feat(ingest): add classification to bigquery, redshift

metadata-ingestion/setup.py

@@ -302,7 +302,8 @@
     | {
         *sqlglot_lib,
         "google-cloud-datacatalog-lineage==0.2.2",
-    },
+    }
+    | classification_lib,
     "clickhouse": sql_common | clickhouse_common,
     "clickhouse-usage": sql_common | usage_common | clickhouse_common,
     "datahub-lineage-file": set(),
@@ -370,6 +371,8 @@
     | redshift_common
     | usage_common
     | sqlglot_lib
+    | classification_lib
+    | {"db-dtypes"}  # Pandas extension data types
     | {"cachetools"},
     "s3": {*s3_base, *data_lake_profiling},
     "gcs": {*s3_base, *data_lake_profiling},

metadata-ingestion/src/datahub/ingestion/api/source.py

@@ -57,6 +57,7 @@ class SourceCapability(Enum):
     TAGS = "Extract Tags"
     SCHEMA_METADATA = "Schema Metadata"
     CONTAINERS = "Asset Containers"
+    CLASSIFICATION = "Classification"
 
 
 @dataclass

metadata-ingestion/src/datahub/ingestion/glossary/classification_mixin.py

@@ -1,16 +1,20 @@
 import concurrent.futures
 import logging
 from dataclasses import dataclass, field
+from functools import partial
 from math import ceil
-from typing import Dict, Iterable, List, Optional
+from typing import Callable, Dict, Iterable, List, Optional, Union
 
 from datahub_classify.helper_classes import ColumnInfo, Metadata
 from pydantic import Field
 
 from datahub.configuration.common import ConfigModel, ConfigurationError
 from datahub.emitter.mce_builder import get_sys_time, make_term_urn, make_user_urn
+from datahub.emitter.mcp import MetadataChangeProposalWrapper
+from datahub.ingestion.api.workunit import MetadataWorkUnit
 from datahub.ingestion.glossary.classifier import ClassificationConfig, Classifier
 from datahub.ingestion.glossary.classifier_registry import classifier_registry
+from datahub.ingestion.source.sql.data_reader import DataReader
 from datahub.metadata.com.linkedin.pegasus2avro.common import (
     AuditStamp,
     GlossaryTermAssociation,
@@ -25,9 +29,12 @@
 
 @dataclass
 class ClassificationReportMixin:
+
+    num_tables_fetch_sample_values_failed: int = 0
+
     num_tables_classification_attempted: int = 0
     num_tables_classification_failed: int = 0
-    num_tables_classified: int = 0
+    num_tables_classification_found: int = 0
 
     info_types_detected: LossyDict[str, LossyList[str]] = field(
         default_factory=LossyDict
@@ -99,8 +106,22 @@ def classify_schema_fields(
         self,
         dataset_name: str,
         schema_metadata: SchemaMetadata,
-        sample_data: Dict[str, list],
+        sample_data: Union[Dict[str, list], Callable[[], Dict[str, list]]],
     ) -> None:
+
+        if not isinstance(sample_data, Dict):
+            try:
+                # TODO: In future, sample_data fetcher can be lazily called if classification
+                # requires values as prediction factor
+                sample_data = sample_data()
+            except Exception as e:
+                self.report.num_tables_fetch_sample_values_failed += 1
+                logger.warning(
+                    f"Failed to get sample values for dataset. Make sure you have granted SELECT permissions on dataset. {dataset_name}",
+                )
+                sample_data = dict()
+                logger.debug("Error", exc_info=e)
+
         column_infos = self.get_columns_to_classify(
             dataset_name, schema_metadata, sample_data
         )
@@ -137,7 +158,7 @@ def classify_schema_fields(
                 )
 
         if field_terms:
-            self.report.num_tables_classified += 1
+            self.report.num_tables_classification_found += 1
             self.populate_terms_in_schema_metadata(schema_metadata, field_terms)
 
     def update_field_terms(
@@ -234,8 +255,11 @@ def get_columns_to_classify(
                 )
                 continue
 
-            # TODO: Let's auto-skip passing sample_data for complex(array/struct) columns
-            # for initial rollout
+            # As a result of custom field path specification e.g. [version=2.0].[type=struct].[type=struct].service'
+            # Sample values for a nested field (an array , union or struct) are not read / passed in classifier correctly.
+            # TODO: Fix this behavior for nested fields. This would probably involve:
+            # 1. Preprocessing field path spec v2 back to native field representation. (without [*] constructs)
+            # 2. Preprocessing retrieved structured sample data to pass in sample values correctly for nested fields.
 
             column_infos.append(
                 ColumnInfo(
@@ -256,3 +280,47 @@ def get_columns_to_classify(
             )
 
         return column_infos
+
+
+def classification_workunit_processor(
+    table_wu_generator: Iterable[MetadataWorkUnit],
+    classification_handler: ClassificationHandler,
+    data_reader: Optional[DataReader],
+    table_id: List[str],
+    data_reader_kwargs: dict = {},
+) -> Iterable[MetadataWorkUnit]:
+    table_name = ".".join(table_id)
+    if not classification_handler.is_classification_enabled_for_table(table_name):
+        yield from table_wu_generator
+    for wu in table_wu_generator:
+        maybe_schema_metadata = wu.get_aspect_of_type(SchemaMetadata)
+        if maybe_schema_metadata:
+            try:
+                classification_handler.classify_schema_fields(
+                    table_name,
+                    maybe_schema_metadata,
+                    (
+                        partial(
+                            data_reader.get_sample_data_for_table,
+                            table_id,
+                            classification_handler.config.classification.sample_size
+                            * 1.2,
+                            **data_reader_kwargs,
+                        )
+                        if data_reader
+                        else dict()
+                    ),
+                )
+                yield MetadataChangeProposalWrapper(
+                    aspect=maybe_schema_metadata, entityUrn=wu.get_urn()
+                ).as_workunit(
+                    is_primary_source=wu.is_primary_source,
+                )
+            except Exception as e:
+                logger.debug(
+                    f"Failed to classify table columns for {table_name} due to error -> {e}",
+                    exc_info=e,
+                )
+                yield wu
+        else:
+            yield wu

metadata-ingestion/src/datahub/ingestion/source/bigquery_v2/bigquery.py

@@ -35,11 +35,16 @@
     TestConnectionReport,
 )
 from datahub.ingestion.api.workunit import MetadataWorkUnit
+from datahub.ingestion.glossary.classification_mixin import (
+    ClassificationHandler,
+    classification_workunit_processor,
+)
 from datahub.ingestion.source.bigquery_v2.bigquery_audit import (
     BigqueryTableIdentifier,
     BigQueryTableRef,
 )
 from datahub.ingestion.source.bigquery_v2.bigquery_config import BigQueryV2Config
+from datahub.ingestion.source.bigquery_v2.bigquery_data_reader import BigQueryDataReader
 from datahub.ingestion.source.bigquery_v2.bigquery_helper import (
     unquote_and_decode_unicode_escape_seq,
 )
@@ -167,6 +172,11 @@ def cleanup(config: BigQueryV2Config) -> None:
     "Optionally enabled via `stateful_ingestion.remove_stale_metadata`",
     supported=True,
 )
+@capability(
+    SourceCapability.CLASSIFICATION,
+    "Optionally enabled via `classification.enabled`",
+    supported=True,
+)
 class BigqueryV2Source(StatefulIngestionSourceBase, TestableSource):
     # https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types
     BIGQUERY_FIELD_TYPE_MAPPINGS: Dict[
@@ -214,6 +224,7 @@ def __init__(self, ctx: PipelineContext, config: BigQueryV2Config):
         super(BigqueryV2Source, self).__init__(config, ctx)
         self.config: BigQueryV2Config = config
         self.report: BigQueryV2Report = BigQueryV2Report()
+        self.classification_handler = ClassificationHandler(self.config, self.report)
         self.platform: str = "bigquery"
 
         BigqueryTableIdentifier._BIGQUERY_DEFAULT_SHARDED_TABLE_REGEX = (
@@ -227,6 +238,12 @@ def __init__(self, ctx: PipelineContext, config: BigQueryV2Config):
         )
         self.sql_parser_schema_resolver = self._init_schema_resolver()
 
+        self.data_reader: Optional[BigQueryDataReader] = None
+        if self.classification_handler.is_classification_enabled():
+            self.data_reader = BigQueryDataReader.create(
+                self.config.get_bigquery_client()
+            )
+
         redundant_lineage_run_skip_handler: Optional[
             RedundantLineageRunSkipHandler
         ] = None
@@ -713,6 +730,7 @@ def _process_schema(
         )
 
         columns = None
+
         if (
             self.config.include_tables
             or self.config.include_views
@@ -732,12 +750,27 @@ def _process_schema(
 
             for table in db_tables[dataset_name]:
                 table_columns = columns.get(table.name, []) if columns else []
-                yield from self._process_table(
+                table_wu_generator = self._process_table(
                     table=table,
                     columns=table_columns,
                     project_id=project_id,
                     dataset_name=dataset_name,
                 )
+                yield from classification_workunit_processor(
+                    table_wu_generator,
+                    self.classification_handler,
+                    self.data_reader,
+                    [project_id, dataset_name, table.name],
+                    data_reader_kwargs=dict(
+                        sample_size_percent=(
+                            self.config.classification.sample_size
+                            * 1.2
+                            / table.rows_count
+                            if table.rows_count
+                            else None
+                        )
+                    ),
+                )
         elif self.store_table_refs:
             # Need table_refs to calculate lineage and usage
             for table_item in self.bigquery_data_dictionary.list_tables(
@@ -1071,14 +1104,16 @@ def gen_dataset_workunits(
         )
 
         yield self.gen_schema_metadata(
-            dataset_urn, table, columns, str(datahub_dataset_name)
+            dataset_urn, table, columns, datahub_dataset_name
         )
 
         dataset_properties = DatasetProperties(
             name=datahub_dataset_name.get_table_display_name(),
-            description=unquote_and_decode_unicode_escape_seq(table.comment)
-            if table.comment
-            else "",
+            description=(
+                unquote_and_decode_unicode_escape_seq(table.comment)
+                if table.comment
+                else ""
+            ),
             qualifiedName=str(datahub_dataset_name),
             created=(
                 TimeStamp(time=int(table.created.timestamp() * 1000))
@@ -1238,10 +1273,10 @@ def gen_schema_metadata(
         dataset_urn: str,
         table: Union[BigqueryTable, BigqueryView, BigqueryTableSnapshot],
         columns: List[BigqueryColumn],
-        dataset_name: str,
+        dataset_name: BigqueryTableIdentifier,
     ) -> MetadataWorkUnit:
         schema_metadata = SchemaMetadata(
-            schemaName=dataset_name,
+            schemaName=str(dataset_name),
             platform=make_data_platform_urn(self.platform),
             version=0,
             hash="",

metadata-ingestion/src/datahub/ingestion/source/bigquery_v2/bigquery_config.py

@@ -10,6 +10,9 @@
 
 from datahub.configuration.common import AllowDenyPattern, ConfigModel
 from datahub.configuration.validate_field_removal import pydantic_removed_field
+from datahub.ingestion.glossary.classification_mixin import (
+    ClassificationSourceConfigMixin,
+)
 from datahub.ingestion.source.sql.sql_config import SQLCommonConfig
 from datahub.ingestion.source.state.stateful_ingestion_base import (
     StatefulLineageConfigMixin,
@@ -64,9 +67,9 @@ def __init__(self, **data: Any):
             )
             os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = self._credentials_path
 
-    def get_bigquery_client(config) -> bigquery.Client:
-        client_options = config.extra_client_options
-        return bigquery.Client(config.project_on_behalf, **client_options)
+    def get_bigquery_client(self) -> bigquery.Client:
+        client_options = self.extra_client_options
+        return bigquery.Client(self.project_on_behalf, **client_options)
 
     def make_gcp_logging_client(
         self, project_id: Optional[str] = None
@@ -96,6 +99,7 @@ class BigQueryV2Config(
     StatefulUsageConfigMixin,
     StatefulLineageConfigMixin,
     StatefulProfilingConfigMixin,
+    ClassificationSourceConfigMixin,
 ):
     project_id_pattern: AllowDenyPattern = Field(
         default=AllowDenyPattern.allow_all(),

metadata-ingestion/src/datahub/ingestion/source/bigquery_v2/bigquery_data_reader.py

@@ -0,0 +1,72 @@
+import logging
+from collections import defaultdict
+from typing import Dict, List, Optional
+
+from google.cloud import bigquery
+
+from datahub.ingestion.source.sql.data_reader import DataReader
+from datahub.utilities.perf_timer import PerfTimer
+
+logger = logging.Logger(__name__)
+
+
+class BigQueryDataReader(DataReader):
+    @staticmethod
+    def create(
+        client: bigquery.Client,
+    ) -> "BigQueryDataReader":
+        return BigQueryDataReader(client)
+
+    def __init__(
+        self,
+        client: bigquery.Client,
+    ) -> None:
+        self.client = client
+
+    def get_sample_data_for_table(
+        self,
+        table_id: List[str],
+        sample_size: int,
+        *,
+        sample_size_percent: Optional[float] = None,
+        filter: Optional[str] = None,
+    ) -> Dict[str, list]:
+        """
+        table_id should be in the form [project, dataset, schema]
+        """
+
+        assert len(table_id) == 3
+        project = table_id[0]
+        dataset = table_id[1]
+        table_name = table_id[2]
+
+        column_values: Dict[str, list] = defaultdict(list)
+        if sample_size_percent is None:
+            return column_values
+        # Ideally we always know the actual row count.
+        # The alternative to perform limit query scans entire BQ table
+        # and is never a recommended option due to cost factor, unless
+        # additional filter clause (e.g. where condition on partition) is available.
+
+        logger.debug(
+            f"Collecting sample values for table {project}.{dataset}.{table_name}"
+        )
+        with PerfTimer() as timer:
+            sample_pc = sample_size_percent * 100
+            # TODO: handle for sharded+compulsory partitioned tables
+            sql = (
+                f"SELECT * FROM `{project}.{dataset}.{table_name}` "
+                + f"TABLESAMPLE SYSTEM ({sample_pc:.8f} percent)"
+            )
+            # Ref: https://cloud.google.com/bigquery/docs/samples/bigquery-query-results-dataframe
+            df = self.client.query_and_wait(sql).to_dataframe()
+            time_taken = timer.elapsed_seconds()
+            logger.debug(
+                f"Finished collecting sample values for table {project}.{dataset}.{table_name};"
+                f"{df.shape[0]} rows; took {time_taken:.3f} seconds"
+            )
+
+        return df.to_dict(orient="list")
+
+    def close(self) -> None:
+        self.client.close()