Update/calculate hourly airqualitydata using bigqdata

[NicholasTurner23]

Description

Just some clean up

Summary by CodeRabbit

• Bug Fixes

  • Enhanced data reliability by filtering out records missing essential date information.
  • Updated timestamp parsing to include timezone details for more accurate time calculations.

• New Features

  • Extended the data extraction period from 3 days to 7 days, allowing for a broader range of data processing.

[coderabbitai]

📝 Walkthrough

Walkthrough

This pull request updates the extract_aggregate_calibrate_raw_data method in the AirQoDataUtils class. A new operation is introduced to drop rows with missing timestamps from the devices DataFrame before processing. Additionally, the timestamp parsing format is modified from "%Y-%m-%dT%H:%M:%SZ" to "%Y-%m-%d %H:%M:%S%z", ensuring that datetime objects are created with timezone awareness. The changes refine the data processing without altering the overall logic flow.

Changes

File	Change Summary
src/.../airqo_utils.py	Updated extract_aggregate_calibrate_raw_data method to drop rows missing timestamps and changed the timestamp format to %Y-%m-%d %H:%M:%S%z for timezone awareness.
src/.../airqo_measurements.py	Modified extract_hourly_data function in airqo_cleanup_measurements DAG to extend data extraction from 3 days to 7 days.

Possibly related PRs

• Update/calculate hourly airqualitydata using bigqdata #4355: Related to the AirQoDataUtils class, affecting data handling in airqo_utils.py.
• Update/calculate hourly airqualitydata using bigqdata #4456: Modifies the extract_aggregate_calibrate_raw_data method in a similar manner, indicating a strong connection.
• Update/calculate hourly airqualitydata using bigqdata #4351: Introduces new columns in the calibrate_data method of the AirQoDataUtils class, indicating a direct relationship in their modifications.

Suggested reviewers

• Baalmart
• Psalmz777

Poem

In code we trust, a tweak so fine,
Nulls are dropped, as timestamps shine.
Formats shift with timezone flair,
Data becomes pristine and fair.
Here's to changes crafted with care! 🚀

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🔥 Problems

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Errors (1)

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Reviewing files that changed from the base of the PR and between 82d2eed and 3f67ec9.

📒 Files selected for processing (1)

• src/workflows/dags/airqo_measurements.py (1 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (2)

• GitHub Check: Analyze (python)
• GitHub Check: Analyze (javascript)

🔇 Additional comments (2)

src/workflows/dags/airqo_measurements.py (2)

238-245: Verify the cleanup frequency relative to the data window.

The DAG runs every 45 minutes (schedule="*/45 * * * *") but processes 7 days of data. This frequent reprocessing of historical data might be unnecessary and resource-intensive.

Consider:

1. Aligning the cleanup window with the actual data update frequency
2. Using incremental processing for recent data
3. Running full historical cleanup less frequently

Would you like me to propose a more efficient scheduling strategy?

---

264-266:

❓ Verification inconclusive

Consider the performance implications of increasing the data extraction window.

The change from 3 to 7 days significantly increases the volume of data being processed every 45 minutes. This could impact:

1. BigQuery costs due to increased data scanning
2. Processing time and resource utilization
3. Potential overlap in data processing between DAG runs

Let's verify the potential impact:

Would you like me to propose an alternative approach that maintains data quality while optimizing resource usage?

---

🏁 Script executed:

#!/bin/bash
# Description: Check the data volume and processing patterns

# Check the table schema and partitioning
bq show --format=prettyjson airqo.hourly_measurements

# Estimate the data volume difference
start_date=$(date -d '7 days ago' '+%Y-%m-%d')
end_date=$(date '+%Y-%m-%d')
bq query --nouse_legacy_sql \
"SELECT COUNT(*) as row_count, 
        (SELECT COUNT(*) FROM airqo.hourly_measurements 
         WHERE DATE(timestamp) BETWEEN DATE_SUB(CURRENT_DATE(), INTERVAL 3 DAY) 
         AND CURRENT_DATE()) as three_day_count
 FROM airqo.hourly_measurements 
 WHERE DATE(timestamp) BETWEEN '${start_date}' AND '${end_date}'"

Length of output: 695

---

Performance & Data Integrity Consideration:
The change from a 3-day window to a 7-day window increases the amount of data processed every 45 minutes, which could lead to:

• Increased BigQuery costs due to more extensive data scanning.
• Higher processing times and greater resource utilization.
• Potential overlaps or reprocessing of data across frequent DAG runs, which might impact data consistency.

The automated shell script verification using the bq command did not produce the expected output (the command wasn’t found), so the actual impact remains inconclusive. Please perform a manual verification of the BigQuery table schema, partitioning, and data volume impact to ensure that this expanded time window does not bring unintended performance or data consistency issues.

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[coderabbitai]

Actionable comments posted: 0

🧹 Nitpick comments (1)

src/workflows/airqo_etl_utils/airqo_utils.py (1)

843-843: Consider adding format validation for timestamps.

While the new format "%Y-%m-%d %H:%M:%S%z" is more robust by including timezone information, it assumes all timestamps will be in this exact format. Consider adding error handling for cases where the timestamp format might be different.

-            end_date_time = datetime.strptime(row.timestamp, "%Y-%m-%d %H:%M:%S%z")
+            try:
+                end_date_time = datetime.strptime(row.timestamp, "%Y-%m-%d %H:%M:%S%z")
+            except ValueError as e:
+                logger.warning(f"Invalid timestamp format for device {row.device_id}: {e}")
+                continue

📜 Review details

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between 32ed5de and 82d2eed.

📒 Files selected for processing (1)

• src/workflows/airqo_etl_utils/airqo_utils.py (1 hunks)

⏰ Context from checks skipped due to timeout of 90000ms (2)

• GitHub Check: Analyze (python)
• GitHub Check: Analyze (javascript)

🔇 Additional comments (1)

src/workflows/airqo_etl_utils/airqo_utils.py (1)

839-839: LGTM! Good data quality practice.

Dropping rows with missing timestamps is a good practice to ensure data integrity and prevent potential issues in downstream processing.