diff --git a/datahub-web-react/src/app/ingest/source/builder/constants.ts b/datahub-web-react/src/app/ingest/source/builder/constants.ts index 58525b3e88f975..18b22c0a37588b 100644 --- a/datahub-web-react/src/app/ingest/source/builder/constants.ts +++ b/datahub-web-react/src/app/ingest/source/builder/constants.ts @@ -17,6 +17,7 @@ import athenaLogo from '../../../../images/awsathenalogo.png'; import mssqlLogo from '../../../../images/mssqllogo.png'; import clickhouseLogo from '../../../../images/clickhouselogo.png'; import cockroachdbLogo from '../../../../images/cockroachdblogo.png'; +import couchbaseLogo from '../../../../images/couchbaselogo.png'; import trinoLogo from '../../../../images/trinologo.png'; import dbtLogo from '../../../../images/dbtlogo.png'; import dremioLogo from '../../../../images/dremiologo.png'; @@ -53,6 +54,8 @@ export const CLICKHOUSE_USAGE = 'clickhouse-usage'; export const CLICKHOUSE_URN = `urn:li:dataPlatform:${CLICKHOUSE}`; export const COCKROACHDB = 'cockroachdb'; export const COCKROACHDB_URN = `urn:li:dataPlatform:${COCKROACHDB}`; +export const COUCHBASE = 'couchbase'; +export const COUCHBASE_URN = `urn:li:dataPlatform:${COUCHBASE}`; export const DBT = 'dbt'; export const DBT_URN = `urn:li:dataPlatform:${DBT}`; export const DREMIO = 'dremio'; @@ -147,6 +150,7 @@ export const PLATFORM_URN_TO_LOGO = { [BIGQUERY_URN]: bigqueryLogo, [CLICKHOUSE_URN]: clickhouseLogo, [COCKROACHDB_URN]: cockroachdbLogo, + [COUCHBASE_URN]: couchbaseLogo, [DBT_URN]: dbtLogo, [DREMIO_URN]: dremioLogo, [DRUID_URN]: druidLogo, diff --git a/datahub-web-react/src/app/ingest/source/builder/sources.json b/datahub-web-react/src/app/ingest/source/builder/sources.json index 102cce0f491e36..ba2c71484a08ce 100644 --- a/datahub-web-react/src/app/ingest/source/builder/sources.json +++ b/datahub-web-react/src/app/ingest/source/builder/sources.json @@ -333,5 +333,13 @@ "description": "Import Nodes and Relationships from Neo4j.", "docsUrl": "https://datahubproject.io/docs/generated/ingestion/sources/neo4j/", "recipe": "source:\n type: 'neo4j'\n config:\n uri: 'neo4j+ssc://host:7687'\n username: 'neo4j'\n password: 'password'\n env: 'PROD'\n\nsink:\n type: \"datahub-rest\"\n config:\n server: 'http://localhost:8080'" + }, + { + "urn": "urn:li:dataPlatform:couchbase", + "name": "couchbase", + "displayName": "Couchbase", + "description": "Import data from Couchbase Server and Capella.", + "docsUrl": "https://datahubproject.io/docs/generated/ingestion/sources/couchbase", + "recipe": "source:\n type: couchbase\n config:\n connect_string: 'couchbases://127.0.0.1'\n username: 'Administrator'\n password: 'password'\n cluster_name: 'testdb'\n classification:\n enabled: true\n classifiers:\n - type: datahub\n profiling:\n enabled: true" } ] diff --git a/metadata-ingestion/docs/sources/couchbase/couchbase_recipe.yml b/metadata-ingestion/docs/sources/couchbase/couchbase_recipe.yml new file mode 100644 index 00000000000000..3816995bf342ab --- /dev/null +++ b/metadata-ingestion/docs/sources/couchbase/couchbase_recipe.yml @@ -0,0 +1,17 @@ +source: + type: couchbase + config: + connect_string: "couchbases://127.0.0.1" + username: "Administrator" + password: "password" + cluster_name: "testdb" + classification: + enabled: true + classifiers: + - type: datahub + profiling: + enabled: true + profile_nested_fields: true + +sink: + # sink configs diff --git a/metadata-ingestion/setup.py b/metadata-ingestion/setup.py index 2cfdf9837f45ad..86ed805c77b7e9 100644 --- a/metadata-ingestion/setup.py +++ b/metadata-ingestion/setup.py @@ -392,6 +392,7 @@ "clickhouse": sql_common | clickhouse_common, "clickhouse-usage": sql_common | usage_common | clickhouse_common, "cockroachdb": sql_common | postgres_common | {"sqlalchemy-cockroachdb<2.0.0"}, + "couchbase": {"couchbase>=4.3.4"}, "datahub-lineage-file": set(), "datahub-business-glossary": set(), "delta-lake": {*data_lake_profiling, *delta_lake}, @@ -622,6 +623,7 @@ "clickhouse", "clickhouse-usage", "cockroachdb", + "couchbase", "delta-lake", "dremio", "druid", @@ -720,6 +722,7 @@ "clickhouse = datahub.ingestion.source.sql.clickhouse:ClickHouseSource", "clickhouse-usage = datahub.ingestion.source.usage.clickhouse_usage:ClickHouseUsageSource", "cockroachdb = datahub.ingestion.source.sql.cockroachdb:CockroachDBSource", + "couchbase = datahub.ingestion.source.couchbase.couchbase_main:CouchbaseDBSource", "delta-lake = datahub.ingestion.source.delta_lake:DeltaLakeSource", "s3 = datahub.ingestion.source.s3:S3Source", "dbt = datahub.ingestion.source.dbt.dbt_core:DBTCoreSource", diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/__init__.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/__init__.py new file mode 100644 index 00000000000000..e69de29bb2d1d6 diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_aggregate.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_aggregate.py new file mode 100644 index 00000000000000..5afb9dfeb11606 --- /dev/null +++ b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_aggregate.py @@ -0,0 +1,115 @@ +import asyncio +import logging +from typing import AsyncGenerator, List + +from acouchbase.collection import AsyncCollection +from acouchbase.scope import AsyncScope +from couchbase.exceptions import DocumentNotFoundException +from couchbase.result import GetResult + +from datahub.ingestion.source.couchbase.couchbase_connect import CouchbaseConnect +from datahub.ingestion.source.couchbase.couchbase_sql import ( + SELECT_COLLECTION_COUNT, + SELECT_DOC_IDS, +) +from datahub.ingestion.source.couchbase.retry import retry + +logger = logging.getLogger(__name__) + + +class CouchbaseAggregate: + scope: AsyncScope + collection: AsyncCollection + + def __init__( + self, + connector: CouchbaseConnect, + keyspace: str, + batch_size: int = 100, + max_sample_size: int = 0, + ): + self.connector = connector + self.keyspace = keyspace + self.batch_size = batch_size + self.max_sample_size = max_sample_size + + if batch_size <= 0: + raise ValueError("batch_size must be greater than 0") + + async def init(self): + await self.connector.cluster_init_async() + self.scope, self.collection = await self.connector.connect_keyspace_async( + self.keyspace + ) + + @retry(factor=0.05) + async def collection_get(self, key: str) -> dict: + try: + result: GetResult = await self.collection.get(key) + return result.content_as[dict] + except DocumentNotFoundException: + logger.warning(f"Document ID {key} not found") + return {} + + @retry(factor=0.05) + async def run_query( + self, query: str, offset: int = 0, limit: int = 0 + ) -> List[dict]: + if offset > 0: + query += f" OFFSET {offset}" + if limit > 0: + query += f" LIMIT {limit}" + result = self.scope.query(query) + documents = [row async for row in result] + return documents + + async def collection_count(self) -> int: + query = SELECT_COLLECTION_COUNT.format(self.connector.collection_name) + + result = await self.run_query(query) + document = [row for row in result] + return document[0].get("count") if document else 0 + + async def get_keys(self): + query = SELECT_DOC_IDS.format(self.connector.collection_name) + + results = await self.run_query(query, limit=self.max_sample_size) + for row in results: + yield row.get("id") + + async def get_key_chunks(self) -> AsyncGenerator[List[str], None]: + keys = [] + async for key in self.get_keys(): + keys.append(key) + if len(keys) == self.batch_size: + yield keys + keys.clear() + if len(keys) > 0: + yield keys + + async def get_documents(self) -> AsyncGenerator[List[dict], None]: + tasks = [] + await self.init() + + async for chunk in self.get_key_chunks(): + for key in chunk: + tasks.append(asyncio.create_task(self.collection_get(key))) + + errors = 0 + if len(tasks) > 0: + batch = [] + await asyncio.sleep(0) + results = await asyncio.gather(*tasks, return_exceptions=True) + for result in results: + if isinstance(result, Exception): + logger.error(result) + errors += 1 + elif isinstance(result, dict): + if result: + batch.append(result) + yield batch + + if errors > 0: + raise RuntimeError(f"batch get: {errors} errors retrieving documents") + + tasks.clear() diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_common.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_common.py new file mode 100644 index 00000000000000..a0cc17c92b7f38 --- /dev/null +++ b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_common.py @@ -0,0 +1,124 @@ +from dataclasses import dataclass, field +from typing import Any, Dict, Iterable, List, Optional, Tuple + +from pydantic import PositiveInt +from pydantic.fields import Field + +from datahub.configuration.common import AllowDenyPattern +from datahub.configuration.source_common import ( + EnvConfigMixin, + PlatformInstanceConfigMixin, +) +from datahub.ingestion.glossary.classification_mixin import ( + ClassificationReportMixin, + ClassificationSourceConfigMixin, +) +from datahub.ingestion.source.ge_profiling_config import GEProfilingConfig +from datahub.ingestion.source.state.stale_entity_removal_handler import ( + StaleEntityRemovalSourceReport, + StatefulIngestionConfigBase, +) +from datahub.ingestion.source.state.stateful_ingestion_base import ( + StatefulProfilingConfigMixin, +) +from datahub.ingestion.source_config.operation_config import is_profiling_enabled +from datahub.ingestion.source_report.ingestion_stage import IngestionStageReport +from datahub.utilities.perf_timer import PerfTimer +from datahub.utilities.stats_collections import TopKDict, int_top_k_dict + + +def flatten( + field_path: List[str], data: Any, truncate: bool = True +) -> Iterable[Tuple[str, Any]]: + if isinstance(data, dict): + for key, value in data.items(): + field_path.append(key) + yield from flatten(field_path, value) + if isinstance(value, dict) or isinstance(value, list): + del field_path[-1] + elif isinstance(data, list): + for value in data: + yield from flatten(field_path, value, False) + else: + yield ".".join(field_path), data + if len(field_path) > 0 and truncate: + del field_path[-1] + + +class CouchbaseDBConfig( + PlatformInstanceConfigMixin, + EnvConfigMixin, + StatefulIngestionConfigBase, + ClassificationSourceConfigMixin, + StatefulProfilingConfigMixin, +): + connect_string: str = Field( + default="couchbases://127.0.0.1", description="Couchbase connect string." + ) + username: str = Field(default="Administrator", description="Couchbase username.") + password: str = Field(default="password", description="Couchbase password.") + cluster_name: str = Field(default="cbdb", description="Couchbase cluster name.") + kv_timeout: Optional[PositiveInt] = Field(default=5, description="KV timeout.") + query_timeout: Optional[PositiveInt] = Field( + default=75, description="Query timeout." + ) + schema_sample_size: Optional[PositiveInt] = Field( + default=10000, description="Number of documents to sample." + ) + options: dict = Field( + default={}, description="Additional options to pass to `ClusterOptions()`." + ) + maxSchemaSize: Optional[PositiveInt] = Field( + default=300, description="Maximum number of fields to include in the schema." + ) + keyspace_pattern: AllowDenyPattern = Field( + default=AllowDenyPattern.allow_all(), + description="regex patterns for keyspace to filter in ingestion.", + ) + domain: Dict[str, AllowDenyPattern] = Field( + default=dict(), + description="regex patterns for keyspaces to filter to assign domain_key.", + ) + + # Profiling + profile_pattern: AllowDenyPattern = Field( + default=AllowDenyPattern.allow_all(), + description="Regex patterns for tables to profile", + ) + + profiling: GEProfilingConfig = Field( + default=GEProfilingConfig(), + description="Configuration for profiling", + ) + + def is_profiling_enabled(self) -> bool: + return self.profiling.enabled and is_profiling_enabled( + self.profiling.operation_config + ) + + +@dataclass +class CouchbaseDBSourceReport( + StaleEntityRemovalSourceReport, ClassificationReportMixin, IngestionStageReport +): + filtered: List[str] = field(default_factory=list) + documents_processed: int = 0 + keyspaces_profiled: int = 0 + collection_aggregate_timer: PerfTimer = field(default_factory=PerfTimer) + profiling_skipped_other: TopKDict[str, int] = field(default_factory=int_top_k_dict) + profiling_skipped_table_profile_pattern: TopKDict[str, int] = field( + default_factory=int_top_k_dict + ) + + def report_dropped(self, name: str) -> None: + self.filtered.append(name) + + def report_entity_profiled(self) -> None: + self.keyspaces_profiled += 1 + + +@dataclass +class CouchbaseEntity: + dataset: str + schema: dict + count: int diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_connect.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_connect.py new file mode 100644 index 00000000000000..df215272f41090 --- /dev/null +++ b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_connect.py @@ -0,0 +1,221 @@ +import logging +from datetime import timedelta +from typing import Iterable, List, Tuple + +from acouchbase.bucket import AsyncBucket +from acouchbase.cluster import AsyncCluster +from acouchbase.collection import AsyncCollection +from acouchbase.scope import AsyncScope +from couchbase.auth import PasswordAuthenticator +from couchbase.bucket import Bucket +from couchbase.cluster import Cluster +from couchbase.diagnostics import ServiceType +from couchbase.exceptions import BucketNotFoundException, InternalServerFailureException +from couchbase.management.buckets import BucketManager, BucketSettings +from couchbase.management.collections import CollectionManager, ScopeSpec +from couchbase.options import ( + ClusterOptions, + ClusterTimeoutOptions, + LockMode, + TLSVerifyMode, + WaitUntilReadyOptions, +) + +from datahub.ingestion.source.couchbase.retry import retry + +logger = logging.getLogger(__name__) + + +class CouchbaseConnect: + cluster: Cluster + cluster_async: AsyncCluster + bucket_manager: BucketManager + cb_connect_string: str + cluster_options: ClusterOptions + bucket_name: str + scope_name: str + collection_name: str + + def __init__( + self, + connect_string: str, + username: str, + password: str, + kv_timeout: float = 5, + query_timeout: float = 60, + ): + self.cb_connect_string = connect_string + + auth = PasswordAuthenticator(username, password) + timeouts = ClusterTimeoutOptions( + query_timeout=timedelta(seconds=query_timeout), + kv_timeout=timedelta(seconds=kv_timeout), + bootstrap_timeout=timedelta(seconds=kv_timeout * 2), + resolve_timeout=timedelta(seconds=kv_timeout), + connect_timeout=timedelta(seconds=kv_timeout), + management_timeout=timedelta(seconds=kv_timeout * 2), + ) + + self.cluster_options = ClusterOptions( + auth, + timeout_options=timeouts, + tls_verify=TLSVerifyMode.NO_VERIFY, + lockmode=LockMode.WAIT, + ) + + logger.debug( + f"couchbase: connect string = {connect_string} username = {username}" + ) + + @retry() + def connect(self) -> Cluster: + cluster = Cluster.connect(self.cb_connect_string, self.cluster_options) + cluster.wait_until_ready( + timedelta(seconds=10), + WaitUntilReadyOptions( + service_types=[ServiceType.KeyValue, ServiceType.Query] + ), + ) + return cluster + + @retry(always_raise_list=(BucketNotFoundException,)) + def bucket(self, cluster: Cluster, name: str) -> Bucket: + if name is None: + raise TypeError("bucket name can not be None") + logger.debug(f"bucket: connect {name}") + return cluster.bucket(name) + + @retry() + async def connect_async(self) -> AsyncCluster: + cluster = await AsyncCluster.connect( + self.cb_connect_string, self.cluster_options + ) + await cluster.on_connect() + await cluster.wait_until_ready( + timedelta(seconds=10), + WaitUntilReadyOptions( + service_types=[ServiceType.KeyValue, ServiceType.Query] + ), + ) + return cluster + + @retry(always_raise_list=(BucketNotFoundException,)) + async def bucket_async(self, cluster: AsyncCluster, name: str) -> AsyncBucket: + if name is None: + raise TypeError("bucket name can not be None") + logger.debug(f"bucket: connect async {name}") + bucket = cluster.bucket(name) + await bucket.on_connect() + return bucket + + def cluster_init(self): + self.cluster = self.connect() + self.bucket_manager = self.cluster.buckets() + + async def cluster_init_async(self): + self.cluster_async = await self.connect_async() + + async def connect_keyspace_async( + self, keyspace: str + ) -> Tuple[AsyncScope, AsyncCollection]: + if not self.cluster_async: + raise RuntimeError("cluster is not initialized") + + keyspace_vector: List[str] = keyspace.split(".") + if len(keyspace_vector) != 3: + raise ValueError( + f"keyspace: invalid format: {keyspace}. Expected format is .." + ) + + self.bucket_name = keyspace_vector[0] + self.scope_name = keyspace_vector[1] + self.collection_name = keyspace_vector[2] + + bucket = await self.bucket_async(self.cluster_async, self.bucket_name) + scope = bucket.scope(self.scope_name) + collection = scope.collection(self.collection_name) + return scope, collection + + def bucket_list(self) -> List[str]: + if not self.bucket_manager: + raise RuntimeError("cluster is not initialized") + buckets: List[BucketSettings] = self.bucket_manager.get_all_buckets() + return [b.name for b in buckets] + + def scope_list(self, bucket_name: str) -> List[str]: + if not self.cluster: + raise RuntimeError("cluster is not initialized") + bucket = self.bucket(self.cluster, bucket_name) + collection_manager: CollectionManager = bucket.collections() + scopes: Iterable[ScopeSpec] = collection_manager.get_all_scopes() + return [scope.name for scope in scopes] + + def collection_list(self, bucket_name: str, scope_name: str) -> List[str]: + if not self.cluster: + raise RuntimeError("cluster is not initialized") + bucket = self.bucket(self.cluster, bucket_name) + collection_manager: CollectionManager = bucket.collections() + scopes: Iterable[ScopeSpec] = collection_manager.get_all_scopes() + scope = next((scope for scope in scopes if scope.name == scope_name), None) + if scope is None: + raise ValueError(f"Scope {scope_name} not found in bucket {bucket_name}") + return [c.name for c in scope.collections] + + @staticmethod + def expand_keyspace(keyspace: str) -> Tuple[str, ...]: + keyspace_vector: List[str] = keyspace.split(".") + if len(keyspace_vector) != 3: + raise ValueError( + f"keyspace: invalid format: {keyspace}. Expected format is .." + ) + return tuple(keyspace_vector) + + @retry(factor=0.05) + def collection_count(self, keyspace: str) -> int: + if not self.cluster: + raise RuntimeError("cluster is not initialized") + + bucket_name, scope_name, collection_name = self.expand_keyspace(keyspace) + + bucket = self.bucket(self.cluster, bucket_name) + scope = bucket.scope(scope_name) + + query = f"SELECT COUNT(*) AS count FROM {collection_name}" + + results = scope.query(query) + for row in results: + return int(row.get("count")) + return 0 + + def bucket_info(self, bucket_name: str) -> BucketSettings: + if not self.bucket_manager: + raise RuntimeError("cluster is not initialized") + settings: BucketSettings = self.bucket_manager.get_bucket(bucket_name) + return settings + + @retry(factor=0.05) + def collection_infer( + self, sample_size: int, sample_values: int, keyspace: str + ) -> dict: + if not self.cluster: + raise RuntimeError("cluster is not initialized") + + schemas = [] + + bucket_name, scope_name, collection_name = self.expand_keyspace(keyspace) + + bucket = self.bucket(self.cluster, bucket_name) + scope = bucket.scope(scope_name) + + query = f'INFER {collection_name} WITH {{"sample_size": {sample_size}, "num_sample_values": {sample_values}, "similarity_metric": 0.0}}' + + results = scope.query(query) + try: + for row in results: + schemas.extend(row) + return schemas[0] + except InternalServerFailureException as e: + if e.error_code == 7014: + return {} + else: + raise e diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_data_reader.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_data_reader.py new file mode 100644 index 00000000000000..4d2daf8d4189cc --- /dev/null +++ b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_data_reader.py @@ -0,0 +1,62 @@ +from __future__ import annotations + +import asyncio +from collections import defaultdict +from typing import Any, Dict, List + +from datahub.ingestion.source.common.data_reader import DataReader +from datahub.ingestion.source.couchbase.couchbase_aggregate import CouchbaseAggregate +from datahub.ingestion.source.couchbase.couchbase_common import flatten +from datahub.ingestion.source.couchbase.couchbase_connect import CouchbaseConnect + +PAGE_SIZE = 100 + + +class CouchbaseCollectionItemsReader(DataReader): + """ + Couchbase Data Reader for use cases that can't use the SQL++ INFER query + """ + + @staticmethod + def create(client: CouchbaseConnect) -> CouchbaseCollectionItemsReader: + return CouchbaseCollectionItemsReader(client) + + def __init__(self, client: CouchbaseConnect) -> None: + # The lifecycle of this client is managed externally + self.client = client + + try: + self.loop = asyncio.get_running_loop() + except RuntimeError: + self.loop = asyncio.new_event_loop() + + async def get_documents(self, keyspace: str, sample_size: int) -> List[dict]: + documents = [] + aggregator = CouchbaseAggregate( + self.client, keyspace, max_sample_size=sample_size + ) + async for chunk in aggregator.get_documents(): + documents.extend(chunk) + + return documents + + def get_sample_data_for_table( + self, table_id: List[str], sample_size: int, **kwargs: Any + ) -> Dict[str, list]: + """ + For Couchbase, table_id should be in formation (bucket, scope, collection) + """ + column_values: Dict[str, list] = defaultdict(list) + keyspace = ".".join(table_id) + + documents = self.loop.run_until_complete( + self.get_documents(keyspace, sample_size) + ) + for document in documents: + for field, data in flatten([], document): + column_values[field].append(data) + + return column_values + + def close(self) -> None: + pass diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_kv_schema.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_kv_schema.py new file mode 100644 index 00000000000000..d1352ba45ca7ed --- /dev/null +++ b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_kv_schema.py @@ -0,0 +1,170 @@ +from collections import Counter +from typing import Any, Counter as CounterType, Dict, Iterable, List, Tuple, Union + +from typing_extensions import TypedDict + + +class BasicSchemaDescription(TypedDict): + types: CounterType[Union[type, str]] # field types and times seen + count: int # times the field was seen + + +class SchemaDescription(BasicSchemaDescription): + delimited_name: str # collapsed field name + # we use 'mixed' to denote mixed types, so we need a str here + type: Union[type, str] # collapsed type + nullable: bool # if field is ever missing + + +class CouchbaseFieldData: + types: Tuple[str, ...] = () + samples: List[Any] = [] + + +def json_schema( + schema: dict, + upper_key: Union[str, None] = None, + samples: Union[List[Any], None] = None, +) -> Union[Dict[str, Any], List[Any], CouchbaseFieldData]: + def process_samples(_key: Union[str, None], _samples: List[Any]) -> List[Any]: + if len(_samples) > 0 and type(_samples[0]) is list: + if type(_samples[0][0]) is dict and _key is not None: + subset = [] + for array in _samples: + for item in array: + if item.get(_key): + subset.append(item[_key]) + return subset + subset = [] + for array in _samples: + subset.extend(array) + return subset + return _samples + + if schema.get("type") == "object": + if upper_key is None: + return json_schema(schema["properties"], samples=samples) + else: + return {upper_key: json_schema(schema["properties"], samples=samples)} + + elif schema.get("type") == "array": + if upper_key is None: + return [json_schema(schema["items"], samples=schema.get("samples"))] + else: + return { + upper_key: [json_schema(schema["items"], samples=schema.get("samples"))] + } + + elif schema.get("type"): + field_data_ = CouchbaseFieldData() + if isinstance(schema.get("type"), list): + types_: List[str] = schema.get("type", []) + for type_ in types_: + field_data_.types += (type_,) + else: + _type_string: str = str(schema.get("type")) + field_data_.types += (_type_string,) + field_data_.samples = ( + process_samples(upper_key, samples) + if samples + else process_samples(upper_key, schema.get("samples", [])) + ) + if upper_key is None: + return field_data_ + else: + return {upper_key: field_data_} + + else: + result: Dict[str, Any] = {} + for key, value in schema.items(): + struct: Any = json_schema(value, key, samples=samples) + if isinstance(struct, dict): + result.update(struct) + return result + + +def flatten( + path: List[str], data: Any, truncate: bool = True +) -> Iterable[Tuple[Tuple[str, ...], CouchbaseFieldData]]: + if isinstance(data, dict): + if path and truncate: + field_data_ = CouchbaseFieldData() + field_data_.types = ("object",) + yield tuple(path), field_data_ + for key, value in data.items(): + path.append(key) + yield from flatten(path, value) + if isinstance(value, dict) or isinstance(value, list): + del path[-1] + + elif isinstance(data, list): + if path and not isinstance(data[0], CouchbaseFieldData): + field_data_ = CouchbaseFieldData() + field_data_.types = ("array",) + yield tuple(path), field_data_ + else: + data[0].types += ("array",) + for value in data: + yield from flatten(path, value, False) + + else: + yield tuple(path), data + if len(path) > 0 and truncate: + del path[-1] + + +def discard(_tuple: Tuple[str, ...], element: str) -> Tuple[str, ...]: + if element in _tuple: + _list = list(_tuple) + _list.remove(element) + _tuple = tuple(_list) + return _tuple + return _tuple + + +def construct_schema( + collection: Dict[str, Any], +) -> Dict[Tuple[str, ...], SchemaDescription]: + """ + Construct JSON schema. + + For each field (represented as a tuple to handle nested items), reports the following: + - `types`: Python types of field values + - `count`: Number of times the field was encountered + - `type`: type of the field if `types` is just a single value, otherwise `mixed` + - `nullable`: if field is ever null/missing + - `delimited_name`: name of the field, joined by a given delimiter + + Parameters + ---------- + collection: + the JSON schema of the collection. + """ + + extended_schema: Dict[Tuple[str, ...], SchemaDescription] = {} + + parsed = json_schema(collection) + + field_path: Tuple[str, ...] + field_data: CouchbaseFieldData + for field_path, field_data in flatten([], parsed): + field_type: Union[str, type] = "mixed" + + if len(field_data.types) == 1: + field_type = next(iter(field_data.types)) + elif len(field_data.types) > 1 and "array" in field_data.types: + field_data.types = discard(field_data.types, "array") + if len(field_data.types) == 1: + field_type = next(iter(field_data.types)) + is_nullable = "null" in field_data.types + field_extended: SchemaDescription = { + "types": Counter(field_data.types), + "count": len(field_path), + "nullable": is_nullable, + "delimited_name": ".".join(field_path), + "type": field_type, + } + + extended_schema[field_path] = field_extended + + return extended_schema diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_main.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_main.py new file mode 100644 index 00000000000000..02450df3f3c8bb --- /dev/null +++ b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_main.py @@ -0,0 +1,441 @@ +import logging +from dataclasses import dataclass +from typing import Dict, Iterable, List, Optional, Type, Union, ValuesView + +from couchbase.cluster import Cluster +from couchbase.management.buckets import BucketSettings + +from datahub.emitter.mce_builder import ( + make_data_platform_urn, + make_dataplatform_instance_urn, + make_dataset_urn_with_platform_instance, + make_domain_urn, +) +from datahub.emitter.mcp import MetadataChangeProposalWrapper +from datahub.emitter.mcp_builder import ( + ContainerKey, + add_dataset_to_container, + add_domain_to_entity_wu, + gen_containers, +) +from datahub.ingestion.api.common import PipelineContext +from datahub.ingestion.api.decorators import ( + SourceCapability, + SupportStatus, + capability, + config_class, + platform_name, + support_status, +) +from datahub.ingestion.api.source import MetadataWorkUnitProcessor +from datahub.ingestion.api.workunit import MetadataWorkUnit +from datahub.ingestion.glossary.classification_mixin import ( + ClassificationHandler, + classification_workunit_processor, +) +from datahub.ingestion.source.common.subtypes import DatasetContainerSubTypes +from datahub.ingestion.source.couchbase.couchbase_common import ( + CouchbaseDBConfig, + CouchbaseDBSourceReport, +) +from datahub.ingestion.source.couchbase.couchbase_connect import CouchbaseConnect +from datahub.ingestion.source.couchbase.couchbase_kv_schema import construct_schema +from datahub.ingestion.source.couchbase.couchbase_profiling import CouchbaseProfiler +from datahub.ingestion.source.couchbase.couchbase_schema_reader import ( + CouchbaseCollectionItemsReader, +) +from datahub.ingestion.source.schema_inference.object import SchemaDescription +from datahub.ingestion.source.state.stale_entity_removal_handler import ( + StaleEntityRemovalHandler, +) +from datahub.ingestion.source.state.stateful_ingestion_base import ( + StatefulIngestionSourceBase, +) +from datahub.metadata.schema_classes import ( + ArrayTypeClass, + BooleanTypeClass, + DataPlatformInstanceClass, + DatasetPropertiesClass, + NullTypeClass, + NumberTypeClass, + RecordTypeClass, + SchemaFieldClass as SchemaField, + SchemaFieldDataTypeClass as SchemaFieldDataType, + SchemalessClass, + SchemaMetadataClass as SchemaMetadata, + StringTypeClass, + UnionTypeClass, +) +from datahub.utilities.registries.domain_registry import DomainRegistry + +logger = logging.getLogger(__name__) + + +# map Python types to DataHub classes +_field_type_mapping: Dict[str, Type] = { + "array": ArrayTypeClass, + "boolean": BooleanTypeClass, + "null": NullTypeClass, + "number": NumberTypeClass, + "string": StringTypeClass, + "object": RecordTypeClass, + "mixed": UnionTypeClass, +} + + +# map Python types to DataHub classes +_field_type_reverse_mapping: Dict[Union[Type, str], str] = { + list: "array", + bool: "boolean", + type(None): "null", + int: "number", + float: "number", + str: "string", + dict: "object", + "mixed": "mixed", +} + + +class KeyspaceKey(ContainerKey): + keyspace: str + + +@platform_name("Couchbase") +@config_class(CouchbaseDBConfig) +@support_status(SupportStatus.INCUBATING) +@capability( + SourceCapability.PLATFORM_INSTANCE, + "The platform_instance is derived from the configured cluster name", +) +@capability(SourceCapability.SCHEMA_METADATA, "Enabled by default") +@capability(SourceCapability.CONTAINERS, "Enabled by default") +@capability( + SourceCapability.CLASSIFICATION, + "Optionally enabled via `classification.enabled`", + supported=True, +) +@capability( + SourceCapability.DATA_PROFILING, + "Optionally enabled via configuration `profiling.enabled`", +) +@capability(SourceCapability.DOMAINS, "Supported via the `domain` config field") +@dataclass +class CouchbaseDBSource(StatefulIngestionSourceBase): + """ + This plugin extracts the following: + + - Buckets (databases), scopes and collections + - Schemas for each collection (via schema inference) + + The plugin will sample 10,000 documents by default. Use the setting `schema_sample_size` to define how many documents will be sampled per collection. + + """ + + config: CouchbaseDBConfig + report: CouchbaseDBSourceReport + couchbase_cluster: Cluster + platform: str = "couchbase" + + def __init__(self, ctx: PipelineContext, config: CouchbaseDBConfig): + super().__init__(config, ctx) + self.config = config + self.report = CouchbaseDBSourceReport() + self.classification_handler = ClassificationHandler(self.config, self.report) + + if self.config.domain: + self.domain_registry = DomainRegistry( + cached_domains=[domain_id for domain_id in self.config.domain], + graph=self.ctx.graph, + ) + + query_timeout = float(self.config.query_timeout or 75) + kv_timeout = float(self.config.kv_timeout or 5) + + self.couchbase_connect = CouchbaseConnect( + self.config.connect_string, + self.config.username, + self.config.password, + kv_timeout, + query_timeout, + ) + self.couchbase_connect.cluster_init() + self.couchbase_cluster = self.couchbase_connect.connect() + + self.profiler = CouchbaseProfiler( + self.config, self.report, self.couchbase_connect + ) + + @classmethod + def create(cls, config_dict: dict, ctx: PipelineContext) -> "CouchbaseDBSource": + config = CouchbaseDBConfig.parse_obj(config_dict) + return cls(ctx, config) + + def get_workunit_processors(self) -> List[Optional[MetadataWorkUnitProcessor]]: + return [ + *super().get_workunit_processors(), + StaleEntityRemovalHandler.create( + self, self.config, self.ctx + ).workunit_processor, + ] + + def get_field_type( + self, field_type: Union[Type, str], collection_name: str + ) -> SchemaFieldDataType: + type_class: Optional[Type] + if isinstance(field_type, str): + type_class = _field_type_mapping.get(field_type) + else: + type_class = field_type + + if type_class is None: + self.report.warning( + message="Unrecognized column type found", + context=f"Collection: {collection_name}, field type {field_type}", + ) + type_class = NullTypeClass + + return SchemaFieldDataType(type=type_class()) + + def get_field_type_string( + self, field_type: Union[Type, str], collection_name: str + ) -> str: + type_class: Optional[str] + if isinstance(field_type, str): + type_class = field_type + else: + type_class = _field_type_reverse_mapping.get(field_type) + + if type_class is None: + self.report.warning( + message="Unrecognized column type found", + context=f"Collection: {collection_name}, field type {field_type}", + ) + type_class = "null" + + return type_class + + def get_workunits_internal(self) -> Iterable[MetadataWorkUnit]: + keyspaces: List[str] = [] + bucket_names: List[str] = self.couchbase_connect.bucket_list() + + for bucket_name in sorted(bucket_names): + bucket_settings: BucketSettings = self.couchbase_connect.bucket_info( + bucket_name + ) + scope_names: List[str] = self.couchbase_connect.scope_list(bucket_name) + + for scope_name in sorted(scope_names): + if scope_name == "_system": + continue + collection_names: List[str] = self.couchbase_connect.collection_list( + bucket_name, scope_name + ) + + for collection_name in sorted(collection_names): + dataset_name = f"{bucket_name}.{scope_name}.{collection_name}" + if not self.config.keyspace_pattern.allowed(dataset_name): + self.report.report_dropped(dataset_name) + continue + + collection_count: int = self.couchbase_connect.collection_count( + dataset_name + ) + if collection_count == 0: + self.report.report_dropped(dataset_name) + continue + + yield from self._generate_keyspace_container( + dataset_name, bucket_settings + ) + + value_sample_size: int = self.config.classification.sample_size + schema_sample_size: int = self.config.schema_sample_size or 10000 + schema: dict = self.couchbase_connect.collection_infer( + schema_sample_size, value_sample_size, dataset_name + ) + keyspaces.append(dataset_name) + + table_wu_generator = self.process_keyspace( + dataset_name, schema, collection_count + ) + + data_reader = CouchbaseCollectionItemsReader.create(schema) + + yield from classification_workunit_processor( + table_wu_generator, + self.classification_handler, + data_reader, + [bucket_name, scope_name, collection_name], + ) + + # Profiling + if self.config.is_profiling_enabled(): + yield from self.profiler.get_workunits(keyspaces) + + def process_keyspace( + self, dataset_name: str, schema: dict, doc_count: int + ) -> Iterable[MetadataWorkUnit]: + platform_instance = self.config.cluster_name + + dataset_urn = make_dataset_urn_with_platform_instance( + platform=self.platform, + platform_instance=platform_instance, + name=dataset_name, + ) + dataset_properties = DatasetPropertiesClass( + tags=[], + customProperties={ + "collection.totalItems": str(doc_count), + }, + ) + + schema_metadata = self.construct_schema_metadata( + keyspace=dataset_name, + schema=schema, + dataset_urn=dataset_urn, + dataset_properties=dataset_properties, + ) + + yield MetadataChangeProposalWrapper( + entityUrn=dataset_urn, + aspect=schema_metadata, + ).as_workunit() + + yield MetadataChangeProposalWrapper( + entityUrn=dataset_urn, + aspect=dataset_properties, + ).as_workunit() + + yield from self._get_domain_wu( + dataset_name=dataset_name, + entity_urn=dataset_urn, + ) + + yield from add_dataset_to_container( + container_key=self._generate_keyspace_container_key(dataset_name), + dataset_urn=dataset_urn, + ) + + platform_instance_aspect = DataPlatformInstanceClass( + platform=make_data_platform_urn(self.platform), + instance=make_dataplatform_instance_urn(self.platform, platform_instance), + ) + + yield MetadataChangeProposalWrapper( + entityUrn=dataset_urn, + aspect=platform_instance_aspect, + ).as_workunit() + + def construct_schema_metadata( + self, + keyspace: str, + schema: dict, + dataset_urn: str, + dataset_properties: DatasetPropertiesClass, + ) -> SchemaMetadata: + collection_schema = construct_schema(schema) + + # initialize the schema for the collection + canonical_schema: List[SchemaField] = [] + max_schema_size = self.config.maxSchemaSize + collection_schema_size = len(collection_schema.values()) + collection_fields: Union[ + List[SchemaDescription], ValuesView[SchemaDescription] + ] = collection_schema.values() + assert max_schema_size is not None + if collection_schema_size > max_schema_size: + # downsample the schema, using frequency as the sort key + self.report.report_warning( + title="Too many schema fields", + message=f"Downsampling the collection schema because it has too many schema fields. Configured threshold is {max_schema_size}", + context=f"Schema Size: {collection_schema_size}, Collection: {dataset_urn}", + ) + # Add this information to the custom properties so user can know they are looking at downsampled schema + dataset_properties.customProperties["schema.downsampled"] = "True" + dataset_properties.customProperties["schema.totalFields"] = ( + f"{collection_schema_size}" + ) + + logger.debug(f"Size of collection fields = {len(collection_fields)}") + # append each schema field (sort so output is consistent) + for schema_field in sorted( + collection_fields, + key=lambda x: ( + -x["count"], + x["delimited_name"], + ), # Negate `count` for descending order, `delimited_name` stays the same for ascending + )[0:max_schema_size]: + field = SchemaField( + fieldPath=schema_field["delimited_name"], + nativeDataType=self.get_field_type_string( + schema_field["type"], dataset_urn + ), + type=self.get_field_type(schema_field["type"], dataset_urn), + description=None, + nullable=schema_field["nullable"], + recursive=False, + ) + canonical_schema.append(field) + + # create schema metadata object for collection + return SchemaMetadata( + schemaName=keyspace, + platform=f"urn:li:dataPlatform:{self.platform}", + version=0, + hash="", + platformSchema=SchemalessClass(), + fields=canonical_schema, + ) + + def get_report(self) -> CouchbaseDBSourceReport: + return self.report + + def _get_domain_wu( + self, dataset_name: str, entity_urn: str + ) -> Iterable[MetadataWorkUnit]: + domain_urn = None + for domain, pattern in self.config.domain.items(): + if pattern.allowed(dataset_name): + domain_urn = make_domain_urn( + self.domain_registry.get_domain_urn(domain) + ) + break + + if domain_urn: + yield from add_domain_to_entity_wu( + entity_urn=entity_urn, + domain_urn=domain_urn, + ) + + def _generate_keyspace_container( + self, + keyspace: str, + bucket_settings: BucketSettings, + ) -> Iterable[MetadataWorkUnit]: + yield from gen_containers( + container_key=self._generate_keyspace_container_key(keyspace), + name=keyspace, + qualified_name=keyspace, + extra_properties={ + "bucket_type": str(bucket_settings.bucket_type.name.lower()), + "bucket_storage_backend": str( + bucket_settings.storage_backend.name.lower() + ), + "bucket_quota": str(bucket_settings.get("ram_quota_mb")), + "bucket_max_expiry": str(bucket_settings.max_expiry.seconds), + "bucket_num_replicas": str(bucket_settings.get("num_replicas")), + }, + sub_types=[DatasetContainerSubTypes.KEYSPACE], + ) + + def _generate_keyspace_container_key(self, keyspace: str) -> ContainerKey: + return KeyspaceKey( + keyspace=keyspace, + platform=self.platform, + instance=self.config.platform_instance, + env=self.config.env, + ) + + def close(self): + self.couchbase_cluster.close() + super().close() diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_profiling.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_profiling.py new file mode 100644 index 00000000000000..fd35f525e5f553 --- /dev/null +++ b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_profiling.py @@ -0,0 +1,337 @@ +import asyncio +import logging +import time +from collections import defaultdict +from dataclasses import dataclass, field +from typing import Any, Dict, Iterable, List, Optional, Union + +import numpy as np + +from datahub.emitter.mce_builder import make_dataset_urn_with_platform_instance +from datahub.emitter.mcp import MetadataChangeProposalWrapper +from datahub.ingestion.api.workunit import MetadataWorkUnit +from datahub.ingestion.source.couchbase.couchbase_aggregate import CouchbaseAggregate +from datahub.ingestion.source.couchbase.couchbase_common import ( + CouchbaseDBConfig, + CouchbaseDBSourceReport, + flatten, +) +from datahub.ingestion.source.couchbase.couchbase_connect import CouchbaseConnect +from datahub.ingestion.source.couchbase.couchbase_schema_reader import ( + CouchbaseCollectionItemsReader, +) +from datahub.metadata.schema_classes import ( + DatasetFieldProfileClass, + DatasetProfileClass, + QuantileClass, +) + +logger = logging.getLogger(__name__) + + +@dataclass +class ColumnMetric: + col_type: Union[str, None] = None + values: List[Any] = field(default_factory=list) + null_count: int = 0 + total_count: int = 0 + distinct_count: Optional[int] = None + min: Optional[Any] = None + max: Optional[Any] = None + mean: Optional[float] = None + stdev: Optional[float] = None + median: Optional[float] = None + quantiles: Optional[List[float]] = None + sample_values: Optional[Any] = None + + +@dataclass +class ProfileData: + row_count: Optional[int] = 0 + column_count: Optional[int] = 0 + column_metrics: Dict[str, ColumnMetric] = field(default_factory=dict) + + +class CouchbaseProfiler: + config: CouchbaseDBConfig + report: CouchbaseDBSourceReport + client: CouchbaseConnect + + def __init__( + self, + config: CouchbaseDBConfig, + report: CouchbaseDBSourceReport, + client: CouchbaseConnect, + ) -> None: + self.config = config + self.report = report + self.client = client + + if self.config.profiling.use_sampling: + self.sample_size = self.config.profiling.sample_size + else: + self.sample_size = 0 + + self.field_sample_count = self.config.profiling.field_sample_values_limit + + if self.config.profiling.max_number_of_fields_to_profile: + self.sample_fields = self.config.profiling.max_number_of_fields_to_profile + else: + self.sample_fields = 0 + + try: + self.loop = asyncio.get_running_loop() + except RuntimeError: + self.loop = asyncio.new_event_loop() + + def get_workunits(self, datasets: List[str]) -> Iterable[MetadataWorkUnit]: + logger.info(f"Profiling {len(datasets)} keyspaces") + for keyspace in datasets: + logger.info(f"Profiling Keyspace {keyspace}") + + try: + yield from self.generate_profile(keyspace) + except Exception as exc: + self.report.profiling_skipped_other[keyspace] += 1 + self.report.failure( + message="Failed to profile keyspace", + context=f"{keyspace}", + exc=exc, + ) + + def generate_profile(self, keyspace: str) -> Iterable[MetadataWorkUnit]: + dataset_urn = make_dataset_urn_with_platform_instance( + platform="couchbase", + name=keyspace, + env=self.config.env, + platform_instance=self.config.cluster_name, + ) + + if ( + not self.config.profile_pattern.allowed(keyspace) + and self.config.profiling.report_dropped_profiles + ): + self.report.profiling_skipped_table_profile_pattern[keyspace] += 1 + logger.info(f"Profiling not allowed for Keyspace {keyspace}") + return + + try: + profile_data = self.profile_table(keyspace) + except Exception as exc: + self.report.warning( + message="Profiling Failed", + context=f"{keyspace}", + exc=exc, + ) + return + + profile_aspect = self.populate_profile_aspect(profile_data) + + if profile_aspect: + self.report.report_entity_profiled() + mcp = MetadataChangeProposalWrapper( + entityUrn=dataset_urn, aspect=profile_aspect + ) + yield mcp.as_workunit() + + def populate_profile_aspect(self, profile_data: ProfileData) -> DatasetProfileClass: + field_profiles = [ + self._create_field_profile(column_name, column_metrics) + for column_name, column_metrics in profile_data.column_metrics.items() + ] + return DatasetProfileClass( + timestampMillis=round(time.time() * 1000), + rowCount=profile_data.row_count, + columnCount=profile_data.column_count, + fieldProfiles=field_profiles, + ) + + @staticmethod + def _create_field_profile( + field_name: str, field_stats: ColumnMetric + ) -> DatasetFieldProfileClass: + quantiles = field_stats.quantiles + return DatasetFieldProfileClass( + fieldPath=field_name, + uniqueCount=field_stats.distinct_count, + nullCount=field_stats.null_count, + min=str(field_stats.min) if field_stats.min else None, + max=str(field_stats.max) if field_stats.max else None, + mean=str(field_stats.mean) if field_stats.mean else None, + median=str(field_stats.median) if field_stats.median else None, + stdev=str(field_stats.stdev) if field_stats.stdev else None, + quantiles=[ + QuantileClass(quantile=str(0.25), value=str(quantiles[0])), + QuantileClass(quantile=str(0.75), value=str(quantiles[1])), + ] + if quantiles + else None, + sampleValues=field_stats.sample_values + if field_stats.sample_values + else None, + ) + + def profile_table(self, keyspace: str) -> ProfileData: + profile_data = ProfileData() + + if not self.config.profiling.profile_table_level_only: + return self.loop.run_until_complete( + self._collect_column_data(keyspace, profile_data) + ) + else: + return self._collect_keyspace_data(keyspace, profile_data) + + def _collect_keyspace_data( + self, keyspace: str, profile_data: ProfileData + ) -> ProfileData: + collection_count: int = self.client.collection_count(keyspace) + + value_sample_size: int = self.config.classification.sample_size + schema_sample_size: int = ( + self.config.schema_sample_size if self.config.schema_sample_size else 10000 + ) + schema: dict = self.client.collection_infer( + schema_sample_size, value_sample_size, keyspace + ) + + schema_reader = CouchbaseCollectionItemsReader.create(schema) + schema_data: dict = schema_reader.get_sample_data_for_table( + keyspace.split("."), value_sample_size + ) + + profile_data.row_count = collection_count + profile_data.column_count = len(list(schema_data.keys())) + + return profile_data + + async def _collect_column_data( + self, keyspace: str, profile_data: ProfileData + ) -> ProfileData: + document_total_count: int = 0 + dropped_fields = set() + dropped_nested_fields = set() + + aggregator = CouchbaseAggregate( + self.client, keyspace, max_sample_size=self.sample_size + ) + + async for chunk in aggregator.get_documents(): + for document in chunk: + column_values: Dict[str, list] = defaultdict(list) + document_total_count += 1 + + for _field, data in flatten([], document): + column_values[_field].append(data) + + for n, (field_name, values) in enumerate(column_values.items()): + if 0 < self.sample_fields <= n: + dropped_fields.add(field_name) + continue + + if ( + not self.config.profiling.profile_nested_fields + and len(field_name.split(".")) > 1 + ): + dropped_nested_fields.add(field_name) + continue + + if field_name not in profile_data.column_metrics: + profile_data.column_metrics[field_name] = ColumnMetric() + if not profile_data.column_count: + profile_data.column_count = 1 + else: + profile_data.column_count += 1 + for value in values: + col_type = type(value).__name__ + if not profile_data.column_metrics[field_name].col_type: + profile_data.column_metrics[field_name].col_type = col_type + else: + if ( + profile_data.column_metrics[field_name].col_type + != col_type + ): + profile_data.column_metrics[ + field_name + ].col_type = "mixed" + profile_data.column_metrics[field_name].total_count += 1 + if value is None: + profile_data.column_metrics[field_name].null_count += 1 + else: + profile_data.column_metrics[field_name].values.append(value) + + if len(dropped_fields) > 0: + if self.config.profiling.report_dropped_profiles: + self.report.report_dropped( + f"The max_number_of_fields_to_profile={self.sample_fields} reached. Dropped fields for {keyspace} ({', '.join(sorted(dropped_fields))})" + ) + + if len(dropped_nested_fields) > 0: + if self.config.profiling.report_dropped_profiles: + self.report.report_dropped( + f"Dropped nested fields for {keyspace} ({', '.join(sorted(dropped_nested_fields))})" + ) + + profile_data.row_count = document_total_count + + return self._add_field_statistics(profile_data) + + def _add_field_statistics(self, profile_data: ProfileData) -> ProfileData: + for field_name, column_metrics in profile_data.column_metrics.items(): + if column_metrics.values: + try: + self._compute_field_statistics(column_metrics) + except Exception as exc: + self.report.warning( + message="Profiling Failed For Column Stats", + context=field_name, + exc=exc, + ) + raise exc + + return profile_data + + def _compute_field_statistics(self, column_metrics: ColumnMetric) -> None: + values = column_metrics.values + if not values: + return + + # ByDefault Null count is added + if not self.config.profiling.include_field_null_count: + column_metrics.null_count = 0 + + if self.config.profiling.include_field_distinct_count: + column_metrics.distinct_count = len(set(values)) + + if self.config.profiling.include_field_min_value: + column_metrics.min = min(values) + + if self.config.profiling.include_field_max_value: + column_metrics.max = max(values) + + if values and self._is_numeric_type(column_metrics.col_type): + if self.config.profiling.include_field_mean_value: + column_metrics.mean = round(float(np.mean(values)), 2) + if self.config.profiling.include_field_stddev_value: + column_metrics.stdev = round(float(np.std(values)), 2) + if self.config.profiling.include_field_median_value: + column_metrics.median = round(float(np.median(values)), 2) + if self.config.profiling.include_field_quantiles: + column_metrics.quantiles = [ + float(np.percentile(values, 25)), + float(np.percentile(values, 75)), + ] + + if values and self.config.profiling.include_field_sample_values: + column_metrics.sample_values = [ + str(v) for v in values[: self.field_sample_count] + ] + + @staticmethod + def _is_numeric_type(data_type: Union[str, None]) -> bool: + if not data_type: + return False + else: + return data_type.lower() in [ + "int", + "float", + ] diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_schema_reader.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_schema_reader.py new file mode 100644 index 00000000000000..46eb642e48253b --- /dev/null +++ b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_schema_reader.py @@ -0,0 +1,33 @@ +from __future__ import annotations + +from collections import defaultdict +from typing import Any, Dict, List + +from datahub.ingestion.source.common.data_reader import DataReader +from datahub.ingestion.source.couchbase.couchbase_kv_schema import flatten, json_schema + +PAGE_SIZE = 100 + + +class CouchbaseCollectionItemsReader(DataReader): + @staticmethod + def create(schema: dict) -> CouchbaseCollectionItemsReader: + return CouchbaseCollectionItemsReader(schema) + + def __init__(self, schema: dict) -> None: + # The lifecycle of this client is managed externally + self.schema = schema + + def get_sample_data_for_table( + self, table_id: List[str], sample_size: int, **kwargs: Any + ) -> Dict[str, list]: + column_values: Dict[str, list] = defaultdict(list) + + parsed = json_schema(self.schema) + for field_path, field_data in flatten([], parsed): + column_values[".".join(field_path)].extend(field_data.samples) + + return column_values + + def close(self) -> None: + pass diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_sql.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_sql.py new file mode 100644 index 00000000000000..f78b4bf5d45c38 --- /dev/null +++ b/metadata-ingestion/src/datahub/ingestion/source/couchbase/couchbase_sql.py @@ -0,0 +1,4 @@ +## + +SELECT_DOC_IDS = "SELECT meta().id FROM {}" +SELECT_COLLECTION_COUNT = "SELECT COUNT(*) AS count FROM {}" diff --git a/metadata-ingestion/src/datahub/ingestion/source/couchbase/retry.py b/metadata-ingestion/src/datahub/ingestion/source/couchbase/retry.py new file mode 100644 index 00000000000000..6f3548dfebc348 --- /dev/null +++ b/metadata-ingestion/src/datahub/ingestion/source/couchbase/retry.py @@ -0,0 +1,89 @@ +import asyncio +import logging +import time +import traceback +from functools import wraps +from typing import Callable, Tuple, Type, Union + +logger = logging.getLogger(__name__) + + +def retry_inline(func, *args, retry_count=5, factor=0.02, **kwargs): + for retry_number in range(retry_count + 1): + try: + return func(*args, **kwargs) + except Exception as err: + if retry_number == retry_count: + logger.debug(f"{func.__name__} retry limit exceeded: {err}") + raise + logger.debug(f"{func.__name__} will retry, number {retry_number + 1}") + wait = factor + wait *= 2 ** (retry_number + 1) + time.sleep(wait) + + +def retry( + retry_count: int = 5, + factor: float = 0.02, + allow_list: Union[Tuple[Type[Exception], ...], None] = None, + always_raise_list: Union[Tuple[Type[Exception], ...], None] = None, +) -> Callable: + def retry_handler(func): + if not asyncio.iscoroutinefunction(func): + + @wraps(func) + def f_wrapper(*args, **kwargs): + for retry_number in range(retry_count + 1): + try: + return func(*args, **kwargs) + except Exception as err: + if always_raise_list and isinstance(err, always_raise_list): + raise + + if allow_list and not isinstance(err, allow_list): + raise + + if retry_number == retry_count: + logger.debug(f"{func.__name__} retry limit exceeded") + logger.debug(f"Error: {err}") + logger.debug(traceback.format_exc()) + raise + + logger.debug( + f"{func.__name__} will retry, number {retry_number + 1}" + ) + wait = factor + wait *= 2 ** (retry_number + 1) + time.sleep(wait) + + return f_wrapper + else: + + @wraps(func) + async def f_wrapper(*args, **kwargs): + for retry_number in range(retry_count + 1): + try: + return await func(*args, **kwargs) + except Exception as err: + if always_raise_list and isinstance(err, always_raise_list): + raise + + if allow_list and not isinstance(err, allow_list): + raise + + if retry_number == retry_count: + logger.debug(f"{func.__name__} retry limit exceeded") + logger.debug(f"Error: {err}") + logger.debug(traceback.format_exc()) + raise + + logger.debug( + f"{func.__name__} will retry, number {retry_number + 1}" + ) + wait = factor + wait *= 2 ** (retry_number + 1) + time.sleep(wait) + + return f_wrapper + + return retry_handler diff --git a/metadata-ingestion/tests/integration/couchbase/couchbase_mces_golden.json b/metadata-ingestion/tests/integration/couchbase/couchbase_mces_golden.json new file mode 100644 index 00000000000000..931f95956e79bf --- /dev/null +++ b/metadata-ingestion/tests/integration/couchbase/couchbase_mces_golden.json @@ -0,0 +1,930 @@ +[ +{ + "entityType": "container", + "entityUrn": "urn:li:container:4822a5fdbd864ec3d6e32dabcb2139c1", + "changeType": "UPSERT", + "aspectName": "containerProperties", + "aspect": { + "json": { + "customProperties": { + "platform": "couchbase", + "env": "PROD", + "keyspace": "data.data.customers", + "bucket_type": "couchbase", + "bucket_storage_backend": "couchstore", + "bucket_quota": "128", + "bucket_max_expiry": "0", + "bucket_num_replicas": "1" + }, + "name": "data.data.customers", + "qualifiedName": "data.data.customers", + "env": "PROD" + } + }, + "systemMetadata": { + "lastObserved": 1615443388097, + "runId": "couchbase-test", + "lastRunId": "no-run-id-provided" + } +}, +{ + "entityType": "container", + "entityUrn": "urn:li:container:4822a5fdbd864ec3d6e32dabcb2139c1", + "changeType": "UPSERT", + "aspectName": "status", + "aspect": { + "json": { + "removed": false + } + }, + "systemMetadata": { + "lastObserved": 1615443388097, + "runId": "couchbase-test", + "lastRunId": "no-run-id-provided" + } +}, +{ + "entityType": "container", + "entityUrn": "urn:li:container:4822a5fdbd864ec3d6e32dabcb2139c1", + "changeType": "UPSERT", + "aspectName": "dataPlatformInstance", + "aspect": { + "json": { + "platform": "urn:li:dataPlatform:couchbase" + } + }, + "systemMetadata": { + "lastObserved": 1615443388097, + "runId": "couchbase-test", + "lastRunId": "no-run-id-provided" + } +}, +{ + "entityType": "container", + "entityUrn": "urn:li:container:4822a5fdbd864ec3d6e32dabcb2139c1", + "changeType": "UPSERT", + "aspectName": "subTypes", + "aspect": { + "json": { + "typeNames": [ + "Keyspace" + ] + } + }, + "systemMetadata": { + "lastObserved": 1615443388097, + "runId": "couchbase-test", + "lastRunId": "no-run-id-provided" + } +}, +{ + "entityType": "container", + "entityUrn": "urn:li:container:4822a5fdbd864ec3d6e32dabcb2139c1", + "changeType": "UPSERT", + "aspectName": "browsePathsV2", + "aspect": { + "json": { + "path": [] + } + }, + "systemMetadata": { + "lastObserved": 1615443388097, + "runId": "couchbase-test", + "lastRunId": "no-run-id-provided" + } +}, +{ + "entityType": "dataset", + "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:couchbase,testdb.data.data.customers,PROD)", + "changeType": "UPSERT", + "aspectName": "schemaMetadata", + "aspect": { + "json": { + "schemaName": "data.data.customers", + "platform": "urn:li:dataPlatform:couchbase", + "version": 0, + "created": { + "time": 0, + "actor": "urn:li:corpuser:unknown" + }, + "lastModified": { + "time": 0, + "actor": "urn:li:corpuser:unknown" + }, + "hash": "", + "platformSchema": { + "com.linkedin.schema.Schemaless": {} + }, + "fields": [ + { + "fieldPath": "orders.id", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.NumberType": {} + } + }, + "nativeDataType": "number", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "orders.total", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.NumberType": {} + } + }, + "nativeDataType": "number", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "profile.cardNumber", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "glossaryTerms": { + "terms": [ + { + "urn": "urn:li:glossaryTerm:Credit_Debit_Card_Number" + } + ], + "auditStamp": { + "time": 1615443388097, + "actor": "urn:li:corpuser:datahub" + } + }, + "isPartOfKey": false + }, + { + "fieldPath": "profile.createdOn", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "profile.cvv", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "profile.expiresMonth", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "profile.expiresYear", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "profile.picture", + "nullable": true, + "type": { + "type": { + "com.linkedin.schema.UnionType": {} + } + }, + "nativeDataType": "mixed", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "address", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "glossaryTerms": { + "terms": [ + { + "urn": "urn:li:glossaryTerm:Street_Address" + } + ], + "auditStamp": { + "time": 1615443388097, + "actor": "urn:li:corpuser:datahub" + } + }, + "isPartOfKey": false + }, + { + "fieldPath": "balance", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.NumberType": {} + } + }, + "nativeDataType": "number", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "city", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "email", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "glossaryTerms": { + "terms": [ + { + "urn": "urn:li:glossaryTerm:Email_Address" + } + ], + "auditStamp": { + "time": 1615443388097, + "actor": "urn:li:corpuser:datahub" + } + }, + "isPartOfKey": false + }, + { + "fieldPath": "firstName", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "id", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.NumberType": {} + } + }, + "nativeDataType": "number", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "isActive", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.BooleanType": {} + } + }, + "nativeDataType": "boolean", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "lastName", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "orders", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.ArrayType": {} + } + }, + "nativeDataType": "array", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "phone", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "glossaryTerms": { + "terms": [ + { + "urn": "urn:li:glossaryTerm:Phone_Number" + } + ], + "auditStamp": { + "time": 1615443388097, + "actor": "urn:li:corpuser:datahub" + } + }, + "isPartOfKey": false + }, + { + "fieldPath": "profile", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.RecordType": {} + } + }, + "nativeDataType": "object", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "state", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "tags", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "uuid", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + }, + { + "fieldPath": "zip", + "nullable": false, + "type": { + "type": { + "com.linkedin.schema.StringType": {} + } + }, + "nativeDataType": "string", + "recursive": false, + "isPartOfKey": false + } + ] + } + }, + "systemMetadata": { + "lastObserved": 1615443388097, + "runId": "couchbase-test", + "lastRunId": "no-run-id-provided" + } +}, +{ + "entityType": "dataset", + "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:couchbase,testdb.data.data.customers,PROD)", + "changeType": "UPSERT", + "aspectName": "datasetProperties", + "aspect": { + "json": { + "customProperties": { + "collection.totalItems": "1000" + }, + "tags": [] + } + }, + "systemMetadata": { + "lastObserved": 1615443388097, + "runId": "couchbase-test", + "lastRunId": "no-run-id-provided" + } +}, +{ + "entityType": "dataset", + "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:couchbase,testdb.data.data.customers,PROD)", + "changeType": "UPSERT", + "aspectName": "container", + "aspect": { + "json": { + "container": "urn:li:container:4822a5fdbd864ec3d6e32dabcb2139c1" + } + }, + "systemMetadata": { + "lastObserved": 1615443388097, + "runId": "couchbase-test", + "lastRunId": "no-run-id-provided" + } +}, +{ + "entityType": "dataset", + "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:couchbase,testdb.data.data.customers,PROD)", + "changeType": "UPSERT", + "aspectName": "dataPlatformInstance", + "aspect": { + "json": { + "platform": "urn:li:dataPlatform:couchbase", + "instance": "urn:li:dataPlatformInstance:(urn:li:dataPlatform:couchbase,testdb)" + } + }, + "systemMetadata": { + "lastObserved": 1615443388097, + "runId": "couchbase-test", + "lastRunId": "no-run-id-provided" + } +}, +{ + "entityType": "dataset", + "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:couchbase,testdb.data.data.customers,PROD)", + "changeType": "UPSERT", + "aspectName": "datasetProfile", + "aspect": { + "json": { + "timestampMillis": 1615443388098, + "partitionSpec": { + "partition": "FULL_TABLE_SNAPSHOT", + "type": "FULL_TABLE" + }, + "rowCount": 1000, + "columnCount": 21, + "fieldProfiles": [ + { + "fieldPath": "id", + "uniqueCount": 1000, + "nullCount": 0, + "min": "1", + "max": "1001", + "mean": "500.5", + "median": "500.5", + "stdev": "288.68", + "sampleValues": [ + "216", + "189", + "859", + "367", + "66" + ] + }, + { + "fieldPath": "uuid", + "uniqueCount": 1000, + "nullCount": 0, + "min": "001b3b27-50a6-4533-afbe-a5825389782b", + "max": "ff924934-1858-4d01-a2a3-29d9bd3aa366", + "sampleValues": [ + "e79d024b-7fc9-4375-88a6-1a61c18c61ff", + "56a0322e-8f11-4c29-a6a9-7feb4ce82556", + "f5083e44-72c4-4a2e-8410-36e82dc2ca89", + "6d3c3108-bdcb-4c8c-91c3-57ad5f35ab1d", + "73dd3909-2c14-4f59-a198-1e8c466c77f1" + ] + }, + { + "fieldPath": "firstName", + "uniqueCount": 145, + "nullCount": 0, + "min": "Aaron", + "max": "Zoey", + "sampleValues": [ + "Aubrey", + "Nora", + "Grayson", + "Amelia", + "Sophia" + ] + }, + { + "fieldPath": "lastName", + "uniqueCount": 175, + "nullCount": 0, + "min": "Adams", + "max": "Young", + "sampleValues": [ + "Ward", + "Martin", + "Robinson", + "Bennett", + "Nelson" + ] + }, + { + "fieldPath": "address", + "uniqueCount": 1000, + "nullCount": 0, + "min": "10032 Ash Court", + "max": "99953 Dogwood Avenue", + "sampleValues": [ + "33779 Myrtle Street", + "97912 Ash Street", + "51145 Cedar Boulevard", + "89159 Spruce Street", + "41502 Cypress Court" + ] + }, + { + "fieldPath": "city", + "uniqueCount": 852, + "nullCount": 0, + "min": "Aberdeen", + "max": "Zionsville", + "sampleValues": [ + "Rolfe", + "Pasadena", + "Medicine Lake", + "Maricopa", + "Alapaha" + ] + }, + { + "fieldPath": "state", + "uniqueCount": 49, + "nullCount": 0, + "min": "AK", + "max": "WY", + "sampleValues": [ + "MD", + "NH", + "MD", + "GA", + "MA" + ] + }, + { + "fieldPath": "zip", + "uniqueCount": 963, + "nullCount": 0, + "min": "01009", + "max": "99825", + "sampleValues": [ + "10580", + "55940", + "28479", + "88135", + "77006" + ] + }, + { + "fieldPath": "phone", + "uniqueCount": 999, + "nullCount": 0, + "min": "201-555-0668", + "max": "989-555-7724", + "sampleValues": [ + "660-555-3355", + "248-555-1505", + "267-555-9036", + "585-555-2506", + "850-555-6027" + ] + }, + { + "fieldPath": "email", + "uniqueCount": 970, + "nullCount": 0, + "min": "aaron.barnes@example.com", + "max": "zoey.young@example.com", + "sampleValues": [ + "michael.sanchez@example.com", + "jack.ward@example.com", + "alexander.bennett@example.com", + "hannah.evans@example.com", + "henry.turner@example.com" + ] + }, + { + "fieldPath": "isActive", + "uniqueCount": 2, + "nullCount": 0, + "max": "True", + "sampleValues": [ + "True", + "True", + "False", + "False", + "False" + ] + }, + { + "fieldPath": "balance", + "uniqueCount": 949, + "nullCount": 0, + "min": "10.01", + "max": "99.94", + "mean": "55.52", + "median": "56.59", + "stdev": "26.05", + "sampleValues": [ + "50.86", + "24.21", + "26.49", + "44.52", + "58.1" + ] + }, + { + "fieldPath": "profile.createdOn", + "uniqueCount": 1000, + "nullCount": 0, + "min": "2020-01-09T20:29:36Z", + "max": "2024-07-03T19:24:59Z", + "sampleValues": [ + "2021-04-04T07:13:13Z", + 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+ } ], + "tags" : [ "new", "gold" ] +} ] \ No newline at end of file diff --git a/metadata-ingestion/tests/integration/couchbase/docker-compose.yml b/metadata-ingestion/tests/integration/couchbase/docker-compose.yml new file mode 100644 index 00000000000000..5d9a60f1a528b1 --- /dev/null +++ b/metadata-ingestion/tests/integration/couchbase/docker-compose.yml @@ -0,0 +1,46 @@ +services: + couchbase: + image: couchbase/server + hostname: node + container_name: testdb + ports: + - "8091:8091/tcp" + - "8092:8092/tcp" + - "8093:8093/tcp" + - "8094:8094/tcp" + - "8095:8095/tcp" + - "8096:8096/tcp" + - "8097:8097/tcp" + - "18091:18091/tcp" + - "18092:18092/tcp" + - "18093:18093/tcp" + - "18094:18094/tcp" + - "18095:18095/tcp" + - "18096:18096/tcp" + - "18097:18097/tcp" + - "9102:9102/tcp" + - "11207:11207/tcp" + - "11210:11210/tcp" + networks: + cbnet1: + ipv4_address: 10.10.1.2 + healthcheck: + test: [ "CMD", "curl", "-f", "http://localhost:8091" ] + interval: 1m30s + timeout: 10s + retries: 3 + start_period: 10s + start_interval: 5s + volumes: + - ./scripts/entrypoint.sh:/entrypoint.sh + - ./data/customers.json:/import.json + entrypoint: ["/entrypoint.sh"] + command: ["couchbase-server"] +networks: + cbnet1: + ipam: + driver: default + config: + - subnet: 10.10.0.0/16 + ip_range: 10.10.1.0/24 + gateway: 10.10.1.1 diff --git a/metadata-ingestion/tests/integration/couchbase/scripts/entrypoint.sh b/metadata-ingestion/tests/integration/couchbase/scripts/entrypoint.sh new file mode 100755 index 00000000000000..1ea59f1be52796 --- /dev/null +++ b/metadata-ingestion/tests/integration/couchbase/scripts/entrypoint.sh @@ -0,0 +1,92 @@ +#!/bin/bash + +staticConfigFile=/opt/couchbase/etc/couchbase/static_config +restPortValue=8091 + +# see https://developer.couchbase.com/documentation/server/current/install/install-ports.html +function overridePort() { + portName=$1 + portNameUpper=$(echo $portName | awk '{print toupper($0)}') + portValue=${!portNameUpper} + + # only override port if value available AND not already contained in static_config + if [ "$portValue" != "" ]; then + if grep -Fq "{${portName}," ${staticConfigFile} + then + echo "Don't override port ${portName} because already available in $staticConfigFile" + else + echo "Override port '$portName' with value '$portValue'" + echo "{$portName, $portValue}." >> ${staticConfigFile} + + if [ ${portName} == "rest_port" ]; then + restPortValue=${portValue} + fi + fi + fi +} + +overridePort "rest_port" +overridePort "mccouch_port" +overridePort "memcached_port" +overridePort "query_port" +overridePort "ssl_query_port" +overridePort "fts_http_port" +overridePort "moxi_port" +overridePort "ssl_rest_port" +overridePort "ssl_capi_port" +overridePort "ssl_proxy_downstream_port" +overridePort "ssl_proxy_upstream_port" + +[[ "$1" == "couchbase-server" ]] && { + + if [ "$(whoami)" = "couchbase" ]; then + # Ensure that /opt/couchbase/var is owned by user 'couchbase' and + # is writable + if [ ! -w /opt/couchbase/var -o \ + $(find /opt/couchbase/var -maxdepth 0 -printf '%u') != "couchbase" ]; then + echo "/opt/couchbase/var is not owned and writable by UID 1000" + echo "Aborting as Couchbase Server will likely not run" + exit 1 + fi + fi + echo "Starting Couchbase Server -- Web UI available at http://:$restPortValue" + echo "and logs available in /opt/couchbase/var/lib/couchbase/logs" + runsvdir -P /etc/service & +} + +max_retries=5 +retry_count=0 + +while [ $retry_count -lt $max_retries ]; do + curl http://127.0.0.1:8091 > /dev/null 2>&1 + + if [ $? -eq 0 ]; then + echo "Server started" + break + else + echo "Retrying..." + retry_count=$((retry_count + 1)) + sleep 5 + fi +done + +if [ $retry_count -eq $max_retries ]; then + echo "Wait timeout after $max_retries retries. Exiting." +fi + +echo "Configuring Couchbase Server" + +/opt/couchbase/bin/couchbase-cli cluster-init -c 10.10.1.2 --cluster-username Administrator --cluster-password password --services data,query,index --cluster-ramsize 1024 + +sleep 2 +echo "Importing Test Data" +/opt/couchbase/bin/couchbase-cli bucket-create -c couchbase://127.0.0.1 --username Administrator --password password --bucket data --bucket-type couchbase --bucket-ramsize 128 +/opt/couchbase/bin/couchbase-cli collection-manage -c couchbase://127.0.0.1 --username Administrator --password password --bucket data --create-scope data +/opt/couchbase/bin/couchbase-cli collection-manage -c couchbase://127.0.0.1 --username Administrator --password password --bucket data --create-collection data.customers +sleep 2 +/opt/couchbase/bin/cbimport json --format list -c couchbase://127.0.0.1 -u Administrator -p password -b data --scope-collection-exp data.customers -d file:///import.json --generate-key '#UUID#' + +echo "The following output is now a tail of couchdb.log:" +tail -f /opt/couchbase/var/lib/couchbase/logs/couchdb.log & +childPID=$! +wait $childPID diff --git a/metadata-ingestion/tests/integration/couchbase/test_couchbase.py b/metadata-ingestion/tests/integration/couchbase/test_couchbase.py new file mode 100644 index 00000000000000..05864a2227c768 --- /dev/null +++ b/metadata-ingestion/tests/integration/couchbase/test_couchbase.py @@ -0,0 +1,155 @@ +import base64 +import json +import logging +import os +import shutil +import time +from pathlib import Path + +import pytest +import requests +from requests.adapters import HTTPAdapter +from requests.auth import AuthBase +from urllib3.util.retry import Retry + +from datahub.ingestion.glossary.classification_mixin import ClassificationConfig +from datahub.ingestion.glossary.classifier import DynamicTypedClassifierConfig +from datahub.ingestion.glossary.datahub_classifier import DataHubClassifierConfig +from datahub.ingestion.run.pipeline import Pipeline +from datahub.ingestion.source.couchbase.retry import retry +from tests.test_helpers import mce_helpers +from tests.test_helpers.docker_helpers import wait_for_port + +retries = Retry(total=5, backoff_factor=2, status_forcelist=[404, 405, 500, 503]) +adapter = HTTPAdapter(max_retries=retries) +session = requests.Session() +session.mount("http://", adapter) +session.mount("https://", adapter) + + +class BasicAuth(AuthBase): + def __init__(self, username, password): + self.username = username + self.password = password + + def __call__(self, r): + auth_hash = f"{self.username}:{self.password}" + auth_bytes = auth_hash.encode("ascii") + auth_encoded = base64.b64encode(auth_bytes) + request_headers = { + "Authorization": f"Basic {auth_encoded.decode('ascii')}", + } + r.headers.update(request_headers) + return r + + +def http_test_get(url: str, auth: BasicAuth) -> int: + response = session.get( + url, + verify=False, + timeout=15, + auth=auth, + ) + return response.status_code + + +def http_test_post(url: str, auth: BasicAuth, data: dict) -> dict: + response = session.post( + url, + verify=False, + timeout=15, + auth=auth, + data=data, + ) + assert response.status_code == 200 + return json.loads(response.text) + + +@pytest.mark.integration_batch_2 +def test_couchbase_ingest(docker_compose_runner, pytestconfig, tmp_path, mock_time): + test_resources_dir = pytestconfig.rootpath / "tests/integration/couchbase" + + with docker_compose_runner( + test_resources_dir / "docker-compose.yml", "couchbase" + ) as docker_services: + if "PYTEST_ENABLE_FILE_LOGGING" in os.environ: + log_file = os.path.join(Path.home(), "pytest.log") + file_handler = logging.FileHandler(log_file) + logging.getLogger().addHandler(file_handler) + + wait_for_port(docker_services, "testdb", 8093) + + result = http_test_get( + "http://127.0.0.1:8091/pools/default/buckets/data", + auth=BasicAuth("Administrator", "password"), + ) + assert result == 200 + + @retry(factor=1) + def collection_wait(): + response = http_test_post( + "http://127.0.0.1:8093/query/service", + auth=BasicAuth("Administrator", "password"), + data={"statement": "SELECT count(*) as count FROM data.data.customers"}, + ) + results = response.get("results", [{}]) + assert results[0].get("count", 0) == 1000 + + collection_wait() + + time.sleep(2) + + # Run the metadata ingestion pipeline. + pipeline = Pipeline.create( + { + "run_id": "couchbase-test", + "source": { + "type": "couchbase", + "config": { + "connect_string": "couchbases://127.0.0.1", + "username": "Administrator", + "password": "password", + "cluster_name": "testdb", + "profiling": { + "enabled": True, + "profile_nested_fields": True, + }, + "classification": ClassificationConfig( + enabled=True, + classifiers=[ + DynamicTypedClassifierConfig( + type="datahub", + config=DataHubClassifierConfig( + minimum_values_threshold=1, + ), + ) + ], + ), + }, + }, + "sink": { + "type": "file", + "config": { + "filename": f"{tmp_path}/couchbase_mces.json", + }, + }, + } + ) + pipeline.run() + pipeline.raise_from_status() + + if "PYTEST_SAVE_OUTPUT_FILE" in os.environ: + shutil.copyfile( + f"{tmp_path}/couchbase_mces.json", + os.path.join(Path.home(), "couchbase_mces.json"), + ) + else: + # Verify the output. + mce_helpers.check_golden_file( + pytestconfig, + output_path=tmp_path / "couchbase_mces.json", + golden_path=test_resources_dir / "couchbase_mces_golden.json", + ignore_paths=[ + r"root\[\d+\]\['aspect'\]\['json'\]\['fieldProfiles'\]\[\d+\]\['sampleValues'\]", + ], + ) diff --git a/metadata-ingestion/tests/unit/couchbase/data/customers.json b/metadata-ingestion/tests/unit/couchbase/data/customers.json new file mode 100644 index 00000000000000..8fda71b45479b6 --- /dev/null +++ b/metadata-ingestion/tests/unit/couchbase/data/customers.json @@ -0,0 +1,32001 @@ +[ { + "id" : 5, + "uuid" : "dbc9f30c-f6d8-4d8e-b530-4678bb16ed21", + "firstName" : "Nora", + "lastName" : "Watson", + "address" : "24109 Ash Court", + "city" : "Camarillo", + "state" : 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"profile" : { + "createdOn" : "2024-03-24T23:31:32Z", + "picture" : null, + "cardNumber" : "5397786264234539", + "expiresMonth" : "05", + "expiresYear" : "2028", + "cvv" : "732" + }, + "orders" : [ { + "id" : 1, + "total" : 57.28 + }, { + "id" : 2, + "total" : 91.18 + }, { + "id" : 3, + "total" : 51.05 + } ], + "tags" : [ "new", "bronze" ] +}, { + "id" : 1001, + "uuid" : "820a6121-f51c-41ba-92e1-814943b35651", + "firstName" : "Jackson", + "lastName" : "Powell", + "address" : "53899 Holly Street", + "city" : "Chehalis", + "state" : "NY", + "zip" : "76537", + "phone" : "320-555-4343", + "email" : "mia.green@example.com", + "isActive" : true, + "balance" : 79.57, + "profile" : { + "createdOn" : "2020-04-08T07:16:46Z", + "picture" : "/v1/892404/200/300/image.jpg", + "cardNumber" : "5426615563558881", + "expiresMonth" : "04", + "expiresYear" : "2027", + "cvv" : "348" + }, + "orders" : [ { + "id" : 1, + "total" : 18.55 + }, { + "id" : 2, + "total" : 62.75 + }, { + "id" : 3, + "total" : 73.99 + } ], + "tags" : [ "new", "gold" ] +} ] \ No newline at end of file diff --git a/metadata-ingestion/tests/unit/couchbase/docker-compose.yml b/metadata-ingestion/tests/unit/couchbase/docker-compose.yml new file mode 100644 index 00000000000000..5d9a60f1a528b1 --- /dev/null +++ b/metadata-ingestion/tests/unit/couchbase/docker-compose.yml @@ -0,0 +1,46 @@ +services: + couchbase: + image: couchbase/server + hostname: node + container_name: testdb + ports: + - "8091:8091/tcp" + - "8092:8092/tcp" + - "8093:8093/tcp" + - "8094:8094/tcp" + - "8095:8095/tcp" + - "8096:8096/tcp" + - "8097:8097/tcp" + - "18091:18091/tcp" + - "18092:18092/tcp" + - "18093:18093/tcp" + - "18094:18094/tcp" + - "18095:18095/tcp" + - "18096:18096/tcp" + - "18097:18097/tcp" + - "9102:9102/tcp" + - "11207:11207/tcp" + - "11210:11210/tcp" + networks: + cbnet1: + ipv4_address: 10.10.1.2 + healthcheck: + test: [ "CMD", "curl", "-f", "http://localhost:8091" ] + interval: 1m30s + timeout: 10s + retries: 3 + start_period: 10s + start_interval: 5s + volumes: + - ./scripts/entrypoint.sh:/entrypoint.sh + - ./data/customers.json:/import.json + entrypoint: ["/entrypoint.sh"] + command: ["couchbase-server"] +networks: + cbnet1: + ipam: + driver: default + config: + - subnet: 10.10.0.0/16 + ip_range: 10.10.1.0/24 + gateway: 10.10.1.1 diff --git a/metadata-ingestion/tests/unit/couchbase/scripts/entrypoint.sh b/metadata-ingestion/tests/unit/couchbase/scripts/entrypoint.sh new file mode 100755 index 00000000000000..1ea59f1be52796 --- /dev/null +++ b/metadata-ingestion/tests/unit/couchbase/scripts/entrypoint.sh @@ -0,0 +1,92 @@ +#!/bin/bash + +staticConfigFile=/opt/couchbase/etc/couchbase/static_config +restPortValue=8091 + +# see https://developer.couchbase.com/documentation/server/current/install/install-ports.html +function overridePort() { + portName=$1 + portNameUpper=$(echo $portName | awk '{print toupper($0)}') + portValue=${!portNameUpper} + + # only override port if value available AND not already contained in static_config + if [ "$portValue" != "" ]; then + if grep -Fq "{${portName}," ${staticConfigFile} + then + echo "Don't override port ${portName} because already available in $staticConfigFile" + else + echo "Override port '$portName' with value '$portValue'" + echo "{$portName, $portValue}." >> ${staticConfigFile} + + if [ ${portName} == "rest_port" ]; then + restPortValue=${portValue} + fi + fi + fi +} + +overridePort "rest_port" +overridePort "mccouch_port" +overridePort "memcached_port" +overridePort "query_port" +overridePort "ssl_query_port" +overridePort "fts_http_port" +overridePort "moxi_port" +overridePort "ssl_rest_port" +overridePort "ssl_capi_port" +overridePort "ssl_proxy_downstream_port" +overridePort "ssl_proxy_upstream_port" + +[[ "$1" == "couchbase-server" ]] && { + + if [ "$(whoami)" = "couchbase" ]; then + # Ensure that /opt/couchbase/var is owned by user 'couchbase' and + # is writable + if [ ! -w /opt/couchbase/var -o \ + $(find /opt/couchbase/var -maxdepth 0 -printf '%u') != "couchbase" ]; then + echo "/opt/couchbase/var is not owned and writable by UID 1000" + echo "Aborting as Couchbase Server will likely not run" + exit 1 + fi + fi + echo "Starting Couchbase Server -- Web UI available at http://:$restPortValue" + echo "and logs available in /opt/couchbase/var/lib/couchbase/logs" + runsvdir -P /etc/service & +} + +max_retries=5 +retry_count=0 + +while [ $retry_count -lt $max_retries ]; do + curl http://127.0.0.1:8091 > /dev/null 2>&1 + + if [ $? -eq 0 ]; then + echo "Server started" + break + else + echo "Retrying..." + retry_count=$((retry_count + 1)) + sleep 5 + fi +done + +if [ $retry_count -eq $max_retries ]; then + echo "Wait timeout after $max_retries retries. Exiting." +fi + +echo "Configuring Couchbase Server" + +/opt/couchbase/bin/couchbase-cli cluster-init -c 10.10.1.2 --cluster-username Administrator --cluster-password password --services data,query,index --cluster-ramsize 1024 + +sleep 2 +echo "Importing Test Data" +/opt/couchbase/bin/couchbase-cli bucket-create -c couchbase://127.0.0.1 --username Administrator --password password --bucket data --bucket-type couchbase --bucket-ramsize 128 +/opt/couchbase/bin/couchbase-cli collection-manage -c couchbase://127.0.0.1 --username Administrator --password password --bucket data --create-scope data +/opt/couchbase/bin/couchbase-cli collection-manage -c couchbase://127.0.0.1 --username Administrator --password password --bucket data --create-collection data.customers +sleep 2 +/opt/couchbase/bin/cbimport json --format list -c couchbase://127.0.0.1 -u Administrator -p password -b data --scope-collection-exp data.customers -d file:///import.json --generate-key '#UUID#' + +echo "The following output is now a tail of couchdb.log:" +tail -f /opt/couchbase/var/lib/couchbase/logs/couchdb.log & +childPID=$! +wait $childPID diff --git a/metadata-ingestion/tests/unit/couchbase/test_couchbase_source.py b/metadata-ingestion/tests/unit/couchbase/test_couchbase_source.py new file mode 100644 index 00000000000000..29d18017a95953 --- /dev/null +++ b/metadata-ingestion/tests/unit/couchbase/test_couchbase_source.py @@ -0,0 +1,119 @@ +import asyncio +import base64 +import json +import logging + +import pytest +import requests +from requests.adapters import HTTPAdapter +from requests.auth import AuthBase +from urllib3.util.retry import Retry + +from datahub.ingestion.source.couchbase.couchbase_aggregate import CouchbaseAggregate +from datahub.ingestion.source.couchbase.couchbase_connect import CouchbaseConnect +from datahub.ingestion.source.couchbase.retry import retry +from tests.test_helpers.docker_helpers import wait_for_port + +logger = logging.getLogger(__name__) +retries = Retry(total=5, backoff_factor=2, status_forcelist=[404, 405, 500, 503]) +adapter = HTTPAdapter(max_retries=retries) +session = requests.Session() +session.mount("http://", adapter) +session.mount("https://", adapter) + + +class BasicAuth(AuthBase): + def __init__(self, username, password): + self.username = username + self.password = password + + def __call__(self, r): + auth_hash = f"{self.username}:{self.password}" + auth_bytes = auth_hash.encode("ascii") + auth_encoded = base64.b64encode(auth_bytes) + request_headers = { + "Authorization": f"Basic {auth_encoded.decode('ascii')}", + } + r.headers.update(request_headers) + return r + + +def http_test_get(url: str, auth: BasicAuth) -> int: + response = session.get( + url, + verify=False, + timeout=15, + auth=auth, + ) + return response.status_code + + +def http_test_post(url: str, auth: BasicAuth, data: dict) -> dict: + response = session.post( + url, + verify=False, + timeout=15, + auth=auth, + data=data, + ) + assert response.status_code == 200 + return json.loads(response.text) + + +@pytest.mark.slow +@pytest.mark.asyncio +async def test_couchbase_driver( + docker_compose_runner, pytestconfig, tmp_path, mock_time +): + test_resources_dir = pytestconfig.rootpath / "tests/integration/couchbase" + + with docker_compose_runner( + test_resources_dir / "docker-compose.yml", "couchbase" + ) as docker_services: + wait_for_port(docker_services, "testdb", 8093) + + result = http_test_get( + "http://127.0.0.1:8091/pools/default/buckets/data", + auth=BasicAuth("Administrator", "password"), + ) + assert result == 200 + + @retry(factor=1) + def collection_wait(): + response = http_test_post( + "http://127.0.0.1:8093/query/service", + auth=BasicAuth("Administrator", "password"), + data={"statement": "SELECT count(*) as count FROM data.data.customers"}, + ) + results = response.get("results", [{}]) + assert results[0].get("count", 0) == 1000 + + collection_wait() + + await asyncio.sleep(2) + + # Run the driver test. + couchbase_connect = CouchbaseConnect( + "couchbases://127.0.0.1", "Administrator", "password", 5, 60 + ) + couchbase_connect.cluster_init() + + bucket_list = couchbase_connect.bucket_list() + assert bucket_list is not None and len(bucket_list) == 1 + + for bucket in bucket_list: + assert bucket == "data" + scope_list = couchbase_connect.scope_list(bucket) + assert "_default" in scope_list and "data" in scope_list + for scope in scope_list: + collection_list = couchbase_connect.collection_list(bucket, scope) + if scope == "data": + assert "customers" in collection_list + elif scope == "_default": + assert "_default" in collection_list + + documents = [] + aggregator = CouchbaseAggregate(couchbase_connect, "data.data.customers") + async for chunk in aggregator.get_documents(): + documents.extend(chunk) + assert len(documents) == 1000