAGIC -- MAGIC The cell below modifies some Spark configurations. The first command overrides the retention threshold check to allow us to demonstrate permanent removal of data. -- MAGIC -- MAGIC **NOTE**: Vacuuming a production table with a short retention can lead to data corruption and/or failure of long-running queries. This is for demonstration purposes only and extreme caution should be used when disabling this setting. -- MAGIC --- MAGIC The second command sets `spark.databricks.delta.vacuum.logging.enabled` to `true` to ensures that the `VACUUM` operation is recorded in the transaction log. +-- MAGIC The second command sets **`spark.databricks.delta.vacuum.logging.enabled`** to **`true`** to ensure that the **`VACUUM`** operation is recorded in the transaction log. -- MAGIC --- MAGIC **NOTE**: Because of slight differences in storage protocols on various clouds, logging `VACUUM` commands is not on by default for some clouds as of DBR 9.1. +-- MAGIC **NOTE**: Because of slight differences in storage protocols on various clouds, logging **`VACUUM`** commands is not on by default for some clouds as of DBR 9.1. -- COMMAND ---------- @@ -263,7 +295,9 @@ SET spark.databricks.delta.vacuum.logging.enabled = true; -- COMMAND ---------- -- MAGIC %md --- MAGIC Before permanently deleting data files, review them manually using the `DRY RUN` command. +-- MAGIC +-- MAGIC +-- MAGIC Before permanently deleting data files, review them manually using the **`DRY RUN`** option. -- COMMAND ---------- @@ -272,9 +306,11 @@ VACUUM beans RETAIN 0 HOURS DRY RUN -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC All data files not in the current version of the table will be shown in the preview above. -- MAGIC --- MAGIC Run the command again without `DRY RUN` to permanently delete these files. +-- MAGIC Run the command again without **`DRY RUN`** to permanently delete these files. -- MAGIC -- MAGIC **NOTE**: All previous versions of the table will no longer be accessible. @@ -285,7 +321,9 @@ VACUUM beans RETAIN 0 HOURS -- COMMAND ---------- -- MAGIC %md --- MAGIC Because `VACUUM` can be such a destructive act for important datasets, it's always a good idea to turn the retention duration check back on. Run the cell below to reactive this setting. +-- MAGIC +-- MAGIC +-- MAGIC Because **`VACUUM`** can be such a destructive act for important datasets, it's always a good idea to turn the retention duration check back on. Run the cell below to reactive this setting. -- COMMAND ---------- @@ -294,7 +332,9 @@ SET spark.databricks.delta.retentionDurationCheck.enabled = true -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that the table history will indicate the user that completed the `VACUUM` operation, the number of files deleted, and log that the retention check was disabled during this operation. +-- MAGIC +-- MAGIC +-- MAGIC Note that the table history will indicate the user that completed the **`VACUUM`** operation, the number of files deleted, and log that the retention check was disabled during this operation. -- COMMAND ---------- @@ -303,6 +343,8 @@ DESCRIBE HISTORY beans -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Query your table again to confirm you still have access to the current version. -- COMMAND ---------- @@ -312,32 +354,42 @@ SELECT * FROM beans -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that because Delta Cache stores copies of files queried in the current session on storage volumes deployed to your currently active cluster, you may still be able to temporarily access previous table versions (though systems should **not** be designed to expect this behavior). Restarting the cluster will ensure that these cached data files are permanently purged. +-- MAGIC +-- MAGIC +-- MAGIC Because Delta Cache stores copies of files queried in the current session on storage volumes deployed to your currently active cluster, you may still be able to temporarily access previous table versions (though systems should **not** be designed to expect this behavior). +-- MAGIC +-- MAGIC Restarting the cluster will ensure that these cached data files are permanently purged. +-- MAGIC +-- MAGIC You can see an example of this by uncommenting and running the following cell that may, or may not, fail +-- MAGIC (depending on the state of the cache). -- COMMAND ---------- -SELECT * FROM beans@v1 +-- SELECT * FROM beans@v1 -- COMMAND ---------- -- MAGIC %md --- MAGIC ## Wrapping Up -- MAGIC --- MAGIC Run the following cell to remove the database and all data associated with this lab. +-- MAGIC +-- MAGIC By completing this lab, you should now feel comfortable: +-- MAGIC * Completing standard Delta Lake table creation and data manipulation commands +-- MAGIC * Reviewing table metadata including table history +-- MAGIC * Leverage Delta Lake versioning for snapshot queries and rollbacks +-- MAGIC * Compacting small files and indexing tables +-- MAGIC * Using **`VACUUM`** to review files marked for deletion and committing these deletes -- COMMAND ---------- --- MAGIC %run ../../Includes/sql-setup $course="delta" $mode="cleanup" +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. -- COMMAND ---------- --- MAGIC %md --- MAGIC By completing this lab, you should now feel comfortable: --- MAGIC * Completing standard Delta Lake table creation and data manipulation commands --- MAGIC * Reviewing table metadata including table history --- MAGIC * Leverage Delta Lake versioning for snapshot queries and rollbacks --- MAGIC * Compacting small files and indexing tables --- MAGIC * Using `VACUUM` to review files marked for deletion and committing these deletes +-- MAGIC %python +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.1.1 - Databases and Tables on Databricks.sql b/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.1 - Databases and Tables on Databricks.sql similarity index 51% rename from Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.1.1 - Databases and Tables on Databricks.sql rename to Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.1 - Databases and Tables on Databricks.sql index 9401dd3..d600477 100644 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.1.1 - Databases and Tables on Databricks.sql +++ b/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.1 - Databases and Tables on Databricks.sql @@ -8,36 +8,42 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Databases and Tables on Databricks -- MAGIC In this demonstration, you will create and explore databases and tables. -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC By the end of this lesson, you will be able to: +-- MAGIC By the end of this lesson, you should be able to: -- MAGIC * Use Spark SQL DDL to define databases and tables --- MAGIC * Describe how the `LOCATION` keyword impacts the default storage directory +-- MAGIC * Describe how the **`LOCATION`** keyword impacts the default storage directory -- MAGIC -- MAGIC -- MAGIC -- MAGIC **Resources** --- MAGIC * [Databases and Tables - Databricks Docs](https://docs.databricks.com/user-guide/tables.html) --- MAGIC * [Managed and Unmanaged Tables](https://docs.databricks.com/user-guide/tables.html#managed-and-unmanaged-tables) --- MAGIC * [Creating a Table with the UI](https://docs.databricks.com/user-guide/tables.html#create-a-table-using-the-ui) --- MAGIC * [Create a Local Table](https://docs.databricks.com/user-guide/tables.html#create-a-local-table) --- MAGIC * [Saving to Persistent Tables](https://spark.apache.org/docs/latest/sql-data-sources-load-save-functions.html#saving-to-persistent-tables) +-- MAGIC * Databases and Tables - Databricks Docs +-- MAGIC * Managed and Unmanaged Tables +-- MAGIC * Creating a Table with the UI +-- MAGIC * Create a Local Table +-- MAGIC * Saving to Persistent Tables -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Lesson Setup -- MAGIC The following script clears out previous runs of this demo and configures some Hive variables that will be used in our SQL queries. -- COMMAND ---------- --- MAGIC %run ../Includes/setup-meta +-- MAGIC %run ../Includes/Classroom-Setup-3.1 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Using Hive Variables -- MAGIC -- MAGIC While not a pattern that is generally recommended in Spark SQL, this notebook will use some Hive variables to substitute in string values derived from the account email of the current user. @@ -46,59 +52,76 @@ -- COMMAND ---------- -SELECT "${c.database}"; +SELECT "${da.db_name}" AS db_name, + "${da.paths.working_dir}" AS working_dir -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Because you may be working in a shared workspace, this course uses variables derived from your username so the databases don't conflict with other users. Again, consider this use of Hive variables a hack for our lesson environment rather than a good practice for development. -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Databases -- MAGIC Let's start by creating two databases: --- MAGIC - One with no LOCATION specified --- MAGIC - One with LOCATION specified +-- MAGIC - One with no **`LOCATION`** specified +-- MAGIC - One with **`LOCATION`** specified -- COMMAND ---------- -CREATE DATABASE IF NOT EXISTS ${c.database}_default_location; -CREATE DATABASE IF NOT EXISTS ${c.database}_custom_location LOCATION '${c.userhome}'; +CREATE DATABASE IF NOT EXISTS ${da.db_name}_default_location; +CREATE DATABASE IF NOT EXISTS ${da.db_name}_custom_location LOCATION '${da.paths.working_dir}/_custom_location.db'; -- COMMAND ---------- --- MAGIC %md --- MAGIC Note that the location of the first database is in the default location under `dbfs:/user/hive/warehouse/` +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that the location of the first database is in the default location under **`dbfs:/user/hive/warehouse/`** and that the database directory is the name of the database with the **`.db`** extension -- COMMAND ---------- -DESCRIBE DATABASE EXTENDED ${c.database}_default_location; +DESCRIBE DATABASE EXTENDED ${da.db_name}_default_location; -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that the location of the second database is in the directory specified after the `LOCATION` keyword. +-- MAGIC +-- MAGIC +-- MAGIC Note that the location of the second database is in the directory specified after the **`LOCATION`** keyword. -- COMMAND ---------- -DESCRIBE DATABASE EXTENDED ${c.database}_custom_location; +DESCRIBE DATABASE EXTENDED ${da.db_name}_custom_location; -- COMMAND ---------- --- MAGIC %md --- MAGIC We will create a table in the database with default location and insert data. Note that the schema must be provided because there are no data from which to infer the schema. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We will create a table in the database with default location and insert data. +-- MAGIC +-- MAGIC Note that the schema must be provided because there is no data from which to infer the schema. -- COMMAND ---------- -USE ${c.database}_default_location; +USE ${da.db_name}_default_location; + CREATE OR REPLACE TABLE managed_table_in_db_with_default_location (width INT, length INT, height INT); -INSERT INTO managed_table_in_db_with_default_location VALUES (3, 2, 1); +INSERT INTO managed_table_in_db_with_default_location +VALUES (3, 2, 1); SELECT * FROM managed_table_in_db_with_default_location; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC We can look at the extended table description to find the location (you'll need to scroll down in the results). -- COMMAND ---------- @@ -108,18 +131,30 @@ DESCRIBE EXTENDED managed_table_in_db_with_default_location; -- COMMAND ---------- -- MAGIC %md --- MAGIC By default, managed tables in a database without the location specified will be created in the `dbfs:/user/hive/warehouse/.db/` directory. +-- MAGIC +-- MAGIC +-- MAGIC By default, managed tables in a database without the location specified will be created in the **`dbfs:/user/hive/warehouse/.db/`** directory. -- MAGIC -- MAGIC We can see that, as expected, the data and metadata for our Delta Table are stored in that location. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"dbfs:/user/hive/warehouse/{database}_default_location.db/managed_table_in_db_with_default_location")) +-- MAGIC hive_root = f"dbfs:/user/hive/warehouse" +-- MAGIC db_name = f"{DA.db_name}_default_location.db" +-- MAGIC table_name = f"managed_table_in_db_with_default_location" +-- MAGIC +-- MAGIC tbl_location = f"{hive_root}/{db_name}/{table_name}" +-- MAGIC print(tbl_location) +-- MAGIC +-- MAGIC files = dbutils.fs.ls(tbl_location) +-- MAGIC display(files) -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Drop the table. -- COMMAND ---------- @@ -128,29 +163,41 @@ DROP TABLE managed_table_in_db_with_default_location; -- COMMAND ---------- --- MAGIC %md --- MAGIC Note the table's folder and its log and data file are deleted. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note the table's directory and its log and data files are deleted. Only the database directory remains. -- COMMAND ---------- -- MAGIC %python --- MAGIC dbutils.fs.ls(f"dbfs:/user/hive/warehouse/{database}_default_location.db") +-- MAGIC +-- MAGIC db_location = f"{hive_root}/{db_name}" +-- MAGIC print(db_location) +-- MAGIC dbutils.fs.ls(db_location) -- COMMAND ---------- --- MAGIC %md --- MAGIC We now create a table in the database with custom location and insert data. Note that the schema must be provided because there are no data from which to infer the schema. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We now create a table in the database with custom location and insert data. +-- MAGIC +-- MAGIC Note that the schema must be provided because there is no data from which to infer the schema. -- COMMAND ---------- -USE ${c.database}_custom_location; +USE ${da.db_name}_custom_location; + CREATE OR REPLACE TABLE managed_table_in_db_with_custom_location (width INT, length INT, height INT); INSERT INTO managed_table_in_db_with_custom_location VALUES (3, 2, 1); SELECT * FROM managed_table_in_db_with_custom_location; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Again, we'll look at the description to find the table location. -- COMMAND ---------- @@ -159,17 +206,27 @@ DESCRIBE EXTENDED managed_table_in_db_with_custom_location; -- COMMAND ---------- --- MAGIC %md --- MAGIC As expected, this managed table is created in the path specified with the `LOCATION` keyword during database creation. As such, the data and metadata for the table are persisted in a directory here. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC As expected, this managed table is created in the path specified with the **`LOCATION`** keyword during database creation. As such, the data and metadata for the table are persisted in a directory here. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{userhome}/managed_table_in_db_with_custom_location")) +-- MAGIC +-- MAGIC table_name = f"managed_table_in_db_with_custom_location" +-- MAGIC tbl_location = f"{DA.paths.working_dir}/_custom_location.db/{table_name}" +-- MAGIC print(tbl_location) +-- MAGIC +-- MAGIC files = dbutils.fs.ls(tbl_location) +-- MAGIC display(files) -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Let's drop the table. -- COMMAND ---------- @@ -178,40 +235,51 @@ DROP TABLE managed_table_in_db_with_custom_location; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Note the table's folder and the log file and data file are deleted. -- MAGIC --- MAGIC Only the "datasets" folder remains. +-- MAGIC Only the database location remains -- COMMAND ---------- -- MAGIC %python --- MAGIC dbutils.fs.ls(userhome) +-- MAGIC +-- MAGIC db_location = f"{DA.paths.working_dir}/_custom_location.db" +-- MAGIC print(db_location) +-- MAGIC +-- MAGIC dbutils.fs.ls(db_location) -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Tables --- MAGIC We will create an external (unmanaged) table from sample data. The data we are going to use are in csv format. We want to create a Delta table with a LOCATION provided in the directory of our choice. +-- MAGIC We will create an external (unmanaged) table from sample data. +-- MAGIC +-- MAGIC The data we are going to use are in CSV format. We want to create a Delta table with a **`LOCATION`** provided in the directory of our choice. -- COMMAND ---------- -USE ${c.database}_default_location; +USE ${da.db_name}_default_location; --- mode "FAILFAST" will abort file parsing with a RuntimeException if any malformed lines are encountered CREATE OR REPLACE TEMPORARY VIEW temp_delays USING CSV OPTIONS ( - path '${c.userhome}/datasets/flights/departuredelays.csv', - header "true", - mode "FAILFAST" + path = '${da.paths.working_dir}/flights/departuredelays.csv', + header = "true", + mode = "FAILFAST" -- abort file parsing with a RuntimeException if any malformed lines are encountered ); -CREATE OR REPLACE TABLE external_table LOCATION '${c.userhome}/external_table' AS +CREATE OR REPLACE TABLE external_table LOCATION '${da.paths.working_dir}/external_table' AS SELECT * FROM temp_delays; SELECT * FROM external_table; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Let's note the location of the table's data in this lesson's working directory. -- COMMAND ---------- @@ -220,7 +288,9 @@ DESCRIBE TABLE EXTENDED external_table; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Now, we drop the table. -- COMMAND ---------- @@ -229,34 +299,42 @@ DROP TABLE external_table; -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC The table definition no longer exists in the metastore, but the underlying data remain intact. -- COMMAND ---------- -- MAGIC %python --- MAGIC display(dbutils.fs.ls(f"{userhome}/external_table")) +-- MAGIC tbl_path = f"{DA.paths.working_dir}/external_table" +-- MAGIC files = dbutils.fs.ls(tbl_path) +-- MAGIC display(files) -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Clean up -- MAGIC Drop both databases. -- COMMAND ---------- -DROP DATABASE ${c.database}_default_location CASCADE; -DROP DATABASE ${c.database}_custom_location CASCADE; +DROP DATABASE ${da.db_name}_default_location CASCADE; +DROP DATABASE ${da.db_name}_custom_location CASCADE; -- COMMAND ---------- -- MAGIC %md --- MAGIC Delete the working directory and its contents. +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. -- COMMAND ---------- -- MAGIC %python --- MAGIC dbutils.fs.rm(userhome, True) +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.2A - Views and CTEs on Databricks.sql b/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.2A - Views and CTEs on Databricks.sql new file mode 100644 index 0000000..10fd81d --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.2A - Views and CTEs on Databricks.sql @@ -0,0 +1,210 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Views and CTEs on Databricks +-- MAGIC In this demonstration, you will create and explore views and common table expressions (CTEs). +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC * Use Spark SQL DDL to define views +-- MAGIC * Run queries that use common table expressions +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC **Resources** +-- MAGIC * Create View - Databricks Docs +-- MAGIC * Common Table Expressions - Databricks Docs + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Classroom Setup +-- MAGIC The following script clears out previous runs of this demo and configures some Hive variables that will be used in our SQL queries. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-3.2A + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We start by creating a table of data we can use for the demonstration. + +-- COMMAND ---------- + +-- mode "FAILFAST" will abort file parsing with a RuntimeException if any malformed lines are encountered +CREATE TABLE external_table +USING CSV OPTIONS ( + path = '${da.paths.working_dir}/flight_delays', + header = "true", + mode = "FAILFAST" +); + +SELECT * FROM external_table; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC To show a list of tables (and views), we use the **`SHOW TABLES`** command also demonstrated below. + +-- COMMAND ---------- + +SHOW TABLES; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Views, Temp Views & Global Temp Views +-- MAGIC +-- MAGIC To set this demonstration up, we are going to first create one of each type of view. +-- MAGIC +-- MAGIC Then in the next notebook, we will explore the differences between how each one behaves. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ### Views +-- MAGIC Let's create a view that contains only the data where the origin is "ABQ" and the destination is "LAX". + +-- COMMAND ---------- + +CREATE VIEW view_delays_abq_lax AS + SELECT * + FROM external_table + WHERE origin = 'ABQ' AND destination = 'LAX'; + +SELECT * FROM view_delays_abq_lax; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Note that the **`view_delays_abq_lax`** view has been added to the list below: + +-- COMMAND ---------- + +SHOW TABLES; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ### Temporary Views +-- MAGIC +-- MAGIC Next we'll create a temporary view. +-- MAGIC +-- MAGIC The syntax is very similar but adds **`TEMPORARY`** to the command. + +-- COMMAND ---------- + +CREATE TEMPORARY VIEW temp_view_delays_gt_120 +AS SELECT * FROM external_table WHERE delay > 120 ORDER BY delay ASC; + +SELECT * FROM temp_view_delays_gt_120; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Now if we show our tables again, we will see the one table and both views. +-- MAGIC +-- MAGIC Make note of the values in the **`isTemporary`** column. + +-- COMMAND ---------- + +SHOW TABLES; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ### Global Temp Views +-- MAGIC +-- MAGIC Lastly, we'll create a global temp view. +-- MAGIC +-- MAGIC Here we simply add **`GLOBAL`** to the command. +-- MAGIC +-- MAGIC Also note the **`global_temp`** database qualifer in the subsequent **`SELECT`** statement. + +-- COMMAND ---------- + +CREATE GLOBAL TEMPORARY VIEW global_temp_view_dist_gt_1000 +AS SELECT * FROM external_table WHERE distance > 1000; + +SELECT * FROM global_temp.global_temp_view_dist_gt_1000; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Before we move on, review one last time the database's tables and views... + +-- COMMAND ---------- + +SHOW TABLES; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ...and the tables and views in the **`global_temp`** database: + +-- COMMAND ---------- + +SHOW TABLES IN global_temp; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Next we are going to demonstrate how tables and views are persisted across multiple sessions and how temp views are not. +-- MAGIC +-- MAGIC To do this simply open the next notebook, [DE 3.2B - Views and CTEs on Databricks, Cont]($./DE 3.2B - Views and CTEs on Databricks, Cont), and continue with the lesson. +-- MAGIC +-- MAGIC Note: There are several scenarios in which a new session may be created: +-- MAGIC * Restarting a cluster +-- MAGIC * Detaching and reataching to a cluster +-- MAGIC * Installing a python package which in turn restarts the Python interpreter +-- MAGIC * Or simply opening a new notebook + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.2B - Views and CTEs on Databricks, Cont.sql b/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.2B - Views and CTEs on Databricks, Cont.sql new file mode 100644 index 0000000..b00468c --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.2B - Views and CTEs on Databricks, Cont.sql @@ -0,0 +1,262 @@ +-- Databricks notebook source +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Views and CTEs on Databricks, Cont' +-- MAGIC +-- MAGIC We are picking up from notebook [DE 3.2A - Views and CTEs on Databricks]($./DE 3.2A - Views and CTEs on Databricks) where we just reviewed the following two lists of tables
+-- MAGIC and views with the special note that our global temp view **`global_temp_view_dist_gt_1000`** was not included in the first list. + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC With the Notebook's state reset, we need to re-initialize some of our lesson-specific configuration. +-- MAGIC +-- MAGIC Note: We will **NOT** be recreating the database for the second 1/2 of this lesson. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-3.2B + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC But, we do need to configure this session to use our database by default. + +-- COMMAND ---------- + +USE ${da.db_name}; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Finally, run the following two cells to confirm that: +-- MAGIC 1. The table **`external_table`** still exists. +-- MAGIC 2. The view **`view_delays_abq_lax`** still exists. +-- MAGIC 3. The temp view **`temp_view_delays_gt_120`** does **NOT** exist. +-- MAGIC 3. The global temp view **`global_temp_view_dist_gt_1000`** does exist in the special **`global_temp`** database. +-- MAGIC +-- MAGIC Hint: If you were to go back to the previous notebook and run **`SHOW TABLES`**
+-- MAGIC again, all three tables and views from the current database will still be shown. + +-- COMMAND ---------- + +SHOW TABLES; + +-- COMMAND ---------- + +SHOW TABLES IN global_temp; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC As mentioned previously, temp views are tied to a Spark session and as such are not accessible... +-- MAGIC * After restarting a cluster +-- MAGIC * After detaching and reataching to a cluster +-- MAGIC * After installing a python package which in turn restarts the Python interpreter +-- MAGIC * Or from another notebook +-- MAGIC +-- MAGIC ...with the special exception of global temporary views. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Global temp views behave much like other temporary views but differ in one important way. +-- MAGIC +-- MAGIC They are added to the **`global_temp`** database that exists on the **`cluster`**. +-- MAGIC +-- MAGIC As long as the cluster is running, this database persists and any notebooks attached to the cluster can access its global temporary views. +-- MAGIC +-- MAGIC We can see this in action by running the follow cells: + +-- COMMAND ---------- + +SELECT * FROM global_temp.global_temp_view_dist_gt_1000; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Global temp views are "lost" when the cluster is restarted. +-- MAGIC +-- MAGIC Take our word for it, don't do it now, but if you were to restart the cluster, the above select statement would fail because the table would no longer exist. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Common Table Expressions (CTEs) +-- MAGIC CTEs can be used in a variety of contexts. Below, are a few examples of the different ways a CTE can be used in a query. First, an example of making multiple column aliases using a CTE. + +-- COMMAND ---------- + +WITH flight_delays( + total_delay_time, + origin_airport, + destination_airport +) AS ( + SELECT + delay, + origin, + destination + FROM + external_table +) +SELECT + * +FROM + flight_delays +WHERE + total_delay_time > 120 + AND origin_airport = "ATL" + AND destination_airport = "DEN"; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Next, is an example of a CTE in a CTE definition. + +-- COMMAND ---------- + +WITH lax_bos AS ( + WITH origin_destination (origin_airport, destination_airport) AS ( + SELECT + origin, + destination + FROM + external_table + ) + SELECT + * + FROM + origin_destination + WHERE + origin_airport = 'LAX' + AND destination_airport = 'BOS' +) +SELECT + count(origin_airport) AS `Total Flights from LAX to BOS` +FROM + lax_bos; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Now, here is an example of a CTE in a subquery. + +-- COMMAND ---------- + +SELECT + max(total_delay) AS `Longest Delay (in minutes)` +FROM + ( + WITH delayed_flights(total_delay) AS ( + SELECT + delay + FROM + external_table + ) + SELECT + * + FROM + delayed_flights + ); + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We can also use a CTE in a subquery expression. + +-- COMMAND ---------- + +SELECT + ( + WITH distinct_origins AS ( + SELECT DISTINCT origin FROM external_table + ) + SELECT + count(origin) AS `Number of Distinct Origins` + FROM + distinct_origins + ) AS `Number of Different Origin Airports`; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Finally, here is a CTE in a **`CREATE VIEW`** statement. + +-- COMMAND ---------- + +CREATE OR REPLACE VIEW BOS_LAX +AS WITH origin_destination(origin_airport, destination_airport) +AS (SELECT origin, destination FROM external_table) +SELECT * FROM origin_destination +WHERE origin_airport = 'BOS' AND destination_airport = 'LAX'; + +SELECT count(origin_airport) AS `Number of Delayed Flights from BOS to LAX` FROM BOS_LAX; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Clean up +-- MAGIC We first drop the training database. + +-- COMMAND ---------- + +DROP DATABASE ${da.db_name} CASCADE; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.3L - Databases, Tables & Views Lab.sql b/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.3L - Databases, Tables & Views Lab.sql new file mode 100644 index 0000000..d13ba0c --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/03 - Relational Entities on Databricks/DE 3.3L - Databases, Tables & Views Lab.sql @@ -0,0 +1,514 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Databases, Tables, and Views Lab +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lab, you should be able to: +-- MAGIC - Create and explore interactions between various relational entities, including: +-- MAGIC - Databases +-- MAGIC - Tables (managed and external) +-- MAGIC - Views (views, temp views, and global temp views) +-- MAGIC +-- MAGIC **Resources** +-- MAGIC * Databases and Tables - Databricks Docs +-- MAGIC * Managed and Unmanaged Tables +-- MAGIC * Creating a Table with the UI +-- MAGIC * Create a Local Table +-- MAGIC * Saving to Persistent Tables + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ### Getting Started +-- MAGIC +-- MAGIC Run the following cell to configure variables and datasets for this lesson. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-3.3L + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Overview of the Data +-- MAGIC +-- MAGIC The data include multiple entries from a selection of weather stations, including average temperatures recorded in either Fahrenheit or Celsius. The schema for the table: +-- MAGIC +-- MAGIC |ColumnName | DataType| Description| +-- MAGIC |------------|---------|------------| +-- MAGIC |NAME |string | Station name | +-- MAGIC |STATION |string | Unique ID | +-- MAGIC |LATITUDE |float | Latitude | +-- MAGIC |LONGITUDE |float | Longitude | +-- MAGIC |ELEVATION |float | Elevation | +-- MAGIC |DATE |date | YYYY-MM-DD | +-- MAGIC |UNIT |string | Temperature units | +-- MAGIC |TAVG |float | Average temperature | +-- MAGIC +-- MAGIC This data is stored in the Parquet format; preview the data with the query below. + +-- COMMAND ---------- + +SELECT * +FROM parquet.`${da.paths.working_dir}/weather` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Create a Database +-- MAGIC +-- MAGIC Create a database in the default location using the **`da.db_name`** variable defined in setup script. + +-- COMMAND ---------- + +-- ANSWER +CREATE DATABASE IF NOT EXISTS ${da.db_name} + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.sql(f"SHOW DATABASES").filter(f"databaseName == '{DA.db_name}'").count() == 1, "Database not present" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Change to Your New Database +-- MAGIC +-- MAGIC **`USE`** your newly created database. + +-- COMMAND ---------- + +-- ANSWER +USE ${da.db_name} + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.sql(f"SHOW CURRENT DATABASE").first()["namespace"] == DA.db_name, "Not using the correct database" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Create a Managed Table +-- MAGIC Use a CTAS statement to create a managed table named **`weather_managed`**. + +-- COMMAND ---------- + +-- ANSWER + +CREATE TABLE weather_managed AS +SELECT * +FROM parquet.`${da.paths.working_dir}/weather` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("weather_managed"), "Table named `weather_managed` does not exist" +-- MAGIC assert spark.table("weather_managed").count() == 2559, "Incorrect row count" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Create an External Table +-- MAGIC +-- MAGIC Recall that an external table differs from a managed table through specification of a location. Create an external table called **`weather_external`** below. + +-- COMMAND ---------- + +-- ANSWER + +CREATE TABLE weather_external +LOCATION "${da.paths.working_dir}/lab/external" +AS SELECT * +FROM parquet.`${da.paths.working_dir}/weather` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("weather_external"), "Table named `weather_external` does not exist" +-- MAGIC assert spark.table("weather_external").count() == 2559, "Incorrect row count" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Examine Table Details +-- MAGIC Use the SQL command **`DESCRIBE EXTENDED table_name`** to examine the two weather tables. + +-- COMMAND ---------- + +DESCRIBE EXTENDED weather_managed + +-- COMMAND ---------- + +DESCRIBE EXTENDED weather_external + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following helper code to extract and compare the table locations. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC def getTableLocation(tableName): +-- MAGIC return spark.sql(f"DESCRIBE DETAIL {tableName}").select("location").first()[0] + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC managedTablePath = getTableLocation("weather_managed") +-- MAGIC externalTablePath = getTableLocation("weather_external") +-- MAGIC +-- MAGIC print(f"""The weather_managed table is saved at: +-- MAGIC +-- MAGIC {managedTablePath} +-- MAGIC +-- MAGIC The weather_external table is saved at: +-- MAGIC +-- MAGIC {externalTablePath}""") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC List the contents of these directories to confirm that data exists in both locations. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC files = dbutils.fs.ls(managedTablePath) +-- MAGIC display(files) + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC files = dbutils.fs.ls(externalTablePath) +-- MAGIC display(files) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ### Check Directory Contents after Dropping Database and All Tables +-- MAGIC The **`CASCADE`** keyword will accomplish this. + +-- COMMAND ---------- + +-- ANSWER +DROP DATABASE ${da.db_name} CASCADE + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.sql(f"SHOW DATABASES").filter(f"databaseName == '{DA.db_name}'").count() == 0, "Database present" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC With the database dropped, the files will have been deleted as well. +-- MAGIC +-- MAGIC Uncomment and run the following cell, which will throw a **`FileNotFoundException`** as your confirmation. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC # files = dbutils.fs.ls(managedTablePath) +-- MAGIC # display(files) + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC files = dbutils.fs.ls(externalTablePath) +-- MAGIC display(files) + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC files = dbutils.fs.ls(DA.paths.working_dir) +-- MAGIC display(files) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC **This highlights the main differences between managed and external tables.** By default, the files associated with managed tables will be stored to this location on the root DBFS storage linked to the workspace, and will be deleted when a table is dropped. +-- MAGIC +-- MAGIC Files for external tables will be persisted in the location provided at table creation, preventing users from inadvertently deleting underlying files. **External tables can easily be migrated to other databases or renamed, but these operations with managed tables will require rewriting ALL underlying files.** + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Create a Database with a Specified Path +-- MAGIC +-- MAGIC Assuming you dropped your database in the last step, you can use the same **`database`** name. + +-- COMMAND ---------- + +CREATE DATABASE ${da.db_name} LOCATION '${da.paths.working_dir}/${da.db_name}'; +USE ${da.db_name}; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Recreate your **`weather_managed`** table in this new database and print out the location of this table. + +-- COMMAND ---------- + +-- ANSWER + +CREATE TABLE weather_managed AS +SELECT * +FROM parquet.`${da.paths.working_dir}/weather` + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC getTableLocation("weather_managed") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("weather_managed"), "Table named `weather_managed` does not exist" +-- MAGIC assert spark.table("weather_managed").count() == 2559, "Incorrect row count" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC While here we're using the **`working_dir`** directory created on the DBFS root, _any_ object store can be used as the database directory. **Defining database directories for groups of users can greatly reduce the chances of accidental data exfiltration**. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Views and their Scoping +-- MAGIC +-- MAGIC Using the provided **`AS`** clause, register: +-- MAGIC - a view named **`celsius`** +-- MAGIC - a temporary view named **`celsius_temp`** +-- MAGIC - a global temp view named **`celsius_global`** + +-- COMMAND ---------- + +-- ANSWER + +CREATE OR REPLACE VIEW celsius +AS (SELECT * + FROM weather_managed + WHERE UNIT = "C") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("celsius"), "Table named `celsius` does not exist" +-- MAGIC assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'celsius'").first()["isTemporary"] == False, "Table is temporary" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Now create a temporary view. + +-- COMMAND ---------- + +-- ANSWER + +CREATE OR REPLACE TEMP VIEW celsius_temp +AS (SELECT * + FROM weather_managed + WHERE UNIT = "C") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("celsius_temp"), "Table named `celsius_temp` does not exist" +-- MAGIC assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'celsius_temp'").first()["isTemporary"] == True, "Table is not temporary" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Now register a global temp view. + +-- COMMAND ---------- + +-- ANSWER + +CREATE OR REPLACE GLOBAL TEMP VIEW celsius_global +AS (SELECT * + FROM weather_managed + WHERE UNIT = "C") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC Run the cell below to check your work. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC assert spark.table("global_temp.celsius_global"), "Global temporary view named `celsius_global` does not exist" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Views will be displayed alongside tables when listing from the catalog. + +-- COMMAND ---------- + +SHOW TABLES + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note the following: +-- MAGIC - The view is associated with the current database. This view will be available to any user that can access this database and will persist between sessions. +-- MAGIC - The temp view is not associated with any database. The temp view is ephemeral and is only accessible in the current SparkSession. +-- MAGIC - The global temp view does not appear in our catalog. **Global temp views will always register to the **`global_temp`** database**. The **`global_temp`** database is ephemeral but tied to the lifetime of the cluster; however, it is only accessible by notebooks attached to the same cluster on which it was created. + +-- COMMAND ---------- + +SELECT * FROM global_temp.celsius_global + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC While no job was triggered when defining these views, a job is triggered _each time_ a query is executed against the view. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Clean Up +-- MAGIC Drop the database and all tables to clean up your workspace. + +-- COMMAND ---------- + +DROP DATABASE ${da.db_name} CASCADE + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Synopsis +-- MAGIC +-- MAGIC In this lab we: +-- MAGIC - Created and deleted databases +-- MAGIC - Explored behavior of managed and external tables +-- MAGIC - Learned about the scoping of views + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.1 - Querying Files Directly.sql b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.1 - Querying Files Directly.sql similarity index 57% rename from Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.1 - Querying Files Directly.sql rename to Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.1 - Querying Files Directly.sql index 16a2882..e5710a6 100644 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.2.1 - Querying Files Directly.sql +++ b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.1 - Querying Files Directly.sql @@ -8,40 +8,48 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Extracting Data Directly from Files -- MAGIC -- MAGIC In this notebook, you'll learn to extract data directly from files using Spark SQL on Databricks. -- MAGIC -- MAGIC A number of file formats support this option, but it is most useful for self-describing data formats (such as parquet and JSON). -- MAGIC --- MAGIC ##### Objectives +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: -- MAGIC - Use Spark SQL to directly query data files --- MAGIC - Layer views and CTEs to make referencing data files easier --- MAGIC - Discuss limitations and applications of this approach +-- MAGIC - Leverage **`text`** and **`binaryFile`** methods to review raw file contents -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC -- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. -- COMMAND ---------- --- MAGIC %run ../Includes/setup +-- MAGIC %run ../Includes/Classroom-Setup-4.1 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Data Overview -- MAGIC --- MAGIC In this example, we'll work with a sample of raw Kafka data written as JSON files. Each file contains all records consumed during a 5-second interval, stored with the full Kafka schema as a multiple-record JSON file. +-- MAGIC In this example, we'll work with a sample of raw Kafka data written as JSON files. +-- MAGIC +-- MAGIC Each file contains all records consumed during a 5-second interval, stored with the full Kafka schema as a multiple-record JSON file. -- MAGIC -- MAGIC | field | type | description | -- MAGIC | --- | --- | --- | --- MAGIC | key | BINARY | The `user_id` field is used as the key; this is a unique alphanumeric field that corresponds to session/cookie information | +-- MAGIC | key | BINARY | The **`user_id`** field is used as the key; this is a unique alphanumeric field that corresponds to session/cookie information | -- MAGIC | value | BINARY | This is the full data payload (to be discussed later), sent as JSON | --- MAGIC | topic | STRING | While the Kafka service hosts multiple topics, only those records from the `clickstream` topic are included here | +-- MAGIC | topic | STRING | While the Kafka service hosts multiple topics, only those records from the **`clickstream`** topic are included here | -- MAGIC | partition | INTEGER | Our current Kafka implementation uses only 2 partitions (0 and 1) | -- MAGIC | offset | LONG | This is a unique value, monotonically increasing for each partition | -- MAGIC | timestamp | LONG | This timestamp is recorded as milliseconds since epoch, and represents the time at which the producer appends a record to a partition | @@ -49,59 +57,81 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Note that our source directory contains many JSON files. -- COMMAND ---------- -- MAGIC %python --- MAGIC len(dbutils.fs.ls(f"{Paths.source}/events/events-kafka.json")) +-- MAGIC dataset_path = f"{DA.paths.datasets}/raw/events-kafka" +-- MAGIC print(dataset_path) +-- MAGIC +-- MAGIC files = dbutils.fs.ls(dataset_path) +-- MAGIC display(files) -- COMMAND ---------- -- MAGIC %md --- MAGIC Here, we'll be using relative file paths to data that's been written to the DBFS root. Most workflows will require users to access data from external cloud storage locations. In most companies, a workspace administrative will be responsible for configuring access to these storage locations. -- MAGIC --- MAGIC Instructions for configuring and accessing these locations can be found in the cloud-vendor specific courses titled "Cloud Architecture & Systems Integrations". +-- MAGIC +-- MAGIC Here, we'll be using relative file paths to data that's been written to the DBFS root. +-- MAGIC +-- MAGIC Most workflows will require users to access data from external cloud storage locations. +-- MAGIC +-- MAGIC In most companies, a workspace administrator will be responsible for configuring access to these storage locations. +-- MAGIC +-- MAGIC Instructions for configuring and accessing these locations can be found in the cloud-vendor specific self-paced courses titled "Cloud Architecture & Systems Integrations". -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Query a Single File -- MAGIC -- MAGIC To query the data contained in a single file, execute the query with the following pattern: -- MAGIC --- MAGIC ``` --- MAGIC SELECT * FROM file_format.`/path/to/file` --- MAGIC ``` +-- MAGIC SELECT * FROM file_format.`/path/to/file` +-- MAGIC +-- MAGIC Make special note of the use of back-ticks (not single quotes) around the path. -- COMMAND ---------- -SELECT * FROM json.`${c.source}/events/events-kafka.json/001.json` +SELECT * FROM json.`${da.paths.datasets}/raw/events-kafka/001.json` -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that our preview displays all 94 rows of our source file. +-- MAGIC +-- MAGIC +-- MAGIC Note that our preview displays all 321 rows of our source file. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Query a Directory of Files -- MAGIC --- MAGIC Assuming all of the files in a directory have the same format and schema, all files can be queried simultaneously by specifying the directory path rather than an individual file. +-- MAGIC Assuming all of the files in a directory have the same format and schema, all files can be queried simultaneously by specifying the directory path rather than an individual file. -- COMMAND ---------- -SELECT * FROM json.`${c.source}/events/events-kafka.json` +SELECT * FROM json.`${da.paths.datasets}/raw/events-kafka` -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC By default, this query will only show the first 1000 rows. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Create References to Files -- MAGIC This ability to directly query files and directories means that additional Spark logic can be chained to queries against files. -- MAGIC @@ -110,33 +140,49 @@ SELECT * FROM json.`${c.source}/events/events-kafka.json` -- COMMAND ---------- CREATE OR REPLACE TEMP VIEW events_temp_view -AS SELECT * FROM json.`${c.source}/events/events-kafka.json`; +AS SELECT * FROM json.`${da.paths.datasets}/raw/events-kafka/`; SELECT * FROM events_temp_view -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Extract Text Files as Raw Strings -- MAGIC --- MAGIC When working with text-based files (which include JSON, CSV, TSV, and TXT formats), you can use the `text` format to load each line of the file as a row with one string column named `value`. This can be useful when data sources are prone to corruption and custom text parsing functions will be used to extract value from text fields. +-- MAGIC When working with text-based files (which include JSON, CSV, TSV, and TXT formats), you can use the **`text`** format to load each line of the file as a row with one string column named **`value`**. This can be useful when data sources are prone to corruption and custom text parsing functions will be used to extract value from text fields. -- COMMAND ---------- -SELECT * FROM text.`${c.source}/events/events-kafka.json` +SELECT * FROM text.`${da.paths.datasets}/raw/events-kafka/` -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Extract the Raw Bytes and Metadata of a File -- MAGIC --- MAGIC Some workflows may require working with entire files, such as when dealing with images or unstructured data. Using `binaryFile` to query a directory will provide file metadata alongside the binary representation of the file contents. +-- MAGIC Some workflows may require working with entire files, such as when dealing with images or unstructured data. Using **`binaryFile`** to query a directory will provide file metadata alongside the binary representation of the file contents. +-- MAGIC +-- MAGIC Specifically, the fields created will indicate the **`path`**, **`modificationTime`**, **`length`**, and **`content`**. + +-- COMMAND ---------- + +SELECT * FROM binaryFile.`${da.paths.datasets}/raw/events-kafka/` + +-- COMMAND ---------- + +-- MAGIC %md -- MAGIC --- MAGIC Specifically, the fields created will indicate the `path`, `modificationTime`, `length`, and `content`. +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. -- COMMAND ---------- -SELECT * FROM binaryFile.`${c.source}/events/events-kafka.json` +-- MAGIC %python +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.2 - Providing Options for External Sources.sql b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.2 - Providing Options for External Sources.sql similarity index 68% rename from Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.2 - Providing Options for External Sources.sql rename to Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.2 - Providing Options for External Sources.sql index f6d44eb..cddc30a 100644 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/DE 2.2.2 - Providing Options for External Sources.sql +++ b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.2 - Providing Options for External Sources.sql @@ -8,12 +8,15 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Providing Options for External Sources -- MAGIC While directly querying files works well for self-describing formats, many data sources require additional configurations or schema declaration to properly ingest records. -- MAGIC -- MAGIC In this lesson, we will create tables using external data sources. While these tables will not yet be stored in the Delta Lake format (and therefore not be optimized for the Lakehouse), this technique helps to facilitate extracting data from diverse external systems. -- MAGIC --- MAGIC ##### Objectives +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: -- MAGIC - Use Spark SQL to configure options for extracting data from external sources -- MAGIC - Create tables against external data sources for various file formats -- MAGIC - Describe default behavior when querying tables defined against external sources @@ -21,17 +24,21 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC -- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. -- COMMAND ---------- --- MAGIC %run ../Includes/setup-external +-- MAGIC %run ../Includes/Classroom-Setup-4.2 -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## When Direct Queries Don't Work -- MAGIC -- MAGIC While views can be used to persist direct queries against files between sessions, this approach has limited utility. @@ -40,41 +47,46 @@ -- COMMAND ---------- -SELECT * FROM csv.`${c.source}/sales/sales.csv` +SELECT * FROM csv.`${da.paths.working_dir}/sales-csv` -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC We can see from the above that: -- MAGIC 1. The header row is being extracted as a table row -- MAGIC 1. All columns are being loaded as a single column --- MAGIC 1. The file is pipe-delimited (`|`) +-- MAGIC 1. The file is pipe-delimited (**`|`**) -- MAGIC 1. The final column appears to contain nested data that is being truncated -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Registering Tables on External Data with Read Options -- MAGIC -- MAGIC While Spark will extract some self-describing data sources efficiently using default settings, many formats will require declaration of schema or other options. -- MAGIC --- MAGIC While there are many [additional configurations](https://docs.databricks.com/spark/latest/spark-sql/language-manual/sql-ref-syntax-ddl-create-table-datasource.html) you can set while creating tables against external sources, the syntax below demonstrates the essentials required to extract data from most formats. +-- MAGIC While there are many additional configurations you can set while creating tables against external sources, the syntax below demonstrates the essentials required to extract data from most formats. -- MAGIC --- MAGIC ``` --- MAGIC CREATE TABLE table_identifier --- MAGIC (col_name1 col_type1, ...) --- MAGIC USING data_source --- MAGIC OPTIONS (key1 = val1, key2 = val2, ...) --- MAGIC LOCATION path --- MAGIC ``` +-- MAGIC +-- MAGIC CREATE TABLE table_identifier (col_name1 col_type1, ...)
+-- MAGIC USING data_source
+-- MAGIC OPTIONS (key1 = val1, key2 = val2, ...)
+-- MAGIC LOCATION = path
+-- MAGIC -- MAGIC --- MAGIC Note that options are passed with keys as unquoted text and values in quotes. Spark supports many [data sources](https://docs.databricks.com/data/data-sources/index.html) with custom options, and additional systems may have unofficial support through external [libraries](https://docs.databricks.com/libraries/index.html). +-- MAGIC Note that options are passed with keys as unquoted text and values in quotes. Spark supports many data sources with custom options, and additional systems may have unofficial support through external libraries. -- MAGIC --- MAGIC **NOTE**: Depending on your workspace settings, you may need adminstrator assistance to load libraries and configure the requisite security settings for some data sources. +-- MAGIC **NOTE**: Depending on your workspace settings, you may need administrator assistance to load libraries and configure the requisite security settings for some data sources. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC The cell below demonstrates using Spark SQL DDL to create a table against an external CSV source, specifying: -- MAGIC 1. The column names and types -- MAGIC 1. The file format @@ -84,8 +96,6 @@ SELECT * FROM csv.`${c.source}/sales/sales.csv` -- COMMAND ---------- -DROP TABLE IF EXISTS sales_csv; - CREATE TABLE sales_csv (order_id LONG, email STRING, transactions_timestamp LONG, total_item_quantity INTEGER, purchase_revenue_in_usd DOUBLE, unique_items INTEGER, items STRING) USING CSV @@ -93,11 +103,13 @@ OPTIONS ( header = "true", delimiter = "|" ) -LOCATION "${c.source}/sales/sales.csv" +LOCATION "${da.paths.working_dir}/sales-csv" -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Note that no data has moved during table declaration. Similar to when we directly queried our files and created a view, we are still just pointing to files stored in an external location. -- MAGIC -- MAGIC Run the following cell to confirm that data is now being loaded correctly. @@ -113,11 +125,13 @@ SELECT COUNT(*) FROM sales_csv -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC All the metadata and options passed during table declaration will be persisted to the metastore, ensuring that data in the location will always be read with these options. -- MAGIC -- MAGIC **NOTE**: When working with CSVs as a data source, it's important to ensure that column order does not change if additional data files will be added to the source directory. Because the data format does not have strong schema enforcement, Spark will load columns and apply column names and data types in the order specified during table declaration. -- MAGIC --- MAGIC Running `DESCRIBE EXTENDED` on a table will show all of the metadata associated with the table definition. +-- MAGIC Running **`DESCRIBE EXTENDED`** on a table will show all of the metadata associated with the table definition. -- COMMAND ---------- @@ -126,6 +140,8 @@ DESCRIBE EXTENDED sales_csv -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Limits of Tables with External Data Sources -- MAGIC -- MAGIC If you've taken other courses on Databricks or reviewed any of our company literature, you may have heard about Delta Lake and the Lakehouse. Note that whenever we're defining tables or queries against external data sources, we **cannot** expect the performance guarantees associated with Delta Lake and Lakehouse. @@ -137,11 +153,16 @@ DESCRIBE EXTENDED sales_csv -- COMMAND ---------- -- MAGIC %python --- MAGIC spark.table("sales_csv").write.mode("append").format("csv").save(f"{Paths.source}/sales/sales.csv") +-- MAGIC (spark.table("sales_csv") +-- MAGIC .write.mode("append") +-- MAGIC .format("csv") +-- MAGIC .save(f"{DA.paths.working_dir}/sales-csv")) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC If we look at the current count of records in our table, the number we see will not reflect these newly inserted rows. -- COMMAND ---------- @@ -151,9 +172,13 @@ SELECT COUNT(*) FROM sales_csv -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC At the time we previously queried this data source, Spark automatically cached the underlying data in local storage. This ensures that on subsequent queries, Spark will provide the optimal performance by just querying this local cache. -- MAGIC --- MAGIC Our external data source is not configured to tell Spark is should refresh this data. We **can** manually refresh the cache of our data by running the `REFRESH TABLE` command. +-- MAGIC Our external data source is not configured to tell Spark that it should refresh this data. +-- MAGIC +-- MAGIC We **can** manually refresh the cache of our data by running the **`REFRESH TABLE`** command. -- COMMAND ---------- @@ -162,7 +187,11 @@ REFRESH TABLE sales_csv -- COMMAND ---------- -- MAGIC %md --- MAGIC Note that refreshing our table will invalidate our cache, meaning that we'll need to rescan our original data source and pull all data back into memory. For very large datasets, this may take a significant amount of time. +-- MAGIC +-- MAGIC +-- MAGIC Note that refreshing our table will invalidate our cache, meaning that we'll need to rescan our original data source and pull all data back into memory. +-- MAGIC +-- MAGIC For very large datasets, this may take a significant amount of time. -- COMMAND ---------- @@ -171,37 +200,46 @@ SELECT COUNT(*) FROM sales_csv -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Extracting Data from SQL Databases -- MAGIC SQL databases are an extremely common data source, and Databricks has a standard JDBC driver for connecting with many flavors of SQL. -- MAGIC -- MAGIC The general syntax for creating these connections is: --- MAGIC ``` --- MAGIC CREATE TABLE --- MAGIC USING org.apache.spark.sql.jdbc --- MAGIC OPTIONS ( --- MAGIC url "jdbc:://:", --- MAGIC dbtable ".table", --- MAGIC user "", --- MAGIC password "" +-- MAGIC +-- MAGIC +-- MAGIC CREATE TABLE
+-- MAGIC USING JDBC
+-- MAGIC OPTIONS (
+-- MAGIC     url = "jdbc:{databaseServerType}://{jdbcHostname}:{jdbcPort}",
+-- MAGIC     dbtable = "{jdbcDatabase}.table",
+-- MAGIC     user = "{jdbcUsername}",
+-- MAGIC     password = "{jdbcPassword}"
-- MAGIC ) --- MAGIC ``` +-- MAGIC -- MAGIC --- MAGIC In the code sample below, we'll connect with SQLite (which uses a local file to store a database, and doesn't have users and passwords). +-- MAGIC In the code sample below, we'll connect with SQLite. +-- MAGIC +-- MAGIC **NOTE:** SQLite uses a local file to store a database, and doesn't require a port, username, or password. +-- MAGIC +-- MAGIC **WARNING**: The backend-configuration of the JDBC server assume you are running this notebook on a single-node cluster. If you are running on a cluster with multiple workers, the client running in the executors will not be able to connect to the driver. -- COMMAND ---------- DROP TABLE IF EXISTS users_jdbc; CREATE TABLE users_jdbc -USING org.apache.spark.sql.jdbc +USING JDBC OPTIONS ( - url "jdbc:sqlite:/${c.username}_ecommerce.db", - dbtable "users" + url = "jdbc:sqlite:/${da.username}_ecommerce.db", + dbtable = "users" ) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Now we can query this table as if it were defined locally. -- COMMAND ---------- @@ -211,6 +249,8 @@ SELECT * FROM users_jdbc -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Looking at the table metadata reveals that we have captured the schema information from the external system. Storage properties (which would include the username and password associated with the connection) are automatically redacted. -- COMMAND ---------- @@ -220,12 +260,14 @@ DESCRIBE EXTENDED users_jdbc -- COMMAND ---------- -- MAGIC %md --- MAGIC While the table is listed as `MANAGED`, listing the contents of the specified location confirms that no data is being persisted locally. +-- MAGIC +-- MAGIC +-- MAGIC While the table is listed as **`MANAGED`**, listing the contents of the specified location confirms that no data is being persisted locally. -- COMMAND ---------- -- MAGIC %python --- MAGIC jdbc_users_path = f"{Paths.database_location}/jdbc_users" +-- MAGIC jdbc_users_path = f"{DA.paths.user_db}/users_jdbc/" -- MAGIC print(jdbc_users_path) -- MAGIC -- MAGIC files = dbutils.fs.ls(jdbc_users_path) @@ -234,6 +276,8 @@ DESCRIBE EXTENDED users_jdbc -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Note that some SQL systems such as data warehouses will have custom drivers. Spark will interact with various external databases differently, but the two basic approaches can be summarized as either: -- MAGIC 1. Moving the entire source table(s) to Databricks and then executing logic on the currently active cluster -- MAGIC 1. Pushing down the query to the external SQL database and only transferring the results back to Databricks @@ -244,6 +288,18 @@ DESCRIBE EXTENDED users_jdbc -- COMMAND ---------- +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + -- MAGIC %md-sandbox -- MAGIC © 2022 Databricks, Inc. All rights reserved.
-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
diff --git a/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.3 - Creating Delta Tables.sql b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.3 - Creating Delta Tables.sql new file mode 100644 index 0000000..ff80a07 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.3 - Creating Delta Tables.sql @@ -0,0 +1,380 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Creating Delta Tables +-- MAGIC +-- MAGIC After extracting data from external data sources, load data into the Lakehouse to ensure that all of the benefits of the Databricks platform can be fully leveraged. +-- MAGIC +-- MAGIC While different organizations may have varying policies for how data is initially loaded into Databricks, we typically recommend that early tables represent a mostly raw version of the data, and that validation and enrichment occur in later stages. This pattern ensures that even if data doesn't match expectations with regards to data types or column names, no data will be dropped, meaning that programmatic or manual intervention can still salvage data in a partially corrupted or invalid state. +-- MAGIC +-- MAGIC This lesson will focus primarily on the pattern used to create most tables, **`CREATE TABLE _ AS SELECT`** (CTAS) statements. +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC - Use CTAS statements to create Delta Lake tables +-- MAGIC - Create new tables from existing views or tables +-- MAGIC - Enrich loaded data with additional metadata +-- MAGIC - Declare table schema with generated columns and descriptive comments +-- MAGIC - Set advanced options to control data location, quality enforcement, and partitioning + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Run Setup +-- MAGIC +-- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-4.3 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Create Table as Select (CTAS) +-- MAGIC +-- MAGIC **`CREATE TABLE AS SELECT`** statements create and populate Delta tables using data retrieved from an input query. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE sales AS +SELECT * FROM parquet.`${da.paths.datasets}/raw/sales-historical/`; + +DESCRIBE EXTENDED sales; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC CTAS statements automatically infer schema information from query results and do **not** support manual schema declaration. +-- MAGIC +-- MAGIC This means that CTAS statements are useful for external data ingestion from sources with well-defined schema, such as Parquet files and tables. +-- MAGIC +-- MAGIC CTAS statements also do not support specifying additional file options. +-- MAGIC +-- MAGIC We can see how this would present significant limitations when trying to ingest data from CSV files. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE sales_unparsed AS +SELECT * FROM csv.`${da.paths.datasets}/raw/sales-csv/`; + +SELECT * FROM sales_unparsed; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC To correctly ingest this data to a Delta Lake table, we'll need to use a reference to the files that allows us to specify options. +-- MAGIC +-- MAGIC In the previous lesson, we showed doing this by registering an external table. Here, we'll slightly evolve this syntax to specify the options to a temporary view, and then use this temp view as the source for a CTAS statement to successfully register the Delta table. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW sales_tmp_vw + (order_id LONG, email STRING, transactions_timestamp LONG, total_item_quantity INTEGER, purchase_revenue_in_usd DOUBLE, unique_items INTEGER, items STRING) +USING CSV +OPTIONS ( + path = "${da.paths.datasets}/raw/sales-csv", + header = "true", + delimiter = "|" +); + +CREATE TABLE sales_delta AS + SELECT * FROM sales_tmp_vw; + +SELECT * FROM sales_delta + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Filtering and Renaming Columns from Existing Tables +-- MAGIC +-- MAGIC Simple transformations like changing column names or omitting columns from target tables can be easily accomplished during table creation. +-- MAGIC +-- MAGIC The following statement creates a new table containing a subset of columns from the **`sales`** table. +-- MAGIC +-- MAGIC Here, we'll presume that we're intentionally leaving out information that potentially identifies the user or that provides itemized purchase details. We'll also rename our fields with the assumption that a downstream system has different naming conventions than our source data. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE purchases AS +SELECT order_id AS id, transaction_timestamp, purchase_revenue_in_usd AS price +FROM sales; + +SELECT * FROM purchases + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that we could have accomplished this same goal with a view, as shown below. + +-- COMMAND ---------- + +CREATE OR REPLACE VIEW purchases_vw AS +SELECT order_id AS id, transaction_timestamp, purchase_revenue_in_usd AS price +FROM sales; + +SELECT * FROM purchases_vw + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Declare Schema with Generated Columns +-- MAGIC +-- MAGIC As noted previously, CTAS statements do not support schema declaration. We note above that the timestamp column appears to be some variant of a Unix timestamp, which may not be the most useful for our analysts to derive insights. This is a situation where generated columns would be beneficial. +-- MAGIC +-- MAGIC Generated columns are a special type of column whose values are automatically generated based on a user-specified function over other columns in the Delta table (introduced in DBR 8.3). +-- MAGIC +-- MAGIC The code below demonstrates creating a new table while: +-- MAGIC 1. Specifying column names and types +-- MAGIC 1. Adding a generated column to calculate the date +-- MAGIC 1. Providing a descriptive column comment for the generated column + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE purchase_dates ( + id STRING, + transaction_timestamp STRING, + price STRING, + date DATE GENERATED ALWAYS AS ( + cast(cast(transaction_timestamp/1e6 AS TIMESTAMP) AS DATE)) + COMMENT "generated based on `transactions_timestamp` column") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Because **`date`** is a generated column, if we write to **`purchase_dates`** without providing values for the **`date`** column, Delta Lake automatically computes them. +-- MAGIC +-- MAGIC **NOTE**: The cell below configures a setting to allow for generating columns when using a Delta Lake **`MERGE`** statement. We'll see more on this syntax later in the course. + +-- COMMAND ---------- + +SET spark.databricks.delta.schema.autoMerge.enabled=true; + +MERGE INTO purchase_dates a +USING purchases b +ON a.id = b.id +WHEN NOT MATCHED THEN + INSERT * + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We can see below that all dates were computed correctly as data was inserted, although neither our source data or insert query specified the values in this field. +-- MAGIC +-- MAGIC As with any Delta Lake source, the query automatically reads the most recent snapshot of the table for any query; you never need to run **`REFRESH TABLE`**. + +-- COMMAND ---------- + +SELECT * FROM purchase_dates + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC It's important to note that if a field that would otherwise be generated is included in an insert to a table, this insert will fail if the value provided does not exactly match the value that would be derived by the logic used to define the generated column. +-- MAGIC +-- MAGIC We can see this error by uncommenting and running the cell below: + +-- COMMAND ---------- + +-- INSERT INTO purchase_dates VALUES +-- (1, 600000000, 42.0, "2020-06-18") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Add a Table Constraint +-- MAGIC +-- MAGIC The error message above refers to a **`CHECK constraint`**. Generated columns are a special implementation of check constraints. +-- MAGIC +-- MAGIC Because Delta Lake enforces schema on write, Databricks can support standard SQL constraint management clauses to ensure the quality and integrity of data added to a table. +-- MAGIC +-- MAGIC Databricks currently support two types of constraints: +-- MAGIC * **`NOT NULL`** constraints +-- MAGIC * **`CHECK`** constraints +-- MAGIC +-- MAGIC In both cases, you must ensure that no data violating the constraint is already in the table prior to defining the constraint. Once a constraint has been added to a table, data violating the constraint will result in write failure. +-- MAGIC +-- MAGIC Below, we'll add a **`CHECK`** constraint to the **`date`** column of our table. Note that **`CHECK`** constraints look like standard **`WHERE`** clauses you might use to filter a dataset. + +-- COMMAND ---------- + +ALTER TABLE purchase_dates ADD CONSTRAINT valid_date CHECK (date > '2020-01-01'); + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Table constraints are shown in the **`TBLPROPERTIES`** field. + +-- COMMAND ---------- + +DESCRIBE EXTENDED purchase_dates + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Enrich Tables with Additional Options and Metadata +-- MAGIC +-- MAGIC So far we've only scratched the surface as far as the options for enriching Delta Lake tables. +-- MAGIC +-- MAGIC Below, we show evolving a CTAS statement to include a number of additional configurations and metadata. +-- MAGIC +-- MAGIC Our **`SELECT`** clause leverages two built-in Spark SQL commands useful for file ingestion: +-- MAGIC * **`current_timestamp()`** records the timestamp when the logic is executed +-- MAGIC * **`input_file_name()`** records the source data file for each record in the table +-- MAGIC +-- MAGIC We also include logic to create a new date column derived from timestamp data in the source. +-- MAGIC +-- MAGIC The **`CREATE TABLE`** clause contains several options: +-- MAGIC * A **`COMMENT`** is added to allow for easier discovery of table contents +-- MAGIC * A **`LOCATION`** is specified, which will result in an external (rather than managed) table +-- MAGIC * The table is **`PARTITIONED BY`** a date column; this means that the data from each data will exist within its own directory in the target storage location +-- MAGIC +-- MAGIC **NOTE**: Partitioning is shown here primarily to demonstrate syntax and impact. Most Delta Lake tables (especially small-to-medium sized data) will not benefit from partitioning. Because partitioning physically separates data files, this approach can result in a small files problem and prevent file compaction and efficient data skipping. The benefits observed in Hive or HDFS do not translate to Delta Lake, and you should consult with an experienced Delta Lake architect before partitioning tables. +-- MAGIC +-- MAGIC **As a best practice, you should default to non-partitioned tables for most use cases when working with Delta Lake.** + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE users_pii +COMMENT "Contains PII" +LOCATION "${da.paths.working_dir}/tmp/users_pii" +PARTITIONED BY (first_touch_date) +AS + SELECT *, + cast(cast(user_first_touch_timestamp/1e6 AS TIMESTAMP) AS DATE) first_touch_date, + current_timestamp() updated, + input_file_name() source_file + FROM parquet.`${da.paths.datasets}/raw/users-historical/`; + +SELECT * FROM users_pii; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC The metadata fields added to the table provide useful information to understand when records were inserted and from where. This can be especially helpful if troubleshooting problems in the source data becomes necessary. +-- MAGIC +-- MAGIC All of the comments and properties for a given table can be reviewed using **`DESCRIBE TABLE EXTENDED`**. +-- MAGIC +-- MAGIC **NOTE**: Delta Lake automatically adds several table properties on table creation. + +-- COMMAND ---------- + +DESCRIBE EXTENDED users_pii + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Listing the location used for the table reveals that the unique values in the partition column **`first_touch_date`** are used to create data directories. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC files = dbutils.fs.ls(f"{DA.paths.working_dir}/tmp/users_pii") +-- MAGIC display(files) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Cloning Delta Lake Tables +-- MAGIC Delta Lake has two options for efficiently copying Delta Lake tables. +-- MAGIC +-- MAGIC **`DEEP CLONE`** fully copies data and metadata from a source table to a target. This copy occurs incrementally, so executing this command again can sync changes from the source to the target location. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE purchases_clone +DEEP CLONE purchases + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Because all the data files must be copied over, this can take quite a while for large datasets. +-- MAGIC +-- MAGIC If you wish to create a copy of a table quickly to test out applying changes without the risk of modifying the current table, **`SHALLOW CLONE`** can be a good option. Shallow clones just copy the Delta transaction logs, meaning that the data doesn't move. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE purchases_shallow_clone +SHALLOW CLONE purchases + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC In either case, data modifications applied to the cloned version of the table will be tracked and stored separately from the source. Cloning is a great way to set up tables for testing SQL code while still in development. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Summary +-- MAGIC +-- MAGIC In this notebook, we focused primarily on DDL and syntax for creating Delta Lake tables. In the next notebook, we'll explore options for writing updates to tables. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.4 - Writing to Tables.sql b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.4 - Writing to Tables.sql new file mode 100644 index 0000000..80a61dc --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.4 - Writing to Tables.sql @@ -0,0 +1,263 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Writing to Delta Tables +-- MAGIC Delta Lake tables provide ACID compliant updates to tables backed by data files in cloud object storage. +-- MAGIC +-- MAGIC In this notebook, we'll explore SQL syntax to process updates with Delta Lake. While many operations are standard SQL, slight variations exist to accommodate Spark and Delta Lake execution. +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC - Overwrite data tables using **`INSERT OVERWRITE`** +-- MAGIC - Append to a table using **`INSERT INTO`** +-- MAGIC - Append, update, and delete from a table using **`MERGE INTO`** +-- MAGIC - Ingest data incrementally into tables using **`COPY INTO`** + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Run Setup +-- MAGIC +-- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-4.4 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Complete Overwrites +-- MAGIC +-- MAGIC We can use overwrites to atomically replace all of the data in a table. There are multiple benefits to overwriting tables instead of deleting and recreating tables: +-- MAGIC - Overwriting a table is much faster because it doesn’t need to list the directory recursively or delete any files. +-- MAGIC - The old version of the table still exists; can easily retrieve the old data using Time Travel. +-- MAGIC - It’s an atomic operation. Concurrent queries can still read the table while you are deleting the table. +-- MAGIC - Due to ACID transaction guarantees, if overwriting the table fails, the table will be in its previous state. +-- MAGIC +-- MAGIC Spark SQL provides two easy methods to accomplish complete overwrites. +-- MAGIC +-- MAGIC Some students may have noticed previous lesson on CTAS statements actually used CRAS statements (to avoid potential errors if a cell was run multiple times). +-- MAGIC +-- MAGIC **`CREATE OR REPLACE TABLE`** (CRAS) statements fully replace the contents of a table each time they execute. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE events AS +SELECT * FROM parquet.`${da.paths.datasets}/raw/events-historical` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Reviewing the table history shows a previous version of this table was replaced. + +-- COMMAND ---------- + +DESCRIBE HISTORY events + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC **`INSERT OVERWRITE`** provides a nearly identical outcome as above: data in the target table will be replaced by data from the query. +-- MAGIC +-- MAGIC **`INSERT OVERWRITE`**: +-- MAGIC +-- MAGIC - Can only overwrite an existing table, not create a new one like our CRAS statement +-- MAGIC - Can overwrite only with new records that match the current table schema -- and thus can be a "safer" technique for overwriting an existing table without disrupting downstream consumers +-- MAGIC - Can overwrite individual partitions + +-- COMMAND ---------- + +INSERT OVERWRITE sales +SELECT * FROM parquet.`${da.paths.datasets}/raw/sales-historical/` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that different metrics are displayed than a CRAS statement; the table history also records the operation differently. + +-- COMMAND ---------- + +DESCRIBE HISTORY sales + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC A primary difference here has to do with how Delta Lake enforces schema on write. +-- MAGIC +-- MAGIC Whereas a CRAS statement will allow us to completely redefine the contents of our target table, **`INSERT OVERWRITE`** will fail if we try to change our schema (unless we provide optional settings). +-- MAGIC +-- MAGIC Uncomment and run the cell below to generated an expected error message. + +-- COMMAND ---------- + +-- INSERT OVERWRITE sales +-- SELECT *, current_timestamp() FROM parquet.`${da.paths.datasets}/raw/sales-historical` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Append Rows +-- MAGIC +-- MAGIC We can use **`INSERT INTO`** to atomically append new rows to an existing Delta table. This allows for incremental updates to existing tables, which is much more efficient than overwriting each time. +-- MAGIC +-- MAGIC Append new sale records to the **`sales`** table using **`INSERT INTO`**. + +-- COMMAND ---------- + +INSERT INTO sales +SELECT * FROM parquet.`${da.paths.datasets}/raw/sales-30m` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Note that **`INSERT INTO`** does not have any built-in guarantees to prevent inserting the same records multiple times. Re-executing the above cell would write the same records to the target table, resulting in duplicate records. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Merge Updates +-- MAGIC +-- MAGIC You can upsert data from a source table, view, or DataFrame into a target Delta table using the **`MERGE`** SQL operation. Delta Lake supports inserts, updates and deletes in **`MERGE`**, and supports extended syntax beyond the SQL standards to facilitate advanced use cases. +-- MAGIC +-- MAGIC +-- MAGIC MERGE INTO target a
+-- MAGIC USING source b
+-- MAGIC ON {merge_condition}
+-- MAGIC WHEN MATCHED THEN {matched_action}
+-- MAGIC WHEN NOT MATCHED THEN {not_matched_action}
+-- MAGIC +-- MAGIC +-- MAGIC We will use the **`MERGE`** operation to update historic users data with updated emails and new users. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW users_update AS +SELECT *, current_timestamp() AS updated +FROM parquet.`${da.paths.datasets}/raw/users-30m` + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC The main benefits of **`MERGE`**: +-- MAGIC * updates, inserts, and deletes are completed as a single transaction +-- MAGIC * multiple conditionals can be added in addition to matching fields +-- MAGIC * provides extensive options for implementing custom logic +-- MAGIC +-- MAGIC Below, we'll only update records if the current row has a **`NULL`** email and the new row does not. +-- MAGIC +-- MAGIC All unmatched records from the new batch will be inserted. + +-- COMMAND ---------- + +MERGE INTO users a +USING users_update b +ON a.user_id = b.user_id +WHEN MATCHED AND a.email IS NULL AND b.email IS NOT NULL THEN + UPDATE SET email = b.email, updated = b.updated +WHEN NOT MATCHED THEN INSERT * + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that we explicitly specify the behavior of this function for both the **`MATCHED`** and **`NOT MATCHED`** conditions; the example demonstrated here is just an example of logic that can be applied, rather than indicative of all **`MERGE`** behavior. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Insert-Only Merge for Deduplication +-- MAGIC +-- MAGIC A common ETL use case is to collect logs or other every-appending datasets into a Delta table through a series of append operations. +-- MAGIC +-- MAGIC Many source systems can generate duplicate records. With merge, you can avoid inserting the duplicate records by performing an insert-only merge. +-- MAGIC +-- MAGIC This optimized command uses the same **`MERGE`** syntax but only provided a **`WHEN NOT MATCHED`** clause. +-- MAGIC +-- MAGIC Below, we use this to confirm that records with the same **`user_id`** and **`event_timestamp`** aren't already in the **`events`** table. + +-- COMMAND ---------- + +MERGE INTO events a +USING events_update b +ON a.user_id = b.user_id AND a.event_timestamp = b.event_timestamp +WHEN NOT MATCHED AND b.traffic_source = 'email' THEN + INSERT * + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Load Incrementally +-- MAGIC +-- MAGIC **`COPY INTO`** provides SQL engineers an idempotent option to incrementally ingest data from external systems. +-- MAGIC +-- MAGIC Note that this operation does have some expectations: +-- MAGIC - Data schema should be consistent +-- MAGIC - Duplicate records should try to be excluded or handled downstream +-- MAGIC +-- MAGIC This operation is potentially much cheaper than full table scans for data that grows predictably. +-- MAGIC +-- MAGIC While here we'll show simple execution on a static directory, the real value is in multiple executions over time picking up new files in the source automatically. + +-- COMMAND ---------- + +COPY INTO sales +FROM "${da.paths.datasets}/raw/sales-30m" +FILEFORMAT = PARQUET + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.2.5L - Extract and Load Data Lab.sql b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.5L - Extract and Load Data Lab.sql similarity index 66% rename from Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.2.5L - Extract and Load Data Lab.sql rename to Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.5L - Extract and Load Data Lab.sql index 3479d10..157c2e8 100644 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.2.5L - Extract and Load Data Lab.sql +++ b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.5L - Extract and Load Data Lab.sql @@ -8,11 +8,14 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Extract and Load Data Lab -- MAGIC -- MAGIC In this lab, you will extract and load raw data from JSON files into a Delta table. -- MAGIC --- MAGIC ##### Objectives +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lab, you should be able to: -- MAGIC - Create an external table to extract data from JSON files -- MAGIC - Create an empty Delta table with a provided schema -- MAGIC - Insert records from an existing table into a Delta table @@ -21,84 +24,111 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC -- MAGIC Run the following cell to configure variables and datasets for this lesson. -- COMMAND ---------- --- MAGIC %run ../../Includes/setup +-- MAGIC %run ../Includes/Classroom-Setup-4.5L -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Overview of the Data -- MAGIC --- MAGIC We will work with a sample of raw Kafka data written as JSON files. Each file contains all records consumed during a 5-second interval, stored with the full Kafka schema as a multiple-record JSON file. The schema for the table: +-- MAGIC We will work with a sample of raw Kafka data written as JSON files. +-- MAGIC +-- MAGIC Each file contains all records consumed during a 5-second interval, stored with the full Kafka schema as a multiple-record JSON file. +-- MAGIC +-- MAGIC The schema for the table: -- MAGIC -- MAGIC | field | type | description | -- MAGIC | ------ | ---- | ----------- | --- MAGIC | key | BINARY | The `user_id` field is used as the key; this is a unique alphanumeric field that corresponds to session/cookie information | +-- MAGIC | key | BINARY | The **`user_id`** field is used as the key; this is a unique alphanumeric field that corresponds to session/cookie information | -- MAGIC | offset | LONG | This is a unique value, monotonically increasing for each partition | -- MAGIC | partition | INTEGER | Our current Kafka implementation uses only 2 partitions (0 and 1) | -- MAGIC | timestamp | LONG | This timestamp is recorded as milliseconds since epoch, and represents the time at which the producer appends a record to a partition | --- MAGIC | topic | STRING | While the Kafka service hosts multiple topics, only those records from the `clickstream` topic are included here | +-- MAGIC | topic | STRING | While the Kafka service hosts multiple topics, only those records from the **`clickstream`** topic are included here | -- MAGIC | value | BINARY | This is the full data payload (to be discussed later), sent as JSON | -- COMMAND ---------- --- MAGIC %md +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Extract Raw Events From JSON Files -- MAGIC To load this data into Delta properly, we first need to extract the JSON data using the correct schema. -- MAGIC --- MAGIC Create an external table against JSON files located at the filepath provided below. Name this table `events_json` and declare the schema above. +-- MAGIC Create an external table against JSON files located at the filepath provided below. Name this table **`events_json`** and declare the schema above. -- COMMAND ---------- -- ANSWER CREATE TABLE IF NOT EXISTS events_json -(key BINARY, offset BIGINT, partition INT, timestamp BIGINT, topic STRING, value BINARY) -USING JSON OPTIONS (path = "${c.source}/events/events-kafka.json") + (key BINARY, offset BIGINT, partition INT, timestamp BIGINT, topic STRING, value BINARY) +USING JSON +OPTIONS (path = "${da.paths.datasets}/raw/events-kafka") -- COMMAND ---------- -- MAGIC %md --- MAGIC **NOTE**: We'll use Python to run checks occasionally throughout the lab. The following cell will return as error with a message on what needs to change if you have not followed instructions. No output from cell execution means that you have completed this step. +-- MAGIC +-- MAGIC +-- MAGIC **NOTE**: We'll use Python to run checks occasionally throughout the lab. The following cell will return an error with a message on what needs to change if you have not followed instructions. No output from cell execution means that you have completed this step. -- COMMAND ---------- -- MAGIC %python -- MAGIC assert spark.table("events_json"), "Table named `events_json` does not exist" -- MAGIC assert spark.table("events_json").columns == ['key', 'offset', 'partition', 'timestamp', 'topic', 'value'], "Please name the columns in the order provided above" --- MAGIC assert spark.table("events_json").dtypes == [('key', 'binary'), ('offset', 'int'), ('partition', 'bigint'), ('timestamp', 'bigint'), ('topic', 'string'), ('value', 'binary')], "Please make sure the column types are identical to those provided above" --- MAGIC assert spark.table("events_json").count() == 45105, "The table should have 45105 records" +-- MAGIC assert spark.table("events_json").dtypes == [('key', 'binary'), ('offset', 'bigint'), ('partition', 'int'), ('timestamp', 'bigint'), ('topic', 'string'), ('value', 'binary')], "Please make sure the column types are identical to those provided above" +-- MAGIC +-- MAGIC total = spark.table("events_json").count() +-- MAGIC assert total == 2252, f"Expected 2252 records, found {total}" -- COMMAND ---------- --- MAGIC %md ## Insert Raw Events Into Delta Table --- MAGIC Create a managed Delta table named `events_raw` using the same schema - we'll load the data extracted above into this empty table. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Insert Raw Events Into Delta Table +-- MAGIC Create an empty managed Delta table named **`events_raw`** using the same schema. -- COMMAND ---------- -- ANSWER CREATE OR REPLACE TABLE events_raw -(key BINARY, offset BIGINT, partition INT, timestamp BIGINT, topic STRING, value BINARY); + (key BINARY, offset BIGINT, partition INT, timestamp BIGINT, topic STRING, value BINARY); -- COMMAND ---------- --- MAGIC %md Run the cell below to confirm the table was created correctly. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Run the cell below to confirm the table was created correctly. -- COMMAND ---------- -- MAGIC %python --- MAGIC assert spark.table("events_raw"), "Table named `events_json` does not exist" +-- MAGIC assert spark.table("events_raw"), "Table named `events_raw` does not exist" -- MAGIC assert spark.table("events_raw").columns == ['key', 'offset', 'partition', 'timestamp', 'topic', 'value'], "Please name the columns in the order provided above" -- MAGIC assert spark.table("events_raw").dtypes == [('key', 'binary'), ('offset', 'bigint'), ('partition', 'int'), ('timestamp', 'bigint'), ('topic', 'string'), ('value', 'binary')], "Please make sure the column types are identical to those provided above" -- MAGIC assert spark.table("events_raw").count() == 0, "The table should have 0 records" -- COMMAND ---------- --- MAGIC %md Once the extracted data and Delta table are ready, insert the JSON records from the `events_json` table into the new `events_raw` Delta table. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Once the extracted data and Delta table are ready, insert the JSON records from the **`events_json`** table into the new **`events_raw`** Delta table. -- COMMAND ---------- @@ -109,6 +139,8 @@ SELECT * FROM events_json -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC Manually review the table contents to ensure data was written as expected. -- COMMAND ---------- @@ -119,29 +151,39 @@ SELECT * FROM events_raw -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to confirm the data has been loaded correctly. -- COMMAND ---------- -- MAGIC %python --- MAGIC assert spark.table("events_raw").count() == 45105, "The table should have 45105 records" --- MAGIC assert set(row['timestamp'] for row in spark.table("events_raw").select("timestamp").limit(5).collect()) == {1593879300053, 1593879300372, 1593879300607, 1593879300739, 1593879300821}, "Make sure you have not modified the data provided" +-- MAGIC assert spark.table("events_raw").count() == 2252, "The table should have 2252 records" +-- MAGIC assert set(row['timestamp'] for row in spark.table("events_raw").select("timestamp").limit(5).collect()) == {1593880885085, 1593880892303, 1593880889174, 1593880886106, 1593880889725}, "Make sure you have not modified the data provided" -- COMMAND ---------- --- MAGIC %md ## Create Delta Table from a Query +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Create Delta Table from a Query -- MAGIC In addition to new events data, let's also load a small lookup table that provides product details that we'll use later in the course. --- MAGIC Use a CTAS statement to create a managed Delta table named `item_lookup` that extracts data from the parquet directory provided below. +-- MAGIC Use a CTAS statement to create a managed Delta table named **`item_lookup`** that extracts data from the parquet directory provided below. -- COMMAND ---------- -- ANSWER -CREATE OR REPLACE TABLE item_lookup AS -SELECT * FROM parquet.`${c.source}/products/products.parquet` +CREATE OR REPLACE TABLE item_lookup +AS SELECT * FROM parquet.`${da.paths.datasets}/raw/item-lookup` -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC Run the cell below to confirm the lookup table has been loaded correctly. -- COMMAND ---------- @@ -152,6 +194,18 @@ SELECT * FROM parquet.`${c.source}/products/products.parquet` -- COMMAND ---------- +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + -- MAGIC %md-sandbox -- MAGIC © 2022 Databricks, Inc. All rights reserved.
-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
diff --git a/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.6 - Cleaning Data.sql b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.6 - Cleaning Data.sql new file mode 100644 index 0000000..56c1bb3 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.6 - Cleaning Data.sql @@ -0,0 +1,326 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Cleaning Data +-- MAGIC +-- MAGIC Most transformations completed with Spark SQL will be familiar to SQL-savvy developers. +-- MAGIC +-- MAGIC As we inspect and clean our data, we'll need to construct various column expressions and queries to express transformations to apply on our dataset. +-- MAGIC +-- MAGIC Column expressions are constructed from existing columns, operators, and built-in Spark SQL functions. They can be used in **`SELECT`** statements to express transformations that create new columns from datasets. +-- MAGIC +-- MAGIC Along with **`SELECT`**, many additional query commands can be used to express transformations in Spark SQL, including **`WHERE`**, **`DISTINCT`**, **`ORDER BY`**, **`GROUP BY`**, etc. +-- MAGIC +-- MAGIC In this notebook, we'll review a few concepts that might differ from other systems you're used to, as well as calling out a few useful functions for common operations. +-- MAGIC +-- MAGIC We'll pay special attention to behaviors around **`NULL`** values, as well as formatting strings and datetime fields. +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC - Summarize datasets and describe null behaviors +-- MAGIC - Retrieve and removing duplicates +-- MAGIC - Validate datasets for expected counts, missing values, and duplicate records +-- MAGIC - Apply common transformations to clean and transform data + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Run Setup +-- MAGIC +-- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-4.6 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC We'll work with new users records in **`users_dirty`** table for this lesson. + +-- COMMAND ---------- + +SELECT * FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Inspect Data +-- MAGIC +-- MAGIC Let's start by counting values in each field of our data. + +-- COMMAND ---------- + +SELECT count(user_id), count(user_first_touch_timestamp), count(email), count(updated), count(*) +FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that **`count(col)`** skips **`NULL`** values when counting specific columns or expressions. +-- MAGIC +-- MAGIC However, **`count(*)`** is a special case that counts the total number of rows (including rows that are only **`NULL`** values). +-- MAGIC +-- MAGIC To count null values, use the **`count_if`** function or **`WHERE`** clause to provide a condition that filters for records where the value **`IS NULL`**. + +-- COMMAND ---------- + +SELECT + count_if(user_id IS NULL) AS missing_user_ids, + count_if(user_first_touch_timestamp IS NULL) AS missing_timestamps, + count_if(email IS NULL) AS missing_emails, + count_if(updated IS NULL) AS missing_updates +FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Clearly there are at least a handful of null values in all of our fields. Let's try to discover what is causing this. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Distinct Records +-- MAGIC +-- MAGIC Start by looking for distinct rows. + +-- COMMAND ---------- + +SELECT count(DISTINCT(*)) +FROM users_dirty + +-- COMMAND ---------- + +SELECT count(DISTINCT(user_id)) +FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Because **`user_id`** is generated alongside the **`user_first_touch_timestamp`**, these fields should always be in parity for counts. + +-- COMMAND ---------- + +SELECT count(DISTINCT(user_first_touch_timestamp)) +FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Here we note that while there are some duplicate records relative to our total row count, we have a much higher number of distinct values. +-- MAGIC +-- MAGIC Let's go ahead and combine our distinct counts with columnar counts to see these values side-by-side. + +-- COMMAND ---------- + +SELECT + count(user_id) AS total_ids, + count(DISTINCT user_id) AS unique_ids, + count(email) AS total_emails, + count(DISTINCT email) AS unique_emails, + count(updated) AS total_updates, + count(DISTINCT(updated)) AS unique_updates, + count(*) AS total_rows, + count(DISTINCT(*)) AS unique_non_null_rows +FROM users_dirty + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Based on the above summary, we know: +-- MAGIC * All of our emails are unique +-- MAGIC * Our emails contain the largest number of null values +-- MAGIC * The **`updated`** column contains only 1 distinct value, but most are non-null + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Deduplicate Rows +-- MAGIC Based on the above behavior, what do you expect will happen if we use **`DISTINCT *`** to try to remove duplicate records? + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW users_deduped AS + SELECT DISTINCT(*) FROM users_dirty; + +SELECT * FROM users_deduped + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note in the preview above that there appears to be null values, even though our **`COUNT(DISTINCT(*))`** ignored these nulls. +-- MAGIC +-- MAGIC How many rows do you expect passed through this **`DISTINCT`** command? + +-- COMMAND ---------- + +SELECT COUNT(*) FROM users_deduped + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that we now have a completely new number. +-- MAGIC +-- MAGIC Spark skips null values while counting values in a column or counting distinct values for a field, but does not omit rows with nulls from a **`DISTINCT`** query. +-- MAGIC +-- MAGIC Indeed, the reason we're seeing a new number that is 1 higher than previous counts is because we have 3 rows that are all nulls (here included as a single distinct row). + +-- COMMAND ---------- + +SELECT * FROM users_dirty +WHERE + user_id IS NULL AND + user_first_touch_timestamp IS NULL AND + email IS NULL AND + updated IS NULL + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Deduplicate Based on Specific Columns +-- MAGIC +-- MAGIC Recall that **`user_id`** and **`user_first_touch_timestamp`** should form unique tuples, as they are both generated when a given user is first encountered. +-- MAGIC +-- MAGIC We can see that we have some null values in each of these fields; exclude nulls counting the distinct number of pairs for these fields will get us the correct count for distinct values in our table. + +-- COMMAND ---------- + +SELECT COUNT(DISTINCT(user_id, user_first_touch_timestamp)) +FROM users_dirty +WHERE user_id IS NOT NULL + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Here, we'll use these distinct pairs to remove unwanted rows from our data. +-- MAGIC +-- MAGIC The code below uses **`GROUP BY`** to remove duplicate records based on **`user_id`** and **`user_first_touch_timestamp`**. +-- MAGIC +-- MAGIC The **`max()`** aggregate function is used on the **`email`** column as a hack to capture non-null emails when multiple records are present; in this batch, all **`updated`** values were equivalent, but we need to use an aggregate function to keep this value in the result of our group by. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW deduped_users AS +SELECT user_id, user_first_touch_timestamp, max(email) AS email, max(updated) AS updated +FROM users_dirty +WHERE user_id IS NOT NULL +GROUP BY user_id, user_first_touch_timestamp; + +SELECT count(*) FROM deduped_users + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Validate Datasets +-- MAGIC We've visually confirmed that our counts are as expected, based our manual review. +-- MAGIC +-- MAGIC Below, we programmatically do some validation using simple filters and **`WHERE`** clauses. +-- MAGIC +-- MAGIC Validate that the **`user_id`** for each row is unique. + +-- COMMAND ---------- + +SELECT max(row_count) <= 1 no_duplicate_ids FROM ( + SELECT user_id, count(*) AS row_count + FROM deduped_users + GROUP BY user_id) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Confirm that each email is associated with at most one **`user_id`**. + +-- COMMAND ---------- + +SELECT max(user_id_count) <= 1 at_most_one_id FROM ( + SELECT email, count(user_id) AS user_id_count + FROM deduped_users + WHERE email IS NOT NULL + GROUP BY email) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Date Format and Regex +-- MAGIC Now that we've removed null fields and eliminated duplicates, we may wish to extract further value out of the data. +-- MAGIC +-- MAGIC The code below: +-- MAGIC - Correctly scales and casts the **`user_first_touch_timestamp`** to a valid timestamp +-- MAGIC - Extracts the calendar data and clock time for this timestamp in human readable format +-- MAGIC - Uses **`regexp_extract`** to extract the domains from the email column using regex + +-- COMMAND ---------- + +SELECT *, + date_format(first_touch, "MMM d, yyyy") AS first_touch_date, + date_format(first_touch, "HH:mm:ss") AS first_touch_time, + regexp_extract(email, "(?<=@).+", 0) AS email_domain +FROM ( + SELECT *, + CAST(user_first_touch_timestamp / 1e6 AS timestamp) AS first_touch + FROM deduped_users +) + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.7 - Advanced SQL Transformations.sql b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.7 - Advanced SQL Transformations.sql new file mode 100644 index 0000000..e689ca3 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.7 - Advanced SQL Transformations.sql @@ -0,0 +1,426 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # Advanced SQL Transformations +-- MAGIC +-- MAGIC Querying tabular data stored in the data lakehouse with Spark SQL is easy, efficient, and fast. +-- MAGIC +-- MAGIC This gets more complicated as the data structure becomes less regular, when many tables need to be used in a single query, or when the shape of data needs to be changed dramatically. This notebook introduces a number of functions present in Spark SQL to help engineers complete even the most complicated transformations. +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC - Use **`.`** and **`:`** syntax to query nested data +-- MAGIC - Work with JSON +-- MAGIC - Flatten and unpacking arrays and structs +-- MAGIC - Combine datasets using joins and set operators +-- MAGIC - Reshape data using pivot tables +-- MAGIC - Use higher order functions for working with arrays + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Run Setup +-- MAGIC +-- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-4.7 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Interacting with JSON Data +-- MAGIC +-- MAGIC The **`events_raw`** table was registered against data representing a Kafka payload. +-- MAGIC +-- MAGIC In most cases, Kafka data will be binary-encoded JSON values. We'll cast the **`key`** and **`value`** as strings below to look at these in a human-readable format. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW events_strings AS + SELECT string(key), string(value) + FROM events_raw; + +SELECT * FROM events_strings + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Spark SQL has built-in functionality to directly interact with JSON data stored as strings. We can use the **`:`** syntax to traverse nested data structures. + +-- COMMAND ---------- + +SELECT value:device, value:geo:city +FROM events_strings + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Spark SQL also has the ability to parse JSON objects into struct types (a native Spark type with nested attributes). +-- MAGIC +-- MAGIC However, the **`from_json`** function requires a schema. To derive the schema of our current data, we'll start by executing a query we know will return a JSON value with no null fields. + +-- COMMAND ---------- + +SELECT value +FROM events_strings +WHERE value:event_name = "finalize" +ORDER BY key +LIMIT 1 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Spark SQL also has a **`schema_of_json`** function to derive the JSON schema from an example. Here, we copy and paste an example JSON to the function and chain it into the **`from_json`** function to cast our **`value`** field to a struct type. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW parsed_events AS + SELECT from_json(value, schema_of_json('{"device":"Linux","ecommerce":{"purchase_revenue_in_usd":1075.5,"total_item_quantity":1,"unique_items":1},"event_name":"finalize","event_previous_timestamp":1593879231210816,"event_timestamp":1593879335779563,"geo":{"city":"Houston","state":"TX"},"items":[{"coupon":"NEWBED10","item_id":"M_STAN_K","item_name":"Standard King Mattress","item_revenue_in_usd":1075.5,"price_in_usd":1195.0,"quantity":1}],"traffic_source":"email","user_first_touch_timestamp":1593454417513109,"user_id":"UA000000106116176"}')) AS json + FROM events_strings; + +SELECT * FROM parsed_events + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Once a JSON string is unpacked to a struct type, Spark supports **`*`** (star) unpacking to flatten fields into columns. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW new_events_final AS + SELECT json.* + FROM parsed_events; + +SELECT * FROM new_events_final + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Explore Data Structures +-- MAGIC +-- MAGIC Spark SQL has robust syntax for working with complex and nested data types. +-- MAGIC +-- MAGIC Start by looking at the fields in the **`events`** table. + +-- COMMAND ---------- + +DESCRIBE events + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC The **`ecommerce`** field is a struct that contains a double and 2 longs. +-- MAGIC +-- MAGIC We can interact with the subfields in this field using standard **`.`** syntax similar to how we might traverse nested data in JSON. + +-- COMMAND ---------- + +SELECT ecommerce.purchase_revenue_in_usd +FROM events +WHERE ecommerce.purchase_revenue_in_usd IS NOT NULL + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Explode Arrays +-- MAGIC The **`items`** field in the **`events`** table is an array of structs. +-- MAGIC +-- MAGIC Spark SQL has a number of functions specifically to deal with arrays. +-- MAGIC +-- MAGIC The **`explode`** function lets us put each element in an array on its own row. + +-- COMMAND ---------- + +SELECT user_id, event_timestamp, event_name, explode(items) AS item +FROM events + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Collect Arrays +-- MAGIC +-- MAGIC The **`collect_set`** function can collect unique values for a field, including fields within arrays. +-- MAGIC +-- MAGIC The **`flatten`** function allows multiple arrays to be combined into a single array. +-- MAGIC +-- MAGIC The **`array_distinct`** function removes duplicate elements from an array. +-- MAGIC +-- MAGIC Here, we combine these queries to create a simple table that shows the unique collection of actions and the items in a user's cart. + +-- COMMAND ---------- + +SELECT user_id, + collect_set(event_name) AS event_history, + array_distinct(flatten(collect_set(items.item_id))) AS cart_history +FROM events +GROUP BY user_id + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Join Tables +-- MAGIC +-- MAGIC Spark SQL supports standard join operations (inner, outer, left, right, anti, cross, semi). +-- MAGIC +-- MAGIC Here we chain a join with a lookup table to an **`explode`** operation to grab the standard printed item name. + +-- COMMAND ---------- + +CREATE OR REPLACE VIEW sales_enriched AS +SELECT * +FROM ( + SELECT *, explode(items) AS item + FROM sales) a +INNER JOIN item_lookup b +ON a.item.item_id = b.item_id; + +SELECT * FROM sales_enriched + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Set Operators +-- MAGIC Spark SQL supports **`UNION`**, **`MINUS`**, and **`INTERSECT`** set operators. +-- MAGIC +-- MAGIC **`UNION`** returns the collection of two queries. +-- MAGIC +-- MAGIC The query below returns the same results as if we inserted our **`new_events_final`** into the **`events`** table. + +-- COMMAND ---------- + +SELECT * FROM events +UNION +SELECT * FROM new_events_final + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC **`INTERSECT`** returns all rows found in both relations. + +-- COMMAND ---------- + +SELECT * FROM events +INTERSECT +SELECT * FROM new_events_final + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC The above query returns no results because our two datasets have no values in common. +-- MAGIC +-- MAGIC **`MINUS`** returns all the rows found in one dataset but not the other; we'll skip executing this here as our previous query demonstrates we have no values in common. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Pivot Tables +-- MAGIC The **`PIVOT`** clause is used for data perspective. We can get the aggregated values based on specific column values, which will be turned to multiple columns used in **`SELECT`** clause. The **`PIVOT`** clause can be specified after the table name or subquery. +-- MAGIC +-- MAGIC **`SELECT * FROM ()`**: The **`SELECT`** statement inside the parentheses is the input for this table. +-- MAGIC +-- MAGIC **`PIVOT`**: The first argument in the clause is an aggregate function and the column to be aggregated. Then, we specify the pivot column in the **`FOR`** subclause. The **`IN`** operator contains the pivot column values. +-- MAGIC +-- MAGIC Here we use **`PIVOT`** to create a new **`transactions`** table that flattens out the information contained in the **`sales`** table. +-- MAGIC +-- MAGIC This flattened data format can be useful for dashboarding, but also useful for applying machine learning algorithms for inference or prediction. + +-- COMMAND ---------- + +CREATE OR REPLACE TABLE transactions AS + +SELECT * FROM ( + SELECT + email, + order_id, + transaction_timestamp, + total_item_quantity, + purchase_revenue_in_usd, + unique_items, + item.item_id AS item_id, + item.quantity AS quantity + FROM sales_enriched +) PIVOT ( + sum(quantity) FOR item_id in ( + 'P_FOAM_K', + 'M_STAN_Q', + 'P_FOAM_S', + 'M_PREM_Q', + 'M_STAN_F', + 'M_STAN_T', + 'M_PREM_K', + 'M_PREM_F', + 'M_STAN_K', + 'M_PREM_T', + 'P_DOWN_S', + 'P_DOWN_K' + ) +); + +SELECT * FROM transactions + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ## Higher Order Functions +-- MAGIC Higher order functions in Spark SQL allow you to work directly with complex data types. When working with hierarchical data, records are frequently stored as array or map type objects. Higher-order functions allow you to transform data while preserving the original structure. +-- MAGIC +-- MAGIC Higher order functions include: +-- MAGIC - **`FILTER`** filters an array using the given lambda function. +-- MAGIC - **`EXIST`** tests whether a statement is true for one or more elements in an array. +-- MAGIC - **`TRANSFORM`** uses the given lambda function to transform all elements in an array. +-- MAGIC - **`REDUCE`** takes two lambda functions to reduce the elements of an array to a single value by merging the elements into a buffer, and the apply a finishing function on the final buffer. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Filter +-- MAGIC Remove items that are not king-sized from all records in our **`items`** column. We can use the **`FILTER`** function to create a new column that excludes that value from each array. +-- MAGIC +-- MAGIC **`FILTER (items, i -> i.item_id LIKE "%K") AS king_items`** +-- MAGIC +-- MAGIC In the statement above: +-- MAGIC - **`FILTER`** : the name of the higher-order function
+-- MAGIC - **`items`** : the name of our input array
+-- MAGIC - **`i`** : the name of the iterator variable. You choose this name and then use it in the lambda function. It iterates over the array, cycling each value into the function one at a time.
+-- MAGIC - **`->`** : Indicates the start of a function
+-- MAGIC - **`i.item_id LIKE "%K"`** : This is the function. Each value is checked to see if it ends with the capital letter K. If it is, it gets filtered into the new column, **`king_items`** + +-- COMMAND ---------- + +-- filter for sales of only king sized items +SELECT + order_id, + items, + FILTER (items, i -> i.item_id LIKE "%K") AS king_items +FROM sales + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC You may write a filter that produces a lot of empty arrays in the created column. When that happens, it can be useful to use a **`WHERE`** clause to show only non-empty array values in the returned column. +-- MAGIC +-- MAGIC In this example, we accomplish that by using a subquery (a query within a query). They are useful for performing an operation in multiple steps. In this case, we're using it to create the named column that we will use with a **`WHERE`** clause. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMP VIEW king_size_sales AS + +SELECT order_id, king_items +FROM ( + SELECT + order_id, + FILTER (items, i -> i.item_id LIKE "%K") AS king_items + FROM sales) +WHERE size(king_items) > 0; + +SELECT * FROM king_size_sales + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Transform +-- MAGIC Built-in functions are designed to operate on a single, simple data type within a cell; they cannot process array values. **`TRANSFORM`** can be particularly useful when you want to apply an existing function to each element in an array. +-- MAGIC +-- MAGIC Compute the total revenue from king-sized items per order. +-- MAGIC +-- MAGIC **`TRANSFORM(king_items, k -> CAST(k.item_revenue_in_usd * 100 AS INT)) AS item_revenues`** +-- MAGIC +-- MAGIC In the statement above, for each value in the input array, we extract the item's revenue value, multiply it by 100, and cast the result to integer. Note that we're using the same kind as references as in the previous command, but we name the iterator with a new variable, **`k`**. + +-- COMMAND ---------- + +-- get total revenue from king items per order +CREATE OR REPLACE TEMP VIEW king_item_revenues AS + +SELECT + order_id, + king_items, + TRANSFORM ( + king_items, + k -> CAST(k.item_revenue_in_usd * 100 AS INT) + ) AS item_revenues +FROM king_size_sales; + +SELECT * FROM king_item_revenues + + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Summary +-- MAGIC Spark SQL offers a comprehensive set of native functionality for interacting with and manipulating highly nested data. +-- MAGIC +-- MAGIC While some syntax for this functionality may be unfamiliar to SQL users, leveraging built-in functions like higher order functions can prevent SQL engineers from needing to rely on custom logic when dealing with highly complex data structures. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.8 - SQL UDFs and Control Flow.sql b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.8 - SQL UDFs and Control Flow.sql new file mode 100644 index 0000000..3afadab --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.8 - SQL UDFs and Control Flow.sql @@ -0,0 +1,205 @@ +-- Databricks notebook source +-- MAGIC %md-sandbox +-- MAGIC +-- MAGIC
+-- MAGIC Databricks Learning +-- MAGIC
+ +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC # SQL UDFs and Control Flow +-- MAGIC +-- MAGIC Databricks added support for User Defined Functions (UDFs) registered natively in SQL starting in DBR 9.1. +-- MAGIC +-- MAGIC This feature allows users to register custom combinations of SQL logic as functions in a database, making these methods reusable anywhere SQL can be run on Databricks. These functions leverage Spark SQL directly, maintaining all of the optimizations of Spark when applying your custom logic to large datasets. +-- MAGIC +-- MAGIC In this notebook, we'll first have a simple introduction to these methods, and then explore how this logic can be combined with **`CASE`** / **`WHEN`** clauses to provide reusable custom control flow logic. +-- MAGIC +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC * Define and registering SQL UDFs +-- MAGIC * Describe the security model used for sharing SQL UDFs +-- MAGIC * Use **`CASE`** / **`WHEN`** statements in SQL code +-- MAGIC * Leverage **`CASE`** / **`WHEN`** statements in SQL UDFs for custom control flow + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Setup +-- MAGIC Run the following cell to setup your environment. + +-- COMMAND ---------- + +-- MAGIC %run ../Includes/Classroom-Setup-4.8 + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Create a Simple Dataset +-- MAGIC +-- MAGIC For this notebook, we'll consider the following dataset, registered here as a temporary view. + +-- COMMAND ---------- + +CREATE OR REPLACE TEMPORARY VIEW foods(food) AS VALUES +("beef"), +("beans"), +("potatoes"), +("bread"); + +SELECT * FROM foods + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## SQL UDFs +-- MAGIC At minimum, a SQL UDF requires a function name, optional parameters, the type to be returned, and some custom logic. +-- MAGIC +-- MAGIC Below, a simple function named **`yelling`** takes one parameter named **`text`**. It returns a string that will be in all uppercase letters with three exclamation points added to the end. + +-- COMMAND ---------- + +CREATE OR REPLACE FUNCTION yelling(text STRING) +RETURNS STRING +RETURN concat(upper(text), "!!!") + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Note that this function is applied to all values of the column in a parallel fashion within the Spark processing engine. SQL UDFs are an efficient way to define custom logic that is optimized for execution on Databricks. + +-- COMMAND ---------- + +SELECT yelling(food) FROM foods + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Scoping and Permissions of SQL UDFs +-- MAGIC +-- MAGIC Note that SQL UDFs will persist between execution environments (which can include notebooks, DBSQL queries, and jobs). +-- MAGIC +-- MAGIC We can describe the function to see where it was registered and basic information about expected inputs and what is returned. + +-- COMMAND ---------- + +DESCRIBE FUNCTION yelling + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC By describing extended, we can get even more information. +-- MAGIC +-- MAGIC Note that the **`Body`** field at the bottom of the function description shows the SQL logic used in the function itself. + +-- COMMAND ---------- + +DESCRIBE FUNCTION EXTENDED yelling + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC SQL UDFs exist as objects in the metastore and are governed by the same Table ACLs as databases, tables, or views. +-- MAGIC +-- MAGIC In order to use a SQL UDF, a user must have **`USAGE`** and **`SELECT`** permissions on the function. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## CASE/WHEN +-- MAGIC +-- MAGIC The standard SQL syntactic construct **`CASE`** / **`WHEN`** allows the evaluation of multiple conditional statements with alternative outcomes based on table contents. +-- MAGIC +-- MAGIC Again, everything is evaluated natively in Spark, and so is optimized for parallel execution. + +-- COMMAND ---------- + +SELECT *, + CASE + WHEN food = "beans" THEN "I love beans" + WHEN food = "potatoes" THEN "My favorite vegetable is potatoes" + WHEN food <> "beef" THEN concat("Do you have any good recipes for ", food ,"?") + ELSE concat("I don't eat ", food) + END +FROM foods + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC ## Simple Control Flow Functions +-- MAGIC +-- MAGIC Combining SQL UDFs with control flow in the form of **`CASE`** / **`WHEN`** clauses provides optimized execution for control flows within SQL workloads. +-- MAGIC +-- MAGIC Here, we demonstrate wrapping the previous logic in a function that will be reusable anywhere we can execute SQL. + +-- COMMAND ---------- + +CREATE FUNCTION foods_i_like(food STRING) +RETURNS STRING +RETURN CASE + WHEN food = "beans" THEN "I love beans" + WHEN food = "potatoes" THEN "My favorite vegetable is potatoes" + WHEN food <> "beef" THEN concat("Do you have any good recipes for ", food ,"?") + ELSE concat("I don't eat ", food) +END; + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Using this method on our data provides the desired outcome. + +-- COMMAND ---------- + +SELECT foods_i_like(food) FROM foods + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC While the example provided here are simple string methods, these same basic principles can be used to add custom computations and logic for native execution in Spark SQL. +-- MAGIC +-- MAGIC Especially for enterprises that might be migrating users from systems with many defined procedures or custom-defined formulas, SQL UDFs can allow a handful of users to define the complex logic needed for common reporting and analytic queries. + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() + +-- COMMAND ---------- + +-- MAGIC %md-sandbox +-- MAGIC © 2022 Databricks, Inc. All rights reserved.
+-- MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+-- MAGIC
+-- MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.2.9L - Reshaping Data.sql b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.9L - Reshaping Data Lab.sql similarity index 59% rename from Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.2.9L - Reshaping Data.sql rename to Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.9L - Reshaping Data Lab.sql index f617fda..0078fe7 100644 --- a/Data-Engineering-With-Databricks/Solutions/02 - ELT with Spark SQL and Python/Labs/DE 2.2.9L - Reshaping Data.sql +++ b/Data-Engineering-with-Databricks/Solutions/04 - ETL with Spark SQL/DE 4.9L - Reshaping Data Lab.sql @@ -8,39 +8,50 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Reshaping Data Lab -- MAGIC --- MAGIC In this lab, you will create a `clickpaths` table that aggregates the number of times each user took a particular action in `events` and then join this information with the flattened view of `transactions` created in the previous notebook. +-- MAGIC In this lab, you will create a **`clickpaths`** table that aggregates the number of times each user took a particular action in **`events`** and then join this information with the flattened view of **`transactions`** created in the previous notebook. -- MAGIC --- MAGIC You'll also explore a new higher order function to flag items recorded in `sales` based on information extracted from item names. +-- MAGIC You'll also explore a new higher order function to flag items recorded in **`sales`** based on information extracted from item names. -- MAGIC --- MAGIC ##### Objectives +-- MAGIC ## Learning Objectives +-- MAGIC By the end of this lab, you should be able to: -- MAGIC - Pivot and join tables to create clickpaths for each user -- MAGIC - Apply higher order functions to flag types of products purchased -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Run Setup -- MAGIC -- MAGIC The setup script will create the data and declare necessary values for the rest of this notebook to execute. -- COMMAND ---------- --- MAGIC %run ../../Includes/setup-transactions +-- MAGIC %run ../Includes/Classroom-Setup-4.9L -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Reshape Datasets to Create Click Paths --- MAGIC This operation will join data from your `events` and `transactions` tables in order to create a record of all actions a user took on the site and what their final order looked like. +-- MAGIC This operation will join data from your **`events`** and **`transactions`** tables in order to create a record of all actions a user took on the site and what their final order looked like. -- MAGIC --- MAGIC The `clickpaths` table should contain all the fields from your `transactions` table, as well as a count of every `event_name` in its own column. Each user that completed a purchase should have a single row in the final table. Let's start by pivoting the `events` table to get counts for each `event_name`. +-- MAGIC The **`clickpaths`** table should contain all the fields from your **`transactions`** table, as well as a count of every **`event_name`** in its own column. Each user that completed a purchase should have a single row in the final table. Let's start by pivoting the **`events`** table to get counts for each **`event_name`**. -- COMMAND ---------- --- MAGIC %md ### 1. Pivot `events` to count actions for each user --- MAGIC We want to aggregate the number of times each user performed a specific event, specified in the `event_name` column. To do this, group by `user` and pivot on `event_name` to provide a count of every event type in its own column, resulting in the schema below. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC ### 1. Pivot **`events`** to count actions for each user +-- MAGIC We want to aggregate the number of times each user performed a specific event, specified in the **`event_name`** column. To do this, group by **`user`** and pivot on **`event_name`** to provide a count of every event type in its own column, resulting in the schema below. -- MAGIC -- MAGIC | field | type | -- MAGIC | --- | --- | @@ -86,31 +97,42 @@ SELECT * FROM ( -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC -- MAGIC **NOTE**: We'll use Python to run checks occasionally throughout the lab. The helper functions below will return an error with a message on what needs to change if you have not followed instructions. No output means that you have completed this step. -- COMMAND ---------- -- MAGIC %python -- MAGIC def check_table_results(table_name, column_names, num_rows): --- MAGIC assert spark.table(table_name), f"Table named `{table_name}` does not exist" +-- MAGIC assert spark.table(table_name), f"Table named **`{table_name}`** does not exist" -- MAGIC assert spark.table(table_name).columns == column_names, "Please name the columns in the order provided above" -- MAGIC assert spark.table(table_name).count() == num_rows, f"The table should have {num_rows} records" -- COMMAND ---------- --- MAGIC %md Run the cell below to confirm the view was created correctly. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Run the cell below to confirm the view was created correctly. -- COMMAND ---------- -- MAGIC %python -- MAGIC event_columns = ['user', 'cart', 'pillows', 'login', 'main', 'careers', 'guest', 'faq', 'down', 'warranty', 'finalize', 'register', 'shipping_info', 'checkout', 'mattresses', 'add_item', 'press', 'email_coupon', 'cc_info', 'foam', 'reviews', 'original', 'delivery', 'premium'] --- MAGIC check_table_results("events_pivot", event_columns, 4085296) +-- MAGIC check_table_results("events_pivot", event_columns, 204586) -- COMMAND ---------- --- MAGIC %md ### 2. Join event counts and transactions for all users +-- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC --- MAGIC Next, join `events_pivot` with `transactions` to create the table `clickpaths`. This table should have the same event name columns from the `events_pivot` table created above, followed by columns from the `transactions` table, as shown below. +-- MAGIC ### 2. Join event counts and transactions for all users +-- MAGIC +-- MAGIC Next, join **`events_pivot`** with **`transactions`** to create the table **`clickpaths`**. This table should have the same event name columns from the **`events_pivot`** table created above, followed by columns from the **`transactions`** table, as shown below. -- MAGIC -- MAGIC | field | type | -- MAGIC | --- | --- | @@ -147,30 +169,36 @@ JOIN transactions b -- COMMAND ---------- --- MAGIC %md Run the cell below to confirm the table was created correctly. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Run the cell below to confirm the table was created correctly. -- COMMAND ---------- -- MAGIC %python -- MAGIC clickpath_columns = event_columns + ['user_id', 'order_id', 'transaction_timestamp', 'total_item_quantity', 'purchase_revenue_in_usd', 'unique_items', 'P_FOAM_K', 'M_STAN_Q', 'P_FOAM_S', 'M_PREM_Q', 'M_STAN_F', 'M_STAN_T', 'M_PREM_K', 'M_PREM_F', 'M_STAN_K', 'M_PREM_T', 'P_DOWN_S', 'P_DOWN_K'] --- MAGIC check_table_results("clickpaths", clickpath_columns, 180680) +-- MAGIC check_table_results("clickpaths", clickpath_columns, 9085) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Flag Types of Products Purchased --- MAGIC Here, you'll use the higher order function `EXISTS` to create boolean columns `mattress` and `pillow` that indicate whether the item purchased was a mattress or pillow product. +-- MAGIC Here, you'll use the higher order function **`EXISTS`** to create boolean columns **`mattress`** and **`pillow`** that indicate whether the item purchased was a mattress or pillow product. -- MAGIC --- MAGIC For example, if `item_name` from the `items` column ends with the string `"Mattress"`, the column value for `mattress` should be `true` and the value for `pillow` should be `false`. Here are a few examples of items and the resulting values. +-- MAGIC For example, if **`item_name`** from the **`items`** column ends with the string **`"Mattress"`**, the column value for **`mattress`** should be **`true`** and the value for **`pillow`** should be **`false`**. Here are a few examples of items and the resulting values. -- MAGIC -- MAGIC | items | mattress | pillow | -- MAGIC | ------- | -------- | ------ | --- MAGIC | `[{..., "item_id": "M_PREM_K", "item_name": "Premium King Mattress", ...}]` | true | false | --- MAGIC | `[{..., "item_id": "P_FOAM_S", "item_name": "Standard Foam Pillow", ...}]` | false | true | --- MAGIC | `[{..., "item_id": "M_STAN_F", "item_name": "Standard Full Mattress", ...}]` | true | false | +-- MAGIC | **`[{..., "item_id": "M_PREM_K", "item_name": "Premium King Mattress", ...}]`** | true | false | +-- MAGIC | **`[{..., "item_id": "P_FOAM_S", "item_name": "Standard Foam Pillow", ...}]`** | false | true | +-- MAGIC | **`[{..., "item_id": "M_STAN_F", "item_name": "Standard Full Mattress", ...}]`** | true | false | -- MAGIC --- MAGIC See documentation for the [exists](https://docs.databricks.com/sql/language-manual/functions/exists.html) function. --- MAGIC You can use the condition expression `item_name LIKE "%Mattress"` to check whether the string `item_name` ends with the word "Mattress". +-- MAGIC See documentation for the exists function. +-- MAGIC You can use the condition expression **`item_name LIKE "%Mattress"`** to check whether the string **`item_name`** ends with the word "Mattress". -- COMMAND ---------- @@ -184,14 +212,30 @@ FROM sales -- COMMAND ---------- --- MAGIC %md Run the cell below to confirm the table was created correctly. +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC +-- MAGIC Run the cell below to confirm the table was created correctly. -- COMMAND ---------- -- MAGIC %python --- MAGIC check_table_results("sales_product_flags", ['items', 'mattress', 'pillow'], 210370) +-- MAGIC check_table_results("sales_product_flags", ['items', 'mattress', 'pillow'], 10539) -- MAGIC product_counts = spark.sql("SELECT sum(CAST(mattress AS INT)) num_mattress, sum(CAST(pillow AS INT)) num_pillow FROM sales_product_flags").first().asDict() --- MAGIC assert product_counts == {'num_mattress': 199764, 'num_pillow': 27481}, "There should be 199764 rows where mattress is true, and 27481 where pillow is true" +-- MAGIC assert product_counts == {'num_mattress': 10015, 'num_pillow': 1386}, "There should be 10015 rows where mattress is true, and 1386 where pillow is true" + +-- COMMAND ---------- + +-- MAGIC %md +-- MAGIC +-- MAGIC +-- MAGIC Run the following cell to delete the tables and files associated with this lesson. + +-- COMMAND ---------- + +-- MAGIC %python +-- MAGIC DA.cleanup() -- COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.3.1 - Python Basics.py b/Data-Engineering-with-Databricks/Solutions/05 - OPTIONAL Python for Spark SQL/DE 5.1 - Python Basics.py similarity index 77% rename from Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.3.1 - Python Basics.py rename to Data-Engineering-with-Databricks/Solutions/05 - OPTIONAL Python for Spark SQL/DE 5.1 - Python Basics.py index 5d8e634..8e97427 100644 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.3.1 - Python Basics.py +++ b/Data-Engineering-with-Databricks/Solutions/05 - OPTIONAL Python for Spark SQL/DE 5.1 - Python Basics.py @@ -8,6 +8,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Just Enough Python for Databricks SQL # MAGIC # MAGIC While Databricks SQL provides an ANSI-compliant flavor of SQL with many additional custom methods (including the entire Delta Lake SQL syntax), users migrating from some systems may run into missing features, especially around control flow and error handling. @@ -17,7 +19,7 @@ # MAGIC Mastering just a handful of Python concepts will unlock powerful new design practices for engineers and analysts proficient in SQL. Rather than trying to teach the entire language, this lesson focuses on those features that can immediately be leveraged to write more extensible SQL programs on Databricks. # MAGIC # MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, students should be able to: +# MAGIC By the end of this lesson, you should be able to: # MAGIC * Print and manipulate multi-line Python strings # MAGIC * Define variables and functions # MAGIC * Use f-strings for variable substitution @@ -25,8 +27,10 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Strings -# MAGIC Characters enclosed in single (`'`) or double (`"`) quotes are considered strings. +# MAGIC Characters enclosed in single (**`'`**) or double (**`"`**) quotes are considered strings. # COMMAND ---------- @@ -35,7 +39,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC To preview how a string will render, we can call `print()`. +# MAGIC +# MAGIC +# MAGIC To preview how a string will render, we can call **`print()`**. # COMMAND ---------- @@ -44,7 +50,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC By wrapping a string in triple quotes (`"""`), it's possible to use multiple lines. +# MAGIC +# MAGIC +# MAGIC By wrapping a string in triple quotes (**`"""`**), it's possible to use multiple lines. # COMMAND ---------- @@ -59,6 +67,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC This makes it easy to turn SQL queries into Python strings. # COMMAND ---------- @@ -71,7 +81,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC When we execute SQL from a Python cell, we will pass a string as an argument to `spark.sql()`. +# MAGIC +# MAGIC +# MAGIC When we execute SQL from a Python cell, we will pass a string as an argument to **`spark.sql()`**. # COMMAND ---------- @@ -80,7 +92,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC To render a query the way it would appear in a normal SQL notebook, we call `display()` on this function. +# MAGIC +# MAGIC +# MAGIC To render a query the way it would appear in a normal SQL notebook, we call **`display()`** on this function. # COMMAND ---------- @@ -89,21 +103,25 @@ # COMMAND ---------- # MAGIC %md -# MAGIC **NOTE**: Executing a cell with only a Python string in it will just print the string. Using `print()` with a string just renders it back to the notebook. # MAGIC -# MAGIC To execute a string that contains SQL using Python, it must be passed within a call to `spark.sql()`. +# MAGIC +# MAGIC **NOTE**: Executing a cell with only a Python string in it will just print the string. Using **`print()`** with a string just renders it back to the notebook. +# MAGIC +# MAGIC To execute a string that contains SQL using Python, it must be passed within a call to **`spark.sql()`**. # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Variables -# MAGIC Python variables are assigned using the `=`. +# MAGIC Python variables are assigned using the **`=`**. # MAGIC -# MAGIC Python variable names need to start with a letter, and can only contain letters, numbers, hyphens, and underscores. +# MAGIC Python variable names need to start with a letter, and can only contain letters, numbers, and underscores. (Variable names starting with underscores are valid but typically reserved for special use cases.) # MAGIC # MAGIC Many Python programmers favor snake casing, which uses only lowercase letters and underscores for all variables. # MAGIC -# MAGIC The cell below creates the variable `my_string`. +# MAGIC The cell below creates the variable **`my_string`**. # COMMAND ---------- @@ -112,6 +130,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Executing a cell with this variable will return its value. # COMMAND ---------- @@ -121,7 +141,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC The output here is the same as if we typed `"This is a string"` into the cell and ran it. +# MAGIC +# MAGIC +# MAGIC The output here is the same as if we typed **`"This is a string"`** into the cell and ran it. # MAGIC # MAGIC Note that the quotation marks aren't part of the string, as shown when we print it. @@ -132,9 +154,11 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC This variable can be used the same way a string would be. # MAGIC -# MAGIC String concatenation (joining to strings together) can be performed with a `+`. +# MAGIC String concatenation (joining to strings together) can be performed with a **`+`**. # COMMAND ---------- @@ -143,6 +167,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC We can join string variables with other string variables. # COMMAND ---------- @@ -153,12 +179,14 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Functions -# MAGIC Functions allow you to specify local variables as arguments and then apply custom logic. We define a function using the keyword `def` followed by the function name and, enclosed in parentheses, any variable arguments we wish to pass into the function. Finally, the function header has a `:` at the end. +# MAGIC Functions allow you to specify local variables as arguments and then apply custom logic. We define a function using the keyword **`def`** followed by the function name and, enclosed in parentheses, any variable arguments we wish to pass into the function. Finally, the function header has a **`:`** at the end. # MAGIC # MAGIC Note: In Python, indentation matters. You can see in the cell below that the logic of the function is indented in from the left margin. Any code that is indented to this level is part of the function. # MAGIC -# MAGIC The function below takes one argument (`arg`) and then prints it. +# MAGIC The function below takes one argument (**`arg`**) and then prints it. # COMMAND ---------- @@ -168,6 +196,8 @@ def print_string(arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC When we pass a string as the argument, it will be printed. # COMMAND ---------- @@ -177,6 +207,8 @@ def print_string(arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC We can also pass a variable as an argument. # COMMAND ---------- @@ -186,7 +218,9 @@ def print_string(arg): # COMMAND ---------- # MAGIC %md -# MAGIC Oftentimes we want to return the results of our function for use elsewhere. For this we use the `return` keyword. +# MAGIC +# MAGIC +# MAGIC Oftentimes we want to return the results of our function for use elsewhere. For this we use the **`return`** keyword. # MAGIC # MAGIC The function below constructs a new string by concatenating our argument. Note that both functions and arguments can have arbitrary names, just like variables (and follow the same rules). @@ -198,6 +232,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Running this function returns the output. # COMMAND ---------- @@ -207,6 +243,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Assigning it to a variable captures the output for reuse elsewhere. # COMMAND ---------- @@ -216,6 +254,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC This variable doesn't contain our function, just the results of our function (a string). # COMMAND ---------- @@ -225,8 +265,10 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## F-strings -# MAGIC By adding the letter `f` before a Python string, you can inject variables or evaluated Python code by inserted them inside curly braces (`{}`). +# MAGIC By adding the letter **`f`** before a Python string, you can inject variables or evaluated Python code by inserted them inside curly braces (**`{}`**). # MAGIC # MAGIC Evaluate the cell below to see string variable substitution. @@ -237,6 +279,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC The following cell inserts the string returned by a function. # COMMAND ---------- @@ -246,6 +290,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Combine this with triple quotes and you can format a paragraph or list, like below. # COMMAND ---------- @@ -261,6 +307,8 @@ def return_new_string(string_arg): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Or you could format a SQL query. # COMMAND ---------- diff --git a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.3.2 - Python Control Flow.py b/Data-Engineering-with-Databricks/Solutions/05 - OPTIONAL Python for Spark SQL/DE 5.2 - Python Control Flow.py similarity index 60% rename from Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.3.2 - Python Control Flow.py rename to Data-Engineering-with-Databricks/Solutions/05 - OPTIONAL Python for Spark SQL/DE 5.2 - Python Control Flow.py index 3b73ffa..251c62f 100644 --- a/Data-Engineering-With-Databricks/02 - ELT with Spark SQL and Python/DE 2.3.2 - Python Control Flow.py +++ b/Data-Engineering-with-Databricks/Solutions/05 - OPTIONAL Python for Spark SQL/DE 5.2 - Python Control Flow.py @@ -8,27 +8,33 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Just Enough Python for Databricks SQL # MAGIC # MAGIC ## Learning Objectives -# MAGIC By the end of this lesson, students should be able to: -# MAGIC * Leverage `if/else` +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Leverage **`if`** / **`else`** # MAGIC * Describe how errors impact notebook execution -# MAGIC * Write simple tests with `assert` -# MAGIC * Use `try/except` to handle errors +# MAGIC * Write simple tests with **`assert`** +# MAGIC * Use **`try`** / **`except`** to handle errors # COMMAND ---------- # MAGIC %md -# MAGIC ## `if/else` # MAGIC -# MAGIC `if/else` clauses are common in many programming languages. # MAGIC -# MAGIC Note that SQL has the `CASE WHEN ... ELSE` construct, which is similar. +# MAGIC ## if/else +# MAGIC +# MAGIC **`if`** / **`else`** clauses are common in many programming languages. +# MAGIC +# MAGIC Note that SQL has the **`CASE WHEN ... ELSE`** construct, which is similar. # MAGIC -# MAGIC **If you're seeking to evaluate conditions within your tables or queries, use `CASE WHEN`.** Python control flow should be reserved for evaluating conditions outside of your query. +# MAGIC If you're seeking to evaluate conditions within your tables or queries, use **`CASE WHEN`**. # MAGIC -# MAGIC More on this later. First, an example with `"beans"`. +# MAGIC Python control flow should be reserved for evaluating conditions outside of your query. +# MAGIC +# MAGIC More on this later. First, an example with **`"beans"`**. # COMMAND ---------- @@ -37,18 +43,20 @@ # COMMAND ---------- # MAGIC %md -# MAGIC Working with `if` and `else` is all about evaluating whether or not certain conditions are true in your execution environment. +# MAGIC +# MAGIC +# MAGIC Working with **`if`** and **`else`** is all about evaluating whether or not certain conditions are true in your execution environment. # MAGIC # MAGIC Note that in Python, we have the following comparison operators: # MAGIC # MAGIC | Syntax | Operation | # MAGIC | --- | --- | -# MAGIC | `==` | equals | -# MAGIC | `>` | greater than | -# MAGIC | `<` | less than | -# MAGIC | `>=` | greater than or equal | -# MAGIC | `<=` | less than or equal | -# MAGIC | `!=` | not equal | +# MAGIC | **`==`** | equals | +# MAGIC | **`>`** | greater than | +# MAGIC | **`<`** | less than | +# MAGIC | **`>=`** | greater than or equal | +# MAGIC | **`<=`** | less than or equal | +# MAGIC | **`!=`** | not equal | # MAGIC # MAGIC If you read the sentence below out loud, you will be describing the control flow of your program. @@ -62,7 +70,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC As expected, because the variable `food` is the string literal `"beans"`, the `if` statement evaluated to `True` and the first print statement evaluated. +# MAGIC +# MAGIC +# MAGIC As expected, because the variable **`food`** is the string literal **`"beans"`**, the **`if`** statement evaluated to **`True`** and the first print statement evaluated. # MAGIC # MAGIC Let's assign a different value to the variable. @@ -73,7 +83,11 @@ # COMMAND ---------- # MAGIC %md -# MAGIC Now the first condition will evaluate as `False`. What do you think will happen when you run the following cell? +# MAGIC +# MAGIC +# MAGIC Now the first condition will evaluate as **`False`**. +# MAGIC +# MAGIC What do you think will happen when you run the following cell? # COMMAND ---------- @@ -85,6 +99,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Note that each time we assign a new value to a variable, this completely erases the old variable. # COMMAND ---------- @@ -95,14 +111,16 @@ # COMMAND ---------- # MAGIC %md -# MAGIC The Python keyword `elif` (short for `else if`) allows us to evaluate multiple conditions. +# MAGIC +# MAGIC +# MAGIC The Python keyword **`elif`** (short for **`else`** + **`if`**) allows us to evaluate multiple conditions. # MAGIC # MAGIC Note that conditions are evaluated from top to bottom. Once a condition evaluates to true, no further conditions will be evaluated. # MAGIC -# MAGIC `if/else` control flow patterns: -# MAGIC 1. Must contain an `if` clause -# MAGIC 1. Can contain any number of `elif` clauses -# MAGIC 1. Can contain at most one `else` clause +# MAGIC **`if`** / **`else`** control flow patterns: +# MAGIC 1. Must contain an **`if`** clause +# MAGIC 1. Can contain any number of **`elif`** clauses +# MAGIC 1. Can contain at most one **`else`** clause # COMMAND ---------- @@ -118,6 +136,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC By encapsulating the above logic in a function, we can reuse this logic and formatting with arbitrary arguments rather than referencing globally-defined variables. # COMMAND ---------- @@ -135,7 +155,9 @@ def foods_i_like(food): # COMMAND ---------- # MAGIC %md -# MAGIC Here, we pass the string `"bread"` to the function. +# MAGIC +# MAGIC +# MAGIC Here, we pass the string **`"bread"`** to the function. # COMMAND ---------- @@ -144,9 +166,11 @@ def foods_i_like(food): # COMMAND ---------- # MAGIC %md -# MAGIC As we evaluate the function, we locally assign the string `"bread"` to the `food` variable, and the logic behaves as expected. # MAGIC -# MAGIC Note that we don't overwrite the value of the `food` variable as previously defined in the notebook. +# MAGIC +# MAGIC As we evaluate the function, we locally assign the string **`"bread"`** to the **`food`** variable, and the logic behaves as expected. +# MAGIC +# MAGIC Note that we don't overwrite the value of the **`food`** variable as previously defined in the notebook. # COMMAND ---------- @@ -155,9 +179,11 @@ def foods_i_like(food): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## try/except # MAGIC -# MAGIC While `if/else` clauses allow us to define conditional logic based on evaluating conditional statements, `try/except` focuses on providing robust error handling. +# MAGIC While **`if`** / **`else`** clauses allow us to define conditional logic based on evaluating conditional statements, **`try`** / **`except`** focuses on providing robust error handling. # MAGIC # MAGIC Let's begin by considering a simple function. @@ -169,6 +195,8 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Let's assume that the desired use of this function is to multiply an integer value by 3. # MAGIC # MAGIC The below cell demonstrates this behavior. @@ -180,6 +208,8 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Note what happens if a string is passed to the function. # COMMAND ---------- @@ -189,11 +219,15 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC In this case, we don't get an error, but we also do not get the desired outcome. # MAGIC -# MAGIC `assert` statements allow us to run simple tests of Python code. If an `assert` statement evaluates to true, nothing happens. If it evaluates to false, an error is raised. +# MAGIC **`assert`** statements allow us to run simple tests of Python code. If an **`assert`** statement evaluates to true, nothing happens. # MAGIC -# MAGIC Run the two cells below to assert the types of `2` and `"2"`. +# MAGIC If it evaluates to false, an error is raised. +# MAGIC +# MAGIC Run the following cell to assert that the number **`2`** is an integer # COMMAND ---------- @@ -201,14 +235,26 @@ def three_times(number): # COMMAND ---------- -assert type("2") == int +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC Uncomment the following cell and then run it to assert that the string **`"2"`"** is an integer. +# MAGIC +# MAGIC It should throw an **`AssertionError`**. + +# COMMAND ---------- + +# assert type("2") == int # COMMAND ---------- # MAGIC %md -# MAGIC As expected, the string `"2"` does not evaluate as an integer. # MAGIC -# MAGIC Python strings have a property to report whether or not they can be safely cast as numeric values. +# MAGIC +# MAGIC As expected, the string **`"2"`** is not an integer. +# MAGIC +# MAGIC Python strings have a property to report whether or not they can be safely cast as numeric value as seen below. # COMMAND ---------- @@ -217,9 +263,13 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC String numbers are common; you may see them as results from an API query, raw records in a JSON or CSV file, or returned by a SQL query. # MAGIC -# MAGIC `int()` and `float()` are two common methods for casting values to numeric types. An `int` will always be a whole number, while a `float` will always have a decimal. +# MAGIC **`int()`** and **`float()`** are two common methods for casting values to numeric types. +# MAGIC +# MAGIC An **`int`** will always be a whole number, while a **`float`** will always have a decimal. # COMMAND ---------- @@ -228,18 +278,24 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC While Python will gladly cast a string containing numeric characters to a numeric type, it will not allow you to change other strings to numbers. +# MAGIC +# MAGIC Uncomment the following cell and give it a try: # COMMAND ---------- -int("two") +# int("two") # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Note that errors will stop the execution of a notebook script; all cells after an error will be skipped when a notebook is scheduled as a production job. # MAGIC -# MAGIC If we enclose code that might throw an error in a `try` statement, we can define alternate logic when an error is encountered. +# MAGIC If we enclose code that might throw an error in a **`try`** statement, we can define alternate logic when an error is encountered. # MAGIC # MAGIC Below is a simple function that demonstrates this. @@ -247,13 +303,18 @@ def three_times(number): def try_int(num_string): try: - return int(num_string) + int(num_string) + result = f"{num_string} is a number." except: - print(f"{num_string} is not a number!") + result = f"{num_string} is not a number!" + + print(result) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC When a numeric string is passed, the function will return the result as an integer. # COMMAND ---------- @@ -263,6 +324,8 @@ def try_int(num_string): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC When a non-numeric string is passed, an informative message is printed out. # MAGIC # MAGIC **NOTE**: An error is **not** raised, even though an error occurred, and no value was returned. Implementing logic that suppresses errors can lead to logic silently failing. @@ -274,6 +337,8 @@ def try_int(num_string): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Below, our earlier function is updated to include logic for handling errors to return an informative message. # COMMAND ---------- @@ -282,15 +347,15 @@ def three_times(number): try: return int(number) * 3 except ValueError as e: - print(f""" - You passed the string variable '{number}'. - The result of using this function would be to return the string '{number * 3}'. - Try passing an integer instead. - """) + print(f"You passed the string variable '{number}'.\n") + print(f"Try passing an integer instead.") + return None # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Now our function can process numbers passed as strings. # COMMAND ---------- @@ -300,6 +365,8 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC And prints an informative message when a string is passed. # COMMAND ---------- @@ -309,11 +376,15 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Note that as implemented, this logic would only be useful for interactive execution of this logic (the message isn't currently being logged anywhere, and the code will not return the data in the desired format; human intervention would be required to act upon the printed message). # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Applying Python Control Flow for SQL Queries # MAGIC # MAGIC While the above examples demonstrate the basic principles of using these designs in Python, the goal of this lesson is to learn how to apply these concepts to executing SQL logic on Databricks. @@ -333,6 +404,8 @@ def three_times(number): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Run the SQL cell below to preview the contents of this temp view. # COMMAND ---------- @@ -343,7 +416,9 @@ def three_times(number): # COMMAND ---------- # MAGIC %md -# MAGIC Running SQL in a Python cell simply requires passing the string query to `spark.sql()`. +# MAGIC +# MAGIC +# MAGIC Running SQL in a Python cell simply requires passing the string query to **`spark.sql()`**. # COMMAND ---------- @@ -353,7 +428,9 @@ def three_times(number): # COMMAND ---------- # MAGIC %md -# MAGIC But recall that executing a query with `spark.sql()` returns the results as a DataFrame rather than displaying them; below, the code is augmented to capture the result and display it. +# MAGIC +# MAGIC +# MAGIC But recall that executing a query with **`spark.sql()`** returns the results as a DataFrame rather than displaying them; below, the code is augmented to capture the result and display it. # COMMAND ---------- @@ -364,7 +441,9 @@ def three_times(number): # COMMAND ---------- # MAGIC %md -# MAGIC Using a simple `if` clause with a function allows us to execute arbitrary SQL queries, optionally displaying the results, and always returning the resultant DataFrame. +# MAGIC +# MAGIC +# MAGIC Using a simple **`if`** clause with a function allows us to execute arbitrary SQL queries, optionally displaying the results, and always returning the resultant DataFrame. # COMMAND ---------- @@ -381,7 +460,9 @@ def simple_query_function(query, preview=True): # COMMAND ---------- # MAGIC %md -# MAGIC Below, we execute a different query and set preview to `False`, as the purpose of the query is to create a temp view rather than return a preview of data. +# MAGIC +# MAGIC +# MAGIC Below, we execute a different query and set preview to **`False`**, as the purpose of the query is to create a temp view rather than return a preview of data. # COMMAND ---------- @@ -392,6 +473,8 @@ def simple_query_function(query, preview=True): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC We now have a simple extensible function that could be further parameterized depending on the needs of our organization. # MAGIC # MAGIC For example, suppose we want to protect our company from malicious SQL, like the query below. @@ -403,30 +486,71 @@ def simple_query_function(query, preview=True): # COMMAND ---------- # MAGIC %md -# MAGIC Below, we define a simple search for a semi-colon in the text, then use an assert statement with `try/except` to raise a custom error message. +# MAGIC +# MAGIC +# MAGIC +# MAGIC We can use the **`find()`** method to test for multiple SQL statements by looking for a semicolon. + +# COMMAND ---------- + +injection_query.find(";") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC If it's not found it will return **`-1`** + +# COMMAND ---------- + +injection_query.find("x") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC With that knowledge, we can define a simple search for a semicolon in the query string and raise a custom error message if it was found (not **`-1`**) # COMMAND ---------- def injection_check(query): semicolon_index = query.find(";") - try: - assert semicolon_index < 0, f"Query contains semi-colon at index {semicolon_index}\nBlocking execution to avoid SQL injection attack" - except AssertionError as e: - print(query) - raise e + if semicolon_index >= 0: + raise ValueError(f"Query contains semi-colon at index {semicolon_index}\nBlocking execution to avoid SQL injection attack") # COMMAND ---------- # MAGIC %md -# MAGIC **NOTE**: The example shown here is not sophisticated, but seeks to demonstrate a general principle. Always be wary of allowing untrusted users to pass text that will be passed to SQL queries. Also note that only one query can be executed using `spark.sql()`, so text with a semi-colon will always throw an error. +# MAGIC +# MAGIC +# MAGIC +# MAGIC **NOTE**: The example shown here is not sophisticated, but seeks to demonstrate a general principle. +# MAGIC +# MAGIC Always be wary of allowing untrusted users to pass text that will be passed to SQL queries. +# MAGIC +# MAGIC Also note that only one query can be executed using **`spark.sql()`**, so text with a semi-colon will always throw an error. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC Uncomment the following cell and give it a try: # COMMAND ---------- -injection_check(injection_query) +# injection_check(injection_query) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC If we add this method to our earlier query function, we now have a more robust function that will assess each query for potential threats before execution. # COMMAND ---------- @@ -441,6 +565,8 @@ def secure_query_function(query, preview=True): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC As expected, we see normal performance with a safe query. # COMMAND ---------- @@ -450,6 +576,8 @@ def secure_query_function(query, preview=True): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC But prevent execution when when bad logic is run. # COMMAND ---------- diff --git a/Data-Engineering-with-Databricks/Solutions/05 - OPTIONAL Python for Spark SQL/DE 5.3L - Python for SQL Lab.py b/Data-Engineering-with-Databricks/Solutions/05 - OPTIONAL Python for Spark SQL/DE 5.3L - Python for SQL Lab.py new file mode 100644 index 0000000..7ec3cce --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/05 - OPTIONAL Python for Spark SQL/DE 5.3L - Python for SQL Lab.py @@ -0,0 +1,292 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Just Enough Python for Databricks SQL Lab +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Review basic Python code and describe expected outcomes of code execution +# MAGIC * Reason through control flow statements in Python functions +# MAGIC * Add parameters to a SQL query by wrapping it in a Python function + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-5.3L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Reviewing Python Basics +# MAGIC +# MAGIC In the previous notebook, we briefly explored using **`spark.sql()`** to execute arbitrary SQL commands from Python. +# MAGIC +# MAGIC Look at the following 3 cells. Before executing each cell, identify: +# MAGIC 1. The expected output of cell execution +# MAGIC 1. What logic is being executed +# MAGIC 1. Changes to the resultant state of the environment +# MAGIC +# MAGIC Then execute the cells, compare the results to your expectations, and see the explanations below. + +# COMMAND ---------- + +course = "dewd" + +# COMMAND ---------- + +spark.sql(f"SELECT '{course}' AS course_name") + +# COMMAND ---------- + +df = spark.sql(f"SELECT '{course}' AS course_name") +display(df) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC 1. **Cmd 5** assigns a string to a variable. When a variable assignment is successful, no output is displayed to the notebook. A new variable is added to the current execution environment. +# MAGIC 1. **Cmd 6** executes a SQL query and displays the schema for the DataFrame alongside the word **`DataFrame`**. In this case, the SQL query is just to select a string, so no changes to our environment occur. +# MAGIC 1. **Cmd 7** executes the same SQL query and displays the output of the DataFrame. This combination of **`display()`** and **`spark.sql()`** most closely mirrors executing logic in a **`%sql`** cell; the results will always be printed in a formatted table, assuming results are returned by the query; some queries will instead manipulate tables or databases, in which case the work **`OK`** will print to show successful execution. In this case, no changes to our environment occur from running this code. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Setting Up a Development Environment +# MAGIC +# MAGIC Throughout this course, we use logic similar to the follow cell to capture information about the user currently executing the notebook and create an isolated development database. +# MAGIC +# MAGIC The **`re`** library is the standard Python library for regex. +# MAGIC +# MAGIC Databricks SQL has a special method to capture the username of the **`current_user()`**; and the **`.first()[0]`** code is a quick hack to capture the first row of the first column of a query executed with **`spark.sql()`** (in this case, we do this safely knowing that there will only be 1 row and 1 column). +# MAGIC +# MAGIC All other logic below is just string formatting. + +# COMMAND ---------- + +import re + +username = spark.sql("SELECT current_user()").first()[0] +clean_username = re.sub("[^a-zA-Z0-9]", "_", username) +db_name = f"dbacademy_{clean_username}_{course}_5_3l" +working_dir = f"dbfs:/user/{username}/dbacademy/{course}/5.3l" + +print(f"username: {username}") +print(f"db_name: {db_name}") +print(f"working_dir: {working_dir}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Below, we add a simple control flow statement to this logic to create and use this user-specific database. +# MAGIC +# MAGIC Optionally, we will reset this database and drop all of the contents on repeat execution. (Note the the default value for the parameter **`reset`** is **`True`**). + +# COMMAND ---------- + +def create_database(course, reset=True): + import re + + username = spark.sql("SELECT current_user()").first()[0] + clean_username = re.sub("[^a-zA-Z0-9]", "_", username) + db_name = f"dbacademy_{clean_username}_{course}_5_3l" + working_dir = f"dbfs:/user/{username}/dbacademy/{course}/5.3l" + + print(f"username: {username}") + print(f"db_name: {db_name}") + print(f"working_dir: {working_dir}") + + if reset: + spark.sql(f"DROP DATABASE IF EXISTS {db_name} CASCADE") + dbutils.fs.rm(working_dir, True) + + spark.sql(f"CREATE DATABASE IF NOT EXISTS {db_name} LOCATION '{working_dir}/{db_name}.db'") + spark.sql(f"USE {db_name}") + +create_database(course) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC While this logic as defined is geared toward isolating students in shared workspaces for instructional purposes, the same basic design could be leveraged for testing new logic in an isolated environment before pushing to production. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Handling Errors Gracefully +# MAGIC +# MAGIC Review the logic in the function below. +# MAGIC +# MAGIC Note that we've just declared a new database that currently contains no tables. + +# COMMAND ---------- + +def query_or_make_demo_table(table_name): + try: + display(spark.sql(f"SELECT * FROM {table_name}")) + print(f"Displayed results for the table {table_name}") + + except: + spark.sql(f"CREATE TABLE {table_name} (id INT, name STRING, value DOUBLE, state STRING)") + spark.sql(f"""INSERT INTO {table_name} + VALUES (1, "Yve", 1.0, "CA"), + (2, "Omar", 2.5, "NY"), + (3, "Elia", 3.3, "OH"), + (4, "Rebecca", 4.7, "TX"), + (5, "Ameena", 5.3, "CA"), + (6, "Ling", 6.6, "NY"), + (7, "Pedro", 7.1, "KY")""") + + display(spark.sql(f"SELECT * FROM {table_name}")) + print(f"Created the table {table_name}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Try to identify the following before executing the next cell: +# MAGIC 1. The expected output of cell execution +# MAGIC 1. What logic is being executed +# MAGIC 1. Changes to the resultant state of the environment + +# COMMAND ---------- + +query_or_make_demo_table("demo_table") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Now answer the same three questions before running the same query below. + +# COMMAND ---------- + +query_or_make_demo_table("demo_table") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC - On the first execution, the table **`demo_table`** did not yet exist. As such, the attempt to return the contents of the table created an error, which resulted in our **`except`** block of logic executing. This block: +# MAGIC 1. Created the table +# MAGIC 1. Inserted values +# MAGIC 1. Printed or displayed the contents of the table +# MAGIC - On the second execution, the table **`demo_table`** already exists, and so the first query in the **`try`** block executes without error. As a result, we just display the results of the query without modifying anything in our environment. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Adapting SQL to Python +# MAGIC Let's consider the following SQL query against our demo table created above. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT id, value +# MAGIC FROM demo_table +# MAGIC WHERE state = "CA" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC which can also be expressed using the PySpark API and the **`display`** function as seen here: + +# COMMAND ---------- + +results = spark.sql("SELECT id, value FROM demo_table WHERE state = 'CA'") +display(results) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Let's use this simple example to practice creating a Python function that adds optional functionality. +# MAGIC +# MAGIC Our target function will: +# MAGIC * Be based upon a query that only includes the **`id`** and **`value`** columns from the a table named **`demo_table`** +# MAGIC * Will allow filtering of that query by **`state`** where the the default behavior is to include all states +# MAGIC * Will optionally render the results of the query using the **`display`** function where the default behavior is to not render +# MAGIC * Will return: +# MAGIC * The query result object (a PySpark DataFrame) if **`render_results`** is False +# MAGIC * The **`None`** value if **`render_results`** is True +# MAGIC +# MAGIC Stretch Goal: +# MAGIC * Add an assert statement to verify that the value passed for the **`state`** parameter contains two, uppercase letters +# MAGIC +# MAGIC Some starter logic has been provided below: + +# COMMAND ---------- + +# ANSWER +def preview_values(state=None, render_results=False): + query = "SELECT id, value FROM demo_table" + + if state is not None: + assert state == state.upper() and len(state) == 2, "Please use the standard 2-letter, uppercase, state abbreviations" + query += f" WHERE state = '{state}'" + + query_results = spark.sql(query) + + if render_results: + display(query_results) + return None + else: + return query_results + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The assert statements below can be used to check whether or not your function works as intended. + +# COMMAND ---------- + +import pyspark.sql.dataframe + +assert type(preview_values()) == pyspark.sql.dataframe.DataFrame, "Function should return the results as a DataFrame" +assert preview_values().columns == ["id", "value"], "Query should only return **`id`** and **`value`** columns" + +assert preview_values(render_results=True) is None, "Function should not return None when rendering" +assert preview_values(render_results=False) is not None, "Function should return DataFrame when not rendering" + +assert preview_values(state=None).count() == 7, "Function should allow no state" +assert preview_values(state="NY").count() == 2, "Function should allow filtering by state" +assert preview_values(state="CA").count() == 2, "Function should allow filtering by state" +assert preview_values(state="OH").count() == 1, "Function should allow filtering by state" + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/06 - Incremental Data Processing/DE 6.1 - Incremental Data Ingestion with Auto Loader.py b/Data-Engineering-with-Databricks/Solutions/06 - Incremental Data Processing/DE 6.1 - Incremental Data Ingestion with Auto Loader.py new file mode 100644 index 0000000..7904cf1 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/06 - Incremental Data Processing/DE 6.1 - Incremental Data Ingestion with Auto Loader.py @@ -0,0 +1,279 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Incremental Data Ingestion with Auto Loader +# MAGIC +# MAGIC Incremental ETL is important since it allows us to deal solely with new data that has been encountered since the last ingestion. Reliably processing only the new data reduces redundant processing and helps enterprises reliably scale data pipelines. +# MAGIC +# MAGIC The first step for any successful data lakehouse implementation is ingesting into a Delta Lake table from cloud storage. +# MAGIC +# MAGIC Historically, ingesting files from a data lake into a database has been a complicated process. +# MAGIC +# MAGIC Databricks Auto Loader provides an easy-to-use mechanism for incrementally and efficiently processing new data files as they arrive in cloud file storage. In this notebook, you'll see Auto Loader in action. +# MAGIC +# MAGIC Due to the benefits and scalability that Auto Loader delivers, Databricks recommends its use as general **best practice** when ingesting data from cloud object storage. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Execute Auto Loader code to incrementally ingest data from cloud storage to Delta Lake +# MAGIC * Describe what happens when a new file arrives in a directory configured for Auto Loader +# MAGIC * Query a table fed by a streaming Auto Loader query +# MAGIC +# MAGIC ## Dataset Used +# MAGIC This demo uses simplified artificially generated medical data representing heart rate recordings delivered in the JSON format. +# MAGIC +# MAGIC | Field | Type | +# MAGIC | --- | --- | +# MAGIC | device_id | int | +# MAGIC | mrn | long | +# MAGIC | time | double | +# MAGIC | heartrate | double | + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Getting Started +# MAGIC +# MAGIC Run the following cell to reset the demo and configure required variables and help functions. + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-6.1 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Using Auto Loader +# MAGIC +# MAGIC In the cell below, a function is defined to demonstrate using Databricks Auto Loader with the PySpark API. This code includes both a Structured Streaming read and write. +# MAGIC +# MAGIC The following notebook will provide a more robust overview of Structured Streaming. If you wish to learn more about Auto Loader options, refer to the documentation. +# MAGIC +# MAGIC Note that when using Auto Loader with automatic schema inference and evolution, the 4 arguments shown here should allow ingestion of most datasets. These arguments are explained below. +# MAGIC +# MAGIC | argument | what it is | how it's used | +# MAGIC | --- | --- | --- | +# MAGIC | **`data_source`** | The directory of the source data | Auto Loader will detect new files as they arrive in this location and queue them for ingestion; passed to the **`.load()`** method | +# MAGIC | **`source_format`** | The format of the source data | While the format for all Auto Loader queries will be **`cloudFiles`**, the format of the source data should always be specified for the **`cloudFiles.format`** option | +# MAGIC | **`table_name`** | The name of the target table | Spark Structured Streaming supports writing directly to Delta Lake tables by passing a table name as a string to the **`.table()`** method. Note that you can either append to an existing table or create a new table | +# MAGIC | **`checkpoint_directory`** | The location for storing metadata about the stream | This argument is pass to the **`checkpointLocation`** and **`cloudFiles.schemaLocation`** options. Checkpoints keep track of streaming progress, while the schema location tracks updates to the fields in the source dataset | +# MAGIC +# MAGIC **NOTE**: The code below has been streamlined to demonstrate Auto Loader functionality. We'll see in later lessons that additional transformations can be applied to source data before saving them to Delta Lake. + +# COMMAND ---------- + +def autoload_to_table(data_source, source_format, table_name, checkpoint_directory): + query = (spark.readStream + .format("cloudFiles") + .option("cloudFiles.format", source_format) + .option("cloudFiles.schemaLocation", checkpoint_directory) + .load(data_source) + .writeStream + .option("checkpointLocation", checkpoint_directory) + .option("mergeSchema", "true") + .table(table_name)) + return query + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC In the following cell, we use the previously defined function and some path variables defined in the setup script to begin an Auto Loader stream. +# MAGIC +# MAGIC Here, we're reading from a source directory of JSON files. + +# COMMAND ---------- + +query = autoload_to_table(data_source = f"{DA.paths.working_dir}/tracker", + source_format = "json", + table_name = "target_table", + checkpoint_directory = f"{DA.paths.checkpoints}/target_table") + + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Because Auto Loader uses Spark Structured Streaming to load data incrementally, the code above doesn't appear to finish executing. +# MAGIC +# MAGIC We can think of this as a **continuously active query**. This means that as soon as new data arrives in our data source, it will be processed through our logic and loaded into our target table. We'll explore this in just a second. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Helper Function for Streaming Lessons +# MAGIC +# MAGIC Our notebook-based lessons combine streaming functions with batch and streaming queries against the results of those operations. These notebooks are for instructional purposes and intended for interactive, cell-by-cell execution. This pattern is not intended for production. +# MAGIC +# MAGIC Below, we define a helper function that prevents our notebook from executing the next cell just long enough to ensure data has been written out by a given streaming query. This code should not be necessary in a production job. + +# COMMAND ---------- + +def block_until_stream_is_ready(query, min_batches=2): + import time + while len(query.recentProgress) < min_batches: + time.sleep(5) # Give it a couple of seconds + + print(f"The stream has processed {len(query.recentProgress)} batchs") + +block_until_stream_is_ready(query) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Query Target Table +# MAGIC +# MAGIC Once data has been ingested to Delta Lake with Auto Loader, users can interact with it the same way they would any table. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM target_table + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Note that the **`_rescued_data`** column is added by Auto Loader automatically to capture any data that might be malformed and not fit into the table otherwise. +# MAGIC +# MAGIC While Auto Loader captured the field names for our data correctly, note that it encoded all fields as **`STRING`** type. Because JSON is a text-based format, this is the safest and most permissive type, ensuring that the least amount of data is dropped or ignored at ingestion due to type mismatch. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC DESCRIBE TABLE target_table + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Use the cell below to define a temporary view that summarizes the recordings in our target table. +# MAGIC +# MAGIC We'll use this view below to demonstrate how new data is automatically ingested with Auto Loader. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC CREATE OR REPLACE TEMP VIEW device_counts AS +# MAGIC SELECT device_id, count(*) total_recordings +# MAGIC FROM target_table +# MAGIC GROUP BY device_id; +# MAGIC +# MAGIC SELECT * FROM device_counts + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Land New Data +# MAGIC +# MAGIC As mentioned previously, Auto Loader is configured to incrementally process files from a directory in cloud object storage into a Delta Lake table. +# MAGIC +# MAGIC We have configured and are currently executing a query to process JSON files from the location specified by **`source_path`** into a table named **`target_table`**. Let's review the contents of the **`source_path`** directory. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.working_dir}/tracker") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC At present, you should see a single JSON file listed in this location. +# MAGIC +# MAGIC The method in the cell below was configured in our setup script to allow us to model an external system writing data to this directory. Each time you execute the cell below, a new file will land in the **`source_path`** directory. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC List the contents of the **`source_path`** again using the cell below. You should see an additional JSON file for each time you ran the previous cell. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.working_dir}/tracker") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Tracking Ingestion Progress +# MAGIC +# MAGIC Historically, many systems have been configured to either reprocess all records in a source directory to calculate current results or require data engineers to implement custom logic to identify new data that's arrived since the last time a table was updated. +# MAGIC +# MAGIC With Auto Loader, your table has already been updated. +# MAGIC +# MAGIC Run the query below to confirm that new data has been ingested. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM device_counts + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The Auto Loader query we configured earlier automatically detects and processes records from the source directory into the target table. There is a slight delay as records are ingested, but an Auto Loader query executing with default streaming configuration should update results in near real time. +# MAGIC +# MAGIC The query below shows the table history. A new table version should be indicated for each **`STREAMING UPDATE`**. These update events coincide with new batches of data arriving at the source. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC DESCRIBE HISTORY target_table + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Clean Up +# MAGIC Feel free to continue landing new data and exploring the table results with the cells above. +# MAGIC +# MAGIC When you're finished, run the following cell to stop all active streams and remove created resources before continuing. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/06 - Incremental Data Processing/DE 6.2 - Reasoning about Incremental Data.py b/Data-Engineering-with-Databricks/Solutions/06 - Incremental Data Processing/DE 6.2 - Reasoning about Incremental Data.py new file mode 100644 index 0000000..fe8cc79 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/06 - Incremental Data Processing/DE 6.2 - Reasoning about Incremental Data.py @@ -0,0 +1,397 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Reasoning about Incremental Data +# MAGIC +# MAGIC Spark Structured Streaming extends the functionality of Apache Spark to allow for simplified configuration and bookkeeping when processing incremental datasets. In the past, much of the emphasis for streaming with big data has focused on reducing latency to provide near real time analytic insights. While Structured Streaming provides exceptional performance in achieving these goals, this lesson will focus more on the applications of incremental data processing. +# MAGIC +# MAGIC While incremental processing is not absolutely necessary to work successfully in the data lakehouse, our experience helping some of the world's largest companies derive insights from the world's largest datasets has led to the conclusion that many workloads can benefit substantially from an incremental processing approach. Many of the core features at the heart of Databricks have been optimized specifically to handle these ever-growing datasets. +# MAGIC +# MAGIC Consider the following datasets and use cases: +# MAGIC * Data scientists need secure, de-identified, versioned access to frequently updated records in an operational database +# MAGIC * Credit card transactions need to be compared to past customer behavior to identify and flag fraud +# MAGIC * A multi-national retailer seeks to serve custom product recommendations using purchase history +# MAGIC * Log files from distributed systems need to be analayzed to detect and respond to instabilities +# MAGIC * Clickstream data from millions of online shoppers needs to be leveraged for A/B testing of UX +# MAGIC +# MAGIC The above are just a small sample of datasets that grow incrementally and infinitely over time. +# MAGIC +# MAGIC In this lesson, we'll explore the basics of working with Spark Structured Streaming to allow incremental processing of data. In the next lesson, we'll talk more about how this incremental processing model simplifies data processing in the data lakehouse. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Describe the programming model used by Spark Structured Streaming +# MAGIC * Configure required options to perform a streaming read on a source +# MAGIC * Describe the requirements for end-to-end fault tolerance +# MAGIC * Configure required options to perform a streaming write to a sink + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Getting Started +# MAGIC +# MAGIC Run the following cell to configure our "classroom." + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-6.2 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Treating Infinite Data as a Table +# MAGIC +# MAGIC The magic behind Spark Structured Streaming is that it allows users to interact with ever-growing data sources as if they were just a static table of records. +# MAGIC +# MAGIC +# MAGIC +# MAGIC In the graphic above, a **data stream** describes any data source that grows over time. New data in a data stream might correspond to: +# MAGIC * A new JSON log file landing in cloud storage +# MAGIC * Updates to a database captured in a CDC feed +# MAGIC * Events queued in a pub/sub messaging feed +# MAGIC * A CSV file of sales closed the previous day +# MAGIC +# MAGIC Many organizations have traditionally taken an approach of reprocessing the entire source dataset each time they want to update their results. Another approach would be to write custom logic to only capture those files or records that have been added since the last time an update was run. +# MAGIC +# MAGIC Structured Streaming lets us define a query against the data source and automatically detect new records and propagate them through previously defined logic. +# MAGIC +# MAGIC **Spark Structured Streaming is optimized on Databricks to integrate closely with Delta Lake and Auto Loader.** + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Basic Concepts +# MAGIC +# MAGIC - The developer defines an **input table** by configuring a streaming read against a **source**. The syntax for doing this is similar to working with static data. +# MAGIC - A **query** is defined against the input table. Both the DataFrames API and Spark SQL can be used to easily define transformations and actions against the input table. +# MAGIC - This logical query on the input table generates the **results table**. The results table contains the incremental state information of the stream. +# MAGIC - The **output** of a streaming pipeline will persist updates to the results table by writing to an external **sink**. Generally, a sink will be a durable system such as files or a pub/sub messaging bus. +# MAGIC - New rows are appended to the input table for each **trigger interval**. These new rows are essentially analogous to micro-batch transactions and will be automatically propagated through the results table to the sink. +# MAGIC +# MAGIC +# MAGIC +# MAGIC +# MAGIC For more information, see the analogous section in the Structured Streaming Programming Guide (from which several images have been borrowed). + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## End-to-end Fault Tolerance +# MAGIC +# MAGIC Structured Streaming ensures end-to-end exactly-once fault-tolerance guarantees through _checkpointing_ (discussed below) and Write Ahead Logs. +# MAGIC +# MAGIC Structured Streaming sources, sinks, and the underlying execution engine work together to track the progress of stream processing. If a failure occurs, the streaming engine attempts to restart and/or reprocess the data. +# MAGIC For best practices on recovering from a failed streaming query see docs. +# MAGIC +# MAGIC This approach _only_ works if the streaming source is replayable; replayable sources include cloud-based object storage and pub/sub messaging services. +# MAGIC +# MAGIC At a high level, the underlying streaming mechanism relies on a couple of approaches: +# MAGIC +# MAGIC * First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval. +# MAGIC * Next, the streaming sinks are designed to be _idempotent_ - that is, multiple writes of the same data (as identified by the offset) do _not_ result in duplicates being written to the sink. +# MAGIC +# MAGIC Taken together, replayable data sources and idempotent sinks allow Structured Streaming to ensure **end-to-end, exactly-once semantics** under any failure condition. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Reading a Stream +# MAGIC +# MAGIC The **`spark.readStream()`** method returns a **`DataStreamReader`** used to configure and query the stream. +# MAGIC +# MAGIC In the previous lesson, we saw code configured for incrementally reading with Auto Loader. Here, we'll show how easy it is to incrementally read a Delta Lake table. +# MAGIC +# MAGIC The code uses the PySpark API to incrementally read a Delta Lake table named **`bronze`** and register a streaming temp view named **`streaming_tmp_vw`**. +# MAGIC +# MAGIC **NOTE**: A number of optional configurations (not shown here) can be set when configuring incremental reads, the most important of which allows you to limit the input rate. + +# COMMAND ---------- + +(spark.readStream + .table("bronze") + .createOrReplaceTempView("streaming_tmp_vw")) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC When we execute a query on a streaming temporary view, we'll continue to update the results of the query as new data arrives in the source. +# MAGIC +# MAGIC Think of a query executed against a streaming temp view as an **always-on incremental query**. +# MAGIC +# MAGIC **NOTE**: Generally speaking, unless a human is actively monitoring the output of a query during development or live dashboarding, we won't return streaming results to a notebook. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM streaming_tmp_vw + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC You will recognize the data as being the same as the Delta table written out in our previous lesson. +# MAGIC +# MAGIC Before continuing, click **`Stop Execution`** at the top of the notebook, **`Cancel`** immediately under the cell, or run the following cell to stop all active streaming queries. + +# COMMAND ---------- + +for s in spark.streams.active: + print("Stopping " + s.id) + s.stop() + s.awaitTermination() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Working with Streaming Data +# MAGIC We can execute most transformation against streaming temp views the same way we would with static data. Here, we'll run a simple aggregation to get counts of records for each **`device_id`**. +# MAGIC +# MAGIC Because we are querying a streaming temp view, this becomes a streaming query that executes indefinitely, rather than completing after retrieving a single set of results. For streaming queries like this, Databricks Notebooks include interactive dashboards that allow users to monitor streaming performance. Explore this below. +# MAGIC +# MAGIC One important note regarding this example: this is merely displaying an aggregation of input as seen by the stream. **None of these records are being persisted anywhere at this point.** + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT device_id, count(device_id) AS total_recordings +# MAGIC FROM streaming_tmp_vw +# MAGIC GROUP BY device_id + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Before continuing, click **`Stop Execution`** at the top of the notebook, **`Cancel`** immediately under the cell, or run the following cell to stop all active streaming queries. + +# COMMAND ---------- + +for s in spark.streams.active: + print("Stopping " + s.id) + s.stop() + s.awaitTermination() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Unsupported Operations +# MAGIC +# MAGIC Most operations on a streaming DataFrame are identical to a static DataFrame. There are some exceptions to this. +# MAGIC +# MAGIC Consider the model of the data as a constantly appending table. Sorting is one of a handful of operations that is either too complex or logically not possible to do when working with streaming data. +# MAGIC +# MAGIC A full discussion of these exceptions is out of scope for this course. Note that advanced streaming methods like windowing and watermarking can be used to add additional functionality to incremental workloads. +# MAGIC +# MAGIC Uncomment and run the following cell how this failure may appear: + +# COMMAND ---------- + +# %sql +# SELECT * +# FROM streaming_tmp_vw +# ORDER BY time + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Persisting Streaming Results +# MAGIC +# MAGIC In order to persist incremental results, we need to pass our logic back to the PySpark Structured Streaming DataFrames API. +# MAGIC +# MAGIC Above, we created a temp view from a PySpark streaming DataFrame. If we create another temp view from the results of a query against a streaming temp view, we'll again have a streaming temp view. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC CREATE OR REPLACE TEMP VIEW device_counts_tmp_vw AS ( +# MAGIC SELECT device_id, COUNT(device_id) AS total_recordings +# MAGIC FROM streaming_tmp_vw +# MAGIC GROUP BY device_id +# MAGIC ) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Writing a Stream +# MAGIC +# MAGIC To persist the results of a streaming query, we must write them out to durable storage. The **`DataFrame.writeStream`** method returns a **`DataStreamWriter`** used to configure the output. +# MAGIC +# MAGIC When writing to Delta Lake tables, we typically will only need to worry about 3 settings, discussed here. +# MAGIC +# MAGIC ### Checkpointing +# MAGIC +# MAGIC Databricks creates checkpoints by storing the current state of your streaming job to cloud storage. +# MAGIC +# MAGIC Checkpointing combines with write ahead logs to allow a terminated stream to be restarted and continue from where it left off. +# MAGIC +# MAGIC Checkpoints cannot be shared between separate streams. A checkpoint is required for every streaming write to ensure processing guarantees. +# MAGIC +# MAGIC ### Output Modes +# MAGIC +# MAGIC Streaming jobs have output modes similar to static/batch workloads. More details here. +# MAGIC +# MAGIC | Mode | Example | Notes | +# MAGIC | ------------- | ----------- | --- | +# MAGIC | **Append** | **`.outputMode("append")`** | **This is the default.** Only newly appended rows are incrementally appended to the target table with each batch | +# MAGIC | **Complete** | **`.outputMode("complete")`** | The Results Table is recalculated each time a write is triggered; the target table is overwritten with each batch | +# MAGIC +# MAGIC +# MAGIC ### Trigger Intervals +# MAGIC +# MAGIC When defining a streaming write, the **`trigger`** method specifies when the system should process the next set of data.. +# MAGIC +# MAGIC +# MAGIC | Trigger Type | Example | Behavior | +# MAGIC |----------------------------------------|----------|----------| +# MAGIC | Unspecified | | **This is the default.** This is equivalent to using **`processingTime="500ms"`** | +# MAGIC | Fixed interval micro-batches | **`.trigger(processingTime="2 minutes")`** | The query will be executed in micro-batches and kicked off at the user-specified intervals | +# MAGIC | Triggered micro-batch | **`.trigger(once=True)`** | The query will execute a single micro-batch to process all the available data and then stop on its own | +# MAGIC | Triggered micro-batches | **`.trigger(availableNow=True)`** | The query will execute multiple micro-batches to process all the available data and then stop on its own | +# MAGIC +# MAGIC Triggers are specified when defining how data will be written to a sink and control the frequency of micro-batches. By default, Spark will automatically detect and process all data in the source that has been added since the last trigger. +# MAGIC +# MAGIC **NOTE:** **`Trigger.AvailableNow`** is a new trigger type that is available in DBR 10.1 for Scala only and available in DBR 10.2 and above for Python and Scala. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Pulling It All Together +# MAGIC +# MAGIC The code below demonstrates using **`spark.table()`** to load data from a streaming temp view back to a DataFrame. Note that Spark will always load streaming views as a streaming DataFrame and static views as static DataFrames (meaning that incremental processing must be defined with read logic to support incremental writing). +# MAGIC +# MAGIC In this first query, we'll demonstrate using **`trigger(availableNow=True)`** to perform incremental batch processing. + +# COMMAND ---------- + +(spark.table("device_counts_tmp_vw") + .writeStream + .option("checkpointLocation", f"{DA.paths.checkpoints}/silver") + .outputMode("complete") + .trigger(availableNow=True) + .table("device_counts") + .awaitTermination() # This optional method blocks execution of the next cell until the incremental batch write has succeeded +) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Below, we change our trigger method to change this query from a triggered incremental batch to an always-on query triggered every 4 seconds. +# MAGIC +# MAGIC **NOTE**: As we start this query, no new records exist in our source table. We'll add new data shortly. + +# COMMAND ---------- + +query = (spark.table("device_counts_tmp_vw") + .writeStream + .option("checkpointLocation", f"{DA.paths.checkpoints}/silver") + .outputMode("complete") + .trigger(processingTime='4 seconds') + .table("device_counts")) + +# Like before, wait until our stream has processed some data +DA.block_until_stream_is_ready(query) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Querying the Output +# MAGIC Now let's query the output we've written from SQL. Because the result is a table, we only need to deserialize the data to return the results. +# MAGIC +# MAGIC Because we are now querying a table (not a streaming DataFrame), the following will **not** be a streaming query. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * +# MAGIC FROM device_counts + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Land New Data +# MAGIC +# MAGIC As in our previous lesson, we have configured a helper function to process new records into our source table. +# MAGIC +# MAGIC Run the cell below to land another batch of data. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Query the target table again to see the updated counts for each **`device_id`**. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * +# MAGIC FROM device_counts + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Clean Up +# MAGIC Feel free to continue landing new data and exploring the table results with the cells above. +# MAGIC +# MAGIC When you're finished, run the following cell to stop all active streams and remove created resources before continuing. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/06 - Incremental Data Processing/DE 6.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py b/Data-Engineering-with-Databricks/Solutions/06 - Incremental Data Processing/DE 6.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py new file mode 100644 index 0000000..231b970 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/06 - Incremental Data Processing/DE 6.3L - Using Auto Loader and Structured Streaming with Spark SQL Lab.py @@ -0,0 +1,186 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Using Auto Loader and Structured Streaming with Spark SQL +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Ingest data using Auto Loader +# MAGIC * Aggregate streaming data +# MAGIC * Stream data to a Delta table + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Setup +# MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-6.3L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Configure Streaming Read +# MAGIC +# MAGIC This lab uses a collection of customer-related CSV data from DBFS found in */databricks-datasets/retail-org/customers/*. +# MAGIC +# MAGIC Read this data using Auto Loader using its schema inference (use **`customers_checkpoint_path`** to store the schema info). Create a streaming temporary view called **`customers_raw_temp`**. + +# COMMAND ---------- + +# ANSWER +customers_checkpoint_path = f"{DA.paths.checkpoints}/customers" + +(spark.readStream + .format("cloudFiles") + .option("cloudFiles.format", "csv") + .option("cloudFiles.schemaLocation", customers_checkpoint_path) + .load("/databricks-datasets/retail-org/customers/") + .createOrReplaceTempView("customers_raw_temp")) + +# COMMAND ---------- + +from pyspark.sql import Row +assert Row(tableName="customers_raw_temp", isTemporary=True) in spark.sql("show tables").select("tableName", "isTemporary").collect(), "Table not present or not temporary" +assert spark.table("customers_raw_temp").dtypes == [('customer_id', 'string'), + ('tax_id', 'string'), + ('tax_code', 'string'), + ('customer_name', 'string'), + ('state', 'string'), + ('city', 'string'), + ('postcode', 'string'), + ('street', 'string'), + ('number', 'string'), + ('unit', 'string'), + ('region', 'string'), + ('district', 'string'), + ('lon', 'string'), + ('lat', 'string'), + ('ship_to_address', 'string'), + ('valid_from', 'string'), + ('valid_to', 'string'), + ('units_purchased', 'string'), + ('loyalty_segment', 'string'), + ('_rescued_data', 'string')], "Incorrect Schema" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Define a streaming aggregation +# MAGIC +# MAGIC Using CTAS syntax, define a new streaming view called **`customer_count_by_state_temp`** that counts the number of customers per **`state`**, in a field called **`customer_count`**. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- ANSWER +# MAGIC +# MAGIC CREATE OR REPLACE TEMPORARY VIEW customer_count_by_state_temp AS +# MAGIC SELECT +# MAGIC state, +# MAGIC count(customer_id) AS customer_count +# MAGIC FROM customers_raw_temp +# MAGIC GROUP BY +# MAGIC state + +# COMMAND ---------- + +assert Row(tableName="customer_count_by_state_temp", isTemporary=True) in spark.sql("show tables").select("tableName", "isTemporary").collect(), "Table not present or not temporary" +assert spark.table("customer_count_by_state_temp").dtypes == [('state', 'string'), ('customer_count', 'bigint')], "Incorrect Schema" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Write aggregated data to a Delta table +# MAGIC +# MAGIC Stream data from the **`customer_count_by_state_temp`** view to a Delta table called **`customer_count_by_state`**. + +# COMMAND ---------- + +# ANSWER +customers_count_checkpoint_path = f"{DA.paths.checkpoints}/customers_count" + +query = (spark.table("customer_count_by_state_temp") + .writeStream + .format("delta") + .option("checkpointLocation", customers_count_checkpoint_path) + .outputMode("complete") + .table("customer_count_by_state")) + +# COMMAND ---------- + +DA.block_until_stream_is_ready(query) + +# COMMAND ---------- + +assert Row(tableName="customer_count_by_state", isTemporary=False) in spark.sql("show tables").select("tableName", "isTemporary").collect(), "Table not present or not temporary" +assert spark.table("customer_count_by_state").dtypes == [('state', 'string'), ('customer_count', 'bigint')], "Incorrect Schema" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Query the results +# MAGIC +# MAGIC Query the **`customer_count_by_state`** table (this will not be a streaming query). Plot the results as a bar graph and also using the map plot. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- ANSWER +# MAGIC SELECT * FROM customer_count_by_state + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Wrapping Up +# MAGIC +# MAGIC Run the following cell to remove the database and all data associated with this lab. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC By completing this lab, you should now feel comfortable: +# MAGIC * Using PySpark to configure Auto Loader for incremental data ingestion +# MAGIC * Using Spark SQL to aggregate streaming data +# MAGIC * Streaming data to a Delta table + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.2.1 - Incremental Multi-Hop in the Lakehouse.py b/Data-Engineering-with-Databricks/Solutions/07 - Multi-Hop Architecture/DE 7.1 - Incremental Multi-Hop in the Lakehouse.py similarity index 80% rename from Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.2.1 - Incremental Multi-Hop in the Lakehouse.py rename to Data-Engineering-with-Databricks/Solutions/07 - Multi-Hop Architecture/DE 7.1 - Incremental Multi-Hop in the Lakehouse.py index 3a82aac..ff85bf7 100644 --- a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/DE 3.2.1 - Incremental Multi-Hop in the Lakehouse.py +++ b/Data-Engineering-with-Databricks/Solutions/07 - Multi-Hop Architecture/DE 7.1 - Incremental Multi-Hop in the Lakehouse.py @@ -8,6 +8,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Incremental Multi-Hop in the Lakehouse # MAGIC # MAGIC Now that we have a better understanding of how to work with incremental data processing by combining Structured Streaming APIs and Spark SQL, we can explore the tight integration between Structured Streaming and Delta Lake. @@ -22,6 +24,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Incremental Updates in the Lakehouse # MAGIC # MAGIC Delta Lake allows users to easily combine streaming and batch workloads in a unified multi-hop pipeline. Each stage of the pipeline represents a state of our data valuable to driving core use cases within the business. Because all data and metadata lives in object storage in the cloud, multiple users and applications can access data in near-real time, allowing analysts to access the freshest data as it's being processed. @@ -43,6 +47,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Datasets Used # MAGIC # MAGIC This demo uses simplified artificially generated medical data. The schema of our two datasets is represented below. Note that we will be manipulating these schema during various steps. @@ -68,34 +74,42 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Getting Started # MAGIC # MAGIC Run the following cell to configure the lab environment. # COMMAND ---------- -# MAGIC %run "../Includes/multi-hop-setup" $mode="reset" +# MAGIC %run ../Includes/Classroom-Setup-7.1 # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Data Simulator -# MAGIC Databricks Auto Loader can automatically process files as they land in your cloud object stores. To simulate this process, you will be asked to run the following operation several times throughout the course. +# MAGIC Databricks Auto Loader can automatically process files as they land in your cloud object stores. +# MAGIC +# MAGIC To simulate this process, you will be asked to run the following operation several times throughout the course. # COMMAND ---------- -File.newData() +DA.data_factory.load() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Bronze Table: Ingesting Raw JSON Recordings # MAGIC # MAGIC Below, we configure a read on a raw JSON source using Auto Loader with schema inference. # MAGIC # MAGIC Note that while you need to use the Spark DataFrame API to set up an incremental read, once configured you can immediately register a temp view to leverage Spark SQL for streaming transformations on your data. # MAGIC -# MAGIC **NOTE**: For a JSON data source, Auto Loader will default to inferring each column as a string. Here, we demonstrate specifying the data type for the `time` column using the `cloudFiles.schemaHints` option. Note that specifying improper types for a field will result in null values. +# MAGIC **NOTE**: For a JSON data source, Auto Loader will default to inferring each column as a string. Here, we demonstrate specifying the data type for the **`time`** column using the **`cloudFiles.schemaHints`** option. Note that specifying improper types for a field will result in null values. # COMMAND ---------- @@ -103,13 +117,15 @@ .format("cloudFiles") .option("cloudFiles.format", "json") .option("cloudFiles.schemaHints", "time DOUBLE") - .option("cloudFiles.schemaLocation", bronzeCheckpoint) - .load(dataLandingLocation) + .option("cloudFiles.schemaLocation", f"{DA.paths.checkpoints}/bronze") + .load(DA.paths.data_landing_location) .createOrReplaceTempView("recordings_raw_temp")) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Here, we'll enrich our raw data with additional metadata describing the source file and the time it was ingested. This additional metadata can be ignored during downstream processing while providing useful information for troubleshooting errors if corrupt data is encountered. # COMMAND ---------- @@ -123,29 +139,35 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC The code below passes our enriched raw data back to PySpark API to process an incremental write to a Delta Lake table. # COMMAND ---------- (spark.table("recordings_bronze_temp") - .writeStream - .format("delta") - .option("checkpointLocation", bronzeCheckpoint) - .outputMode("append") - .table("bronze")) + .writeStream + .format("delta") + .option("checkpointLocation", f"{DA.paths.checkpoints}/bronze") + .outputMode("append") + .table("bronze")) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Trigger another file arrival with the following cell and you'll see the changes immediately detected by the streaming query you've written. # COMMAND ---------- -File.newData() +DA.data_factory.load() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ### Load Static Lookup Table # MAGIC The ACID guarantees that Delta Lake brings to your data are managed at the table level, ensuring that only fully successfully commits are reflected in your tables. If you choose to merge these data with other data sources, be aware of how those sources version data and what sort of consistency guarantees they have. # MAGIC @@ -153,13 +175,12 @@ # COMMAND ---------- -(spark - .read - .format("csv") - .schema("mrn STRING, name STRING") - .option("header", True) - .load(f"{dataSource}/patient/patient_info.csv") - .createOrReplaceTempView("pii")) +(spark.read + .format("csv") + .schema("mrn STRING, name STRING") + .option("header", True) + .load(f"{DA.paths.data_source}/patient/patient_info.csv") + .createOrReplaceTempView("pii")) # COMMAND ---------- @@ -169,10 +190,12 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Silver Table: Enriched Recording Data # MAGIC As a second hop in our silver level, we will do the follow enrichments and checks: # MAGIC - Our recordings data will be joined with the PII to add patient names -# MAGIC - The time for our recordings will be parsed to the format `'yyyy-MM-dd HH:mm:ss'` to be human-readable +# MAGIC - The time for our recordings will be parsed to the format **`'yyyy-MM-dd HH:mm:ss'`** to be human-readable # MAGIC - We will exclude heart rates that are <= 0, as we know that these either represent the absence of the patient or an error in transmission # COMMAND ---------- @@ -194,15 +217,17 @@ # COMMAND ---------- (spark.table("recordings_w_pii") - .writeStream - .format("delta") - .option("checkpointLocation", recordingsEnrichedCheckpoint) - .outputMode("append") - .table("recordings_enriched")) + .writeStream + .format("delta") + .option("checkpointLocation", f"{DA.paths.checkpoints}/recordings_enriched") + .outputMode("append") + .table("recordings_enriched")) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Trigger another new file and wait for it propagate through both previous queries. # COMMAND ---------- @@ -212,14 +237,16 @@ # COMMAND ---------- -File.newData() +DA.data_factory.load() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Gold Table: Daily Averages # MAGIC -# MAGIC Here we read a stream of data from `recordingsEnrichedPath` and write another stream to create an aggregate gold table of daily averages for each patient. +# MAGIC Here we read a stream of data from **`recordings_enriched`** and write another stream to create an aggregate gold table of daily averages for each patient. # COMMAND ---------- @@ -238,7 +265,9 @@ # COMMAND ---------- # MAGIC %md -# MAGIC Note that we're using `.trigger(once=True)` below. This provides us the ability to continue to use the strengths of structured streaming while trigger this job as a single batch. To recap, these strengths include: +# MAGIC +# MAGIC +# MAGIC Note that we're using **`.trigger(availableNow=True)`** below. This provides us the ability to continue to use the strengths of structured streaming while trigger this job one-time to process all available data in micro-batches. To recap, these strengths include: # MAGIC - exactly once end-to-end fault tolerant processing # MAGIC - automatic detection of changes in upstream data sources # MAGIC @@ -249,22 +278,23 @@ # COMMAND ---------- (spark.table("patient_avg") - .writeStream - .format("delta") - .outputMode("complete") - .option("checkpointLocation", dailyAvgCheckpoint) - .trigger(once=True) - .table("daily_patient_avg") -) + .writeStream + .format("delta") + .outputMode("complete") + .option("checkpointLocation", f"{DA.paths.checkpoints}/daily_avg") + .trigger(availableNow=True) + .table("daily_patient_avg")) # COMMAND ---------- # MAGIC %md -# MAGIC #### Important Considerations for `complete` Output with Delta # MAGIC -# MAGIC When using `complete` output mode, we rewrite the entire state of our table each time our logic runs. While this is ideal for calculating aggregates, we **cannot** read a stream from this directory, as Structured Streaming assumes data is only being appended in the upstream logic. # MAGIC -# MAGIC **NOTE**: Certain options can be set to change this behavior, but have other limitations attached. For more details, refer to [Delta Streaming: Ignoring Updates and Deletes](https://docs.databricks.com/delta/delta-streaming.html#ignoring-updates-and-deletes). +# MAGIC #### Important Considerations for complete Output with Delta +# MAGIC +# MAGIC When using **`complete`** output mode, we rewrite the entire state of our table each time our logic runs. While this is ideal for calculating aggregates, we **cannot** read a stream from this directory, as Structured Streaming assumes data is only being appended in the upstream logic. +# MAGIC +# MAGIC **NOTE**: Certain options can be set to change this behavior, but have other limitations attached. For more details, refer to Delta Streaming: Ignoring Updates and Deletes. # MAGIC # MAGIC The gold Delta table we have just registered will perform a static read of the current state of the data each time we run the following query. @@ -276,6 +306,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Note the above table includes all days for all users. If the predicates for our ad hoc queries match the data encoded here, we can push down our predicates to files at the source and very quickly generate more limited aggregate views. # COMMAND ---------- @@ -288,27 +320,33 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Process Remaining Records -# MAGIC The following cell will land additional files for the rest of 2020 in your source directory. You'll be able to see these process through the first 3 tables in your Delta Lake, but will need to re-run your final query to update your `daily_patient_avg` table, since this query uses the trigger once syntax. +# MAGIC The following cell will land additional files for the rest of 2020 in your source directory. You'll be able to see these process through the first 3 tables in your Delta Lake, but will need to re-run your final query to update your **`daily_patient_avg`** table, since this query uses the trigger available now syntax. # COMMAND ---------- -File.newData(continuous=True) +DA.data_factory.load(continuous=True) # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Wrapping Up # MAGIC # MAGIC Finally, make sure all streams are stopped. # COMMAND ---------- -# MAGIC %run "../Includes/multi-hop-setup" $mode="clean" +DA.cleanup() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Summary # MAGIC # MAGIC Delta Lake and Structured Streaming combine to provide near real-time analytic access to data in the lakehouse. @@ -316,6 +354,8 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Additional Topics & Resources # MAGIC # MAGIC * Table Streaming Reads and Writes diff --git a/Data-Engineering-with-Databricks/Solutions/07 - Multi-Hop Architecture/DE 7.2L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py b/Data-Engineering-with-Databricks/Solutions/07 - Multi-Hop Architecture/DE 7.2L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py new file mode 100644 index 0000000..486fa7f --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/07 - Multi-Hop Architecture/DE 7.2L - Propagating Incremental Updates with Structured Streaming and Delta Lake Lab.py @@ -0,0 +1,290 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Propagating Incremental Updates with Structured Streaming and Delta Lake +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Apply your knowledge of structured streaming and Auto Loader to implement a simple multi-hop architecture + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Setup +# MAGIC Run the following script to setup necessary variables and clear out past runs of this notebook. Note that re-executing this cell will allow you to start the lab over. + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-7.2L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Ingest data +# MAGIC +# MAGIC This lab uses a collection of customer-related CSV data from DBFS found in */databricks-datasets/retail-org/customers/*. +# MAGIC +# MAGIC Read this data using Auto Loader using its schema inference (use **`customers_checkpoint_path`** to store the schema info). Stream the raw data to a Delta table called **`bronze`**. + +# COMMAND ---------- + +# ANSWER +customers_checkpoint_path = f"{DA.paths.checkpoints}/customers" + +query = (spark.readStream + .format("cloudFiles") + .option("cloudFiles.format", "csv") + .option("cloudFiles.schemaLocation", customers_checkpoint_path) + .load("/databricks-datasets/retail-org/customers/") + .writeStream + .format("delta") + .option("checkpointLocation", customers_checkpoint_path) + .outputMode("append") + .table("bronze")) + +# COMMAND ---------- + +DA.block_until_stream_is_ready(query) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC Run the cell below to check your work. + +# COMMAND ---------- + +assert spark.table("bronze"), "Table named `bronze` does not exist" +assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'bronze'").first()["isTemporary"] == False, "Table is temporary" +assert spark.table("bronze").dtypes == [('customer_id', 'string'), ('tax_id', 'string'), ('tax_code', 'string'), ('customer_name', 'string'), ('state', 'string'), ('city', 'string'), ('postcode', 'string'), ('street', 'string'), ('number', 'string'), ('unit', 'string'), ('region', 'string'), ('district', 'string'), ('lon', 'string'), ('lat', 'string'), ('ship_to_address', 'string'), ('valid_from', 'string'), ('valid_to', 'string'), ('units_purchased', 'string'), ('loyalty_segment', 'string'), ('_rescued_data', 'string')], "Incorrect Schema" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC Let's create a streaming temporary view into the bronze table, so that we can perform transforms using SQL. + +# COMMAND ---------- + +(spark + .readStream + .table("bronze") + .createOrReplaceTempView("bronze_temp")) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Clean and enhance data +# MAGIC +# MAGIC Using CTAS syntax, define a new streaming view called **`bronze_enhanced_temp`** that does the following: +# MAGIC * Skips records with a null **`postcode`** (set to zero) +# MAGIC * Inserts a column called **`receipt_time`** containing a current timestamp +# MAGIC * Inserts a column called **`source_file`** containing the input filename + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- ANSWER +# MAGIC CREATE OR REPLACE TEMPORARY VIEW bronze_enhanced_temp AS +# MAGIC SELECT +# MAGIC *, current_timestamp() receipt_time, input_file_name() source_file +# MAGIC FROM bronze_temp +# MAGIC WHERE postcode > 0 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC Run the cell below to check your work. + +# COMMAND ---------- + +assert spark.table("bronze_enhanced_temp"), "Table named `bronze_enhanced_temp` does not exist" +assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'bronze_enhanced_temp'").first()["isTemporary"] == True, "Table is not temporary" +assert spark.table("bronze_enhanced_temp").dtypes == [('customer_id', 'string'), ('tax_id', 'string'), ('tax_code', 'string'), ('customer_name', 'string'), ('state', 'string'), ('city', 'string'), ('postcode', 'string'), ('street', 'string'), ('number', 'string'), ('unit', 'string'), ('region', 'string'), ('district', 'string'), ('lon', 'string'), ('lat', 'string'), ('ship_to_address', 'string'), ('valid_from', 'string'), ('valid_to', 'string'), ('units_purchased', 'string'), ('loyalty_segment', 'string'), ('_rescued_data', 'string'), ('receipt_time', 'timestamp'), ('source_file', 'string')], "Incorrect Schema" +assert spark.table("bronze_enhanced_temp").isStreaming, "Not a streaming table" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Silver table +# MAGIC +# MAGIC Stream the data from **`bronze_enhanced_temp`** to a table called **`silver`**. + +# COMMAND ---------- + +# ANSWER +silver_checkpoint_path = f"{DA.paths.checkpoints}/silver" + +query = (spark.table("bronze_enhanced_temp") + .writeStream + .format("delta") + .option("checkpointLocation", silver_checkpoint_path) + .outputMode("append") + .table("silver")) + +# COMMAND ---------- + +DA.block_until_stream_is_ready(query) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC Run the cell below to check your work. + +# COMMAND ---------- + +assert spark.table("silver"), "Table named `silver` does not exist" +assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'silver'").first()["isTemporary"] == False, "Table is temporary" +assert spark.table("silver").dtypes == [('customer_id', 'string'), ('tax_id', 'string'), ('tax_code', 'string'), ('customer_name', 'string'), ('state', 'string'), ('city', 'string'), ('postcode', 'string'), ('street', 'string'), ('number', 'string'), ('unit', 'string'), ('region', 'string'), ('district', 'string'), ('lon', 'string'), ('lat', 'string'), ('ship_to_address', 'string'), ('valid_from', 'string'), ('valid_to', 'string'), ('units_purchased', 'string'), ('loyalty_segment', 'string'), ('_rescued_data', 'string'), ('receipt_time', 'timestamp'), ('source_file', 'string')], "Incorrect Schema" +assert spark.table("silver").filter("postcode <= 0").count() == 0, "Null postcodes present" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC Let's create a streaming temporary view into the silver table, so that we can perform business-level using SQL. + +# COMMAND ---------- + +(spark + .readStream + .table("silver") + .createOrReplaceTempView("silver_temp")) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Gold tables +# MAGIC +# MAGIC Using CTAS syntax, define a new streaming view called **`customer_count_temp`** that counts customers per state. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- ANSWER +# MAGIC CREATE OR REPLACE TEMPORARY VIEW customer_count_temp AS +# MAGIC SELECT state, count(customer_id) AS customer_count +# MAGIC FROM silver_temp +# MAGIC GROUP BY +# MAGIC state + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC Run the cell below to check your work. + +# COMMAND ---------- + +assert spark.table("customer_count_temp"), "Table named `customer_count_temp` does not exist" +assert spark.sql(f"SHOW TABLES").filter(f"tableName == 'customer_count_temp'").first()["isTemporary"] == True, "Table is not temporary" +assert spark.table("customer_count_temp").dtypes == [('state', 'string'), ('customer_count', 'bigint')], "Incorrect Schema" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Finally, stream the data from the **`customer_count_temp`** view to a Delta table called **`gold_customer_count_by_state`**. + +# COMMAND ---------- + +# ANSWER +customers_count_checkpoint_path = f"{DA.paths.checkpoints}/customers_counts" + +query = (spark.table("customer_count_temp") + .writeStream + .format("delta") + .option("checkpointLocation", customers_count_checkpoint_path) + .outputMode("complete") + .table("gold_customer_count_by_state")) + +# COMMAND ---------- + +DA.block_until_stream_is_ready(query) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC Run the cell below to check your work. + +# COMMAND ---------- + +assert spark.table("gold_customer_count_by_state"), "Table named `gold_customer_count_by_state` does not exist" +assert spark.sql(f"show tables").filter(f"tableName == 'gold_customer_count_by_state'").first()["isTemporary"] == False, "Table is temporary" +assert spark.table("gold_customer_count_by_state").dtypes == [('state', 'string'), ('customer_count', 'bigint')], "Incorrect Schema" +assert spark.table("gold_customer_count_by_state").count() == 51, "Incorrect number of rows" + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Query the results +# MAGIC +# MAGIC Query the **`gold_customer_count_by_state`** table (this will not be a streaming query). Plot the results as a bar graph and also using the map plot. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM gold_customer_count_by_state + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Wrapping Up +# MAGIC +# MAGIC Run the following cell to remove the database and all data associated with this lab. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC By completing this lab, you should now feel comfortable: +# MAGIC * Using PySpark to configure Auto Loader for incremental data ingestion +# MAGIC * Using Spark SQL to aggregate streaming data +# MAGIC * Streaming data to a Delta table + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.3.1 - DLT UI Walkthrough.py b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.1 - DLT UI Walkthrough.py similarity index 54% rename from Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.3.1 - DLT UI Walkthrough.py rename to Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.1 - DLT UI Walkthrough.py index c35c33d..4f05fca 100644 --- a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.3.1 - DLT UI Walkthrough.py +++ b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.1 - DLT UI Walkthrough.py @@ -8,10 +8,15 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Using the Delta Live Tables UI # MAGIC -# MAGIC This demo will explore the DLT UI. By the end of this lesson you will be able to: +# MAGIC This demo will explore the DLT UI. +# MAGIC # MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: # MAGIC * Deploy a DLT pipeline # MAGIC * Explore the resultant DAG # MAGIC * Execute an update of the pipeline @@ -20,65 +25,99 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Run Setup # MAGIC # MAGIC The following cell is configured to reset this demo. # COMMAND ---------- -# MAGIC %run ../Includes/dlt-setup $course="dlt_demo" $mode="reset" +# MAGIC %run ../../Includes/Classroom-Setup-8.1.1 # COMMAND ---------- # MAGIC %md -# MAGIC Execute the following cell to print out two values that will be used during the following configuration steps. +# MAGIC +# MAGIC +# MAGIC Execute the following cell to print out values that will be used during the following configuration steps. + +# COMMAND ---------- + +DA.print_pipeline_config() # COMMAND ---------- -print(f"Target: {database}") -print(f"Storage location: {userhome.split(':')[1]}") +# ANSWER + +# This function is provided for students who do not +# want to work through the exercise of creating the pipeline. +DA.create_pipeline() + +# This function is provided to start the pipeline and block +# until it has completed, canceled or failed +DA.start_pipeline() # COMMAND ---------- # MAGIC %md -# MAGIC ## Create and configure a pipeline +# MAGIC +# MAGIC +# MAGIC ## Create and Configure a Pipeline # MAGIC # MAGIC In this section you will create a pipeline using a notebook provided with the courseware. We'll explore the contents of the notebook in the following lesson. # MAGIC -# MAGIC 1. Click the **Jobs** button on the sidebar, +# MAGIC 1. Click the **Jobs** button on the sidebar. # MAGIC 1. Select the **Delta Live Tables** tab. # MAGIC 1. Click **Create Pipeline**. -# MAGIC 1. Fill in a **Pipeline Name** of your choosing. -# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the companion notebook called **3.3.2 - SQL for Delta Live Tables**. -# MAGIC * Though this document is a standard Databricks Notebook, the SQL syntax is specialized to DLT table declarations. We will be exploring the syntax in the exercise that follows. -# MAGIC 1. In the **Target** field, specify the database name printed out next to **Target** in the cell above. (This should follow the pattern `dbacademy__dlt_demo`) +# MAGIC 1. Leave **Product Edition** as **Advanced**. +# MAGIC 1. Fill in a **Pipeline Name** - because these names must be unique, we suggest using the **`Pipeline Name`** provided by the cell above. +# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the companion notebook called **DE 8.1.2 - SQL for Delta Live Tables**. +# MAGIC * Alternatively, you can copy the **`Notebook Path`** provided by the cell above and paste it into the provided field. +# MAGIC * Even though this document is a standard Databricks Notebook, the SQL syntax is specialized to DLT table declarations. +# MAGIC * We will be exploring the syntax in the exercise that follows. +# MAGIC 1. In the **Target** field, specify the database name printed out next to **`Target`** in the cell above.
+# MAGIC This should follow the pattern **`dbacademy__dewd_dlt_demo_81`** # MAGIC * This field is optional; if not specified, then tables will not be registered to a metastore, but will still be available in the DBFS. Refer to the documentation for more information on this option. -# MAGIC 1. In the **Storage location** field, copy the directory as printed above. -# MAGIC * This optional field allows the user to specify a location to store logs, tables, and other information related to pipeline execution. If not specified, DLT will automatically generate a directory. +# MAGIC 1. In the **Storage location** field, copy the **`Storage Location`** path printed by the cell above. +# MAGIC * This optional field allows the user to specify a location to store logs, tables, and other information related to pipeline execution. +# MAGIC * If not specified, DLT will automatically generate a directory. # MAGIC 1. For **Pipeline Mode**, select **Triggered** -# MAGIC * This field specifies how the pipeline will be run. **Triggered** pipelines run once and then shut down until the next manual or scheduled update. **Continuous** pipelines run continuously, ingesting new data as it arrives. Choose the mode based on latency and cost requirements. -# MAGIC 1. Uncheck the **Enable autoscaling** box, and set the number of workers to 1., +# MAGIC * This field specifies how the pipeline will be run. +# MAGIC * **Triggered** pipelines run once and then shut down until the next manual or scheduled update. +# MAGIC * **Continuous** pipelines run continuously, ingesting new data as it arrives. Choose the mode based on latency and cost requirements. +# MAGIC 1. Uncheck the **Enable autoscaling** box, and set the number of workers to **`1`** (one). # MAGIC * **Enable autoscaling**, **Min Workers** and **Max Workers** control the worker configuration for the underlying cluster processing the pipeline. Notice the DBU estimate provided, similar to that provided when configuring interactive clusters. # MAGIC 1. Click **Create**. # COMMAND ---------- # MAGIC %md -# MAGIC ## Run a pipeline +# MAGIC +# MAGIC +# MAGIC ## Run a Pipeline # MAGIC # MAGIC With a pipeline created, you will now run the pipeline. # MAGIC -# MAGIC 1. Select **Development** to run the pipeline in development mode. Development mode provides for more expeditious iterative development by reusing the cluster (as opposed to creating a new cluster for each run) and disabling retries so that you can readily identify and fix errors. Refer to the documentation for more information on this feature. +# MAGIC 1. Select **Development** to run the pipeline in development mode. +# MAGIC * Development mode provides for more expeditious iterative development by reusing the cluster (as opposed to creating a new cluster for each run) and disabling retries so that you can readily identify and fix errors. +# MAGIC * Refer to the documentation for more information on this feature. # MAGIC 2. Click **Start**. # MAGIC -# MAGIC The initial run will take several minutes while a cluster is provisioned. Subsequent runs will be appreciably quicker. +# MAGIC The initial run will take several minutes while a cluster is provisioned. +# MAGIC +# MAGIC Subsequent runs will be appreciably quicker. # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Exploring the DAG # MAGIC -# MAGIC As the pipeline completes, the execution flow is graphed. Select the tables review the details. +# MAGIC As the pipeline completes, the execution flow is graphed. +# MAGIC +# MAGIC Selecting the tables reviews the details. # MAGIC # MAGIC Select **sales_orders_cleaned**. Notice the results reported in the **Data Quality** section. Because this flow has data expectations declared, those metrics are tracked here. No records are dropped because the constraint is declared in a way that allows violating records to be included in the output. This will be covered in more details in the next exercise. diff --git a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.3.2 - SQL for Delta Live Tables.sql b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.2 - SQL for Delta Live Tables.sql similarity index 50% rename from Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.3.2 - SQL for Delta Live Tables.sql rename to Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.2 - SQL for Delta Live Tables.sql index aa76e29..f3a5966 100644 --- a/Data-Engineering-With-Databricks/03 - Incremental Data and Delta Live Tables/DE 3.3.2 - SQL for Delta Live Tables.sql +++ b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.2 - SQL for Delta Live Tables.sql @@ -8,62 +8,66 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # SQL for Delta Live Tables -- MAGIC -- MAGIC In the last lesson, we walked through the process of scheduling this notebook as a Delta Live Table (DLT) pipeline. Now we'll explore the contents of this notebook to better understand the syntax used by Delta Live Tables. -- MAGIC -- MAGIC This notebook uses SQL to declare Delta Live Tables that together implement a simple multi-hop architecture based on a Databricks-provided example dataset loaded by default into Databricks workspaces. -- MAGIC --- MAGIC At its simplest, you can think of DLT SQL as a slight modification to traditional CTAS statements. DLT tables and views will always be preceded by the `LIVE` keyword. +-- MAGIC At its simplest, you can think of DLT SQL as a slight modification to traditional CTAS statements. DLT tables and views will always be preceded by the **`LIVE`** keyword. -- MAGIC -- MAGIC ## Learning Objectives --- MAGIC --- MAGIC By the end of this lesson, students should feel confident: --- MAGIC * Defining tables and views with Delta Live Tables --- MAGIC * Using SQL to incrementally ingest raw data with Auto Loader --- MAGIC * Performing incremental reads on Delta tables with SQL --- MAGIC * Updating code and redeploying a pipeline +-- MAGIC By the end of this lesson, you should be able to: +-- MAGIC * Define tables and views with Delta Live Tables +-- MAGIC * Use SQL to incrementally ingest raw data with Auto Loader +-- MAGIC * Perform incremental reads on Delta tables with SQL +-- MAGIC * Update code and redeploy a pipeline -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Declare Bronze Layer Tables -- MAGIC --- MAGIC Below we declare a table and view implementing the bronze layer. This represents data in its rawest form, but captured in a format that can be retained indefinitely and queried with the performance and benefits that Delta Lake has to offer. +-- MAGIC Below we declare two tables implementing the bronze layer. This represents data in its rawest form, but captured in a format that can be retained indefinitely and queried with the performance and benefits that Delta Lake has to offer. -- COMMAND ---------- -- MAGIC %md --- MAGIC ### `sales_orders_raw` -- MAGIC --- MAGIC `sales_orders_raw` ingests JSON data incrementally from the example dataset found in */databricks-datasets/retail-org/sales_orders/*. -- MAGIC --- MAGIC Incremental processing via Auto Loader (which uses the same processing model as Structured Streaming), requires the addition of the `INCREMENTAL` keyword in the declaration as seen below. The `cloud_files()` method enables Auto Loader to be used natively with SQL. This method takes the following positional parameters: +-- MAGIC ### sales_orders_raw +-- MAGIC +-- MAGIC **`sales_orders_raw`** ingests JSON data incrementally from the example dataset found in */databricks-datasets/retail-org/sales_orders/*. +-- MAGIC +-- MAGIC Incremental processing via Auto Loader (which uses the same processing model as Structured Streaming), requires the addition of the **`STREAMING`** keyword in the declaration as seen below. The **`cloud_files()`** method enables Auto Loader to be used natively with SQL. This method takes the following positional parameters: -- MAGIC * The source location, as mentioned above -- MAGIC * The source data format, which is JSON in this case --- MAGIC * An arbitrarily sized array of optional reader options. In this case, we set `cloudFiles.inferColumnTypes` to `true` +-- MAGIC * An arbitrarily sized array of optional reader options. In this case, we set **`cloudFiles.inferColumnTypes`** to **`true`** -- MAGIC -- MAGIC The following declaration also demonstrates the declaration of additional table metadata (a comment and properties in this case) that would be visible to anyone exploring the data catalog. -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE sales_orders_raw +CREATE OR REFRESH STREAMING LIVE TABLE sales_orders_raw COMMENT "The raw sales orders, ingested from /databricks-datasets." -AS -SELECT * FROM cloud_files("/databricks-datasets/retail-org/sales_orders/", "json", map("cloudFiles.inferColumnTypes", "true")) +AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/sales_orders/", "json", map("cloudFiles.inferColumnTypes", "true")) -- COMMAND ---------- -- MAGIC %md --- MAGIC ### `customers` -- MAGIC --- MAGIC `customers` presents a **view** into CSV customer data found in */databricks-datasets/retail-org/customers/*. A view differs from a table in that there is no actual data bound to the view; it can be thought of as a stored query. -- MAGIC --- MAGIC This view will soon be used in a join operation to look up customer data based on sales records. +-- MAGIC ### customers +-- MAGIC +-- MAGIC **`customers`** presents CSV customer data found in */databricks-datasets/retail-org/customers/*. This table will soon be used in a join operation to look up customer data based on sales records. -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE customers +CREATE OR REFRESH STREAMING LIVE TABLE customers COMMENT "The customers buying finished products, ingested from /databricks-datasets." AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv"); @@ -71,6 +75,8 @@ AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv" -- MAGIC %md -- MAGIC +-- MAGIC +-- MAGIC -- MAGIC ## Declare Silver Layer Tables -- MAGIC -- MAGIC Now we declare tables implementing the silver layer. This layer represents a refined copy of data from the bronze layer, with the intention of optimizing downstream applications. At this level we apply operations like data cleansing and enrichment. @@ -78,7 +84,9 @@ AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv" -- COMMAND ---------- -- MAGIC %md --- MAGIC ### `sales_orders_cleaned` +-- MAGIC +-- MAGIC +-- MAGIC ### sales_orders_cleaned -- MAGIC -- MAGIC Here we declare our first silver table, which enriches the sales transaction data with customer information in addition to implementing quality control by rejecting records with a null order number. -- MAGIC @@ -86,60 +94,65 @@ AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv" -- MAGIC -- MAGIC #### Quality Control -- MAGIC --- MAGIC The `CONSTRAINT` keyword introduces quality control. Similar in function to a traditional `WHERE` clause, `CONSTRAINT` integrates with DLT, enabling it to collect metrics on constraint violations. Constraints provide an optional `ON VIOLATION` clause, specifying an action to take on records that violate the constraint. The three modes currently supported by DLT include: +-- MAGIC The **`CONSTRAINT`** keyword introduces quality control. Similar in function to a traditional **`WHERE`** clause, **`CONSTRAINT`** integrates with DLT, enabling it to collect metrics on constraint violations. Constraints provide an optional **`ON VIOLATION`** clause, specifying an action to take on records that violate the constraint. The three modes currently supported by DLT include: -- MAGIC --- MAGIC | `ON VIOLATION` | Behavior | +-- MAGIC | **`ON VIOLATION`** | Behavior | -- MAGIC | --- | --- | --- MAGIC | `FAIL UPDATE` | Pipeline failure when constraint is violated | --- MAGIC | `DROP ROW` | Discard records that violate constraints | +-- MAGIC | **`FAIL UPDATE`** | Pipeline failure when constraint is violated | +-- MAGIC | **`DROP ROW`** | Discard records that violate constraints | -- MAGIC | Omitted | Records violating constraints will be included (but violations will be reported in metrics) | -- MAGIC -- MAGIC #### References to DLT Tables and Views --- MAGIC References to other DLT tables and views will always include the `live.` prefix. A target database name will automatically be substituted at runtime, allowing for easily migration of pipelines between DEV/QA/PROD environments. +-- MAGIC References to other DLT tables and views will always include the **`live.`** prefix. A target database name will automatically be substituted at runtime, allowing for easily migration of pipelines between DEV/QA/PROD environments. -- MAGIC -- MAGIC #### References to Streaming Tables -- MAGIC --- MAGIC References to streaming DLT tables use the `STREAM()`, supplying the table name as an argument. +-- MAGIC References to streaming DLT tables use the **`STREAM()`**, supplying the table name as an argument. -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE sales_orders_cleaned( +CREATE OR REFRESH STREAMING LIVE TABLE sales_orders_cleaned( CONSTRAINT valid_order_number EXPECT (order_number IS NOT NULL) ON VIOLATION DROP ROW ) COMMENT "The cleaned sales orders with valid order_number(s) and partitioned by order_datetime." AS -SELECT f.customer_id, f.customer_name, f.number_of_line_items, - TIMESTAMP(from_unixtime((cast(f.order_datetime as long)))) as order_datetime, - DATE(from_unixtime((cast(f.order_datetime as long)))) as order_date, - f.order_number, f.ordered_products, c.state, c.city, c.lon, c.lat, c.units_purchased, c.loyalty_segment + SELECT f.customer_id, f.customer_name, f.number_of_line_items, + timestamp(from_unixtime((cast(f.order_datetime as long)))) as order_datetime, + date(from_unixtime((cast(f.order_datetime as long)))) as order_date, + f.order_number, f.ordered_products, c.state, c.city, c.lon, c.lat, c.units_purchased, c.loyalty_segment FROM STREAM(LIVE.sales_orders_raw) f LEFT JOIN LIVE.customers c - ON c.customer_id = f.customer_id - AND c.customer_name = f.customer_name + ON c.customer_id = f.customer_id + AND c.customer_name = f.customer_name -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Declare Gold Table -- MAGIC -- MAGIC At the most refined level of the architecture, we declare a table delivering an aggregation with business value, in this case a collection of sales order data based in a specific region. In aggregating, the report generates counts and totals of orders by date and customer. -- COMMAND ---------- -CREATE LIVE TABLE sales_order_in_la +CREATE OR REFRESH LIVE TABLE sales_order_in_la COMMENT "Sales orders in LA." AS -SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, SUM(ordered_products_explode.price) as sales, SUM(ordered_products_explode.qty) as quantity, COUNT(ordered_products_explode.id) as product_count -FROM ( - SELECT city, order_date, customer_id, customer_name, EXPLODE(ordered_products) as ordered_products_explode - FROM LIVE.sales_orders_cleaned - WHERE city = 'Los Angeles' - ) -GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode.curr + SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, + sum(ordered_products_explode.price) as sales, + sum(ordered_products_explode.qty) as quantity, + count(ordered_products_explode.id) as product_count + FROM (SELECT city, order_date, customer_id, customer_name, explode(ordered_products) as ordered_products_explode + FROM LIVE.sales_orders_cleaned + WHERE city = 'Los Angeles') + GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode.curr -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Explore Results -- MAGIC -- MAGIC Explore the DAG (Directed Acyclic Graph) representing the entities involved in the pipeline and the relationships between them. Click on each to view a summary, which includes: @@ -148,27 +161,37 @@ GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode. -- MAGIC * Schema -- MAGIC * Data quality metrics -- MAGIC --- MAGIC Refer to this companion notebook to inspect tables and logs. +-- MAGIC Refer to this companion notebook to inspect tables and logs. -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Update Pipeline -- MAGIC --- MAGIC Uncomment the following cell to declare another gold table. Similar to the previous gold table declaration, this filters for the `city` of Chicago. Re-run your pipeline to examine the updated results. Does it run as expected? Can you identify any issues? +-- MAGIC Uncomment the following cell to declare another gold table. Similar to the previous gold table declaration, this filters for the **`city`** of Chicago. +-- MAGIC +-- MAGIC Re-run your pipeline to examine the updated results. +-- MAGIC +-- MAGIC Does it run as expected? +-- MAGIC +-- MAGIC Can you identify any issues? -- COMMAND ---------- --- CREATE LIVE TABLE sales_order_in_chicago --- COMMENT "Sales orders in Chicago." --- AS --- SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, SUM(ordered_products_explode.price) as sales, SUM(ordered_products_explode.qty) as quantity, COUNT(ordered_products_explode.id) as product_count --- FROM ( --- SELECT city, order_date, customer_id, customer_name, EXPLODE(ordered_products) as ordered_products_explode --- FROM sales_orders_cleaned --- WHERE city = 'Chicago' --- ) --- GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode.curr +-- ANSWER +CREATE OR REFRESH LIVE TABLE sales_order_in_chicago +COMMENT "Sales orders in Chicago." +AS + SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, + sum(ordered_products_explode.price) as sales, + sum(ordered_products_explode.qty) as quantity, + count(ordered_products_explode.id) as product_count + FROM (SELECT city, order_date, customer_id, customer_name, explode(ordered_products) as ordered_products_explode + FROM LIVE.sales_orders_cleaned + WHERE city = 'Chicago') + GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode.curr -- COMMAND ---------- diff --git a/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.3 - Pipeline Results.py b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.3 - Pipeline Results.py new file mode 100644 index 0000000..d3b6cb8 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.1 DLT/DE 8.1.3 - Pipeline Results.py @@ -0,0 +1,76 @@ +# Databricks notebook source +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Exploring the Results of a DLT Pipeline +# MAGIC +# MAGIC This notebook explores the execution results of a DLT pipeline. + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-8.1.3 + +# COMMAND ---------- + +files = dbutils.fs.ls(DA.paths.storage_location) +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The **`system`** directory captures events associated with the pipeline. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.storage_location}/system/events") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC These event logs are stored as a Delta table. Let's query the table. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM delta.`${da.paths.storage_location}/system/events` + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Let's view the contents of the *tables* directory. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.storage_location}/tables") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Let's query the gold table. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM ${da.db_name}.sales_order_in_la + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Run the following cell to delete the tables and files associated with this lesson. + +# COMMAND ---------- + +DA.cleanup() + diff --git a/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.1L - Lab Instructions.py b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.1L - Lab Instructions.py new file mode 100644 index 0000000..0787c72 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.1L - Lab Instructions.py @@ -0,0 +1,176 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Lab: Migrating SQL Notebooks to Delta Live Tables +# MAGIC +# MAGIC This notebook describes the overall structure for the lab exercise, configures the environment for the lab, provides simulated data streaming, and performs cleanup once you are done. A notebook like this is not typically needed in a production pipeline scenario. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Convert existing data pipelines to Delta Live Tables + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Datasets Used +# MAGIC +# MAGIC This demo uses simplified artificially generated medical data. The schema of our two datasets is represented below. Note that we will be manipulating these schema during various steps. +# MAGIC +# MAGIC #### Recordings +# MAGIC The main dataset uses heart rate recordings from medical devices delivered in the JSON format. +# MAGIC +# MAGIC | Field | Type | +# MAGIC | --- | --- | +# MAGIC | device_id | int | +# MAGIC | mrn | long | +# MAGIC | time | double | +# MAGIC | heartrate | double | +# MAGIC +# MAGIC #### PII +# MAGIC These data will later be joined with a static table of patient information stored in an external system to identify patients by name. +# MAGIC +# MAGIC | Field | Type | +# MAGIC | --- | --- | +# MAGIC | mrn | long | +# MAGIC | name | string | + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Getting Started +# MAGIC +# MAGIC Begin by running the following cell to configure the lab environment. + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-8.2.1L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Land Initial Data +# MAGIC Seed the landing zone with some data before proceeding. You will re-run this command to land additional data later. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Execute the following cell to print out values that will be used during the following configuration steps. + +# COMMAND ---------- + +DA.print_pipeline_config() + +# COMMAND ---------- + +# ANSWER + +# This function is provided for those students that do not +# want to work through the exercise of creating the pipline. +DA.create_pipeline() + +# This function is provided to start the pipline and block +# until it has comleted, is canceled or failed +DA.start_pipeline() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create and Configure a Pipeline +# MAGIC +# MAGIC 1. Click the **Jobs** button on the sidebar, then select the **Delta Live Tables** tab. +# MAGIC 1. Click **Create Pipeline**. +# MAGIC 1. Leave **Product Edition** as **Advanced**. +# MAGIC 1. Fill in a **Pipeline Name** - because these names must be unique, we suggest using the **Pipline Name** provided in the cell above. +# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the notebook **`DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab`**. +# MAGIC 1. Configure the Source +# MAGIC * Click **`Add configuration`** +# MAGIC * Enter the word **`source`** in the **Key** field +# MAGIC * Enter the **Source** value specified above to the **`Value`** field +# MAGIC 1. Enter the database name printed next to **`Target`** below in the **Target** field. +# MAGIC 1. Enter the location printed next to **`Storage Location`** below in the **Storage Location** field. +# MAGIC 1. Set **Pipeline Mode** to **Triggered**. +# MAGIC 1. Disable autoscaling. +# MAGIC 1. Set the number of **`workers`** to **`1`** (one). +# MAGIC 1. Click **Create**. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Open and Complete DLT Pipeline Notebook +# MAGIC +# MAGIC You will perform your work in the companion notebook [DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab]($./DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab),
+# MAGIC which you will ultimately deploy as a pipeline. +# MAGIC +# MAGIC Open the Notebook and, following the guidelines provided therein, fill in the cells where prompted to
+# MAGIC implement a multi-hop architecture similar to the one we worked with in the previous section. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Run your Pipeline +# MAGIC +# MAGIC Select **Development** mode, which accelerates the development lifecycle by reusing the same cluster across runs.
+# MAGIC It will also turn off automatic retries when jobs fail. +# MAGIC +# MAGIC Click **Start** to begin the first update to your table. +# MAGIC +# MAGIC Delta Live Tables will automatically deploy all the necessary infrastructure and resolve the dependencies between all datasets. +# MAGIC +# MAGIC **NOTE**: The first table update may take several minutes as relationships are resolved and infrastructure deploys. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Troubleshooting Code in Development Mode +# MAGIC +# MAGIC Don't despair if your pipeline fails the first time. Delta Live Tables is in active development, and error messages are improving all the time. +# MAGIC +# MAGIC Because relationships between tables are mapped as a DAG, error messages will often indicate that a dataset isn't found. +# MAGIC +# MAGIC Let's consider our DAG below: +# MAGIC +# MAGIC +# MAGIC +# MAGIC If the error message **`Dataset not found: 'recordings_parsed'`** is raised, there may be several culprits: +# MAGIC 1. The logic defining **`recordings_parsed`** is invalid +# MAGIC 1. There is an error reading from **`recordings_bronze`** +# MAGIC 1. A typo exists in either **`recordings_parsed`** or **`recordings_bronze`** +# MAGIC +# MAGIC The safest way to identify the culprit is to iteratively add table/view definitions back into your DAG starting from your initial ingestion tables. You can simply comment out later table/view definitions and uncomment these between runs. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.5L - Migrating a SQL Pipeline to DLT Lab.sql b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab.sql similarity index 61% rename from Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.5L - Migrating a SQL Pipeline to DLT Lab.sql rename to Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab.sql index 56fd65e..7f65c4f 100644 --- a/Data-Engineering-With-Databricks/Solutions/03 - Incremental Data and Delta Live Tables/Labs/DE 3.3.5L - Migrating a SQL Pipeline to DLT Lab.sql +++ b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab.sql @@ -8,37 +8,41 @@ -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC # Lab: Migrating a SQL Pipeline to Delta Live Tables -- MAGIC --- MAGIC This notebook will be completed by you to implement a DLT pipeline using SQL. It is **not intended** to be executed interactively, but rather to be deployed as a pipeline once you have completed your changes. +-- MAGIC This notebook will be completed by you to implement a DLT pipeline using SQL. +-- MAGIC +-- MAGIC It is **not intended** to be executed interactively, but rather to be deployed as a pipeline once you have completed your changes. -- MAGIC -- MAGIC To aid in completion of this Notebook, please refer to the DLT syntax documentation. -- COMMAND ---------- --- MAGIC %run ../../Includes/classic-setup $mode="reset" - --- COMMAND ---------- - -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Declare Bronze Table -- MAGIC --- MAGIC Declare a bronze table that ingests JSON data incrementally (using Auto Loader) from the simulated cloud source. Here you will need to substitute the value obtained from the companion setup Notebook. +-- MAGIC Declare a bronze table that ingests JSON data incrementally (using Auto Loader) from the simulated cloud source. The source location is already supplied as an argument; using this value is illustrated in the cell below. -- MAGIC -- MAGIC As we did previously, include two additional columns: --- MAGIC * `receipt_time` that records a timestamp as returned by `current_timestamp()` --- MAGIC * `source_file` that is obtained by `input_file_name()` +-- MAGIC * **`receipt_time`** that records a timestamp as returned by **`current_timestamp()`** +-- MAGIC * **`source_file`** that is obtained by **`input_file_name()`** -- COMMAND ---------- -- ANSWER -CREATE INCREMENTAL LIVE TABLE recordings_bronze +CREATE OR REFRESH STREAMING LIVE TABLE recordings_bronze AS SELECT current_timestamp() receipt_time, input_file_name() source_file, * FROM cloud_files("${source}", "json", map("cloudFiles.schemaHints", "time DOUBLE")) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ### PII File -- MAGIC -- MAGIC Using a similar CTAS syntax, create a live **table** into the CSV data found at */mnt/training/healthcare/patient*. @@ -47,41 +51,44 @@ AS SELECT current_timestamp() receipt_time, input_file_name() source_file, * -- MAGIC -- MAGIC | option | value | -- MAGIC | --- | --- | --- MAGIC | `header` | `true` | --- MAGIC | `cloudFiles.inferColumnTypes` | `true` | +-- MAGIC | **`header`** | **`true`** | +-- MAGIC | **`cloudFiles.inferColumnTypes`** | **`true`** | -- MAGIC -- MAGIC Auto Loader configurations for CSV can be found here. -- COMMAND ---------- -- ANSWER -CREATE INCREMENTAL LIVE TABLE pii +CREATE OR REFRESH STREAMING LIVE TABLE pii AS SELECT * FROM cloud_files("/mnt/training/healthcare/patient", "csv", map("header", "true", "cloudFiles.inferColumnTypes", "true")) -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Declare Silver Tables -- MAGIC --- MAGIC Our silver tables, `recordings_parsed`, will subscribe to the `recordings` dataset and cast the fields as follows: +-- MAGIC Our silver table, **`recordings_parsed`**, will consist of the following fields: -- MAGIC -- MAGIC | Field | Type | -- MAGIC | --- | --- | --- MAGIC | `device_id` | `INTEGER` | --- MAGIC | `mrn` | `LONG` | --- MAGIC | `heartrate` | `DOUBLE` | --- MAGIC | `time` | `TIMESTAMP` (example provided below) | +-- MAGIC | **`device_id`** | **`INTEGER`** | +-- MAGIC | **`mrn`** | **`LONG`** | +-- MAGIC | **`heartrate`** | **`DOUBLE`** | +-- MAGIC | **`time`** | **`TIMESTAMP`** (example provided below) | +-- MAGIC | **`name`** | **`STRING`** | -- MAGIC --- MAGIC This query should also enrich the data through an inner join with the `pii` table on the common `mrn` field. +-- MAGIC This query should also enrich the data through an inner join with the **`pii`** table on the common **`mrn`** field to obtain the name. -- MAGIC --- MAGIC Implement quality control by applying a contraint to drop records with an invalid `heartrate` (that is, not greater than zero). +-- MAGIC Implement quality control by applying a constraint to drop records with an invalid **`heartrate`** (that is, not greater than zero). -- COMMAND ---------- -- ANSWER -CREATE INCREMENTAL LIVE TABLE recordings_enriched +CREATE OR REFRESH STREAMING LIVE TABLE recordings_enriched (CONSTRAINT positive_heartrate EXPECT (heartrate > 0) ON VIOLATION DROP ROW) AS SELECT CAST(a.device_id AS INTEGER) device_id, @@ -96,26 +103,28 @@ AS SELECT -- COMMAND ---------- -- MAGIC %md +-- MAGIC +-- MAGIC -- MAGIC ## Gold Table -- MAGIC --- MAGIC Create a gold table, `daily_patient_avg`, that aggregates `recordings_enriched` by `mrn`, `name`, and `date` and delivers the following columns: +-- MAGIC Create a gold table, **`daily_patient_avg`**, that aggregates **`recordings_enriched`** by **`mrn`**, **`name`**, and **`date`** and delivers the following columns: -- MAGIC -- MAGIC | Column name | Value | -- MAGIC | --- | --- | --- MAGIC | `mrn` | `mrn` from source | --- MAGIC | `name` | `name` from source | --- MAGIC | `avg_heartrate` | Average `heartrate` from the grouping | --- MAGIC | `date` | Date extracted from `time` | +-- MAGIC | **`mrn`** | **`mrn`** from source | +-- MAGIC | **`name`** | **`name`** from source | +-- MAGIC | **`avg_heartrate`** | Average **`heartrate`** from the grouping | +-- MAGIC | **`date`** | Date extracted from **`time`** | -- COMMAND ---------- -- ANSWER -CREATE INCREMENTAL LIVE TABLE daily_patient_avg +CREATE OR REFRESH STREAMING LIVE TABLE daily_patient_avg COMMENT "Daily mean heartrates by patient" -AS SELECT mrn, name, MEAN(heartrate) avg_heartrate, DATE(time) `date` - FROM STREAM(live.recordings_enriched) - GROUP BY mrn, name, DATE(time) + AS SELECT mrn, name, MEAN(heartrate) avg_heartrate, DATE(time) `date` + FROM STREAM(live.recordings_enriched) + GROUP BY mrn, name, DATE(time) -- COMMAND ---------- diff --git a/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.3L - Lab Conclusion.py b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.3L - Lab Conclusion.py new file mode 100644 index 0000000..31a8de3 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/08 - Delta Live Tables/DE 8.2 DLT Lab/DE 8.2.3L - Lab Conclusion.py @@ -0,0 +1,91 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Lab: Conclusion +# MAGIC Running the following cell to configure the lab environment: + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-8.2.3L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Display Results +# MAGIC +# MAGIC Assuming your pipeline runs successfully, display the contents of the gold table. +# MAGIC +# MAGIC **NOTE**: Because we specified a value for **Target**, tables are published to the specified database. Without a **Target** specification, we would need to query the table based on its underlying location in DBFS (relative to the **Storage Location**). + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM ${da.db_name}.daily_patient_avg + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Trigger another file arrival with the following cell. +# MAGIC +# MAGIC Feel free to run it a couple more times if desired. +# MAGIC +# MAGIC Following this, run the pipeline again and view the results. +# MAGIC +# MAGIC Feel free to re-run the cell above to gain an updated view of the **`daily_patient_avg`** table. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Wrapping Up +# MAGIC +# MAGIC Ensure that you delete your pipeline from the DLT UI, and run the following cell to clean up the files and tables that were created as part of the lab setup and execution. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Summary +# MAGIC +# MAGIC In this lab, you learned to convert an existing data pipeline to a Delta Live Tables SQL pipeline, and deployed that pipeline using the DLT UI. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Additional Topics & Resources +# MAGIC +# MAGIC * Delta Live Tables Documentation +# MAGIC * Delta Live Tables Demo + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.1.1 - Task Orchestration with Databricks Jobs.py b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.1.1 - Task Orchestration with Databricks Jobs.py new file mode 100644 index 0000000..a325efd --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.1.1 - Task Orchestration with Databricks Jobs.py @@ -0,0 +1,196 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Orchestrating Jobs with Databricks +# MAGIC +# MAGIC New updates to the Databricks Jobs UI have added the ability to schedule multiple tasks as part of a job, allowing Databricks Jobs to fully handle orchestration for most production workloads. +# MAGIC +# MAGIC Here, we'll start by reviewing the steps for scheduling a notebook as a triggered standalone job, and then add a dependent job using a DLT pipeline. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Schedule a notebook as a Databricks Job +# MAGIC * Describe job scheduling options and differences between cluster types +# MAGIC * Review Job Runs to track progress and see results +# MAGIC * Schedule a DLT pipeline as a Databricks Job +# MAGIC * Configure linear dependencies between tasks using the Databricks Jobs UI + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-9.1.1 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create and configure a pipeline +# MAGIC The pipeline we create here is nearly identical to the one in the previous unit. +# MAGIC +# MAGIC We will use it as part of a scheduled job in this lesson. +# MAGIC +# MAGIC Execute the following cell to print out the values that will be used during the following configuration steps. + +# COMMAND ---------- + +print_pipeline_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create and configure a pipeline +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the **Jobs** button on the sidebar, +# MAGIC 1. Select the **Delta Live Tables** tab. +# MAGIC 1. Click **Create Pipeline**. +# MAGIC 1. Fill in a **Pipeline Name** - because these names must be unique, we suggest using the **Pipeline Name** provided in the cell above. +# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the companion notebook called **DE 9.1.3 - DLT Job**. Alternatively, you can copy the **Notebook Path** and paste it into the field provided. +# MAGIC 1. In the **Target** field, specify the database name printed out next to **Target** in the cell above.
+# MAGIC This should follow the pattern **`dbacademy__dewd_dlt_demo_91`** +# MAGIC 1. In the **Storage location** field, copy the directory as printed above. +# MAGIC 1. For **Pipeline Mode**, select **Triggered** +# MAGIC 1. Uncheck the **Enable autoscaling** box +# MAGIC 1. Set the number of workers to **`1`** (one) +# MAGIC 1. Click **Create**. +# MAGIC +# MAGIC **Note**: we won't be executing this pipline directly as it will be executed by our job later in this lesson,
+# MAGIC but if you want to test it real quick, you can click the **Start** button now. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## Schedule a Notebook Job +# MAGIC +# MAGIC When using the Jobs UI to orchestrate a workload with multiple tasks, you'll always begin by scheduling a single task. +# MAGIC +# MAGIC Before we start run the following cell to get the values used in this step. + +# COMMAND ---------- + +print_job_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Here, we'll start by scheduling the next notebook +# MAGIC +# MAGIC Steps: +# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. +# MAGIC 1. Click the blue **`Create Job`** button +# MAGIC 1. Configure the task: +# MAGIC 1. Enter **`reset`** for the task name +# MAGIC 1. Select the notebook **`DE 9.1.2 - Reset`** using the notebook picker. +# MAGIC 1. From the **Cluster** dropdown, under **Existing All Purpose Clusters**, select your cluster +# MAGIC 1. Click **Create** +# MAGIC 1. In the top-left of the screen rename the job (not the task) from **`reset`** (the defaulted value) to the **Job Name** provided for you in the previous cell. +# MAGIC 1. Click the blue **Run now** button in the top right to start the job. +# MAGIC +# MAGIC **Note**: When selecting your all-purpose cluster, you will get a warning about how this will be billed as all-purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Chron Scheduling of Databricks Jobs +# MAGIC +# MAGIC Note that on the right hand side of the Jobs UI, directly under the **Job Details** section is a section labeled **Schedule**. +# MAGIC +# MAGIC Click on the **Edit schedule** button to explore scheduling options. +# MAGIC +# MAGIC Changing the **Schedule type** field from **Manual** to **Scheduled** will bring up a chron scheduling UI. +# MAGIC +# MAGIC This UI provides extensive options for setting up chronological scheduling of your Jobs. Settings configured with the UI can also be output in chron syntax, which can be edited if custom configuration not available with the UI is needed. +# MAGIC +# MAGIC At this time, we'll leave our job set with **Manual** scheduling. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Review Run +# MAGIC +# MAGIC As currently configured, our single notebook provides identical performance to the legacy Databricks Jobs UI, which only allowed a single notebook to be scheduled. +# MAGIC +# MAGIC To Review the Job Run +# MAGIC 1. Select the **Runs** tab in the top-left of the screen (you should currently be on the **Tasks** tab) +# MAGIC 1. Find your job. If **the job is still running**, it will be under the **Active runs** section. If **the job finished running**, it will be under the **Completed runs** section +# MAGIC 1. Open the Output details by click on the timestamp field under the **Start time** column +# MAGIC 1. If **the job is still running**, you will see the active state of the notebook with a **Status** of **`Pending`** or **`Running`** in the right side panel. If **the job has completed**, you will see the full execution of the notebook with a **Status** of **`Succeeded`** or **`Failed`** in the right side panel +# MAGIC +# MAGIC The notebook employs the magic command **`%run`** to call an additional notebook using a relative path. Note that while not covered in this course, new functionality added to Databricks Repos allows loading Python modules using relative paths. +# MAGIC +# MAGIC The actual outcome of the scheduled notebook is to reset the environment for our new job and pipeline. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Schedule a DLT Pipeline as a Task +# MAGIC +# MAGIC In this step, we'll add a DLT pipeline to execute after the success of the task we configured at the start of this lesson. +# MAGIC +# MAGIC Steps: +# MAGIC 1. At the top left of your screen, you'll see the **Runs** tab is currently selected; click the **Tasks** tab. +# MAGIC 1. Click the large blue circle with a **+** at the center bottom of the screen to add a new task +# MAGIC 1. Specify the **Task name** as **`dlt`** +# MAGIC 1. From **Type**, select **`Delta Live Tables pipeline`** +# MAGIC 1. Click the **Pipeline** field and select the DLT pipeline you configured previously
+# MAGIC Note: The pipeline will start with **Jobs-Demo-91** and will end with your email address. +# MAGIC 1. The **Depends on** field defaults to your previously defined task but may have renamed itself from the value **reset** that you specified previously to something like **Jobs-Demo-91-youremailaddress**. +# MAGIC 1. Click the blue **Create task** button +# MAGIC +# MAGIC You should now see a screen with 2 boxes and a downward arrow between them. +# MAGIC +# MAGIC Your **`reset`** task (possibly renamed to something like **Jobs-Demo-91-youremailaddress**) will be at the top, +# MAGIC leading into your **`dlt`** task. +# MAGIC +# MAGIC This visualization represents the dependencies between these tasks. +# MAGIC +# MAGIC Click **Run now** to execute your job. +# MAGIC +# MAGIC **NOTE**: You may need to wait a few minutes as infrastructure for your job and pipeline is deployed. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Review Multi-Task Run Results +# MAGIC +# MAGIC Select the **Runs** tab again and then the most recent run under **Active runs** or **Completed runs** depending on if the job has completed or not. +# MAGIC +# MAGIC The visualizations for tasks will update in real time to reflect which tasks are actively running, and will change colors if task failures occur. +# MAGIC +# MAGIC Clicking on a task box will render the scheduled notebook in the UI. +# MAGIC +# MAGIC You can think of this as just an additional layer of orchestration on top of the previous Databricks Jobs UI, if that helps; note that if you have workloads scheduling jobs with the CLI or REST API, the JSON structure used to configure and get results about jobs has seen similar updates to the UI. +# MAGIC +# MAGIC **NOTE**: At this time, DLT pipelines scheduled as tasks do not directly render results in the Runs GUI; instead, you will be directed back to the DLT Pipeline GUI for the scheduled Pipeline. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.1.2 - Reset.py b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.1.2 - Reset.py new file mode 100644 index 0000000..3090a9b --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.1.2 - Reset.py @@ -0,0 +1,18 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-9.1.1 + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/2 - DLT Job.sql b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.1.3 - DLT Job.sql similarity index 89% rename from Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/2 - DLT Job.sql rename to Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.1.3 - DLT Job.sql index 85abadd..a434b53 100644 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.1 - Jobs/2 - DLT Job.sql +++ b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.1.3 - DLT Job.sql @@ -1,18 +1,18 @@ -- Databricks notebook source -CREATE INCREMENTAL LIVE TABLE sales_orders_raw +CREATE OR REFRESH STREAMING LIVE TABLE sales_orders_raw COMMENT "The raw sales orders, ingested from /databricks-datasets." AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/sales_orders/", "json", map("cloudFiles.inferColumnTypes", "true")) -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE customers +CREATE OR REFRESH STREAMING LIVE TABLE customers COMMENT "The customers buying finished products, ingested from /databricks-datasets." AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv"); -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE sales_orders_cleaned( +CREATE OR REFRESH STREAMING LIVE TABLE sales_orders_cleaned( CONSTRAINT valid_order_number EXPECT (order_number IS NOT NULL) ON VIOLATION DROP ROW ) COMMENT "The cleaned sales orders with valid order_number(s) and partitioned by order_datetime." @@ -28,7 +28,7 @@ SELECT f.customer_id, f.customer_name, f.number_of_line_items, -- COMMAND ---------- -CREATE LIVE TABLE sales_order_in_la +CREATE OR REFRESH LIVE TABLE sales_order_in_la COMMENT "Sales orders in LA." AS SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, SUM(ordered_products_explode.price) as sales, SUM(ordered_products_explode.qty) as quantity, COUNT(ordered_products_explode.id) as product_count @@ -41,7 +41,7 @@ GROUP BY order_date, city, customer_id, customer_name, ordered_products_explode. -- COMMAND ---------- -CREATE LIVE TABLE sales_order_in_chicago +CREATE OR REFRESH LIVE TABLE sales_order_in_chicago COMMENT "Sales orders in Chicago." AS SELECT city, order_date, customer_id, customer_name, ordered_products_explode.curr, SUM(ordered_products_explode.price) as sales, SUM(ordered_products_explode.qty) as quantity, COUNT(ordered_products_explode.id) as product_count diff --git a/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.1L - Lab Instructions.py b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.1L - Lab Instructions.py new file mode 100644 index 0000000..dea121f --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.1L - Lab Instructions.py @@ -0,0 +1,179 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Lab: Orchestrating Jobs with Databricks +# MAGIC +# MAGIC In this lab, you'll be configuring a multi-task job comprising of: +# MAGIC * A notebook that lands a new batch of data in a storage directory +# MAGIC * A Delta Live Table pipeline that processes this data through a series of tables +# MAGIC * A notebook that queries the gold table produced by this pipeline as well as various metrics output by DLT +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Schedule a notebook as a Databricks Job +# MAGIC * Schedule a DLT pipeline as a Databricks Job +# MAGIC * Configure linear dependencies between tasks using the Databricks Jobs UI + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-9.2.1L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Land Initial Data +# MAGIC Seed the landing zone with some data before proceeding. You will re-run this command to land additional data later. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create and Configure a Pipeline +# MAGIC +# MAGIC The pipeline we create here is nearly identical to the one in the previous unit. +# MAGIC +# MAGIC We will use it as part of a scheduled job in this lesson. +# MAGIC +# MAGIC Execute the following cell to print out the values that will be used during the following configuration steps. + +# COMMAND ---------- + +print_pipeline_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the **Jobs** button on the sidebar. +# MAGIC 1. Select the **Delta Live Tables** tab. +# MAGIC 1. Click **Create Pipeline**. +# MAGIC 1. Fill in a **Pipeline Name** - because these names must be unique, we suggest using the **Pipeline Name** provided in the cell above. +# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the companion notebook called **DE 9.2.3L - DLT Job**. +# MAGIC * Alternatively, you can copy the **Notebook Path** specified above and paste it into the field provided. +# MAGIC 1. Configure the Source +# MAGIC * Click **`Add configuration`** +# MAGIC * Enter the word **`source`** in the **Key** field +# MAGIC * Enter the **Source** value specified above to the **`Value`** field +# MAGIC 1. In the **Target** field, specify the database name printed out next to **Target** in the cell above.
+# MAGIC This should follow the pattern **`dbacademy__dewd_jobs_lab_92`** +# MAGIC 1. In the **Storage location** field, copy the directory as printed above. +# MAGIC 1. For **Pipeline Mode**, select **Triggered** +# MAGIC 1. Uncheck the **Enable autoscaling** box +# MAGIC 1. Set the number of workers to **`1`** (one) +# MAGIC 1. Click **Create**. +# MAGIC +# MAGIC +# MAGIC **Note**: we won't be executing this pipline directly as it will be executed by our job later in this lesson,
+# MAGIC but if you want to test it real quick, you can click the **Start** button now. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Schedule a Notebook Job +# MAGIC +# MAGIC When using the Jobs UI to orchestrate a workload with multiple tasks, you'll always begin by scheduling a single task. +# MAGIC +# MAGIC Before we start run the following cell to get the values used in this step. + +# COMMAND ---------- + +print_job_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Here, we'll start by scheduling the notebook batch job. +# MAGIC +# MAGIC Steps: +# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. +# MAGIC 1. Click the blue **Create Job** button +# MAGIC 1. Configure the task: +# MAGIC 1. Enter **Batch-Job** for the task name +# MAGIC 1. Select the notebook **DE 9.2.2L - Batch Job** using the notebook picker +# MAGIC 1. From the **Cluster** dropdown, under **Existing All Purpose Cluster**, select your cluster +# MAGIC 1. Click **Create** +# MAGIC 1. In the top-left of the screen rename the job (not the task) from **`Batch-Job`** (the defaulted value) to the **Job Name** provided for you in the previous cell. +# MAGIC 1. Click the blue **Run now** button in the top right to start the job to test the job real quick. +# MAGIC +# MAGIC **Note**: When selecting your all purpose cluster, you will get a warning about how this will be billed as all purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Schedule a DLT Pipeline as a Task +# MAGIC +# MAGIC In this step, we'll add a DLT pipeline to execute after the success of the task we configured at the start of this lesson. +# MAGIC +# MAGIC Steps: +# MAGIC 1. At the top left of your screen, click the **Tasks** tab if it is not already selected. +# MAGIC 1. Click the large blue circle with a **+** at the center bottom of the screen to add a new task +# MAGIC 1. Specify the **Task name** as **DLT-Pipeline** +# MAGIC 1. From **Type**, select **`Delta Live Tables pipeline`** +# MAGIC 1. Click the **Pipeline** field and select the DLT pipeline you configured previously
+# MAGIC Note: The pipeline will start with **Jobs-Labs-92** and will end with your email address. +# MAGIC 1. The **Depends on** field defaults to your previously defined task but may have renamed itself from the value **reset** that you specified previously to something like **Jobs-Lab-92-youremailaddress**. +# MAGIC 1. Click the blue **Create task** button +# MAGIC +# MAGIC You should now see a screen with 2 boxes and a downward arrow between them. +# MAGIC +# MAGIC Your **`Batch-Job`** task (possibly renamed to something like **Jobs-Labs-92-youremailaddress**) will be at the top, +# MAGIC leading into your **`DLT-Pipeline`** task. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Schedule an Additional Notebook Task +# MAGIC +# MAGIC An additional notebook has been provided which queries some of the DLT metrics and the gold table defined in the DLT pipeline. +# MAGIC +# MAGIC We'll add this as a final task in our job. +# MAGIC +# MAGIC Steps: +# MAGIC 1. At the top left of your screen, click the **Tasks** tab if it is not already selected. +# MAGIC 1. Click the large blue circle with a **+** at the center bottom of the screen to add a new task +# MAGIC 1. Specify the **Task name** as **Query-Results** +# MAGIC 1. Leave the **Type** set to **Notebook** +# MAGIC 1. Select the notebook **DE 9.2.4L - Query Results Job** using the notebook picker +# MAGIC 1. Note that the **Depends on** field defaults to your previously defined task, **DLT-Pipeline** +# MAGIC 1. From the **Cluster** dropdown, under **Existing All Purpose Cluster**, select your cluster +# MAGIC 1. Click the blue **Create task** button +# MAGIC +# MAGIC Click the blue **Run now** button in the top right of the screen to run this job. +# MAGIC +# MAGIC From the **Runs** tab, you will be able to click on the start time for this run under the **Active runs** section and visually track task progress. +# MAGIC +# MAGIC Once all your tasks have succeeded, review the contents of each task to confirm expected behavior. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.2L - Batch Job.py b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.2L - Batch Job.py new file mode 100644 index 0000000..55656d3 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.2L - Batch Job.py @@ -0,0 +1,7 @@ +# Databricks notebook source +# MAGIC %run ../../Includes/Classroom-Setup-9.2.2L + +# COMMAND ---------- + +DA.data_factory.load() + diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/1 - DLT Task.sql b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.3L - DLT Job.sql similarity index 81% rename from Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/1 - DLT Task.sql rename to Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.3L - DLT Job.sql index 55e4d4e..a812762 100644 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.4.2 - LAB - End-to-End ELT in the Lakehouse/1 - DLT Task.sql +++ b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.3L - DLT Job.sql @@ -1,17 +1,17 @@ -- Databricks notebook source -CREATE INCREMENTAL LIVE TABLE recordings_bronze +CREATE OR REFRESH STREAMING LIVE TABLE recordings_bronze AS SELECT current_timestamp() receipt_time, input_file_name() source_file, * FROM cloud_files("${source}", "json", map("cloudFiles.schemaHints", "time DOUBLE")) -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE pii +CREATE OR REFRESH STREAMING LIVE TABLE pii AS SELECT * FROM cloud_files("/mnt/training/healthcare/patient", "csv", map("header", "true", "cloudFiles.inferColumnTypes", "true")) -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE recordings_enriched +CREATE OR REFRESH STREAMING LIVE TABLE recordings_enriched (CONSTRAINT positive_heartrate EXPECT (heartrate > 0) ON VIOLATION DROP ROW) AS SELECT CAST(a.device_id AS INTEGER) device_id, @@ -25,7 +25,7 @@ AS SELECT -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE daily_patient_avg +CREATE OR REFRESH STREAMING LIVE TABLE daily_patient_avg COMMENT "Daily mean heartrates by patient" AS SELECT mrn, name, MEAN(heartrate) avg_heartrate, DATE(time) `date` FROM STREAM(live.recordings_enriched) diff --git a/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.4L - Query Results Job.py b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.4L - Query Results Job.py new file mode 100644 index 0000000..158cba1 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/09 - Task Orchestration with Jobs/DE 9.2L - Jobs Lab/DE 9.2.4L - Query Results Job.py @@ -0,0 +1,71 @@ +# Databricks notebook source +# MAGIC %run ../../Includes/Classroom-Setup-9.2.4L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Exploring the Results of a DLT Pipeline +# MAGIC +# MAGIC Run the following cell to enumerate the output of your storage location: + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.working_dir}/storage") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC The **system** directory captures events associated with the pipeline. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.working_dir}/storage/system/events") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC These event logs are stored as a Delta table. +# MAGIC +# MAGIC Let's query the table. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM delta.`${da.paths.working_dir}/storage/system/events` + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Let's view the contents of the *tables* directory. + +# COMMAND ---------- + +files = dbutils.fs.ls(f"{DA.paths.working_dir}/storage/tables") +display(files) + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Let's query the gold table. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT * FROM ${da.db_name}.daily_patient_avg + +# COMMAND ---------- + +DA.cleanup() + diff --git a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.2.1 - Navigating Databricks SQL and Attaching to Endpoints.py b/Data-Engineering-with-Databricks/Solutions/10 - Running a DBSQL Query/DE 10.1 - Navigating Databricks SQL and Attaching to Endpoints.py similarity index 65% rename from Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.2.1 - Navigating Databricks SQL and Attaching to Endpoints.py rename to Data-Engineering-with-Databricks/Solutions/10 - Running a DBSQL Query/DE 10.1 - Navigating Databricks SQL and Attaching to Endpoints.py index 7916b00..1dfdf45 100644 --- a/Data-Engineering-With-Databricks/Solutions/04 - Managing Data Access and Production Pipelines/4.2.1 - Navigating Databricks SQL and Attaching to Endpoints.py +++ b/Data-Engineering-with-Databricks/Solutions/10 - Running a DBSQL Query/DE 10.1 - Navigating Databricks SQL and Attaching to Endpoints.py @@ -8,34 +8,36 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Navigating Databricks SQL and Attaching to Endpoints # MAGIC # MAGIC * Navigate to Databricks SQL # MAGIC * Make sure that SQL is selected from the workspace option in the sidebar (directly below the Databricks logo) # MAGIC * Make sure a SQL endpoint is on and accessible -# MAGIC * Navigate to SQL enpoints in the sidebar -# MAGIC * If a SQL endpoint exists and has the State `Running`, you'll use this endpoint -# MAGIC * If a SQL endpoint exists but is `Stopped`, click the `Start` button if you have this option (**NOTE**: Start the smallest endpoint you have available to you) -# MAGIC * If no endpoints exist and you have the option, click `Create SQL Endpoint`; name the endpoint something you'll recognize and set the cluster size to 2X-Small. Leave all other options as default. +# MAGIC * Navigate to SQL endpoints in the sidebar +# MAGIC * If a SQL endpoint exists and has the State **`Running`**, you'll use this endpoint +# MAGIC * If a SQL endpoint exists but is **`Stopped`**, click the **`Start`** button if you have this option (**NOTE**: Start the smallest endpoint you have available to you) +# MAGIC * If no endpoints exist and you have the option, click **`Create SQL Endpoint`**; name the endpoint something you'll recognize and set the cluster size to 2X-Small. Leave all other options as default. # MAGIC * If you have no way to create or attach to a SQL endpoint, you'll need to contact a workspace administrator and request access to compute resources in Databricks SQL to continue. # MAGIC * Navigate to home page in Databricks SQL # MAGIC * Click the Databricks logo at the top of the side nav bar -# MAGIC * Locate the **Sample dashboards** and click `Visit gallery` -# MAGIC * Click `Import` next to the **Retail Revenue & Supply Chain** option +# MAGIC * Locate the **Sample dashboards** and click **`Visit gallery`** +# MAGIC * Click **`Import`** next to the **Retail Revenue & Supply Chain** option # MAGIC * Assuming you have a SQL endpoint available, this should load a dashboard and immediately display results # MAGIC * Click **Refresh** in the top right (the underlying data has not changed, but this is the button that would be used to pick up changes) # MAGIC # MAGIC # Updating a DBSQL Dashboard # MAGIC # MAGIC * Use the sidebar navigator to find the **Dashboards** -# MAGIC * Locate the sample dashboard you just loaded; it should be called **Retail Revenue & Supply Chain** and have your username under the `Created By` field. **NOTE**: the **My Dashboards** option on the right hand side can serve as a shortcut to filter out other dashboards in the workspace +# MAGIC * Locate the sample dashboard you just loaded; it should be called **Retail Revenue & Supply Chain** and have your username under the **`Created By`** field. **NOTE**: the **My Dashboards** option on the right hand side can serve as a shortcut to filter out other dashboards in the workspace # MAGIC * Click on the dashboard name to view it # MAGIC * View the query behind the **Shifts in Pricing Priorities** plot # MAGIC * Hover over the plot; three vertical dots should appear. Click on these # MAGIC * Select **View Query** from the menu that appears # MAGIC * Review the SQL code used to populate this plot # MAGIC * Note that 3 tier namespacing is used to identify the source table; this is a preview of new functionality to be supported by Unity Catalog -# MAGIC * Click `Run` in the top right of the screen to preview the results of the query +# MAGIC * Click **`Run`** in the top right of the screen to preview the results of the query # MAGIC * Review the visualization # MAGIC * Under the query, a tab named **Table** should be selected; click **Price by Priority over Time** to switch to a preview of your plot # MAGIC * Click **Edit Visualization** at the bottom of the screen to review settings @@ -43,37 +45,37 @@ # MAGIC * If you wish to apply your changes, click **Save**; otherwise, click **Cancel** # MAGIC * Back in the query editor, click the **Add Visualization** button to the right of the visualization name # MAGIC * Create a bar graph -# MAGIC * Set the **X Column** as `Date` -# MAGIC * Set the **Y Column** as `Total Price` -# MAGIC * **Group by** `Priority` -# MAGIC * Set **Stacking** to `Stack` +# MAGIC * Set the **X Column** as **`Date`** +# MAGIC * Set the **Y Column** as **`Total Price`** +# MAGIC * **Group by** **`Priority`** +# MAGIC * Set **Stacking** to **`Stack`** # MAGIC * Leave all other settings as defaults # MAGIC * Click **Save** -# MAGIC * Back in the query editor, click the default name for this visualization to edit it; change the visualization name to `Stacked Price` -# MAGIC * Add the bottom of the screen, click the three vertical dots to the left of the `Edit Visualization` button +# MAGIC * Back in the query editor, click the default name for this visualization to edit it; change the visualization name to **`Stacked Price`** +# MAGIC * Add the bottom of the screen, click the three vertical dots to the left of the **`Edit Visualization`** button # MAGIC * Select **Add to Dashboard** from the menu -# MAGIC * Select your `Retail Revenue & Supply Chain` dashboard +# MAGIC * Select your **`Retail Revenue & Supply Chain`** dashboard # MAGIC * Navigate back to your dashboard to view this change # MAGIC # MAGIC # Create a New Query # MAGIC # MAGIC * Use the sidebar to navigate to **Queries** -# MAGIC * Click the `Create Query` button -# MAGIC * In the **Schema Browser**, click on the current metastore and select `samples` -# MAGIC * Select the `tpch` database -# MAGIC * Click on the `partsupp` table to get a preview of the schema -# MAGIC * While hovering over the `partsupp` table name, click the `>>` button to insert the table name into your query text +# MAGIC * Click the **`Create Query`** button +# MAGIC * Make sure you are connected to an endpoint. In the **Schema Browser**, click on the current metastore and select **`samples`**. +# MAGIC * Select the **`tpch`** database +# MAGIC * Click on the **`partsupp`** table to get a preview of the schema +# MAGIC * While hovering over the **`partsupp`** table name, click the **>>** button to insert the table name into your query text # MAGIC * Write your first query: -# MAGIC * `SELECT * FROM` the `partsupp` table using the full name imported in the last step; click **Run** to preview results -# MAGIC * Modify this query to `GROUP BY ps_partkey` and return the `ps_partkey` and `sum(ps_availqty)`; click **Run** to preview results -# MAGIC * Update your query to alias the 2nd column to be named `total_availqty` and re-execute the query +# MAGIC * **`SELECT * FROM`** the **`partsupp`** table using the full name imported in the last step; click **Run** to preview results +# MAGIC * Modify this query to **`GROUP BY ps_partkey`** and return the **`ps_partkey`** and **`sum(ps_availqty)`**; click **Run** to preview results +# MAGIC * Update your query to alias the 2nd column to be named **`total_availqty`** and re-execute the query # MAGIC * Save your query # MAGIC * Click the **Save** button next to **Run** near the top right of the screen # MAGIC * Give the query a name you'll remember # MAGIC * Add the query to your dashboard # MAGIC * Click the three vertical buttons at the bottom of the screen # MAGIC * Click **Add to Dashboard** -# MAGIC * Select your `Retail Revenue & Supply Chain` dashboard +# MAGIC * Select your **`Retail Revenue & Supply Chain`** dashboard # MAGIC * Navigate back to your dashboard to view this change # MAGIC * If you wish to change the organization of visualizations, click the three vertical buttons in the top right of the screen; click **Edit** in the menu that appears and you'll be able to drag and resize visualizations diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.3.1 - Managing Permissions for Databases, Tables, and Views.py b/Data-Engineering-with-Databricks/Solutions/11 - Managing Permissions/DE 11.1 - Managing Permissions for Databases, Tables, and Views.py similarity index 79% rename from Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.3.1 - Managing Permissions for Databases, Tables, and Views.py rename to Data-Engineering-with-Databricks/Solutions/11 - Managing Permissions/DE 11.1 - Managing Permissions for Databases, Tables, and Views.py index ac3fca8..d8f2d04 100644 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.3.1 - Managing Permissions for Databases, Tables, and Views.py +++ b/Data-Engineering-with-Databricks/Solutions/11 - Managing Permissions/DE 11.1 - Managing Permissions for Databases, Tables, and Views.py @@ -8,12 +8,15 @@ # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC # Managing Permissions for Databases, Tables, and Views # MAGIC # MAGIC The instructions as detailed below are provided for groups of users to explore how Table ACLs on Databricks work. It leverages Databricks SQL and the Data Explorer to accomplish these tasks, and assumes that at least one user in the group has administrator status (or that an admin has previously configured permissions to allow proper permissions for users to create databases, tables, and views). # MAGIC # MAGIC As written, these instructions are for the admin user to complete. The following notebook will have a similar exercise for users to complete in pairs. # MAGIC +# MAGIC ## Learning Objectives # MAGIC By the end of this lesson, you should be able to: # MAGIC * Describe the default permissions for users and admins in DBSQL # MAGIC * Identify the default owner for databases, tables, and views created in DBSQL and change ownership @@ -23,65 +26,43 @@ # COMMAND ---------- +# MAGIC %run ../Includes/Classroom-Setup-11.1 + +# COMMAND ---------- + # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Generate Setup Statements # MAGIC -# MAGIC The following cell uses Python to extract username of the present user and format this into several statements used to create databases, tables, and views. +# MAGIC The following cell uses Python to extract username of the current user and format this into several statements used to create databases, tables, and views. # MAGIC # MAGIC Only the admin needs to execute the following cell. Successful execution will print out a series of formatted SQL queries, which can be copied into the DBSQL query editor and executed. # COMMAND ---------- -def generate_query(course, mode="reset"): - import re - - username = spark.sql("SELECT current_user()").first()[0] - userhome = f"dbfs:/user/{username}/{course}" - database = f"""dbacademy_{re.sub("[^a-zA-Z0-9]", "_", username)}_{course}""" - - if mode == "reset": - spark.sql(f"DROP DATABASE IF EXISTS {database} CASCADE") - dbutils.fs.rm(userhome, True) - - print(f""" -CREATE DATABASE IF NOT EXISTS {database} -LOCATION '{userhome}'; - -USE {database}; - -CREATE TABLE users -(id INT, name STRING, value DOUBLE, state STRING); - -INSERT INTO users -VALUES (1, "Yve", 1.0, "CA"), - (2, "Omar", 2.5, "NY"), - (3, "Elia", 3.3, "OH"), - (4, "Rebecca", 4.7, "TX"), - (5, "Ameena", 5.3, "CA"), - (6, "Ling", 6.6, "NY"), - (7, "Pedro", 7.1, "KY"); - -CREATE VIEW ny_users_vw -AS SELECT * FROM users WHERE state = 'NY'; - """) -generate_query("acls_demo") +DA.generate_users_table() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC Steps: -# MAGIC * Run the cell above -# MAGIC * Copy the entire output to your clipboard -# MAGIC * Navigate to the Databricks SQL workspace -# MAGIC * Make sure that a DBSQL endpoint is running -# MAGIC * Use the left sidebar to select the **SQL Editor** -# MAGIC * Paste the query above and click the blue **Run** in the top right +# MAGIC 1. Run the cell above +# MAGIC 1. Copy the entire output to your clipboard +# MAGIC 1. Navigate to the Databricks SQL workspace +# MAGIC 1. Make sure that a DBSQL endpoint is running +# MAGIC 1. Use the left sidebar to select the **SQL Editor** +# MAGIC 1. Paste the query above and click the blue **Run** in the top right # MAGIC # MAGIC **NOTE**: You will need to be connected to a DBSQL endpoint to execute these queries successfully. If you cannot connect to a DBSQL endpoint, you will need to contact your administrator to give you access. # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Using Data Explorer # MAGIC # MAGIC * Use the left sidebar navigator to select the **Data** tab; this places you in the **Data Explorer** @@ -93,7 +74,7 @@ def generate_query(course, mode="reset"): # MAGIC * Explore data schema, metadata, and history # MAGIC * Set and modify permissions of relational entities # MAGIC -# MAGIC Note that at the moment these instructions are being written, Unity Catalog is not yet generally available. The 3 tier namespacing functionality it adds can be previewed to an extent by switching between the default `hive_metastore` and the `sample` catalog used for example dashboards and queries. Expect the Data Explorer UI and functionality to evolve as Unity Catalog is added to workspaces. +# MAGIC Note that at the moment these instructions are being written, Unity Catalog is not yet generally available. The 3 tier namespacing functionality it adds can be previewed to an extent by switching between the default **`hive_metastore`** and the **`sample`** catalog used for example dashboards and queries. Expect the Data Explorer UI and functionality to evolve as Unity Catalog is added to workspaces. # MAGIC # MAGIC ## Configuring Permissions # MAGIC @@ -114,7 +95,7 @@ def generate_query(course, mode="reset"): # MAGIC | FUNCTION | controls access to a named function. | # MAGIC | ANY FILE | controls access to the underlying filesystem. Users granted access to ANY FILE can bypass the restrictions put on the catalog, databases, tables, and views by reading from the file system directly. | # MAGIC -# MAGIC **NOTE**: At present, the `ANY FILE` object cannot be set from Data Explorer. +# MAGIC **NOTE**: At present, the **`ANY FILE`** object cannot be set from Data Explorer. # MAGIC # MAGIC ## Granting Privileges # MAGIC @@ -145,8 +126,10 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Review the Default Permissions -# MAGIC In the Data Explorer, find the database you created earlier (this should follow the pattern `dbacademy__acls_demo`). +# MAGIC In the Data Explorer, find the database you created earlier (this should follow the pattern **`dbacademy__acls_demo`**). # MAGIC # MAGIC Clicking on the database name should display a list of the contained tables and views on the left hand side. On the right, you'll see some details about the database, including the **Owner** and **Location**. # MAGIC @@ -155,6 +138,8 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Assigning Ownership # MAGIC # MAGIC Click the blue pencil next to the **Owner** field. Note that an owner can be set as an individual OR a group. For most implementations, having one or several small groups of trusted power users as owners will limit admin access to important datasets while ensuring that a single user does not create a choke point in productivity. @@ -164,11 +149,13 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Change Database Permissions # MAGIC # MAGIC Begin by allowing all users to review metadata about the database. # MAGIC -# MAGIC Step: +# MAGIC Steps: # MAGIC 1. Make sure you have the **Permissions** tab selected for the database # MAGIC 1. Click the blue **Grant** button # MAGIC 1. Select the **USAGE** and **READ_METADATA** options @@ -180,14 +167,16 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Change View Permissions # MAGIC # MAGIC While users can now see information about this database, they won't be able to interact with the table of view declared above. # MAGIC # MAGIC Let's start by giving users the ability to query our view. # MAGIC -# MAGIC Step: -# MAGIC 1. Select the `ny_users_vw` +# MAGIC Steps: +# MAGIC 1. Select the **`ny_users_vw`** # MAGIC 1. Select the **Permissions** tab # MAGIC * Users should have inherited the permissions granted at the database level; you'll be able to see which permissions users currently have on an asset, as well as where that permission is inherited from # MAGIC 1. Click the blue **Grant** button @@ -199,25 +188,29 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Run a Query to Confirm # MAGIC # MAGIC In the **SQL Editor**, all users should use the **Schema Browser** on the lefthand side to navigate to the database being controlled by the admin. # MAGIC -# MAGIC Users should start a query by typing `SELECT * FROM ` and then click the **>>** that appears while hovering over the view name to insert it into their query. +# MAGIC Users should start a query by typing **`SELECT * FROM`** and then click the **>>** that appears while hovering over the view name to insert it into their query. # MAGIC # MAGIC This query should return 2 results. # MAGIC -# MAGIC **NOTE**: This view is defined against the `users` table, which has not had any permissions set yet. Note that users have access only to that portion of the data that passes through the filters defined on the view; this pattern demonstrates how a single underlying table can be used to drive controlled access to data for relevant stakeholders. +# MAGIC **NOTE**: This view is defined against the **`users`** table, which has not had any permissions set yet. Note that users have access only to that portion of the data that passes through the filters defined on the view; this pattern demonstrates how a single underlying table can be used to drive controlled access to data for relevant stakeholders. # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Change Table Permissions # MAGIC -# MAGIC Perform the same steps as above, but now for the `users` table. +# MAGIC Perform the same steps as above, but now for the **`users`** table. # MAGIC -# MAGIC Step: -# MAGIC 1. Select the `users` table +# MAGIC Steps: +# MAGIC 1. Select the **`users`** table # MAGIC 1. Select the **Permissions** tab # MAGIC 1. Click the blue **Grant** button # MAGIC 1. Select the **SELECT** and **READ_METADATA** options @@ -227,7 +220,9 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md -# MAGIC ## Have Users Attempt to `DROP TABLE` +# MAGIC +# MAGIC +# MAGIC ## Have Users Attempt to **`DROP TABLE`** # MAGIC # MAGIC In the **SQL Editor**, encourage users to explore the data in this table. # MAGIC @@ -236,6 +231,8 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Create a Database for Derivative Datasets # MAGIC # MAGIC In most cases users will need a location to save out derivative datasets. At present, users may not have the ability to create new tables in any location (depending on existing ACLs in the workspace and databases created during previous lessons students have completed). @@ -246,22 +243,13 @@ def generate_query(course, mode="reset"): # COMMAND ---------- -import re - -username = spark.sql("SELECT current_user()").first()[0] -database = f"""dbacademy_{re.sub("[^a-zA-Z0-9]", "_", username)}_derivative""" - -print(f""" -CREATE DATABASE {database}; - -GRANT USAGE, READ_METADATA, CREATE, MODIFY, SELECT ON DATABASE `{database}` TO `users`; - -SHOW GRANT ON DATABASE `{database}` -""") +DA.generate_create_database_with_grants() # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Have Users Create New Tables or Views # MAGIC # MAGIC Give users a moment to test that they can create tables and views in this new database. @@ -271,21 +259,19 @@ def generate_query(course, mode="reset"): # COMMAND ---------- # MAGIC %md +# MAGIC +# MAGIC # MAGIC ## Admin Configuration # MAGIC -# MAGIC At present, users do not have any Table ACL permissions granted on the default catalog `hive_metastore` by default. The next lab assumes that users will be able to create databases. +# MAGIC At present, users do not have any Table ACL permissions granted on the default catalog **`hive_metastore`** by default. The next lab assumes that users will be able to create databases. # MAGIC # MAGIC To enable the ability to create databases and tables in the default catalog using Databricks SQL, have a workspace admin run the following command in the DBSQL query editor: # MAGIC -# MAGIC ``` -# MAGIC GRANT usage, create ON CATALOG `hive_metastore` TO `users` -# MAGIC ``` +# MAGIC GRANT usage, create ON CATALOG `hive_metastore` TO `users` # MAGIC # MAGIC To confirm this has run successfully, execute the following query: # MAGIC -# MAGIC ``` -# MAGIC SHOW GRANT ON CATALOG `hive_metastore` -# MAGIC ``` +# MAGIC SHOW GRANT ON CATALOG `hive_metastore` # COMMAND ---------- diff --git a/Data-Engineering-with-Databricks/Solutions/11 - Managing Permissions/DE 11.2L - Configuring Privileges for Production Data and Derived Tables Lab.py b/Data-Engineering-with-Databricks/Solutions/11 - Managing Permissions/DE 11.2L - Configuring Privileges for Production Data and Derived Tables Lab.py new file mode 100644 index 0000000..c4328ac --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/11 - Managing Permissions/DE 11.2L - Configuring Privileges for Production Data and Derived Tables Lab.py @@ -0,0 +1,215 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Configuring Privileges for Production Data and Derived Tables +# MAGIC +# MAGIC The instructions as detailed below are provided for pairs of users to explore how Table ACLs on Databricks work. It leverages Databricks SQL and the Data Explorer to accomplish these tasks, and assumes that neither user has admin privileges for the workspace. An admin will need to have previously granted **`CREATE`** and **`USAGE`** privileges on a catalog for users to be able to create databases in Databricks SQL. +# MAGIC +# MAGIC ##Learning Objectives +# MAGIC +# MAGIC By the end of this lab, you should be able to: +# MAGIC * Use Data Explorer to navigate relational entities +# MAGIC * Configure permissions for tables and views with Data Explorer +# MAGIC * Configure minimal permissions to allow for table discovery and querying +# MAGIC * Change ownership for databases, tables, and views created in DBSQL + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-11.2L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Exchange User Names with your Partner +# MAGIC If you are not in a workspace where your usernames correspond with your email address, make sure your partner has your username. +# MAGIC +# MAGIC They will need this when assigning privileges and searching for your database at later steps. +# MAGIC +# MAGIC The following cell will print your username. + +# COMMAND ---------- + +print(f"Your username: {DA.username}") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Generate Setup Statements +# MAGIC +# MAGIC The following cell uses Python to extract the username of the current user and format this into several statements used to create databases, tables, and views. +# MAGIC +# MAGIC Both students should execute the following cell. +# MAGIC +# MAGIC Successful execution will print out a series of formatted SQL queries, which can be copied into the DBSQL query editor and executed. + +# COMMAND ---------- + +DA.generate_query() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Steps: +# MAGIC 1. Run the cell above +# MAGIC 1. Copy the entire output to your clipboard +# MAGIC 1. Navigate to the Databricks SQL workspace +# MAGIC 1. Make sure that a DBSQL endpoint is running +# MAGIC 1. Use the left sidebar to select the **SQL Editor** +# MAGIC 1. Paste the query above and click the blue **Run** in the top right +# MAGIC +# MAGIC **NOTE**: You will need to be connected to a DBSQL endpoint to execute these queries successfully. If you cannot connect to a DBSQL endpoint, you will need to contact your administrator to give you access. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Find Your Database +# MAGIC In the Data Explorer, find the database you created earlier (this should follow the pattern **`dbacademy__dewd_acls_lab`**). +# MAGIC +# MAGIC Clicking on the database name should display a list of the contained tables and views on the left hand side. +# MAGIC +# MAGIC On the right, you'll see some details about the database, including the **Owner** and **Location**. +# MAGIC +# MAGIC Click the **Permissions** tab to review who presently has permissions (depending on your workspace configuration, some permissions may have been inherited from settings on the catalog). + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Change Database Permissions +# MAGIC +# MAGIC Steps: +# MAGIC 1. Make sure you have the **Permissions** tab selected for the database +# MAGIC 1. Click the blue **Grant** button +# MAGIC 1. Select the **USAGE**, **SELECT**, and **READ_METADATA** options +# MAGIC 1. Enter the username of your partner in the field at the top. +# MAGIC 1. Click **OK** +# MAGIC +# MAGIC Confirm with your partner that you can each see each others' databases and tables. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Run a Query to Confirm +# MAGIC +# MAGIC By granting **`USAGE`**, **`SELECT`**, and **`READ_METADATA`** on your database, your partner should now be able to freely query the tables and views in this database, but will not be able to create new tables OR modify your data. +# MAGIC +# MAGIC In the SQL Editor, each user should run a series of queries to confirm this behavior in the database they were just added to. +# MAGIC +# MAGIC **Make sure you specify your partner's database while running the queries below.** +# MAGIC +# MAGIC **NOTE**: These first 3 queries should succeed, but the last should fail. + +# COMMAND ---------- + +# Replace FILL_IN with your partner's username +DA.generate_confirmation_query("FILL_IN") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Execute a Query to Generate the Union of Your Beans +# MAGIC +# MAGIC Execute the query below against your own databases. +# MAGIC +# MAGIC **NOTE**: Because random values were inserted for the **`grams`** and **`delicious`** columns, you should see 2 distinct rows for each **`name`**, **`color`** pair. + +# COMMAND ---------- + +DA.generate_union_query() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Register a Derivative View to Your Database +# MAGIC +# MAGIC Execute the query below to register the results of the previous query to your database. + +# COMMAND ---------- + +DA.generate_derivative_view() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Query Your Partner's View +# MAGIC +# MAGIC Once your partner has successfully completed the previous step, run the following query against each of your tables; you should get the same results: + +# COMMAND ---------- + +# Replace FILL_IN with your partner's username +DA.generate_partner_view("FILL_IN") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Add Modify Permissions +# MAGIC +# MAGIC Now try to drop each other's **`beans`** tables. +# MAGIC +# MAGIC At the moment, this shouldn't work. +# MAGIC +# MAGIC Using the Data Explorer, add the **`MODIFY`** permission for your **`beans`** table for your partner. +# MAGIC +# MAGIC Again, attempt to drop your partner's **`beans`** table. +# MAGIC +# MAGIC It should again fail. +# MAGIC +# MAGIC **Only the owner of a table should be able to issue this statement**.
+# MAGIC (Note that ownership can be transferred from an individual to a group, if desired). +# MAGIC +# MAGIC Instead, execute a query to delete records from your partner's table: + +# COMMAND ---------- + +# Replace FILL_IN with your partner's username +DA.generate_delete_query("FILL_IN") + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC This query should successfully drop all records from the target table. +# MAGIC +# MAGIC Try to re-execute queries against any of the views of tables you'd previously queried in this lab. +# MAGIC +# MAGIC **NOTE**: If steps were completed successfully, none of your previous queries should return results, as the data referenced by your views has been deleted. This demonstrates the risks associated with providing **`MODIFY`** privileges to users on data that will be used in production applications and dashboards. +# MAGIC +# MAGIC If you have additional time, see if you can use the Delta methods **`DESCRIBE HISTORY`** and **`RESTORE`** to revert the records in your table. + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.1 - Last Mile ETL with DBSQL.py b/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.1 - Last Mile ETL with DBSQL.py new file mode 100644 index 0000000..171a1e1 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.1 - Last Mile ETL with DBSQL.py @@ -0,0 +1,304 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC # Last Mile ETL with Databricks SQL +# MAGIC +# MAGIC Before we continue, let's do a recap of some of the things we've learned so far: +# MAGIC 1. The Databricks workspace contains a suite of tools to simplify the data engineering development lifecycle +# MAGIC 1. Databricks notebooks allow users to mix SQL with other programming languages to define ETL workloads +# MAGIC 1. Delta Lake provides ACID compliant transactions and makes incremental data processing easy in the Lakehouse +# MAGIC 1. Delta Live Tables extends the SQL syntax to support many design patterns in the Lakehouse, and simplifies infrastructure deployment +# MAGIC 1. Multi-task jobs allows for full task orchestration, adding dependencies while scheduling a mix of notebooks and DLT pipelines +# MAGIC 1. Databricks SQL allows users to edit and execute SQL queries, build visualizations, and define dashboards +# MAGIC 1. Data Explorer simplifies managing Table ACLs, making Lakehouse data available to SQL analysts (soon to be expanded greatly by Unity Catalog) +# MAGIC +# MAGIC In this section, we'll focus on exploring more DBSQL functionality to support production workloads. +# MAGIC +# MAGIC We'll start by focusing on leveraging Databricks SQL to configure queries that support last mile ETL for analytics. Note that while we'll be using the Databricks SQL UI for this demo, SQL Endpoints integrate with a number of other tools to allow external query execution, as well as having full API support for executing arbitrary queries programmatically. +# MAGIC +# MAGIC From these query results, we'll generate a series of visualizations, which we'll combine into a dashboard. +# MAGIC +# MAGIC Finally, we'll walk through scheduling updates for queries and dashboards, and demonstrate setting alerts to help monitor the state of production datasets over time. +# MAGIC +# MAGIC ## Learning Objectives +# MAGIC By the end of this lesson, you should be able to: +# MAGIC * Use Databricks SQL as a tool to support production ETL tasks backing analytic workloads +# MAGIC * Configure SQL queries and visualizations with the Databricks SQL Editor +# MAGIC * Create dashboards in Databricks SQL +# MAGIC * Schedule updates for queries and dashboards +# MAGIC * Set alerts for SQL queries + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Run Setup Script +# MAGIC The following cells runs a notebook that defines a class we'll use to generate SQL queries. + +# COMMAND ---------- + +# MAGIC %run ../Includes/Classroom-Setup-12.1 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Demo Database +# MAGIC Execute the following cell and copy the results into the Databricks SQL Editor. +# MAGIC +# MAGIC These queries: +# MAGIC * Create a new database +# MAGIC * Declare two tables (we'll use these for loading data) +# MAGIC * Declare two functions (we'll use these for generating data) +# MAGIC +# MAGIC Once copied, execute the query using the **Run** button. + +# COMMAND ---------- + +DA.generate_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC **NOTE**: The queries above are only designed to be run once after resetting the demo completely to reconfigure the environment. Users will need to have **`CREATE`** and **`USAGE`** permissions on the catalog to execute them. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC +# MAGIC **WARNING:** Make sure to select your database before proceeding as the **`USE`** statement
doesn't yet change the database against which your queries will execute + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Query to Load Data +# MAGIC Steps: +# MAGIC 1. Execute the cell below to print out a formatted SQL query for loading data in the **`user_ping`** table created in the previous step. +# MAGIC 1. Save this query with the name **Load Ping Data**. +# MAGIC 1. Run this query to load a batch of data. + +# COMMAND ---------- + +DA.generate_load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Executing the query should load some data and return a preview of the data in the table. +# MAGIC +# MAGIC **NOTE**: Random numbers are being used to define and load data, so each user will have slightly different values present. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Set a Query Refresh Schedule +# MAGIC +# MAGIC Steps: +# MAGIC 1. Locate the **Refresh Schedule** field at the bottom right of the SQL query editor box; click the blue **Never** +# MAGIC 1. Use the drop down to change to Refresh every **1 minute** +# MAGIC 1. For **Ends**, click the **On** radio button +# MAGIC 1. Select tomorrow's date +# MAGIC 1. Click **OK** + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Query to Track Total Records +# MAGIC Steps: +# MAGIC 1. Execute the cell below. +# MAGIC 1. Save this query with the name **User Counts**. +# MAGIC 1. Run the query to calculate the current results. + +# COMMAND ---------- + +DA.generate_user_counts() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Bar Graph Visualization +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the **Add Visualization** button, located beneath the Refresh Schedule button in the bottom right-hand corner of the query window +# MAGIC 1. Click on the name (should default to something like **`Visualization 1`**) and change the name to **Total User Records** +# MAGIC 1. Set **`user_id`** for the **X Column** +# MAGIC 1. Set **`total_records`** for the **Y Columns** +# MAGIC 1. Click **Save** + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a New Dashboard +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. +# MAGIC 1. Click the **Create new dashboard** option +# MAGIC 1. Name your dashboard User Ping Summary **``** +# MAGIC 1. Click **Save** to create the new dashboard +# MAGIC 1. Your newly created dashboard should now be selected as the target; click **OK** to add your visualization + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Query to Calculate the Recent Average Ping +# MAGIC Steps: +# MAGIC 1. Execute the cell below to print out the formatted SQL query. +# MAGIC 1. Save this query with the name **Avg Ping**. +# MAGIC 1. Run the query to calculate the current results. + +# COMMAND ---------- + +DA.generate_avg_ping() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Add a Line Plot Visualization to your Dashboard +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the **Add Visualization** button +# MAGIC 1. Click on the name (should default to something like **`Visualization 1`**) and change the name to **Avg User Ping** +# MAGIC 1. Select **`Line`** for the **Visualization Type** +# MAGIC 1. Set **`end_time`** for the **X Column** +# MAGIC 1. Set **`avg_ping`** for the **Y Columns** +# MAGIC 1. Set **`user_id`** for the **Group by** +# MAGIC 1. Click **Save** +# MAGIC 1. Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. +# MAGIC 1. Select the dashboard you created earlier +# MAGIC 1. Click **OK** to add your visualization + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create a Query to Report Summary Statistics +# MAGIC Steps: +# MAGIC 1. Execute the cell below. +# MAGIC 1. Save this query with the name **Ping Summary**. +# MAGIC 1. Run the query to calculate the current results. + +# COMMAND ---------- + +DA.generate_summary() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Add the Summary Table to your Dashboard +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the button with three vertical dots at the bottom of the screen and select **Add to Dashboard**. +# MAGIC 1. Select the dashboard you created earlier +# MAGIC 1. Click **OK** to add your visualization + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Review and Refresh your Dashboard +# MAGIC +# MAGIC Steps: +# MAGIC 1. Use the left side bar to navigate to **Dashboards** +# MAGIC 1. Find the dashboard you've added your queries to +# MAGIC 1. Click the blue **Refresh** button to update your dashboard +# MAGIC 1. Click the **Schedule** button to review dashboard scheduling options +# MAGIC * Note that scheduling a dashboard to update will execute all queries associated with that dashboard +# MAGIC * Do not schedule the dashboard at this time + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Share your Dashboard +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the blue **Share** button +# MAGIC 1. Select **All Users** from the top field +# MAGIC 1. Choose **Can Run** from the right field +# MAGIC 1. Click **Add** +# MAGIC 1. Change the **Credentials** to **Run as viewer** +# MAGIC +# MAGIC **NOTE**: At present, no other users should have any permissions to run your dashboard, as they have not been granted permissions to the underlying databases and tables using Table ACLs. If you wish other users to be able to trigger updates to your dashboard, you will either need to grant them permissions to **Run as owner** or add permissions for the tables referenced in your queries. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Set Up an Alert +# MAGIC +# MAGIC Steps: +# MAGIC 1. Use the left side bar to navigate to **Alerts** +# MAGIC 1. Click **Create Alert** in the top right +# MAGIC 1. Click the field at the top left of the screen to give the alert a name **` Count Check`** +# MAGIC 1. Select your **User Counts** query +# MAGIC 1. For the **Trigger when** options, configure: +# MAGIC * **Value column**: **`total_records`** +# MAGIC * **Condition**: **`>`** +# MAGIC * **Threshold**: **`15`** +# MAGIC 1. For **Refresh**, select **Never** +# MAGIC 1. Click **Create Alert** +# MAGIC 1. On the next screen, click the blue **Refresh** in the top right to evaluate the alert + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Review Alert Destination Options +# MAGIC +# MAGIC +# MAGIC +# MAGIC Steps: +# MAGIC 1. From the preview of your alert, click the blue **Add** button to the right of **Destinations** on the right side of the screen +# MAGIC 1. At the bottom of the window that pops up, locate the and click the blue text in the message **Create new destinations in Alert Destinations** +# MAGIC 1. Review the available alerting options + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.1L - Instructions and Configuration.py b/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.1L - Instructions and Configuration.py new file mode 100644 index 0000000..41f73f6 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.1L - Instructions and Configuration.py @@ -0,0 +1,236 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC ## End-to-End ETL in the Lakehouse +# MAGIC +# MAGIC In this notebook, you will pull together concepts learned throughout the course to complete an example data pipeline. +# MAGIC +# MAGIC The following is a non-exhaustive list of skills and tasks necessary to successfully complete this exercise: +# MAGIC * Using Databricks notebooks to write queries in SQL and Python +# MAGIC * Creating and modifying databases, tables, and views +# MAGIC * Using Auto Loader and Spark Structured Streaming for incremental data processing in a multi-hop architecture +# MAGIC * Using Delta Live Table SQL syntax +# MAGIC * Configuring a Delta Live Table pipeline for continuous processing +# MAGIC * Using Databricks Jobs to orchestrate tasks from notebooks stored in Repos +# MAGIC * Setting chronological scheduling for Databricks Jobs +# MAGIC * Defining queries in Databricks SQL +# MAGIC * Creating visualizations in Databricks SQL +# MAGIC * Defining Databricks SQL dashboards to review metrics and results + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Run Setup +# MAGIC Run the following cell to reset all the databases and directories associated with this lab. + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-12.2.1L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Land Initial Data +# MAGIC Seed the landing zone with some data before proceeding. + +# COMMAND ---------- + +DA.data_factory.load() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Create and Configure a DLT Pipeline +# MAGIC **NOTE**: The main difference between the instructions here and in previous labs with DLT is that in this instance, we will be setting up our pipeline for **Continuous** execution in **Production** mode. + +# COMMAND ---------- + +print_pipeline_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Steps: +# MAGIC 1. Click the **Jobs** button on the sidebar. +# MAGIC 1. Select the **Delta Live Tables** tab. +# MAGIC 1. Click **Create Pipeline**. +# MAGIC 1. Fill in a **Pipeline Name** - because these names must be unique, we suggest using the **Pipline Name** provided in the cell above. +# MAGIC 1. For **Notebook Libraries**, use the navigator to locate and select the notebook **DE 12.2.2L - DLT Task**. +# MAGIC * Alternatively, you can copy the **Notebook Path** specified above and paste it into the field provided. +# MAGIC 1. Configure the Source +# MAGIC * Click **`Add configuration`** +# MAGIC * Enter the word **`source`** in the **Key** field +# MAGIC * Enter the **Source** value specified above to the **`Value`** field +# MAGIC 1. In the **Target** field, specify the database name printed out next to **Target** in the cell above.
+# MAGIC This should follow the pattern **`dbacademy__dewd_cap_12`** +# MAGIC 1. In the **Storage location** field, copy the directory as printed above. +# MAGIC 1. For **Pipeline Mode**, select **Continuous** +# MAGIC 1. Uncheck the **Enable autoscaling** box +# MAGIC 1. Set the number of workers to **`1`** (one) +# MAGIC 1. Click **Create**. +# MAGIC 1. After the UI updates, change from **Development** to **Production** mode +# MAGIC +# MAGIC This should begin the deployment of infrastructure. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Schedule a Notebook Job +# MAGIC +# MAGIC Our DLT pipeline is setup to process data as soon as it arrives. +# MAGIC +# MAGIC We'll schedule a notebook to land a new batch of data each minute so we can see this functionality in action. +# MAGIC +# MAGIC Before we start run the following cell to get the values used in this step. + +# COMMAND ---------- + +print_job_config() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Steps: +# MAGIC 1. Navigate to the Jobs UI using the Databricks left side navigation bar. +# MAGIC 1. Click the blue **Create Job** button +# MAGIC 1. Configure the task: +# MAGIC 1. Enter **Land-Data** for the task name +# MAGIC 1. Select the notebook **DE 12.2.3L - Land New Data** using the notebook picker +# MAGIC 1. From the **Cluster** dropdown, under **Existing All Purpose Cluster**, select your cluster +# MAGIC 1. Click **Create** +# MAGIC 1. In the top-left of the screen rename the job (not the task) from **`Land-Data`** (the defaulted value) to the **Job Name** provided for you in the previous cell. +# MAGIC +# MAGIC **Note**: When selecting your all purpose cluster, you will get a warning about how this will be billed as all purpose compute. Production jobs should always be scheduled against new job clusters appropriately sized for the workload, as this is billed at a much lower rate. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Set a Chronological Schedule for your Job +# MAGIC Steps: +# MAGIC 1. Navigate to the **Jobs UI** and click on the job you just created. +# MAGIC 1. Locate the **Schedule** section in the side panel on the right. +# MAGIC 1. Click on the **Edit schedule** button to explore scheduling options. +# MAGIC 1. Change the **Schedule type** field from **Manual** to **Scheduled**, which will bring up a chron scheduling UI. +# MAGIC 1. Set the schedule to update **Every 2**, **Minutes** from **00** +# MAGIC 1. Click **Save** +# MAGIC +# MAGIC **NOTE**: If you wish, you can click **Run now** to trigger the first run, or wait until the top of the next minute to make sure your scheduling has worked successfully. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Register DLT Event Metrics for Querying with DBSQL +# MAGIC +# MAGIC The following cell prints out SQL statements to register the DLT event logs to your target database for querying in DBSQL. +# MAGIC +# MAGIC Execute the output code with the DBSQL Query Editor to register these tables and views. +# MAGIC +# MAGIC Explore each and make note of the logged event metrics. + +# COMMAND ---------- + +DA.generate_register_dlt_event_metrics_sql() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Define a Query on the Gold Table +# MAGIC +# MAGIC The **daily_patient_avg** table is automatically updated each time a new batch of data is processed through the DLT pipeline. Each time a query is executed against this table, DBSQL will confirm if there is a newer version and then materialize results from the newest available version. +# MAGIC +# MAGIC Run the following cell to print out a query with your database name. Save this as a DBSQL query. + +# COMMAND ---------- + +DA.generate_daily_patient_avg() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Add a Line Plot Visualization +# MAGIC +# MAGIC To track trends in patient averages over time, create a line plot and add it to a new dashboard. +# MAGIC +# MAGIC Create a line plot with the following settings: +# MAGIC * **X Column**: **`date`** +# MAGIC * **Y Column**: **`avg_heartrate`** +# MAGIC * **Group By**: **`name`** +# MAGIC +# MAGIC Add this visualization to a dashboard. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Track Data Processing Progress +# MAGIC +# MAGIC The code below extracts the **`flow_name`**, **`timestamp`**, and **`num_output_rows`** from the DLT event logs. +# MAGIC +# MAGIC Save this query in DBSQL, then define a bar plot visualization that shows: +# MAGIC * **X Column**: **`timestamp`** +# MAGIC * **Y Column**: **`num_output_rows`** +# MAGIC * **Group By**: **`flow_name`** +# MAGIC +# MAGIC Add your visualization to your dashboard. + +# COMMAND ---------- + +DA.generate_visualization_query() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Refresh your Dashboard and Track Results +# MAGIC +# MAGIC The **Land-Data** notebook scheduled with Jobs above has 12 batches of data, each representing a month of recordings for our small sampling of patients. As configured per our instructions, it should take just over 20 minutes for all of these batches of data to be triggered and processed (we scheduled the Databricks Job to run every 2 minutes, and batches of data will process through our pipeline very quickly after initial ingestion). +# MAGIC +# MAGIC Refresh your dashboard and review your visualizations to see how many batches of data have been processed. (If you followed the instructions as outlined here, there should be 12 distinct flow updates tracked by your DLT metrics.) If all source data has not yet been processed, you can go back to the Databricks Jobs UI and manually trigger additional batches. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC With everything configured, you can now continue to the final part of your lab in the notebook [DE 12.2.4L - Final Steps]($./DE 12.2.4L - Final Steps) + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/2 - DLT Job.sql b/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.2L - DLT Task.sql similarity index 81% rename from Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/2 - DLT Job.sql rename to Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.2L - DLT Task.sql index 55e4d4e..a812762 100644 --- a/Data-Engineering-With-Databricks/04 - Managing Data Access and Production Pipelines/4.1.2 - LAB - Jobs/2 - DLT Job.sql +++ b/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.2L - DLT Task.sql @@ -1,17 +1,17 @@ -- Databricks notebook source -CREATE INCREMENTAL LIVE TABLE recordings_bronze +CREATE OR REFRESH STREAMING LIVE TABLE recordings_bronze AS SELECT current_timestamp() receipt_time, input_file_name() source_file, * FROM cloud_files("${source}", "json", map("cloudFiles.schemaHints", "time DOUBLE")) -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE pii +CREATE OR REFRESH STREAMING LIVE TABLE pii AS SELECT * FROM cloud_files("/mnt/training/healthcare/patient", "csv", map("header", "true", "cloudFiles.inferColumnTypes", "true")) -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE recordings_enriched +CREATE OR REFRESH STREAMING LIVE TABLE recordings_enriched (CONSTRAINT positive_heartrate EXPECT (heartrate > 0) ON VIOLATION DROP ROW) AS SELECT CAST(a.device_id AS INTEGER) device_id, @@ -25,7 +25,7 @@ AS SELECT -- COMMAND ---------- -CREATE INCREMENTAL LIVE TABLE daily_patient_avg +CREATE OR REFRESH STREAMING LIVE TABLE daily_patient_avg COMMENT "Daily mean heartrates by patient" AS SELECT mrn, name, MEAN(heartrate) avg_heartrate, DATE(time) `date` FROM STREAM(live.recordings_enriched) diff --git a/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.3L - Land New Data.py b/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.3L - Land New Data.py new file mode 100644 index 0000000..01adf27 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.3L - Land New Data.py @@ -0,0 +1,7 @@ +# Databricks notebook source +# MAGIC %run ../../Includes/Classroom-Setup-12.2.3L + +# COMMAND ---------- + +DA.data_factory.load() + diff --git a/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.4L - Final Steps.py b/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.4L - Final Steps.py new file mode 100644 index 0000000..73ca1fb --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/12 - Productionalizing Dashboards and Queries in DBSQL/DE 12.2L - OPTIONAL Capstone/DE 12.2.4L - Final Steps.py @@ -0,0 +1,137 @@ +# Databricks notebook source +# MAGIC %md-sandbox +# MAGIC +# MAGIC
+# MAGIC Databricks Learning +# MAGIC
+ +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # End-to-End ETL in the Lakehouse +# MAGIC ## Final Steps +# MAGIC +# MAGIC We are picking up from the first notebook in this lab, [DE 12.2.1L - Instructions and Configuration]($./DE 12.2.1L - Instructions and Configuration) +# MAGIC +# MAGIC If everything is setup correctly, you should have: +# MAGIC * A DLT Pipeline running in **Continuous** mode +# MAGIC * A job that is feeding that pipline new data every 2 minutes +# MAGIC * A series of Databricks SQL Queries analysing the outputs of that pipeline + +# COMMAND ---------- + +# MAGIC %run ../../Includes/Classroom-Setup-12.2.4L + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Execute a Query to Repair Broken Data +# MAGIC +# MAGIC Review the code that defined the **`recordings_enriched`** table to identify the filter applied for the quality check. +# MAGIC +# MAGIC In the cell below, write a query that returns all the records from the **`recordings_bronze`** table that were refused by this quality check. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- ANSWER +# MAGIC SELECT * FROM ${da.db_name}.recordings_bronze WHERE heartrate <= 0 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC For the purposes of our demo, let's assume that thorough manual review of our data and systems has demonstrated that occasionally otherwise valid heartrate recordings are returned as negative values. +# MAGIC +# MAGIC Run the following query to examine these same rows with the negative sign removed. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC SELECT abs(heartrate), * FROM ${da.db_name}.recordings_bronze WHERE heartrate <= 0 + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC To complete our dataset, we wish to insert these fixed records into the silver **`recordings_enriched`** table. +# MAGIC +# MAGIC Use the cell below to update the query used in the DLT pipeline to execute this repair. +# MAGIC +# MAGIC **NOTE**: Make sure you update the code to only process those records that were previously rejected due to the quality check. + +# COMMAND ---------- + +# MAGIC %sql +# MAGIC -- ANSWER +# MAGIC +# MAGIC MERGE INTO ${da.db_name}.recordings_enriched t +# MAGIC USING (SELECT +# MAGIC CAST(a.device_id AS INTEGER) device_id, +# MAGIC CAST(a.mrn AS LONG) mrn, +# MAGIC abs(CAST(a.heartrate AS DOUBLE)) heartrate, +# MAGIC CAST(from_unixtime(a.time, 'yyyy-MM-dd HH:mm:ss') AS TIMESTAMP) time, +# MAGIC b.name +# MAGIC FROM ${da.db_name}.recordings_bronze a +# MAGIC INNER JOIN ${da.db_name}.pii b +# MAGIC ON a.mrn = b.mrn +# MAGIC WHERE heartrate <= 0) v +# MAGIC ON t.mrn=v.mrn AND t.time=v.time +# MAGIC WHEN NOT MATCHED THEN INSERT * + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC Use the cell below to manually or programmatically confirm that this update has been successful. +# MAGIC +# MAGIC (The total number of records in the **`recordings_bronze`** should now be equal to the total records in **`recordings_enriched`**). + +# COMMAND ---------- + +# ANSWER +assert spark.table(f"{DA.db_name}.recordings_bronze").count() == spark.table(f"{DA.db_name}.recordings_enriched").count() + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Consider Production Data Permissions +# MAGIC +# MAGIC Note that while our manual repair of the data was successful, as the owner of these datasets, by default we have permissions to modify or delete these data from any location we're executing code. +# MAGIC +# MAGIC To put this another way: our current permissions would allow us to change or drop our production tables permanently if an errant SQL query is accidentally executed with the current user's permissions (or if other users are granted similar permissions). +# MAGIC +# MAGIC While for the purposes of this lab, we desired to have full permissions on our data, as we move code from development to production, it is safer to leverage service principals when scheduling Jobs and DLT Pipelines to avoid accidental data modifications. + +# COMMAND ---------- + +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC ## Shut Down Production Infrastructure +# MAGIC +# MAGIC Note that Databricks Jobs, DLT Pipelines, and scheduled DBSQL queries and dashboards are all designed to provide sustained execution of production code. In this end-to-end demo, you were instructed to configure a Job and Pipeline for continuous data processing. To prevent these workloads from continuing to execute, you should **Pause** your Databricks Job and **Stop** your DLT pipeline. Deleting these assets will also ensure that production infrastructure is terminated. +# MAGIC +# MAGIC **NOTE**: All instructions for DBSQL asset scheduling in previous lessons instructed users to set the update schedule to end tomorrow. You may choose to go back and also cancel these updates to prevent DBSQL endpoints from staying on until that time. + +# COMMAND ---------- + +DA.cleanup() + +# COMMAND ---------- + +# MAGIC %md-sandbox +# MAGIC © 2022 Databricks, Inc. All rights reserved.
+# MAGIC Apache, Apache Spark, Spark and the Spark logo are trademarks of the Apache Software Foundation.
+# MAGIC
+# MAGIC Privacy Policy | Terms of Use | Support diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-1.2.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-1.2.py new file mode 100644 index 0000000..d0762d6 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-1.2.py @@ -0,0 +1,21 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="1.2" + +# COMMAND ---------- + +def create_demo_tmp_vw(): + print("Creating the temp view demo_tmp_vw") + + spark.sql(""" + CREATE OR REPLACE TEMP VIEW demo_tmp_vw(name, value) AS VALUES + ("Yi", 1), + ("Ali", 2), + ("Selina", 3) + """) + +# COMMAND ---------- + +DA.init(create_db=False) +create_demo_tmp_vw() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-11.1.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-11.1.py new file mode 100644 index 0000000..43c5046 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-11.1.py @@ -0,0 +1,53 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="11.1" + +# COMMAND ---------- + +def print_sql(rows, sql): + html = f"""""" + displayHTML(html) + +# COMMAND ---------- + +def _generate_users_table(): + print_sql(20, f""" +CREATE DATABASE IF NOT EXISTS {DA.db_name} +LOCATION '{DA.paths.user_db}'; + +USE {DA.db_name}; + +CREATE TABLE users (id INT, name STRING, value DOUBLE, state STRING); + +INSERT INTO users +VALUES (1, "Yve", 1.0, "CA"), + (2, "Omar", 2.5, "NY"), + (3, "Elia", 3.3, "OH"), + (4, "Rebecca", 4.7, "TX"), + (5, "Ameena", 5.3, "CA"), + (6, "Ling", 6.6, "NY"), + (7, "Pedro", 7.1, "KY"); + +CREATE VIEW ny_users_vw +AS SELECT * FROM users WHERE state = 'NY'; +""") + +DA.generate_users_table = _generate_users_table + +# COMMAND ---------- + +def _generate_create_database_with_grants(): + print_sql(7, f""" +CREATE DATABASE {DA.db_name}_derivative; + +GRANT USAGE, READ_METADATA, CREATE, MODIFY, SELECT ON DATABASE `{DA.db_name}_derivative` TO `users`; + +SHOW GRANT ON DATABASE `{DA.db_name}_derivative`;""") + +DA.generate_create_database_with_grants = _generate_create_database_with_grants + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-11.2L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-11.2L.py new file mode 100644 index 0000000..af1f1d3 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-11.2L.py @@ -0,0 +1,122 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="acls_lab" + +# COMMAND ---------- + +def print_sql(rows, sql): + displayHTML(f"""""") + +# COMMAND ---------- + +def _generate_query(): + import re + import random + + print_sql(23, f""" +CREATE DATABASE IF NOT EXISTS {DA.db_name} +LOCATION '{DA.paths.user_db}'; + +USE {DA.db_name}; + +CREATE TABLE beans +(name STRING, color STRING, grams FLOAT, delicious BOOLEAN); + +INSERT INTO beans +VALUES ('black', 'black', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('lentils', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('jelly', 'rainbow', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('pinto', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('green', 'green', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('beanbag chair', 'white', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('lentils', 'green', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('kidney', 'red', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}), + ('castor', 'brown', {random.uniform(0, 5000):.2f}, {random.choice(["true", "false"])}); + +CREATE VIEW tasty_beans +AS SELECT * FROM beans WHERE delicious = true; + """) + +DA.generate_query = _generate_query + +# COMMAND ---------- + +def _generate_confirmation_query(username): + import re + clean_username = re.sub("[^a-zA-Z0-9]", "_", username) + database = DA.db_name.replace(DA.clean_username, clean_username) + + print_sql(11, f""" +USE {database}; + +SELECT * FROM beans; +SELECT * FROM tasty_beans; +SELECT * FROM beans MINUS SELECT * FROM tasty_beans; + +UPDATE beans +SET color = 'pink' +WHERE name = 'black' +""") + +DA.generate_confirmation_query = _generate_confirmation_query + +# COMMAND ---------- + +def _generate_union_query(): + print_sql(6, f""" +USE {DA.db_name}; + +SELECT * FROM beans +UNION ALL TABLE beans;""") + +DA.generate_union_query = _generate_union_query + +# COMMAND ---------- + +def _generate_derivative_view(): + print_sql(7, f""" +USE {DA.db_name}; + +CREATE VIEW our_beans +AS SELECT * FROM beans +UNION ALL TABLE beans; +""") + +DA.generate_derivative_view = _generate_derivative_view + +# COMMAND ---------- + +def _generate_partner_view(username): + import re + clean_username = re.sub("[^a-zA-Z0-9]", "_", username) + database = DA.db_name.replace(DA.clean_username, clean_username) + + print_sql(7, f""" +USE {database}; + +SELECT name, color, delicious, sum(grams) +FROM our_beans +GROUP BY name, color, delicious;""") + +DA.generate_partner_view = _generate_partner_view + +# COMMAND ---------- + +def _generate_delete_query(username): + import re + clean_username = re.sub("[^a-zA-Z0-9]", "_", username) + database = DA.db_name.replace(DA.clean_username, clean_username) + + print_sql(5, f""" +USE {database}; + +DELETE FROM beans + """) + +DA.generate_delete_query = _generate_delete_query + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.1.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.1.py new file mode 100644 index 0000000..d2f0100 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.1.py @@ -0,0 +1,118 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="12.1" + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + +# COMMAND ---------- + +def print_sql(rows, sql): + html = f"" + displayHTML(html) + +# COMMAND ---------- + +def _generate_config(): + print_sql(33, f""" +CREATE DATABASE IF NOT EXISTS {DA.db_name} +LOCATION '{DA.paths.working_dir}'; + +USE {DA.db_name}; + +CREATE TABLE user_ping +(user_id STRING, ping INTEGER, time TIMESTAMP); + +CREATE TABLE user_ids (user_id STRING); + +INSERT INTO user_ids VALUES +("potato_luver"), +("beanbag_lyfe"), +("default_username"), +("the_king"), +("n00b"), +("frodo"), +("data_the_kid"), +("el_matador"), +("the_wiz"); + +CREATE FUNCTION get_ping() + RETURNS INT + RETURN int(rand() * 250); + +CREATE FUNCTION is_active() + RETURNS BOOLEAN + RETURN CASE + WHEN rand() > .25 THEN true + ELSE false + END; +""") + +DA.generate_config = _generate_config + +# COMMAND ---------- + +def _generate_load(): + print_sql(12, f""" +USE {DA.db_name}; + +INSERT INTO user_ping +SELECT *, + get_ping() ping, + current_timestamp() time +FROM user_ids +WHERE is_active()=true; + +SELECT * FROM user_ping; +""") + +DA.generate_load = _generate_load + +# COMMAND ---------- + +def _generate_user_counts(): + print_sql(10, f""" +USE {DA.db_name}; + +SELECT user_id, count(*) total_records +FROM user_ping +GROUP BY user_id +ORDER BY + total_records DESC, + user_id ASC; +""") + +DA.generate_user_counts = _generate_user_counts + +# COMMAND ---------- + +def _generate_avg_ping(): + print_sql(10, f""" +USE {DA.db_name}; + +SELECT user_id, window.end end_time, mean(ping) avg_ping +FROM user_ping +GROUP BY user_id, window(time, '3 minutes') +ORDER BY + end_time DESC, + user_id ASC; +""") + +DA.generate_avg_ping = _generate_avg_ping + +# COMMAND ---------- + +def _generate_summary(): + print_sql(8, f""" +USE {DA.db_name}; + +SELECT user_id, min(time) first_seen, max(time) last_seen, count(*) total_records, avg(ping) total_avg_ping +FROM user_ping +GROUP BY user_id +ORDER BY user_id ASC; +""") + +DA.generate_summary = _generate_summary + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.2.1L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.2.1L.py new file mode 100644 index 0000000..4b906bf --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.2.1L.py @@ -0,0 +1,83 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="cap_12" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def print_sql(rows, sql): + displayHTML(f"""""") + + +# COMMAND ---------- + +def _generate_daily_patient_avg(): + sql = f"SELECT * FROM {DA.db_name}.daily_patient_avg" + print_sql(3, sql) + +DA.generate_daily_patient_avg = _generate_daily_patient_avg + +# COMMAND ---------- + +def _generate_visualization_query(): + sql = f""" +SELECT flow_name, timestamp, int(details:flow_progress:metrics:num_output_rows) num_output_rows +FROM {DA.db_name}.dlt_metrics +ORDER BY timestamp DESC;""" + + print_sql(5, sql) + +DA.generate_visualization_query = _generate_visualization_query + +# COMMAND ---------- + +generate_register_dlt_event_metrics_sql_string = "" + +def _generate_register_dlt_event_metrics_sql(): + global generate_register_dlt_event_metrics_sql_string + + generate_register_dlt_event_metrics_sql_string = f""" +CREATE TABLE IF NOT EXISTS {DA.db_name}.dlt_events +LOCATION '{DA.paths.working_dir}/storage/system/events'; + +CREATE VIEW IF NOT EXISTS {DA.db_name}.dlt_success AS +SELECT * FROM {DA.db_name}.dlt_events +WHERE details:flow_progress:metrics IS NOT NULL; + +CREATE VIEW IF NOT EXISTS {DA.db_name}.dlt_metrics AS +SELECT timestamp, origin.flow_name, details +FROM {DA.db_name}.dlt_success +ORDER BY timestamp DESC;""".strip() + + print_sql(13, generate_register_dlt_event_metrics_sql_string) + +DA.generate_register_dlt_event_metrics_sql = _generate_register_dlt_event_metrics_sql + +# COMMAND ---------- + +def print_pipeline_config(): + path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) + path = "/".join(path.split("/")[:-1]) + "/DE 12.2.2L - DLT Task" + + displayHTML(f""" + + + + + +
Pipeline Name:Cap-12-{DA.username}
Source:{DA.paths.working_dir}/source/tracker
Target:{DA.db_name}
Storage Location:{DA.paths.working_dir}/storage
Notebook Path:{path}
""") + +def print_job_config(): + displayHTML(f""" + +
Job Name:Cap-12-{DA.username}
""") + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.2.3L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.2.3L.py new file mode 100644 index 0000000..9777fb2 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.2.3L.py @@ -0,0 +1,15 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="cap_12" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Don't clean up, continue where we left off. +# DA.cleanup() +DA.init(create_db=False) +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.2.4L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.2.4L.py new file mode 100644 index 0000000..d9f6771 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-12.2.4L.py @@ -0,0 +1,14 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="cap_12" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Don't clean up, continue where we left off. +# DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.1.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.1.py new file mode 100644 index 0000000..a4038dd --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.1.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="2.1" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.2L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.2L.py new file mode 100644 index 0000000..16ed498 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.2L.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="2.2L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.3.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.3.py new file mode 100644 index 0000000..60734f0 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.3.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="2.3" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.4L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.4L.py new file mode 100644 index 0000000..b59dd06 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-2.4L.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="2.4L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.1.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.1.py new file mode 100644 index 0000000..4562868 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.1.py @@ -0,0 +1,12 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="3.1" + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +install_dtavod_datasets(reinstall=False) +print() +copy_source_dataset(f"{DA.working_dir_prefix}/source/dtavod/flights/departuredelays.csv", f"{DA.paths.working_dir}/flights/departuredelays.csv", "csv", "flights") +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.2A.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.2A.py new file mode 100644 index 0000000..0d2d298 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.2A.py @@ -0,0 +1,17 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="3.2" + +# COMMAND ---------- + +DA.cleanup() +DA.init() + +# Clean out the global_temp database. +for row in spark.sql("SHOW TABLES IN global_temp").select("tableName").collect(): + table_name = row[0] + spark.sql(f"DROP TABLE global_temp.{table_name}") + +print() +install_dtavod_datasets(reinstall=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.2B.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.2B.py new file mode 100644 index 0000000..397a759 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.2B.py @@ -0,0 +1,22 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="3.2" + +# COMMAND ---------- + +# We want the state from the previous lesson +# DA.cleanup() # DO NOT EXECUTE + +tags = sc._jvm.scala.collection.JavaConversions.mapAsJavaMap(dbutils.entry_point.getDbutils().notebook().getContext().tags()) +is_job = "jobId" in tags +if is_job: print("Mocking global temp view") + +DA.init(create_db=is_job) + +if is_job: + spark.sql(f"""USE {DA.db_name}""") + spark.sql("""CREATE TABLE IF NOT EXISTS external_table USING CSV OPTIONS (path '${da.paths.working_dir}/flight_delays', header "true", mode "FAILFAST");""") + spark.sql("""CREATE OR REPLACE GLOBAL TEMPORARY VIEW global_temp_view_dist_gt_1000 AS SELECT * FROM external_table WHERE distance > 1000;""") + +print() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.3L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.3L.py new file mode 100644 index 0000000..7f5c2c6 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-3.3L.py @@ -0,0 +1,12 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="3.3L" + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +install_dtavod_datasets(reinstall=False) +print() +copy_source_dataset(f"{DA.working_dir_prefix}/source/dtavod/weather", f"{DA.paths.working_dir}/weather", "parquet", "weather") +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.1.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.1.py new file mode 100644 index 0000000..63ef9c5 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.1.py @@ -0,0 +1,10 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.1" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.2.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.2.py new file mode 100644 index 0000000..7d52166 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.2.py @@ -0,0 +1,11 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.2" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) +load_eltwss_external_tables() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.3.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.3.py new file mode 100644 index 0000000..a03307e --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.3.py @@ -0,0 +1,11 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.3" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) +load_eltwss_external_tables() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.4.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.4.py new file mode 100644 index 0000000..d5c8a52 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.4.py @@ -0,0 +1,23 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.4" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) + +print() + +clone_source_table("sales", f"{DA.paths.datasets}/delta", "sales_hist") +clone_source_table("users", f"{DA.paths.datasets}/delta", "users_hist") +clone_source_table("events", f"{DA.paths.datasets}/delta", "events_hist") + +clone_source_table("users_update", f"{DA.paths.datasets}/delta") +clone_source_table("events_update", f"{DA.paths.datasets}/delta") + +# clone_source_table("events_raw", f"{DA.paths.datasets}/delta") +# clone_source_table("item_lookup", f"{DA.paths.datasets}/delta") + +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.5L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.5L.py new file mode 100644 index 0000000..19f59c7 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.5L.py @@ -0,0 +1,10 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.5L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.6.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.6.py new file mode 100644 index 0000000..86ba4b9 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.6.py @@ -0,0 +1,14 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.6" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) + +print() +create_eltwss_users_update() + +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.7.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.7.py new file mode 100644 index 0000000..c4b714f --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.7.py @@ -0,0 +1,23 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.7" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) + +print() + +clone_source_table("sales", f"{DA.paths.datasets}/delta", "sales_hist") +# clone_source_table("users", f"{DA.paths.datasets}/delta", "users_hist") +clone_source_table("events", f"{DA.paths.datasets}/delta", "events_hist") + +# clone_source_table("users_update", f"{DA.paths.datasets}/delta") +clone_source_table("events_update", f"{DA.paths.datasets}/delta") + +clone_source_table("events_raw", f"{DA.paths.datasets}/delta") +clone_source_table("item_lookup", f"{DA.paths.datasets}/delta") + +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.8.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.8.py new file mode 100644 index 0000000..1cdde71 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.8.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.8" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.9L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.9L.py new file mode 100644 index 0000000..a938f41 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-4.9L.py @@ -0,0 +1,12 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="4.9L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +install_eltwss_datasets(reinstall=False) +print() +load_eltwss_tables() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-5.3L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-5.3L.py new file mode 100644 index 0000000..399dba2 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-5.3L.py @@ -0,0 +1,9 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="5.3L" + +# COMMAND ---------- + +DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-6.1.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-6.1.py new file mode 100644 index 0000000..bc915b1 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-6.1.py @@ -0,0 +1,43 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="6.1" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +class DataFactory: + def __init__(self, ): + self.source = "/mnt/training/healthcare/tracker/streaming/" + self.userdir = f"{DA.paths.working_dir}/tracker" + self.curr_mo = 1 + + def load(self, continuous=False): + if self.curr_mo > 12: + print("Data source exhausted\n") + + elif continuous == True: + while self.curr_mo <= 12: + curr_file = f"{self.curr_mo:02}.json" + dbutils.fs.cp(self.source + curr_file, self.userdir + curr_file) + self.curr_mo += 1 + else: + curr_file = f"{str(self.curr_mo).zfill(2)}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the tracker dataset") + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + +# COMMAND ---------- + +DA.init() +DA.paths.checkpoints = f"{DA.paths.working_dir}/checkpoints" +DA.data_factory = DataFactory() + +print() +DA.data_factory.load() +DA.conclude_setup() + +sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-6.2.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-6.2.py new file mode 100644 index 0000000..20eed0d --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-6.2.py @@ -0,0 +1,63 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="6.2" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def autoload_to_table(data_source, source_format, table_name, checkpoint_directory): + (spark.readStream + .format("cloudFiles") + .option("cloudFiles.format", source_format) + .option("cloudFiles.schemaLocation", checkpoint_directory) + .load(data_source) + .writeStream + .option("checkpointLocation", checkpoint_directory) + .option("mergeSchema", "true") + .trigger(availableNow=True) + .table(table_name) + .awaitTermination() + ) + +# COMMAND ---------- + +class DataFactory: + def __init__(self): + self.source = "/mnt/training/healthcare/tracker/streaming/" + self.userdir = f"{DA.paths.working_dir}/tracker" + self.curr_mo = 1 + + def load(self, continuous=False): + if self.curr_mo > 12: + print("Data source exhausted\n") + + elif continuous == True: + while self.curr_mo <= 12: + curr_file = f"{self.curr_mo:02}.json" + dbutils.fs.cp(f"{self.source}/{curr_file}", f"{self.userdir}/{curr_file}") + self.curr_mo += 1 + autoload_to_table(self.userdir, "json", "bronze", f"{DA.paths.checkpoints}/bronze") + else: + curr_file = f"{str(self.curr_mo).zfill(2)}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + + dbutils.fs.cp(f"{self.source}/{curr_file}", f"{self.userdir}/{curr_file}") + self.curr_mo += 1 + autoload_to_table(self.userdir, "json", "bronze", f"{DA.paths.checkpoints}/bronze") + + +# COMMAND ---------- + +DA.init() +DA.paths.checkpoints = f"{DA.paths.working_dir}/_checkpoints" +DA.data_factory = DataFactory() + +print() +DA.data_factory.load() +DA.conclude_setup() + +sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-6.3L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-6.3L.py new file mode 100644 index 0000000..c35fd86 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-6.3L.py @@ -0,0 +1,12 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="6.3L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.paths.checkpoints = f"{DA.paths.working_dir}/_checkpoints" +DA.conclude_setup() + +sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-7.1.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-7.1.py new file mode 100644 index 0000000..c581e45 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-7.1.py @@ -0,0 +1,57 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="7.1" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +class DataFactory: + def __init__(self): + self.source = f"{DA.paths.data_source}/tracker/streaming/" + self.userdir = DA.paths.data_landing_location + self.curr_mo = 1 + + def load(self, continuous=False): + if self.curr_mo > 12: + print("Data source exhausted\n") + elif continuous == True: + while self.curr_mo <= 12: + curr_file = f"{self.curr_mo:02}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + else: + curr_file = f"{str(self.curr_mo).zfill(2)}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + + +# COMMAND ---------- + +DA.init() +DA.paths.checkpoints = f"{DA.paths.working_dir}/_checkpoints" +DA.paths.data_source = "/mnt/training/healthcare" +DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +# bronzePath = f"{DA.paths.wokring_dir}/bronze" +# recordingsParsedPath = f"{DA.paths.wokring_dir}/silver/recordings_parsed" +# recordingsEnrichedPath = f"{DA.paths.wokring_dir}/silver/recordings_enriched" +# dailyAvgPath = f"{DA.paths.wokring_dir}/gold/dailyAvg" + +# checkpointPath = f"{DA.paths.wokring_dir}/checkpoints" +#bronzeCheckpoint = f"{DA.paths.checkpoints}/bronze" +# recordingsParsedCheckpoint = f"{DA.paths.checkpoints}/recordings_parsed" +# recordingsEnrichedCheckpoint = f"{DA.paths.checkpoints}/recordings_enriched" +# dailyAvgCheckpoint = f"{DA.paths.checkpoints}/dailyAvgPath" + +DA.data_factory = DataFactory() +DA.conclude_setup() + +sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-7.2L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-7.2L.py new file mode 100644 index 0000000..e12f6b5 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-7.2L.py @@ -0,0 +1,12 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="7.2L" + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.paths.checkpoints = f"{DA.paths.working_dir}/_checkpoints" +DA.conclude_setup() + +sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.1.1.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.1.1.py new file mode 100644 index 0000000..736ff02 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.1.1.py @@ -0,0 +1,102 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="dlt_demo_81" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def get_pipeline_config(): + path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) + path = "/".join(path.split("/")[:-1]) + "/DE 8.1.2 - SQL for Delta Live Tables" + + return f"DLT-Demo-81-{DA.username}", path + +def _print_pipeline_config(): + "Provided by DBAcademy, this function renders the configuration of the pipeline as HTML" + pipeline_name, path = get_pipeline_config() + + displayHTML(f""" + + + + + + + + + + + + +
Pipeline Name:
Target:
Storage Location:
Notebook Path:
""") + +DA.print_pipeline_config = _print_pipeline_config + +# COMMAND ---------- + +def _create_pipeline(): + "Provided by DBAcademy, this function creates the prescribed pipline" + + from dbacademy.dbrest import DBAcademyRestClient + client = DBAcademyRestClient() + + pipeline_name, path = get_pipeline_config() + + # Delete the existing pipeline if it exists + client.pipelines().delete_by_name(pipeline_name) + + # Create the new pipeline + pipeline = client.pipelines().create( + name = pipeline_name, + storage = DA.paths.storage_location, + target = DA.db_name, + notebooks = [path]) + + DA.pipeline_id = pipeline.get("pipeline_id") + +DA.create_pipeline = _create_pipeline + +# COMMAND ---------- + +def _start_pipeline(): + "Provided by DBAcademy, this function starts the pipeline and then blocks until it has completed, failed or was canceled" + + import time + from dbacademy.dbrest import DBAcademyRestClient + client = DBAcademyRestClient() + + # Start the pipeline + start = client.pipelines().start_by_id(DA.pipeline_id) + update_id = start.get("update_id") + + # Get the status and block until it is done + update = client.pipelines().get_update_by_id(DA.pipeline_id, update_id) + state = update.get("update").get("state") + + done = ["COMPLETED", "FAILED", "CANCELED"] + while state not in done: + duration = 15 + time.sleep(duration) + print(f"Current state is {state}, sleeping {duration} seconds.") + update = client.pipelines().get_update_by_id(DA.pipeline_id, update_id) + state = update.get("update").get("state") + + print(f"The final state is {state}.") + assert state == "COMPLETED", f"Expected the state to be COMPLETED, found {state}" + +DA.start_pipeline = _start_pipeline + +# COMMAND ---------- + +DA.cleanup() +DA.init() + +DA.paths.data_source = "/mnt/training/healthcare" +DA.paths.storage_location = f"{DA.paths.working_dir}/storage" +DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.1.3.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.1.3.py new file mode 100644 index 0000000..d6ca2c5 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.1.3.py @@ -0,0 +1,20 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="dlt_demo_81" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Continues where 8.1.1 picks up, don't remove assets +# DA.cleanup() +DA.init() + +DA.paths.data_source = "/mnt/training/healthcare" +DA.paths.storage_location = f"{DA.paths.working_dir}/storage" +DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.2.1L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.2.1L.py new file mode 100644 index 0000000..88f4cb2 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.2.1L.py @@ -0,0 +1,110 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="dlt_lab_82" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def get_pipeline_config(): + path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) + path = "/".join(path.split("/")[:-1]) + "/DE 8.2.2L - Migrating a SQL Pipeline to DLT Lab" + + pipeline_name = f"DLT-Lab-82L-{DA.username}" + source = f"{DA.paths.working_dir}/source/tracker" + return pipeline_name, path, source + +def _print_pipeline_config(): + "Provided by DBAcademy, this function renders the configuration of the pipeline as HTML" + pipeline_name, path, source = get_pipeline_config() + + displayHTML(f""" + + + + + + + + + + + + + + + + + +
Pipeline Name:
Target:
Storage Location:
Notebook Path:
Source:
""") + +DA.print_pipeline_config = _print_pipeline_config + +# COMMAND ---------- + +def _create_pipeline(): + "Provided by DBAcademy, this function creates the prescribed pipline" + + from dbacademy.dbrest import DBAcademyRestClient + client = DBAcademyRestClient() + + pipeline_name, path, source = get_pipeline_config() + + # Delete the existing pipeline if it exists + client.pipelines().delete_by_name(pipeline_name) + + # Create the new pipeline + pipeline = client.pipelines().create( + name = pipeline_name, + storage = f"{DA.paths.working_dir}/storage", + target = DA.db_name, + notebooks = [path], + configuration = {"source": source}) + + DA.pipline_id = pipeline.get("pipeline_id") + +DA.create_pipeline = _create_pipeline + +# COMMAND ---------- + +def _start_pipeline(): + "Provided by DBAcademy, this function starts the pipline and then blocks until it has completed, failed or was canceled" + + import time + from dbacademy.dbrest import DBAcademyRestClient + client = DBAcademyRestClient() + + # Start the pipeline + start = client.pipelines().start_by_id(DA.pipline_id) + update_id = start.get("update_id") + + # Get the status and block until it is done + update = client.pipelines().get_update_by_id(DA.pipline_id, update_id) + state = update.get("update").get("state") + + done = ["COMPLETED", "FAILED", "CANCELED"] + while state not in done: + duration = 15 + time.sleep(duration) + print(f"Current state is {state}, sleeping {duration} seconds.") + update = client.pipelines().get_update_by_id(DA.pipline_id, update_id) + state = update.get("update").get("state") + + print(f"The final state is {state}.") + assert state == "COMPLETED", f"Expected the state to be COMPLETED, found {state}" + +DA.start_pipeline = _start_pipeline + +# COMMAND ---------- + +DA.cleanup() +DA.init() + +# DA.paths.data_source = "/mnt/training/healthcare" +# DA.paths.storage_location = f"{DA.paths.working_dir}/storage" +# DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.2.3L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.2.3L.py new file mode 100644 index 0000000..8358dc3 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-8.2.3L.py @@ -0,0 +1,21 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="dlt_lab_82" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Continuing from 8.2.3L, do not remove assets +# DA.cleanup() +DA.init() + +DA.paths.data_source = "/mnt/training/healthcare" +DA.paths.storage_location = f"{DA.paths.working_dir}/storage" + +DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.1.1.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.1.1.py new file mode 100644 index 0000000..bb17c2b --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.1.1.py @@ -0,0 +1,37 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="jobs_demo_91" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def print_pipeline_config(): + path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) + path = "/".join(path.split("/")[:-1]) + "/DE 9.1.3 - DLT Job" + + displayHTML(f""" + + + + +
Pipeline Name:Jobs-Demo-91-{DA.username}
Target:{DA.db_name}
Storage Location:{DA.paths.working_dir}/storage
Notebook Path:{path}
""") + +def print_job_config(): +# path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) +# path = "/".join(path.split("/")[:-1]) + "/DE 9.1.2 - Reset" +# Notebook Path:{path} + + displayHTML(f""" + + +
Job Name:Jobs-Demo-91-{DA.username}
""") + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.1.2.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.1.2.py new file mode 100644 index 0000000..2aa9973 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.1.2.py @@ -0,0 +1,14 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="jobs_demo_91" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.2.1L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.2.1L.py new file mode 100644 index 0000000..ebf54cb --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.2.1L.py @@ -0,0 +1,33 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="jobs_lab_92" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def print_pipeline_config(): + path = dbutils.entry_point.getDbutils().notebook().getContext().notebookPath().getOrElse(None) + path = "/".join(path.split("/")[:-1]) + "/DE 9.2.3L - DLT Job" + + displayHTML(f""" + + + + + +
Pipeline Name:Jobs-Lab-92-{DA.username}
Source:{DA.paths.working_dir}/source/tracker
Target:{DA.db_name}
Storage Location:{DA.paths.working_dir}/storage
Notebook Path:{path}
""") + +def print_job_config(): + displayHTML(f""" + +
Job Name:Jobs-Lab-92-{DA.username}
""") + +# COMMAND ---------- + +DA.cleanup() +DA.init() +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.2.2L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.2.2L.py new file mode 100644 index 0000000..a82d3ee --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.2.2L.py @@ -0,0 +1,15 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="jobs_lab_92" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Don't reset our database or other assets +# DA.cleanup() +DA.init(create_db=False) +DA.data_factory = DltDataFactory() +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.2.4L.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.2.4L.py new file mode 100644 index 0000000..f250172 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-9.2.4L.py @@ -0,0 +1,14 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="jobs_lab_92" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +# Don't reset our database or other assets +# DA.cleanup() +DA.init(create_db=False) +DA.conclude_setup() + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-DLT-Lab.py b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-DLT-Lab.py new file mode 100644 index 0000000..f8e0da8 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/Classroom-Setup-DLT-Lab.py @@ -0,0 +1,110 @@ +# Databricks notebook source +# MAGIC %run ./_utility-methods $lesson="dlt_lab" + +# COMMAND ---------- + +# MAGIC %run ./mount-datasets + +# COMMAND ---------- + +def print_sql(rows, sql): + displayHTML(f"""""") + + +# COMMAND ---------- + +generate_register_dlt_event_metrics_sql_string = "" + +def _generate_register_dlt_event_metrics_sql(): + global generate_register_dlt_event_metrics_sql_string + + generate_register_dlt_event_metrics_sql_string = f""" +CREATE TABLE IF NOT EXISTS {DA.db_name}.dlt_events +LOCATION '{DA.paths.storage_location}/system/events'; + +CREATE VIEW IF NOT EXISTS {DA.db_name}.dlt_success AS +SELECT * FROM {DA.db_name}.dlt_events +WHERE details:flow_progress:metrics IS NOT NULL; + +CREATE VIEW IF NOT EXISTS {DA.db_name}.dlt_metrics AS +SELECT timestamp, origin.flow_name, details +FROM {DA.db_name}.dlt_success +ORDER BY timestamp DESC;""".strip() + + print_sql(13, generate_register_dlt_event_metrics_sql_string) +DA.generate_register_dlt_event_metrics_sql = _generate_register_dlt_event_metrics_sql + +# COMMAND ---------- + +def _generate_daily_patient_avg(): + sql = f"SELECT * FROM {DA.db_name}.daily_patient_avg" + print_sql(3, sql) + +DA.generate_daily_patient_avg = _generate_daily_patient_avg + +# COMMAND ---------- + +def _generate_visualization_query(): + sql = f""" +SELECT flow_name, timestamp, int(details:flow_progress:metrics:num_output_rows) num_output_rows +FROM {DA.db_name}.dlt_metrics +ORDER BY timestamp DESC;""" + + print_sql(5, sql) + +DA.generate_visualization_query = _generate_visualization_query + +# COMMAND ---------- + +class DataFactory: + def __init__(self): + self.source = f"{DA.paths.data_source}/tracker/streaming/" + self.userdir = DA.paths.data_landing_location + try: + self.curr_mo = 1 + int(max([x[1].split(".")[0] for x in dbutils.fs.ls(self.userdir)])) + except: + self.curr_mo = 1 + + def load(self, continuous=False): + if self.curr_mo > 12: + print("Data source exhausted\n") + elif continuous == True: + while self.curr_mo <= 12: + curr_file = f"{self.curr_mo:02}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + else: + curr_file = f"{str(self.curr_mo).zfill(2)}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + +# COMMAND ---------- + +DA.init() + +DA.paths.data_source = "/mnt/training/healthcare" +DA.paths.storage_location = f"{DA.paths.working_dir}/storage" + +DA.paths.data_landing_location = f"{DA.paths.working_dir}/source/tracker" + +# bronzePath = f"{DA.paths.wokring_dir}/bronze" +# recordingsParsedPath = f"{DA.paths.wokring_dir}/silver/recordings_parsed" +# recordingsEnrichedPath = f"{DA.paths.wokring_dir}/silver/recordings_enriched" +# dailyAvgPath = f"{DA.paths.wokring_dir}/gold/daily_avg" + +# checkpointPath = f"{DA.paths.wokring_dir}/checkpoints" +#bronzeCheckpoint = f"{DA.paths.checkpoints}/bronze" +# recordingsParsedCheckpoint = f"{DA.paths.checkpoints}/recordings_parsed" +# recordingsEnrichedCheckpoint = f"{DA.paths.checkpoints}/recordings_enriched" +# dailyAvgCheckpoint = f"{DA.paths.checkpoints}/dailyAvgPath" + +DA.data_factory = DataFactory() +DA.conclude_setup() + +# sqlContext.setConf("spark.sql.shuffle.partitions", spark.sparkContext.defaultParallelism) + diff --git a/Data-Engineering-with-Databricks/Solutions/Includes/_utility-methods.py b/Data-Engineering-with-Databricks/Solutions/Includes/_utility-methods.py new file mode 100644 index 0000000..b27e619 --- /dev/null +++ b/Data-Engineering-with-Databricks/Solutions/Includes/_utility-methods.py @@ -0,0 +1,267 @@ +# Databricks notebook source +# MAGIC %pip install \ +# MAGIC git+https://github.com/databricks-academy/dbacademy-gems \ +# MAGIC git+https://github.com/databricks-academy/dbacademy-rest \ +# MAGIC --quiet --disable-pip-version-check + +# COMMAND ---------- + +class Paths(): + def __init__(self, working_dir, clean_lesson): + self.working_dir = working_dir + + if clean_lesson: self.user_db = f"{working_dir}/{clean_lesson}.db" + else: self.user_db = f"{working_dir}/database.db" + + def exists(self, path): + try: return len(dbutils.fs.ls(path)) >= 0 + except Exception:return False + + def print(self, padding=" "): + max_key_len = 0 + for key in self.__dict__: max_key_len = len(key) if len(key) > max_key_len else max_key_len + for key in self.__dict__: + label = f"{padding}DA.paths.{key}:" + print(label.ljust(max_key_len+13) + DA.paths.__dict__[key]) + +class DBAcademyHelper(): + def __init__(self, lesson=None): + import re, time + + self.start = int(time.time()) + + self.course_name = "dewd" + self.lesson = lesson.lower() + self.data_source_uri = "wasbs://courseware@dbacademy.blob.core.windows.net/data-engineering-with-databricks/v02" + + # Define username + self.username = spark.sql("SELECT current_user()").first()[0] + self.clean_username = re.sub("[^a-zA-Z0-9]", "_", self.username) + + self.db_name_prefix = f"dbacademy_{self.clean_username}_{self.course_name}" + self.source_db_name = None + + self.working_dir_prefix = f"dbfs:/user/{self.username}/dbacademy/{self.course_name}" + + if self.lesson: + clean_lesson = re.sub("[^a-zA-Z0-9]", "_", self.lesson) + working_dir = f"{self.working_dir_prefix}/{self.lesson}" + self.paths = Paths(working_dir, clean_lesson) + self.hidden = Paths(working_dir, clean_lesson) + self.db_name = f"{self.db_name_prefix}_{clean_lesson}" + else: + working_dir = self.working_dir_prefix + self.paths = Paths(working_dir, None) + self.hidden = Paths(working_dir, None) + self.db_name = self.db_name_prefix + + def init(self, create_db=True): + spark.catalog.clearCache() + self.create_db = create_db + + if create_db: + print(f"\nCreating the database \"{self.db_name}\"") + spark.sql(f"CREATE DATABASE IF NOT EXISTS {self.db_name} LOCATION '{self.paths.user_db}'") + spark.sql(f"USE {self.db_name}") + + def cleanup(self): + for stream in spark.streams.active: + print(f"Stopping the stream \"{stream.name}\"") + stream.stop() + try: stream.awaitTermination() + except: pass # Bury any exceptions + + if spark.sql(f"SHOW DATABASES").filter(f"databaseName == '{self.db_name}'").count() == 1: + print(f"Dropping the database \"{self.db_name}\"") + spark.sql(f"DROP DATABASE {self.db_name} CASCADE") + + if self.paths.exists(self.paths.working_dir): + print(f"Removing the working directory \"{self.paths.working_dir}\"") + dbutils.fs.rm(self.paths.working_dir, True) + + def conclude_setup(self): + import time + + spark.conf.set("da.username", self.username) + spark.conf.set("da.db_name", self.db_name) + for key in self.paths.__dict__: + spark.conf.set(f"da.paths.{key.lower()}", self.paths.__dict__[key]) + + print("\nPredefined Paths:") + DA.paths.print() + + if self.source_db_name: + print(f"\nPredefined tables in {self.source_db_name}:") + tables = spark.sql(f"SHOW TABLES IN {self.source_db_name}").filter("isTemporary == false").select("tableName").collect() + if len(tables) == 0: print(" -none-") + for row in tables: print(f" {row[0]}") + + if self.create_db: + print(f"\nPredefined tables in {self.db_name}:") + tables = spark.sql(f"SHOW TABLES IN {self.db_name}").filter("isTemporary == false").select("tableName").collect() + if len(tables) == 0: print(" -none-") + for row in tables: print(f" {row[0]}") + + print(f"\nSetup completed in {int(time.time())-self.start} seconds") + + def block_until_stream_is_ready(self, query, min_batches=2): + import time + while len(query.recentProgress) < min_batches: + time.sleep(5) # Give it a couple of seconds + + print(f"The stream has processed {len(query.recentProgress)} batchs") + +dbutils.widgets.text("lesson", "missing") +lesson = dbutils.widgets.get("lesson") +if lesson == "none": lesson = None +assert lesson != "missing", f"The lesson must be passed to the DBAcademyHelper" + +DA = DBAcademyHelper(lesson) + +# COMMAND ---------- + +def install_source_dataset(source_uri, reinstall, subdir): + target_dir = f"{DA.working_dir_prefix}/source/{subdir}" + +# if reinstall and DA.paths.exists(target_dir): +# print(f"Removing existing dataset at {target_dir}") +# dbutils.fs.rm(target_dir, True) + + if DA.paths.exists(target_dir): + print(f"Skipping install to \"{target_dir}\", dataset already exists") + else: + print(f"Installing datasets to \"{target_dir}\"") + dbutils.fs.cp(source_uri, target_dir, True) + + return target_dir + +# COMMAND ---------- + +def install_dtavod_datasets(reinstall): + source_uri = "wasbs://courseware@dbacademy.blob.core.windows.net/databases_tables_and_views_on_databricks/v02" + DA.paths.datasets = install_source_dataset(source_uri, reinstall, "dtavod") + + copy_source_dataset(f"{DA.paths.datasets}/flights/departuredelays.csv", + f"{DA.paths.working_dir}/flight_delays", + format="csv", name="flight_delays") + +# COMMAND ---------- + +def install_eltwss_datasets(reinstall): + source_uri = "wasbs://courseware@dbacademy.blob.core.windows.net/elt-with-spark-sql/v02/small-datasets" + DA.paths.datasets = install_source_dataset(source_uri, reinstall, "eltwss") + +# COMMAND ---------- + +def clone_source_table(table_name, source_path, source_name=None): + import time + start = int(time.time()) + + source_name = table_name if source_name is None else source_name + print(f"Cloning the {table_name} table from {source_path}/{source_name}", end="...") + + spark.sql(f""" + CREATE OR REPLACE TABLE {table_name} + SHALLOW CLONE delta.`{source_path}/{source_name}` + """) + + total = spark.read.table(table_name).count() + print(f"({int(time.time())-start} seconds / {total:,} records)") + +def load_eltwss_tables(): + clone_source_table("events", f"{DA.paths.datasets}/delta") + clone_source_table("sales", f"{DA.paths.datasets}/delta") + clone_source_table("users", f"{DA.paths.datasets}/delta") + clone_source_table("transactions", f"{DA.paths.datasets}/delta") + +# COMMAND ---------- + +def copy_source_dataset(src_path, dst_path, format, name): + import time + start = int(time.time()) + print(f"Creating the {name} dataset", end="...") + + dbutils.fs.cp(src_path, dst_path, True) + + total = spark.read.format(format).load(dst_path).count() + print(f"({int(time.time())-start} seconds / {total:,} records)") + +def load_eltwss_external_tables(): + copy_source_dataset(f"{DA.paths.datasets}/raw/sales-csv", + f"{DA.paths.working_dir}/sales-csv", "csv", "sales-csv") + + import time + start = int(time.time()) + print(f"Creating the users table", end="...") + + # REFACTORING - Making lesson-specific copy + dbutils.fs.cp(f"{DA.paths.datasets}/raw/users-historical", + f"{DA.paths.working_dir}/users-historical", True) + + # https://spark.apache.org/docs/latest/sql-data-sources-jdbc.html + (spark.read + .format("parquet") + .load(f"{DA.paths.working_dir}/users-historical") + .repartition(1) + .write + .format("org.apache.spark.sql.jdbc") + .option("url", f"jdbc:sqlite:/{DA.username}_ecommerce.db") + .option("dbtable", "users") # The table name in sqllight + .mode("overwrite") + .save()) + + total = spark.read.parquet(f"{DA.paths.working_dir}/users-historical").count() + print(f"({int(time.time())-start} seconds / {total:,} records)") + +# COMMAND ---------- + +# lesson: Writing delta +def create_eltwss_users_update(): + import time + start = int(time.time()) + print(f"Creating the users_dirty table", end="...") + + # REFACTORING - Making lesson-specific copy + dbutils.fs.cp(f"{DA.paths.datasets}/raw/users-30m", + f"{DA.paths.working_dir}/users-30m", True) + + spark.sql(f""" + CREATE OR REPLACE TABLE users_dirty AS + SELECT *, current_timestamp() updated + FROM parquet.`{DA.paths.working_dir}/users-30m` + """) + + spark.sql("INSERT INTO users_dirty VALUES (NULL, NULL, NULL, NULL), (NULL, NULL, NULL, NULL), (NULL, NULL, NULL, NULL)") + + total = spark.read.table("users_dirty").count() + print(f"({int(time.time())-start} seconds / {total:,} records)") + +# COMMAND ---------- + +class DltDataFactory: + def __init__(self): + self.source = f"/mnt/training/healthcare/tracker/streaming" + self.userdir = f"{DA.paths.working_dir}/source/tracker" + try: + self.curr_mo = 1 + int(max([x[1].split(".")[0] for x in dbutils.fs.ls(self.userdir)])) + except: + self.curr_mo = 1 + + def load(self, continuous=False): + if self.curr_mo > 12: + print("Data source exhausted\n") + elif continuous == True: + while self.curr_mo <= 12: + curr_file = f"{self.curr_mo:02}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + else: + curr_file = f"{str(self.curr_mo).zfill(2)}.json" + target_dir = f"{self.userdir}/{curr_file}" + print(f"Loading the file {curr_file} to the {target_dir}") + + dbutils.fs.cp(f"{self.source}/{curr_file}", target_dir) + self.curr_mo += 1 + diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/mount-datasets.py b/Data-Engineering-with-Databricks/Solutions/Includes/mount-datasets.py similarity index 100% rename from Data-Engineering-With-Databricks/Solutions/Includes/mount-datasets.py rename to Data-Engineering-with-Databricks/Solutions/Includes/mount-datasets.py diff --git a/Data-Engineering-With-Databricks/Solutions/Includes/setup-entities.py b/Data-Engineering-with-Databricks/Solutions/Includes/setup-entities.py similarity index 100% rename from Data-Engineering-With-Databricks/Solutions/Includes/setup-entities.py rename to Data-Engineering-with-Databricks/Solutions/Includes/setup-entities.py diff --git a/Data-Engineering-with-Databricks/Untitled Notebook 2023-07-26 17:20:55 (1).py b/Data-Engineering-with-Databricks/Untitled Notebook 2023-07-26 17:20:55 (1).py new file mode 100644 index 0000000..e835ace --- /dev/null +++ b/Data-Engineering-with-Databricks/Untitled Notebook 2023-07-26 17:20:55 (1).py @@ -0,0 +1,6 @@ +# Databricks notebook source +print('hello') + +# COMMAND ---------- + + diff --git a/Data-Engineering-With-Databricks/Version Info.py b/Data-Engineering-with-Databricks/Version Info.py similarity index 84% rename from Data-Engineering-With-Databricks/Version Info.py rename to Data-Engineering-with-Databricks/Version Info.py index 63d004c..ad37f88 100644 --- a/Data-Engineering-With-Databricks/Version Info.py +++ b/Data-Engineering-with-Databricks/Version Info.py @@ -7,11 +7,15 @@ # COMMAND ---------- -# MAGIC %md # Project Information +# MAGIC %md +# MAGIC +# MAGIC +# MAGIC +# MAGIC # Project Information # MAGIC # MAGIC * Name: **Data Engineering with Databricks** -# MAGIC * Version: **beta.2** -# MAGIC * Built On: **Jan 19, 2022 at 14:41:04 UTC** +# MAGIC * Version: **2.2.2** +# MAGIC * Built On: **May 12, 2022 at 15:26:04 UTC** # COMMAND ---------- diff --git a/README.md b/README.md index 9589221..9f63c30 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,8 @@ # Data Engineering with Databricks -This is a beta build; please contact the Databricks curriculum team before distributing content. +This repository contains the resources students need to follow along with the instructor teaching this course, in addition to the various labs and their solutions. + +There are two ways to get started (with and w/o Databricks Repos). Your instructor will indicate which procedure you should use and when. + +For your convenience, both procedures are documented for you at +https://www.databricks.training/step-by-step/importing-courseware-from-github.
diff --git a/platform-demos/1-workspace.md b/platform-demos/1-workspace.md deleted file mode 100644 index 3fe8189..0000000 --- a/platform-demos/1-workspace.md +++ /dev/null @@ -1,56 +0,0 @@ -# Navigating the Databricks Data Science and Engineering Workspace - -This demo will explore the Databricks Data Science and Engineering Workspace. By the end of this lesson you will be able to: -* Describe the Databricks architecture and its services -* Navigate the Data Science and Engineering workspace - -## Home page - -Go over the different sections of the home page. Be sure to highlight the `What's new` section that provides information on the current and historical Databricks versions, as well as a service status dashboard. Mention that there are handy links to perform some common tasks, all of which are available elsewhere in the UI. Finally, call out to the `Quickstart Tutorial` which is a great resource for getting started with a Notebook. - -## Sidebar - -The sidebar contains links to different sections of your Workspace. The links available depend on the service selected in the persona switcher. - -## Persona switcher - -Use the persona switcher to demonstrate switching to Databricks SQL and Databrics Machine Learning. Highlight that you can pin a selection to make it the default when logging in to Databricks. - -### Icons - -Explore the various icons. In particular: -* Open the **Create** menu for creating common assets -* Show the **Workspace** file browsing functionality. Show user and home folders at a minimum. Display the options available at folder and file levels. Discuss how permissions can be applied to folders within this structure to facilitate a secure, collaborative Workspace. -* Make a forward mention of **Repos**. While Repos apply the same functionality and UI metaphors as the Workspace, they deliver git integration. -* Highlight that **Recents** can be used to revisit recently accessed assets, and make it easier to switch between Notebooks in a single tab. -* Make a forward mention of **Data**, which will be used to explore tables and files used for processing. -* Make a forward mention of **Compute** for managing your clusters -* Make a forward mention of **Jobs** that can be used to non-interactively execute and schedule workloads. - -## Help - -Explore the different resources available under the **Help** item. - -## Settings - -There are three distinct sections for settings. - -### User Settings - -Highight that this is where we go to update passwords, personal acess tokens, integration with a git server, and various settings related to your Workspace. - -### Admin Console - -For sufficiently privileged users, this option is available to administer various aspects of the Workspace. Configure global settings, create users, groups, permissions; set global cluster initialization scripts. - -### Manage Account - -Configure your Databricks account. - -## Menu options - -Show the options to contol behaviour of the sidebar. - -## User menu - -Highlight the option used to log off or switch Workspaces, when multiple Workspaces are configured. diff --git a/platform-demos/2-clusters.md b/platform-demos/2-clusters.md deleted file mode 100644 index 39a434d..0000000 --- a/platform-demos/2-clusters.md +++ /dev/null @@ -1,54 +0,0 @@ -# Create and manage interactive clusters - -This demo will cover creating and managing all-purpose Databricks clusters using the Databricks Data Science and Engineering Workspace. By the end of this lesson you will be able to: -* Describe cluster structures -* Create and manage clusters - -## Create Cluster - -Create a new cluster by using the link in the **Common Tasks** section of the home page, or by going to the **Compute** page and clicking **Create Cluster**. In either case, be sure to mention that there are a couple ways to do this, like many common tasks. - -Discuss the options. Be sure to mention at least the following: -* **Cluster mode**: as mentioned in the slides, there are three different modes. For the small workloads like the ones we use in the training, you can use the **Single Node** option here, but note that this disables some other options. For more info, see https://docs.databricks.com/clusters/configure.html#cluster-mode -* **Databricks Runtime Version**: this specifies the software to deploy to the clusters. - * Highlight the different familes to choose from (**Standard** vs **ML** for machine learning workloads) - * Generally we choose the latest version though do mention that the versions marked "LTS" carry extended support which might be a preferential option for some production systems. Refer to https://docs.databricks.com/release-notes/runtime/databricks-runtime-ver.html#runtime-support for more info. - * The **Photon** checkbox filters versions that support Databricks' new vectorized query engine. For more info, see https://docs.databricks.com/runtime/photon.html. - For general info on Databricks Runtime, refer to https://docs.databricks.com/runtime/dbr.html, -* **Enable autoscaling** allows the dynamic adjustment (up or down) to the number of worker nodes through a range (**Min Workers** and **Max Worker**) based on workload. Note that this option is not available when **Cluster Mode** is set to **Single Node** -* **Enable autoscaling local storage** allows the additional EBS volumes to be dynamically attached if disk space is getting low (increase only). See https://docs.databricks.com/clusters/configure.html#autoscaling-local-storage for more deteails. -* **Terminate after** to control compute costs by automatically terminating clusters after a period of inactivity (that is, absence of Spark jobs) -* **Worker Type** to specify the cloud-specfic VM profile for the worker nodes, determining the CPU/RAM configuration for each. Note that this option is not available when **Cluster Mode** is set to **Single Node** -* **Workers** to specify the number of worker nodes. Note that this option is not available when **Cluster Mode** is set to **Single Node**, or the **Enable autoscaling** option is selected -* **Min Workers** and **Max Workers** to specify the minimum and maximum number of worker nodes. Note that these are only available when **Enable autoscaling** option is selected. -* **Driver Type** (or **Node Type** when **Cluster Mode** is set to **Single Node**) specifies the cloud-specfic VM profile for the driver node, determining the CPU/RAM configuration. - -With respect to the Worker/Driver/Node Type, here are some general recommendations: -* Memory optimized (eg: r4, DSv2): Memory-intensive applications -* Compute optimized (eg: c5, Fsv2): Structured Streaming, Distributed Analytics, Data Science Applications -* Storage optimized (eg: i3, Lsv2): Applications that require higher disk throughput and IO -* General purpose (eg: m4, DSv2): Enterprise-grade applications, relational databases, and analytics with in-memory caching - -Highlight the DBU estimation. Adjust settings (like Cluster Mode, Workers/Min Workers/Max Workers, and Worker and Driver Types) to see how it impacts this estimate. - -Open the **Advanced Options** to show what's there, but we won't go into details at this point. - -Create the cluster. While the cluster spins up, it's an opportune moment to talk about the merits of using Pools. You can demonstrate the workflow quickly by: -1. Visiting the **Pools** tab -2. Create a pool with all the default settings -3. Go back to the cluster creation page (create a new cluster), and open the **Worker Type** and/or **Driver Type** dropdowns, and notice how the option to attach to a pool is presented. -4. Clean up by deleting the pool. - -## Manage clusters - -With the cluster created, go back to the **Compute** page to view the cluster(s). Remind learners that as their organizations grow so will the list, and so you can mention the options to filter, search and sort the list. - -Select a cluster to adjust configuration. Click the **Edit** button and show how settings can be modified. Call out that some settings may require running clusters to be restarted. - -Though we don't get heavy into permissions, show the **Permissions** dialog and highlight the fact that we can control which users can access the cluster and what they can do with it, referring back to the Access Control table in the slides. - -Show the **Clone** funcitonality, which allows us to quickly rubberstamp cluster configurations. Also highlight the run controls and **Delete** button. - -Show the **Libraries** tab, which allows us to install addtional libraries (jar files) onto the cluster. These are used to install drivers or libraries needed by workloads - -Walk through the various logs. diff --git a/platform-demos/3-dbfs.md b/platform-demos/3-dbfs.md deleted file mode 100644 index 5fb82bc..0000000 --- a/platform-demos/3-dbfs.md +++ /dev/null @@ -1,74 +0,0 @@ -# Manage data with DBFS - -This demo will explore DBFS from the Databricks Data Science and Engineering Workspace. By the end of this lesson you will be able to: - -* Describe and navigate the Databricks File System (DBFS) -* Import files into DBFS - -Before proceeding, ensure that: -* DBFS browsing functionality is enabled in your Workspace (**Settings→Admin Console→Workspace Settings→Advanced→DBFS File Browser**) -* There's a running cluster you can attach to. For basic DBFS browsing from the Workspace, this isn't a strict requirement – but it will be good to have one running to give a better demonstration. -* Have a sample data file on hand for upload. Suggest using uszips.csv, which can be downloaded from here: https://simplemaps.com/data/us-zips. The file contains census and geographical information for each of the tens of thousands of US zip codes. Displaying the website in class satisfies the licensing requirements for using this sample dataset. - -## Explore DBFS - -Go to the **Data** page, where we can access DBFS. Mention that DBFS browsing is a feature that can be turned on or off by the administrator (can show this if desired). - -What we see here represents the *DBFS root* – that is, the top-level directory within DBFS. The DBFS root is backed by a storage bucket that was set up by your Databricks platform administrator. - -Explain some of the special folders: - -* /Filestore: this multi-purpose directory stores files uploaded to DBFS from the Workspace, such as data files, images and libraries. Output files (like images created by Notebook visualizations) are also stored here, from where they can be downloaded. -* /databricks-datasets (not visible in the browser as it is mounted and not physically copied): this directory contains example datasets provided by Databricks to accompany product documentation. See https://docs.databricks.com/data/databricks-datasets.html for more information. -* /databricks-results: this directory contains the full results of your queries. These files are generated when you download the full results of a query that returns more than the 1000 rows displayed by default. -* /databricks-init (only visible if set up): this directory is used to host initialization scripts that are executed on each node of a starting cluster prior to invoking the JVM environment for the driver or worker. -* /user/hive/warehouse: also known as the Databricks Hive metastore, this directory contains metadata related to Databricks tables and, in general, the table data itself. Note that Databricks is capable of using an external Hive metastore if needed. -* /mnt: this directory contains object storage mount points -* /tmp: this directory provides a space to store temporary working files - -Also mention the following general points regarding DBFS: -* All nodes in your clusters will be able to see everything we see here in the Workspace -* Don't confuse DBFS with the filesystem-like organization of the Workspace (feel free to open the **Workspace** page as a reminder). The two are unrelated. -* DBFS does not represent the local file system running on your cluster nodes; each node has its own local file system into which DBFS is mounted. - -Use the browser to explore some of these. **/FileStore** and **/usr/hive** are good examples. Also look at **/mnt** if attached to a running cluster, which is where external data sources are mounted by convention. Remind them that the directories inside **/mnt** are not "real" directories, but rather *mountpoints* (that is, a virtual directory that redirects to an external filesystem-like resources). Because they require some degree of processing, we must be attached to a running cluster to see them. - -## Cluster view of DBFS - -To help reinforce these points about DBFS, demo the following in a new Python Notebook. And though we get more into Notebooks in the next lesson, for now just say enough: that Notebooks provide an interactive environmnet for running code on our clusters. - -Create and execute a cell with the following: - - display(dbutils.fs.ls('/')) - -This is using the DBFS utility class to get a list of entries in the top-level directory of DBFS, then displaying them with the multi-purpose Databricks `display()` function. Raise the following points: -* We see some entries (like **databricks-datasets**) here but not in the Workspace browser - -Create a new cell and contrast the results from above with the following: - - import os - print(os.listdir('/')) - -This is using the standard os utility class to get a list of entries in the top-level directory of the cluster's local file system. Raise the following points: -* there are a collection of standard Linux file system directories and files found here, because we are listing the contents of the local file system, not DBFS -* there is a **/dbfs** directory, where DBFS is actually mounted. - -## Upload from Workspace - -Let’s upload a file to DBFS. - -With the DBFS tab in the Data page selected, click the **Upload** button. Browse to locate the .csv file (contained in the .zip file downloaded from above), or drag the file to the shared area of the dialog. - -Call out that the file will be uploaded into the **/FileStore** directory. We could create additional sub-directories here if desired, but when uploading through he UI it's always going to be somewhere under **FileStore**. - -Show that it's there by navigating to the **/FileStore** in the Workspace. Can also re-run the Notebook created earlier to check the file's presence. - -Bring up the point that this example file represents a relatively small dataset. For production environments: -* uploading large datasets into DBFS is generally not recommended. In that case, the preferred approach is to connect, or mount, an external data source onto DBFS and maintain your data there. -* uploading via the user interface is not recommended. Consider automating this using the CLI or API - -## File Management - -Call out to the prefix searching capabilities of the UI to help you find files as your DBFS scales. - -Also demonstrate usage of the chevron element on each item that allows us to perform some additional operations. For the file we just uploaded, we could move, rename or delete it as needed. diff --git a/platform-demos/4-notebooks.md b/platform-demos/4-notebooks.md deleted file mode 100644 index b18e025..0000000 --- a/platform-demos/4-notebooks.md +++ /dev/null @@ -1,131 +0,0 @@ -# Create and run Databricks notebooks - -This demo will cover the creation and management of some simple Databricks Notebooks. By the end of this lesson you will be able to: - -* Create and populate your own multi-cell Notebook -* Incorporate visual elements into your Notebooks -* Import and export Notebooks - -Before proceeding, ensure that: -* You have a cluster running as it will be needed to run commands -* Have a sample data file for reading. The code example here assumes the existence of **/FileStore/uszips.csv** in DBFS, as per the DBFS demo - -## Create Notebook - -Create a new Notebook. Highlight that because it's such a common thing to do, there are a few ways, including: -* From the home page -* From the **Create** menu -* From any folder item in the Workspace for which you have appropriate permissions (click the chevron, then **Create→Notebook**) - -When choosing a name, be sure to mention that you can easily change the name afterward. - -When selecting default language, mention that since Notebooks are essentially a collection of commands, you should choose the language you anticipate using the most. That said, there's no need to overthink this since it too can be changed after, and individual cells can also override this choice. For the sake of an example, choose **Python**. - -For cluster, choose a running cluster for which you have permissions. These compute resources will be needed when you go to execute commands in the Notebook. - -## Create a Markdown cell - -Start by documenting the Notebook with a bit of text. - - %md - # Example Notebook - This is a description of the Notebook - -Take a moment to talk about the *magic command* in the first line. By default, Databricks will assume this cell contains Python (the default language). The magic command overrides this. Databricks supports a few different magic commands for such designatations and various other tasks. - -Draw attention to the syntax highlight as you type, which start to become very handing when writing SQL, Python or others. - -Add another Markdown cell with the following contents. - - %md - ## Read a data file - Read and display a CSV from DBFS. - -Open the table of contents panel and illustrate how thoughtful application of Markdown can make your Notebooks more easy to read and navigate. - -## Create a Python cell - -Now add another cell containing the following Python code: - - df = spark.read.csv("/FileStore/uszips.csv", - header="true", - inferSchema="true") - display(df) - -Draw attention to the fact that we don't need a magic command for this cell because Python is the default language for the Notebook. - -Here we are leveraging the automatically created SparkSession object, **spark**, to read the CSV file located in the DBFS **FileStore** directory into a **DataFrame** object named `df`. The parameters intercept the header row, and causes the schema to be inferred from sapling the input data. - -Then we display the data using the Databricks `display()` function. Databricks formats this output as a table by default. Highlight that you can scroll through the data, and sort it by clicking on the headers. - -Add another cell with the code: - - df.createOrReplaceTempView('temp') - -This code creates a *temporary view*, that is, a database table local to this Notebook, based on the data referred to by the `df` object. Running it gives us the opportunity to reinforce some important points: -* execution state is maintained across cells in a Notebook (per language). In this cell we can seamlessly refer to `df` that was created in a different cell. -* no output is generated here because we didn't call `display()`. It's not necessary anyway since there's nothing new to display; we're simply creating an alternate representation of data we already read in. - -## Create an SQL cell - -Add another cell: - - %sql - SELECT * FROM temp - -A few things of note: -* here we need a magic command since we're departing from the default language. -* we are simply querying all the data from a table named **temp**; that is, the temporary view we created in the previous cell from Python code -* temporary views can be used to create alternate representations of data that you don't want to make globablly available; they're also useful for querying DataFrames from SQL -* Without having to do anything extra, Databricks is displaying the output in a table -* the data is identical to the earlier cell (as it should be, since it's the same data; the only thing that has changed is how it's being stored) - -## Add Visualizations - -Create a new cell with the following: - - %sql - SELECT `state_id` AS `state`,COUNT(`zip`) AS `nzips` - FROM temp - WHERE `state_id` NOT IN ('AS','GU','MP','PR','VI') - GROUP BY `state` - ORDER BY `nzips` - -This more elaborate query pulls only the **state_id** and **zip** columns, and aggregates by **zip**. It's also filtering out some states and sorting the output by the count. The end result is a record per state with the count of zip codes in that state. Run the cell. Once you get a table output, click the bar graph plot. Let’s run the query to see the output. This gives us a 51-row table, as expected, counting the number of reported zip codes for each state. Click the bar graph to switch visualiations. Highlight how you can customize these and, ultimately using DBSQL, could pull these graphics into a dashboard. - -For another great visual, add the following cell: - - %sql - SELECT `state_id` AS `state`,SUM(`population`) AS `population` - FROM temp - WHERE `state_id` NOT IN ('AS','GU','MP','PR','VI') - GROUP BY `state` - -Run and select the **Map** plot. This gives us a heatmap presenting the population per state. - -## Export and Import Notebooks - -You can share Notebooks by exporting them. Open **File→Export**, and highlight 3 important options: -* **DBC Archive**: an archive that can be easily imported into another Workspace. This includes state and results -* **Source File**: a source code file (in the syntax of the default language) representing the text/code in the Notebook (no state or results are captured) -* **HTML**: a static web page capturing the Notebook (and results), useful for publishing or documentation - -Export the Notebook using HTML, then open the resulting .html file in a web browser. Highlight that the visuals are all included and as interactive as they were in the Notebook. - -Now export as a DBC Archive. - -## Import Notebooks - -Let's highlight two ways to import. First create a folder in your home directory in the Workspace. Call it **Test**. In that folder, select the import option. Now drag the .dbc file downloaded previously to the shared area of the dialog. This highlights the file import method (you can also browse for the file rather than dropping it onto the dialog). This gives us a copy of the Notebook we exported previously. - -Now go back to the **Test** folder and import again. This time use the URL option and paste the link: - - https://docs.databricks.com/_static/notebooks/delta/quickstart-python.html - -## Comments - -Like many online document collaboration platforms, Notebooks support comments. Highlight some code, click the icon and add a comment. As you might expect, you can edit or reply to comments. - -## Revision history - -Notebooks also have built-in revision history. Though not as powerful as an external revisioin control system like git, it does provide some basic abilities to inspect the history of a Notebook, what changed when, by whom, etc. Git integration through Databricks Repos, which we discuss shortly, provide additional capabilities. diff --git a/platform-demos/5-repos.md b/platform-demos/5-repos.md deleted file mode 100644 index a5cf664..0000000 --- a/platform-demos/5-repos.md +++ /dev/null @@ -1,129 +0,0 @@ -# Integrate Git control for Databricks Repos - -This demo will cover the creation and management of Databricks Repos. By the end of this lesson you will be able to: -* Integrate your Workspace with a supported Git service -* Create and manage Repos -* Add Notebooks to a Repo -* Perform various revision control operations on the Repo - -Before proceeding, ensure that: -* Repos are enabled in your Workspace (**Settings→Admin Console→Workspace Settings→Advanced→Repos**) -* If you are using an exising account with a supported git provider, set up the integration now (**Settings→User Settings→Git Integration**) before sharing screen with learners to avoid leakage of credentials. -* If you are not using an existing git account, create a throw-away account with GitHub. - -## Integrating git - -First mention that the Repos feature can be turned on or off by the administrator (can show this if desired). The allow list can also be configured there for improved security. - -Now introduce learners to the page where they configure git integration (**Settings→User Settings→Git Integration**). Click on the **Change settings** button. - -Click on the **Git provider** dropdown to display supported providers (note that **GitHub Enterprise** is currently in private preview; if someone is interested in that integration, then they would need to work with their Databricks representative to get that set up). - -Select a few different ones and draw attention to the fact that the UI provides a link to instructions on crating a token based on the selection. But we will walk through this. - -Select the one that applies to you. - -If you have already configured git integration because you are using an existing account, proceed as follows: -* Show them (in the git provider UI) how to obtain the username -* How to create a personal access token with appropriate permissions, which is displayed in the Workspace UI based on the selection. NOTE: do NOT actually create a token! -* How they would populate the **Username** and **Token** fields -* Do NOT save any changes - -If you are using a throw-away GitHub account: -* Show them where to find the account username in the GitHub UI -* How to create a personal access token with **repo** permissions. See https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token -* Copy the token and transfer that value to the **Token** field in the Workspace UI -* Transfer the username to the **Username** field in the Workspace UI -* Click **Save** - -## Create Repo - -Remind learners of the 1:1 mapping between a Databricks Repo and a git repository. So first we need a git repository. - -### Create git repository - -In your git service UI: -* Create a new repository initialized with some content (for example, the **Add a README file** in GitHub); this way the repository is automatically initialized -* Capture the HTTPS URL. - -### Create Databricks Repo - -In the **Repos** page, click **Add Repo**. Mention that it's possible to start a Repo and add the remote URL later, but that's not the workflow we show here. Click **Clone remote Git repo** and paste the URL. THe **Git provider** and **Repo name** will be inferred though you can change the name if desired. Click **Create**. - -## Add folder - -With a new Repo created, navigate to it so that we can add some content. Remind learners that the UI metaphors are identical to the Workspace and that Repos is merely an extension of that to accomodate integration with git revision control. - -Inside the Repo, create a folder (click on the chevron and select **Create→Folder**). Name the folder anything you like. - -## Add Notebooks - -Now let’s add Notebooks. Let's walk through a few different ways. - -### Create new Notebook - -If beginning a brand new project, you might be starting from a blank slate so in this case, creating a new Notebook might be the way to go. - -On the folder you created, click the chevron and select **Create→Notebook**. From there, the workflow is identical to creating a new Notebook in the Workspace, which we have seen before. - -From there, create a simple markdown cell: - - %md - # Sample notebook - Sample description - -### Clone an existing Notebook - -If you have existing Notebooks in your Workspace or another Repo, cloning is an option. - -Navigate to a Notebook you have created in your Workspace. Click the chevron and select **Clone**. - -You will likely want to overwrite the auto-generated value for **New Name**. For the destination, select **Repos** then navigate to the desired Repo and folder within, and click **Clone**. - -### Import a Notebook - -If you have existing Notebooks from others that you want to add, importing them is an option. Tell learners that they would first need to have them exported (ideally a DBC Archive). - -From there, demonstrate the import workflow. On the folder you created in the Repo, click the chevron and select **Importk**. Again the workflow is similar to importing into the Workspace. If you have a DBC from that lesson then use that. Otherwise you can import by URL and use the following: - - https://docs.databricks.com/_static/notebooks/delta/quickstart-python.html - -## Commit and push - -We have set up a new Repo, and locally we created a new folder containing some Notebooks. Let’s push this upstream to our remote repository. - -We do this from the Repo dialog. Open that from the Repos page or from one of the Notebooks inside the Repo. - -Talk about the dialog and how it itemizes the changes. This is handy because sometimes by the time we finally get around to committing, we forget what we’ve changed. Selecting each change in the list provides a preview of the changes to the associated file. - -We can commit each change individually or as many as we'd like. It’s good practice to logically group commits, and sometimes we forget to commit before we have made additional unrelated changes, so this option helps with that. - -For each commit, we must provide a summary; be descriptive and concise. If desired, additional verbosity can be provided in the description. - -Let’s commit the changes by clicking Commit & Push. - -Point out that what we did here covers functionality, but doesn't necessarily reflect best practice. Things can get a little more complicated when multiple developers are collaborating on the same branch. If someone else has pushed their own changes to the remote repository since you last pulled from it, you will not be able to push your changes. You will have to pull the remote changes first, and potentially deal with any merging conflicts that can sometimes happen if conflicting changes are detected. - -So, if developers must collaborate on the same branch, then pulling is important before you begin your development work. - -## Best practices - -Best practices advocate creating feature branches for your development. This way your changes are isolated and you are largely immune to the potential conflict situations mentioned above. This is particularly appropriate when working on features that aren’t tied tightly to development work being carried out by others in your team. - -Once you complete your changes and they have been unit tested, reviewed and approved, then merging with other branches can be handled in a more controlled manner elsewhere in your process. - -## Branching - -To support best practices, let's create a feature branch called **dev**. Open the Repo dialog, which can be accessed from the **Repos** page or within any Notebook inside the Repo. - -Click the **+** button to the right of the branch dropdown. The dropdown then becomes editable. Specify the name of the new branch. When creating a new branch, it will automatically become the active branch, though you can use the dropdown at any time to switch to a different branch. - -With the development branch created and activated, we can now begin making changes as needed. - -Import a new Notebook into the folder you created in the Repo. As an example you can use the following URL: - - https://docs.databricks.com/_static/notebooks/structured-streaming-python.html - -Revisit the Repo dialog and notice how the changes are itemized before. Fill out a summary and commit and push the changes, just like we did before. The key difference is that now the main branch is unaffected by our changes. Everything we did is isolated in the **dev** branch. Verify this by switching branches back to main, using the dropdown in the Repo dialog. Notice how the new Notebook is closed, and the folder no longer contains that new Notebook. - -Switch back to **dev** to see it reappear. diff --git a/platform-demos/6-LAB.md b/platform-demos/6-LAB.md deleted file mode 100644 index 6250d9d..0000000 --- a/platform-demos/6-LAB.md +++ /dev/null @@ -1,187 +0,0 @@ -# Hands On Lab: Getting Started with the Databricks Platform - -In this lab, you'll apply the skills you've just learned to: -- Create and manage an interactive cluster -- Import a Repo -- Create and clone a notebook -- Load a dataset into the DBFS -- Attach a notebook to a cluster and run a cell -- Review code changes in Repos - -Before proceeding, ensure that you: -- Are logged into Databricks. -- Are in the **Data Science & Engineering Workspace** by selecting the appropriate entry from the persona menu. - -## Create a Cluster - -Running commands and processing data requires compute resources, which in Databricks is supplied by clusters. In this section, we will create a new interactive cluster which we will later use to run commands. - -1. From the home page, select **New Cluster**, or click the **Compute** icon in the sidebar and click **Create Cluster**. -2. Specify *my_cluster* for **Cluster Name**. -3. Select **Single Node** for **Cluster Mode**. This will provide ample compute resources for this lab while minimizing compute costs. -4. Specify *60* to terminate the cluster after 60 minutes of inactivity. This more agressive value will further reduce compute costs. -5. Select the most recent version of the **Standard** runtime for **Databricks Runtime Version**. -6. Leave the remainder of the settings in their default states. -7. Click **Create Cluster**. - -Cluster Creation will take several minutes. In the meantime, you may continue with the next section. - -## Import a Repo - -Databricks Repos is a feature that extends the concept of the Workspace to include revision control through its git integration. Repos also provides an API that enables integration with CI/CD tools. - -In this section, we will create a Repo based on an existing repository on GitHub. - -1. Click the **Repos** icon in the sidebar and click **Add Repo**. -2. With **Clone remote Git repo** selected, paste the following URL into the **Git repo URL** field: - ``` - https://github.com/databricks-academy/data-engineering-with-databricks - ``` - Notice that values for **Git provider** and **Repo name** are inferred. For this exercise, leave the inferred values as they are. -3. Click **Create**. - -We now have a Repo called **data-engineering-with-databricks** that contains a copy of the materials from the underlying git repository. Though parallel to the folders of your Workspace, the user interface metaphors for Repos are identical and it is easy to move material back and forth as we will soon see. - -## Create a Notebook - -A Notebook is an interactive web document that allows use to run commands on our clusters in four different languages: Python, R, Scala and SQL. We can also intermix Markdown to embed structured documentation. In this section we will create a Notebook in the Workspace. - -1. Click the **Workspace** icon in the sidebar. -2. Select the **Users** folder, which contains a personal folder for each user in the Workspace. -3. Identify the folder associated with your user id (this maps to the user id you use to login to Databricks). This is your personal folder for storing Databricks assets. -5. Create a new folder named `Home`: - 1. Click the chevron on the right side of your personal folder. Select **Create→Folder** from the menu. - 2. Specify *Home* for the folder name. - 3. Click **Create Folder**. - 4. Navigate to this new folder; select your personal folder to view the contents, then select **Home**. -6. Create a new Notebook named `my_name`: - 1. Click the chevron on the right side of the **Home** folder. Select **Create→Notebook** from the menu. - 2. Specify *my_name* for **Name**. - 3. Select **Python** as the **Default Language**. - 4. Select **my_cluster** for **Cluster**. - 5. Click **Create**. You now have a blank Notebook at the following path: */Users/<user id>/Home/my_name*. Let's begin adding some content to this Notebook. -7. Add a single line of Python code to the first cell of the Notebook. Substituting your own name on the right-hand side, this will assigne that value to a variable named `name`. - ``` - name = "" - ``` - -## Clone a Notebook - -Let's return our attention to the Repo we created earlier. When setting up Repos, we often want to copy in Notebooks we have been developing in our Workspace. This process of copying Notebooks is referred to as cloning the Notebook. You can clone a Notebook from your Workspace to a different location in your Workspace, from your Workspace to a Repo, from a Repo to your Workspace, or even from one Repo to another Repo. In this section, we clone a Notebook to transfer it from the Workspace into the Repo we created. - -The cloning function is available from both the Workspace navigator, or from within the Notebook if you have it open (where it can be found in the **File** menu). - -**NOTE**: When cloning your notebook, be _very_ specific about the location; we'll leverage another notebook that uses a `%run` to call this notebook from a relative location and confirm success. - -1. With the Notebook open, open the **File** menu and select **Clone**. -2. Replace the auto-generated value of **New Name** with the string `my_name`. -3. Specify the destination. - 1. Select **Repos** in the left-most column. - 2. Select the folder in the right column named after your user id. - 3. Select the **data-engineering-with-databricks** Repo that appears in the rightmost column. - 4. Use the file picker to navigate into the **platform-demos** directory, and then the **assets** folder. - 5. Click **Clone**. - -The newly created copy of the Notebook opens. Note that there is now a Repo control button in the top-left because we are editing a Notebook contained in a Repo. The text appearing in this button reflects the branch of the Repo that is currently active; the Github repository we cloned uses a branch called `published` for its main production branch. - -## Load a Dataset - -NOTE: managing database tables, which we do in the next section, requires compute resources supplied by your running cluster. Let's ensure it's running now. If it isn't, let's restart it. This operation takes a few minutes and we can continue through this section while that completes. - -1. Click the **Compute** icon in the sidebar. -2. Locate the cluster you created earlier. If the state is shown as **Terminated**, then restart it by clicking the **Start** button in the **Actions** column (these buttons only appear when hovering over the row). -3. The operation takes a couple of minutes, but you can complete this section in the meantime. - -In this section we will upload a data file to DBFS using the Databricks UI. While this is a useful training exercise, this approach is not advised in a production scenario for two reasons: -- It's better to automate this, so that the entire environment can be replicated without human intervention -- It's not good practice to store your datasets in the DBFS root. Datasets should be stored in external storage that is mounted onto DBFS under */mnt*. - -For this exercise, we use a sample data file we obtained from SimpleMaps (https://simplemaps.com/data/us-zips) that provides census and geographical information for each of the tens of thousands of US zip codes. Visit their website for more information or related products. - -1. Download the data file. We provide a copy that can be directly downloaded from [here](./assets/uszips.csv). -2. In case you are operating in a shared environment where name conflicts may occur, rename the file using a pattern that's guaranteed to be unique. Use your full name (no spaces or other punctuation characters), but retain the *.csv* extension. -3. Upload the file to DBFS. - 1. Click the **Data** icon in the sidebar. - 2. Select **DBFS** to open the DBFS file browser. - 3. Click **Upload**. - 4. Leave the **DBFS Target Directory** blank; this will upload the file into the */FileStore* directory of DBFS. - 5. Click to use your operating system file browser to locate the file, or drag it from your local storage area to the drop zone. Either way, wait a few moments for it to upload. - 6. Once the upload completes, click **Done**. -4. (Optional) Navigate to the */Filestore* directory in the DBFS file navigator to validate that the file is present. - -## Create a Table - -With a data file loaded to DBFS, we will create a table based on it using the Databricks UI. Once again, this is a useful training exercise, but this approach is not advised in a production scenario for two reasons: -- It's better to automate this, so that the entire environment can be replicated without human intervention -- Particularly for large datasets, creating a Delta table will deliver superior performance. Creating an unmanaged table is also better since it decouples the table data and metadata. Neither of these options is available in the table creation UI. - -1. Click the **Data** icon in the sidebar. -2. Select **Database Tables** to view and manage the databases and tables. -3. Click **Create Table**. -4. Select **DBFS** for **Data Source**, since the data file has been uploaded to DBFS already. -5. Navigate to and select the *.csv* file uploaded previously, located in the */FileStore* directory. -6. Click **Create Table with UI**. -7. Select your cluster from the **Cluster** dropdown. -8. Click **Preview Table**. Note the value for **Table Name** but leave it as is. You will need this value shortly. -9. Enable **First row is header** to promote the first row to be the column names. -10. Enable **Infer schema** to allow Spark to infer the data types for each column. Note that usage of this option is dependent on how you plan to use this table and whether you prefer all values to be treated as STRINGs in the table or if you would prefer the type to reflect the actual nature of the data in that column. -11. With the remainder of the options in their default states, click **Create Table**. - -## Attach a Notebook to a Cluster - -It is often necessary to attach a Notebook to a cluster. There are a number of reason this need might arise, including: -We need compute resources to run cells in our Notebooks, but maybe the Notebook is detached. This can happen for a few different reasons: -- Notebook was created without attaching to any cluster. -- The cluster originally attached to the Notebook was deleted. -- Need to test a Notebook against a different cluster configuration. - -In this section, we will attach a Notebook from the Repo to our running cluster. - -1. Click on the **Repos** icon in the sidebar. -2. Navigate to the **data-engineering-with-databricks** Repo. -3. Find the **platform-demos** directory, and then the **assets** folder. -4. Select the Notebook named **population_heatmap**. The Notebook opens. -5. Locate the cluster dropdown at the top-right corner. From the dropdown, select your cluster. - -With a running cluster attached, we can now run cells within the Notebook. - -## Run a Cell - -Having attached a running cluster to our Notebook, let's run some cells. - -1. Locate the first runnable cell in the Notebook, **Cmd 3**. This cell uses the `%run` *magic command* (code preceded with a percent sign) to source another Notebook named **my_name** (recall that was the Notebook we created earlier). This is a useful technique for modularizing code. -2. Run the cell using either of the following options: - - Click the play button at the top-right corner of the cell, then select **Run Cell** from the menu. - - With the cell selected, use the keyboard shortcut `Shift+Enter`. - Because this cell runs a different Notebook, no output is captured. Instead, a summary of the execution time is provided. -3. Let's advance to the next runnable cell, **Cmd 5**. Note the following with regards to this cell: - - It contains SQL, demonstrating that you can mix and match languages within a single Notebook - - Because the default language for this Notebook is Python, this cell begins with the `%sql` magic command to denote that the cell is SQL. - - It queries the table we created earlier, named in the Notebook **my_name**. This query builds a list of states with population counts for each. -4. Run the cell using one of the aforementioned methods. - - Does it succeed? - - If it did, you can advance to the next section. - - If not, can you determine the reason it failed and fix it? - - The remainder of this section walks through the fix. -5. Understand the root cause of the failure and how to fix it: - - The query failed because of a mismatch between the table named in **my_name** and the actual name of the table. - - Replace the name in **my_name** with the name of the table created earlier. -6. Locate and open the Notebook **my_name** within the Repo (*not* the copy in your Workspace). -7. Replace the value on the right-hand side of the assignment in **Cmd 1** with the table name noted earlier. -8. Return to the **population_heatmap** Notebook. Note, the **Recents** menu, accessible from the sidebar, is very handy for switching between Notebooks quickly. -9. Run the entire Notebook again by clicking **Run All**. This will ensure the updated name is pulled in prior to attempting the query again. This time, we are met with success. - -## Review Changes in Repos - -Let's see how our changes are reflected by the revision control capabilites that Repos provide. - -1. Click the button at the top-right corner of the Notebook to open the **Repo** dialog. -2. Notice the itemized changes. Selecting each one in turn highlights what changed on the right-hand side of the dialog. -3. As write permissions are not available on the provided repository, you will not be able to commit and push. Instead, click **Close**. - -One thing to note: nothing else we did in this lab is reflected in the changes: -- The *Home* directory we created -- The datasets we uploaded -- The first instance of the **my_name** Notebook - -These changes were made outside the Repo and are hence not tracked. Notice also that other metadata (state, results, and comments, if any) are also not tracked. diff --git a/platform-demos/README.md b/platform-demos/README.md deleted file mode 100644 index 60f7845..0000000 --- a/platform-demos/README.md +++ /dev/null @@ -1,3 +0,0 @@ -# Platform Demos - -This collection of markdown files and assets is provided for reference by instructors and students as part of the Data Engineering with Databricks course. diff --git a/platform-demos/assets/population_heatmap.py b/platform-demos/assets/population_heatmap.py deleted file mode 100644 index 6c56f3a..0000000 --- a/platform-demos/assets/population_heatmap.py +++ /dev/null @@ -1,27 +0,0 @@ -# Databricks notebook source -# MAGIC %md -# MAGIC # Population Heatmap by State -# MAGIC Using *uszips.csv* as a data source, aggregate the populations by state. -# MAGIC Note: data file provided courtesy of SimpleMaps (https://simplemaps.com/data/us-zips) - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Source a Notebook to configure the table name. If `my_name` resides in a different relative path, then adjust the code in **Cmd 3** accordingly. - -# COMMAND ---------- - -# MAGIC %run ./my_name - -# COMMAND ---------- - -# MAGIC %md -# MAGIC Query the table that was named in the `my_name` Notebook. Aggregate the population, grouping by state. For maximum effectiveness, select the **Map** plot to visualize the output. - -# COMMAND ---------- - -# MAGIC %sql -# MAGIC SELECT `state_id` AS `state`,SUM(`population`) AS `population` -# MAGIC FROM ${conf.name} -# MAGIC WHERE `state_id` NOT IN ('AS','GU','MP','PR','VI') -# MAGIC GROUP BY `state` diff --git a/platform-demos/assets/uszips.csv b/platform-demos/assets/uszips.csv deleted file mode 100644 index ed619bc..0000000 --- a/platform-demos/assets/uszips.csv +++ /dev/null @@ -1,33122 +0,0 @@ -"zip","lat","lng","city","state_id","state_name","zcta","parent_zcta","population","density","county_fips","county_name","county_weights","county_names_all","county_fips_all","imprecise","military","timezone" -"00601","18.18005","-66.75218","Adjuntas","PR","Puerto Rico","TRUE","","17113","102.7","72001","Adjuntas","{""72001"": ""99.43"", ""72141"": ""0.57""}","Adjuntas|Utuado","72001|72141","FALSE","FALSE","America/Puerto_Rico" -"00602","18.36074","-67.17519","Aguada","PR","Puerto Rico","TRUE","","37751","476.0","72003","Aguada","{""72003"": ""100""}","Aguada","72003","FALSE","FALSE","America/Puerto_Rico" -"00603","18.4544","-67.12201","Aguadilla","PR","Puerto Rico","TRUE","","47081","574.9","72005","Aguadilla","{""72005"": ""100""}","Aguadilla","72005","FALSE","FALSE","America/Puerto_Rico" -"00606","18.16721","-66.93828","Maricao","PR","Puerto Rico","TRUE","","6392","58.3","72093","Maricao","{""72093"": ""94.88"", ""72153"": ""3.78"", ""72121"": ""1.35""}","Maricao|Yauco|Sabana Grande","72093|72153|72121","FALSE","FALSE","America/Puerto_Rico" -"00610","18.29032","-67.12244","Anasco","PR","Puerto Rico","TRUE","","26686","286.9","72011","Añasco","{""72011"": ""99.45"", ""72003"": ""0.55""}","Añasco|Aguada","72011|72003","FALSE","FALSE","America/Puerto_Rico" -"00612","18.40699","-66.70805","Arecibo","PR","Puerto Rico","TRUE","","59369","339.1","72013","Arecibo","{""72013"": ""99.89"", ""72017"": ""0.11""}","Arecibo|Barceloneta","72013|72017","FALSE","FALSE","America/Puerto_Rico" -"00616","18.41752","-66.66814","Bajadero","PR","Puerto Rico","TRUE","","10022","335.6","72013","Arecibo","{""72013"": ""100""}","Arecibo","72013","FALSE","FALSE","America/Puerto_Rico" -"00617","18.44125","-66.55916","Barceloneta","PR","Puerto Rico","TRUE","","23750","603.5","72017","Barceloneta","{""72017"": ""99.43"", ""72054"": ""0.57""}","Barceloneta|Florida","72017|72054","FALSE","FALSE","America/Puerto_Rico" -"00622","17.99174","-67.15248","Boqueron","PR","Puerto Rico","TRUE","","6741","89.8","72023","Cabo Rojo","{""72023"": ""100""}","Cabo Rojo","72023","FALSE","FALSE","America/Puerto_Rico" -"00623","18.08354","-67.15418","Cabo Rojo","PR","Puerto Rico","TRUE","","41746","424.5","72023","Cabo Rojo","{""72023"": ""100""}","Cabo Rojo","72023","FALSE","FALSE","America/Puerto_Rico" -"00624","18.06465","-66.71823","Penuelas","PR","Puerto Rico","TRUE","","21936","196.2","72111","Peñuelas","{""72111"": ""92.53"", ""72113"": ""7.47""}","Peñuelas|Ponce","72111|72113","FALSE","FALSE","America/Puerto_Rico" -"00627","18.419","-66.86033","Camuy","PR","Puerto Rico","TRUE","","31598","263.2","72027","Camuy","{""72027"": ""100""}","Camuy","72027","FALSE","FALSE","America/Puerto_Rico" -"00631","18.18555","-66.83327","Castaner","PR","Puerto Rico","TRUE","","1499","143.9","72081","Lares","{""72081"": ""67.01"", ""72001"": ""32.99""}","Lares|Adjuntas","72081|72001","FALSE","FALSE","America/Puerto_Rico" -"00637","18.0798","-66.94274","Sabana Grande","PR","Puerto Rico","TRUE","","22383","250.9","72121","Sabana Grande","{""72121"": ""99.56"", ""72153"": ""0.44""}","Sabana Grande|Yauco","72121|72153","FALSE","FALSE","America/Puerto_Rico" -"00638","18.28905","-66.5151","Ciales","PR","Puerto Rico","TRUE","","16412","95.9","72039","Ciales","{""72039"": ""98.27"", ""72091"": ""1.73""}","Ciales|Manatí","72039|72091","FALSE","FALSE","America/Puerto_Rico" -"00641","18.26408","-66.71376","Utuado","PR","Puerto Rico","TRUE","","26712","110.3","72141","Utuado","{""72141"": ""99.79"", ""72001"": ""0.21""}","Utuado|Adjuntas","72141|72001","FALSE","FALSE","America/Puerto_Rico" -"00646","18.43401","-66.2833","Dorado","PR","Puerto Rico","TRUE","","37095","691.0","72051","Dorado","{""72051"": ""99.89"", ""72143"": ""0.11""}","Dorado|Vega Alta","72051|72143","FALSE","FALSE","America/Puerto_Rico" -"00647","17.96612","-66.94383","Ensenada","PR","Puerto Rico","TRUE","","5809","147.5","72055","Guánica","{""72055"": ""98.88"", ""72079"": ""1.12""}","Guánica|Lajas","72055|72079","FALSE","FALSE","America/Puerto_Rico" -"00650","18.34321","-66.58601","Florida","PR","Puerto Rico","TRUE","","14534","183.9","72054","Florida","{""72054"": ""83.61"", ""72141"": ""11.84"", ""72017"": ""1.91"", ""72013"": ""1.51"", ""72039"": ""1.13""}","Florida|Utuado|Barceloneta|Arecibo|Ciales","72054|72141|72017|72013|72039","FALSE","FALSE","America/Puerto_Rico" 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