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https://github.com/datahub-project/datahub.git
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fix(ingest/unity): generate sibling and lineage (#9894)
This commit is contained in:
dushayntAW
GitHub
parent
ad4da5747d
commit
3a4bdef44a
@ -15,6 +15,11 @@ source:
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deny:
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- ".*\\.unwanted_schema"
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# emit_siblings: true
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# delta_lake_options:
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# platform_instance_name: null
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# env: 'PROD'
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# profiling:
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# method: "analyze"
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# enabled: true
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@ -60,6 +60,13 @@ class UnityCatalogProfilerConfig(ConfigModel):
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)
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class DeltaLakeDetails(ConfigModel):
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platform_instance_name: Optional[str] = Field(
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default=None, description="Delta-lake paltform instance name"
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)
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env: str = Field(default="PROD", description="Delta-lake environment")
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class UnityCatalogAnalyzeProfilerConfig(UnityCatalogProfilerConfig):
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method: Literal["analyze"] = "analyze"
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@ -253,6 +260,16 @@ class UnityCatalogSourceConfig(
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discriminator="method",
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)
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emit_siblings: bool = pydantic.Field(
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default=True,
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description="Whether to emit siblings relation with corresponding delta-lake platform's table. If enabled, this will also ingest the corresponding delta-lake table.",
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)
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delta_lake_options: DeltaLakeDetails = Field(
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default=DeltaLakeDetails(),
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description="Details about the delta lake, incase to emit siblings",
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)
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scheme: str = DATABRICKS
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def get_sql_alchemy_url(self, database: Optional[str] = None) -> str:
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@ -41,7 +41,10 @@ from datahub.ingestion.api.source import (
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TestConnectionReport,
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)
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from datahub.ingestion.api.workunit import MetadataWorkUnit
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from datahub.ingestion.source.aws.s3_util import make_s3_urn_for_lineage
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from datahub.ingestion.source.aws.s3_util import (
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make_s3_urn_for_lineage,
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strip_s3_prefix,
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)
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from datahub.ingestion.source.common.subtypes import (
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DatasetContainerSubTypes,
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DatasetSubTypes,
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@ -80,9 +83,13 @@ from datahub.ingestion.source.unity.proxy_types import (
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)
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from datahub.ingestion.source.unity.report import UnityCatalogReport
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from datahub.ingestion.source.unity.usage import UnityCatalogUsageExtractor
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from datahub.metadata.com.linkedin.pegasus2avro.common import Siblings
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from datahub.metadata.com.linkedin.pegasus2avro.dataset import (
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DatasetLineageType,
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FineGrainedLineage,
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FineGrainedLineageUpstreamType,
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Upstream,
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UpstreamLineage,
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ViewProperties,
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)
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from datahub.metadata.schema_classes import (
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@ -491,6 +498,25 @@ class UnityCatalogSource(StatefulIngestionSourceBase, TestableSource):
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if table.view_definition:
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self.view_definitions[dataset_urn] = (table.ref, table.view_definition)
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# generate sibling and lineage aspects in case of EXTERNAL DELTA TABLE
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if (
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table_props.customProperties.get("table_type") == "EXTERNAL"
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and table_props.customProperties.get("data_source_format") == "DELTA"
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and self.config.emit_siblings
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):
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storage_location = str(table_props.customProperties.get("storage_location"))
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if storage_location.startswith("s3://"):
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browse_path = strip_s3_prefix(storage_location)
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source_dataset_urn = make_dataset_urn_with_platform_instance(
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"delta-lake",
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browse_path,
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self.config.delta_lake_options.platform_instance_name,
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self.config.delta_lake_options.env,
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)
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yield from self.gen_siblings_workunit(dataset_urn, source_dataset_urn)
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yield from self.gen_lineage_workunit(dataset_urn, source_dataset_urn)
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yield from [
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mcp.as_workunit()
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for mcp in MetadataChangeProposalWrapper.construct_many(
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@ -947,3 +973,38 @@ class UnityCatalogSource(StatefulIngestionSourceBase, TestableSource):
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self.sql_parser_schema_resolver.close()
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super().close()
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def gen_siblings_workunit(
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self,
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dataset_urn: str,
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source_dataset_urn: str,
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) -> Iterable[MetadataWorkUnit]:
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"""
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Generate sibling workunit for both unity-catalog dataset and its connector source dataset
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"""
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yield MetadataChangeProposalWrapper(
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entityUrn=dataset_urn,
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aspect=Siblings(primary=False, siblings=[source_dataset_urn]),
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).as_workunit()
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yield MetadataChangeProposalWrapper(
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entityUrn=source_dataset_urn,
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aspect=Siblings(primary=True, siblings=[dataset_urn]),
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).as_workunit(is_primary_source=False)
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def gen_lineage_workunit(
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self,
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dataset_urn: str,
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source_dataset_urn: str,
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) -> Iterable[MetadataWorkUnit]:
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"""
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Generate dataset to source connector lineage workunit
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"""
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yield MetadataChangeProposalWrapper(
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entityUrn=dataset_urn,
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aspect=UpstreamLineage(
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upstreams=[
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Upstream(dataset=source_dataset_urn, type=DatasetLineageType.VIEW)
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]
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),
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).as_workunit()
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@ -66,17 +66,6 @@ def register_mock_data(workspace_client):
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workspace_client.catalogs.list.return_value = [
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CatalogInfo.from_dict(d)
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for d in [
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{
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"name": "main",
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"owner": "account users",
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"comment": "Main catalog (auto-created)",
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"metastore_id": "2c983545-d403-4f87-9063-5b7e3b6d3736",
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"created_at": 1666185153376,
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"created_by": "abc@acryl.io",
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"updated_at": 1666186071115,
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"updated_by": "abc@acryl.io",
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"catalog_type": "MANAGED_CATALOG",
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},
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{
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"name": "quickstart_catalog",
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"owner": "account users",
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@ -87,50 +76,13 @@ def register_mock_data(workspace_client):
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"updated_at": 1666186064332,
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"updated_by": "abc@acryl.io",
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"catalog_type": "MANAGED_CATALOG",
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},
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{
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"name": "system",
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"owner": SERVICE_PRINCIPAL_ID_2,
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"comment": "System catalog (auto-created)",
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"metastore_id": "2c983545-d403-4f87-9063-5b7e3b6d3736",
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"created_at": 1666185153391,
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"created_by": "System user",
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"updated_at": 1666185153391,
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"updated_by": "System user",
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"catalog_type": "SYSTEM_CATALOG",
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},
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}
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]
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]
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workspace_client.schemas.list.return_value = [
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SchemaInfo.from_dict(d)
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for d in [
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{
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"name": "default",
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"catalog_name": "quickstart_catalog",
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"owner": "abc@acryl.io",
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"comment": "Default schema (auto-created)",
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"metastore_id": "2c983545-d403-4f87-9063-5b7e3b6d3736",
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"full_name": "quickstart_catalog.default",
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"created_at": 1666185610021,
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"created_by": "abc@acryl.io",
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"updated_at": 1666185610021,
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"updated_by": "abc@acryl.io",
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"catalog_type": "MANAGED_CATALOG",
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},
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{
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"name": "information_schema",
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"catalog_name": "quickstart_catalog",
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"owner": SERVICE_PRINCIPAL_ID_1,
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"comment": "Information schema (auto-created)",
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"metastore_id": "2c983545-d403-4f87-9063-5b7e3b6d3736",
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"full_name": "quickstart_catalog.information_schema",
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"created_at": 1666185610024,
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"created_by": "System user",
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"updated_at": 1666185610024,
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"updated_by": "System user",
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"catalog_type": "MANAGED_CATALOG",
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},
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{
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"name": "quickstart_schema",
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"catalog_name": "quickstart_catalog",
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@ -199,7 +151,57 @@ def register_mock_data(workspace_client):
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"updated_by": "abc@acryl.io",
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"table_id": "cff27aa1-1c6a-4d78-b713-562c660c2896",
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}
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)
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),
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databricks.sdk.service.catalog.TableInfo.from_dict(
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{
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"name": "quickstart_table_external",
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"catalog_name": "quickstart_catalog",
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"schema_name": "quickstart_schema",
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"table_type": "EXTERNAL",
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"data_source_format": "DELTA",
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"columns": [
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{
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"name": "columnA",
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"type_text": "int",
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"type_json": '{"name":"columnA","type":"integer","nullable":true,"metadata":{}}',
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"type_name": "INT",
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"type_precision": 0,
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"type_scale": 0,
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"position": 0,
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"nullable": True,
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},
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{
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"name": "columnB",
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"type_text": "string",
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"type_json": '{"name":"columnB","type":"string","nullable":true,"metadata":{}}',
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"type_name": "STRING",
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"type_precision": 0,
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"type_scale": 0,
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"position": 1,
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"nullable": True,
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},
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],
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"storage_location": "s3://db-02eec1f70bfe4115445be9fdb1aac6ac-s3-root-bucket/metastore/2c983545-d403-4f87-9063-5b7e3b6d3736/tables/cff27aa1-1c6a-4d78-b713-562c660c2896",
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"owner": "account users",
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"properties": {
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"delta.lastCommitTimestamp": "1666185711000",
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"delta.lastUpdateVersion": "1",
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"delta.minReaderVersion": "1",
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"delta.minWriterVersion": "2",
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"spark.sql.statistics.numRows": "10",
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"spark.sql.statistics.totalSize": "512",
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},
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"generation": 2,
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"metastore_id": "2c983545-d403-4f87-9063-5b7e3b6d3736",
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"full_name": "quickstart_catalog.quickstart_schema.quickstart_table_external",
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"data_access_configuration_id": "00000000-0000-0000-0000-000000000000",
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"created_at": 1666185698688,
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"created_by": "abc@acryl.io",
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"updated_at": 1666186049633,
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"updated_by": "abc@acryl.io",
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"table_id": "cff27aa1-1c6a-4d78-b713-562c660c2896",
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}
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),
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]
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workspace_client.tables.get = lambda *args, **kwargs: databricks.sdk.service.catalog.TableInfo.from_dict(
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@ -409,6 +411,11 @@ def test_ingestion(pytestconfig, tmp_path, requests_mock):
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"include_ownership": True,
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"include_hive_metastore": True,
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"warehouse_id": "test",
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"emit_siblings": True,
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"delta_lake_options": {
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"platform_instance_name": None,
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"env": "PROD",
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},
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"profiling": {
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"enabled": True,
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"method": "analyze",
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