-
The #1 Open Source Data Catalog
+
The #1 Open Source Metadata Platform
- DataHub's extensible metadata platform enables data discovery, data observability and federated governance that helps tame the
+ DataHub is an extensible metadata platform that enables data discovery, data observability and federated governance to help tame the
complexity of your data ecosystem.
+
+Built with ❤️ by Acryl Data and LinkedIn.
+
Get Started →
diff --git a/metadata-ingestion/docs/sources/vertica/README.md b/metadata-ingestion/docs/sources/vertica/README.md
index 3c41b744b321f..fb016fbdb2d28 100644
--- a/metadata-ingestion/docs/sources/vertica/README.md
+++ b/metadata-ingestion/docs/sources/vertica/README.md
@@ -8,7 +8,6 @@ The DataHub Vertica Plugin extracts the following:
* Metadata for databases, schemas, views, tables, and projections
* Table level lineage
* Metadata for ML Models
-* Metadata for Vertica OAuth
### Concept Mapping
diff --git a/metadata-ingestion/docs/sources/vertica/vertica_pre.md b/metadata-ingestion/docs/sources/vertica/vertica_pre.md
index dbcda212cf02f..a4e71de3c908c 100644
--- a/metadata-ingestion/docs/sources/vertica/vertica_pre.md
+++ b/metadata-ingestion/docs/sources/vertica/vertica_pre.md
@@ -2,5 +2,5 @@
In order to ingest metadata from Vertica, you will need:
-- Vertica Server Version 10.1.1-0 and avobe. It may also work for older versions.
+- Vertica Server Version 10.1.1-0 and above. It may also work with, but is not been tested with, older versions .
- Vertica Credentials (Username/Password)
diff --git a/metadata-ingestion/docs/sources/vertica/vertica_recipe.yml b/metadata-ingestion/docs/sources/vertica/vertica_recipe.yml
index 88ac74cc22c5f..ad4f0173b0111 100644
--- a/metadata-ingestion/docs/sources/vertica/vertica_recipe.yml
+++ b/metadata-ingestion/docs/sources/vertica/vertica_recipe.yml
@@ -12,7 +12,6 @@ source:
include_tables: true
include_views: true
include_projections: true
- include_oauth: true
include_models: true
include_view_lineage: true
include_projection_lineage: true
diff --git a/metadata-ingestion/setup.py b/metadata-ingestion/setup.py
index 0421ab61ffe61..4f156591eb756 100644
--- a/metadata-ingestion/setup.py
+++ b/metadata-ingestion/setup.py
@@ -374,7 +374,9 @@ def get_long_description():
"nifi": {"requests", "packaging", "requests-gssapi"},
"powerbi": microsoft_common | {"lark[regex]==1.1.4", "sqlparse"},
"powerbi-report-server": powerbi_report_server,
- "vertica": sql_common | {"vertica-sqlalchemy-dialect[vertica-python]==0.0.1"},
+
+ "vertica": sql_common | {"vertica-sqlalchemy-dialect[vertica-python]==0.0.8"},
+
"unity-catalog": databricks | sqllineage_lib,
}
@@ -401,7 +403,9 @@ def get_long_description():
"types-cachetools",
# versions 0.1.13 and 0.1.14 seem to have issues
"types-click==0.1.12",
- "boto3-stubs[s3,glue,sagemaker,sts]>=1.28.4",
+ # The boto3-stubs package seems to have regularly breaking minor releases,
+ # we pin to a specific version to avoid this.
+ "boto3-stubs[s3,glue,sagemaker,sts]==1.28.15",
"types-tabulate",
# avrogen package requires this
"types-pytz",
@@ -487,7 +491,8 @@ def get_long_description():
"powerbi-report-server",
"salesforce",
"unity-catalog",
- "nifi"
+ "nifi",
+ "vertica"
# airflow is added below
]
if plugin
@@ -521,7 +526,7 @@ def get_long_description():
"mysql",
"mariadb",
"redash",
- # "vertica",
+ "vertica",
]
for dependency in plugins[plugin]
),
diff --git a/metadata-ingestion/src/datahub/ingestion/extractor/json_schema_util.py b/metadata-ingestion/src/datahub/ingestion/extractor/json_schema_util.py
index cfa1b7609c0b1..8e313e92cbf84 100644
--- a/metadata-ingestion/src/datahub/ingestion/extractor/json_schema_util.py
+++ b/metadata-ingestion/src/datahub/ingestion/extractor/json_schema_util.py
@@ -254,11 +254,13 @@ def _field_from_primitive(
isPartOfKey=field_path.is_key_schema,
)
elif datahub_field_type in [EnumTypeClass]:
+ # Convert enums to string representation
+ schema_enums = list(map(json.dumps, schema["enum"]))
yield SchemaField(
fieldPath=field_path.expand_type("enum", schema).as_string(),
type=type_override or SchemaFieldDataTypeClass(type=EnumTypeClass()),
nativeDataType="Enum",
- description=f"one of {','.join(schema['enum'])}",
+ description=f"One of: {', '.join(schema_enums)}",
nullable=nullable,
jsonProps=JsonSchemaTranslator._get_jsonprops_for_any_schema(
schema, required=required
diff --git a/metadata-ingestion/src/datahub/ingestion/source/aws/sagemaker_processors/feature_groups.py b/metadata-ingestion/src/datahub/ingestion/source/aws/sagemaker_processors/feature_groups.py
index 75fae47c966fd..b8b96c6306a3b 100644
--- a/metadata-ingestion/src/datahub/ingestion/source/aws/sagemaker_processors/feature_groups.py
+++ b/metadata-ingestion/src/datahub/ingestion/source/aws/sagemaker_processors/feature_groups.py
@@ -24,7 +24,7 @@
from mypy_boto3_sagemaker import SageMakerClient
from mypy_boto3_sagemaker.type_defs import (
DescribeFeatureGroupResponseTypeDef,
- FeatureDefinitionOutputTypeDef,
+ FeatureDefinitionTypeDef,
FeatureGroupSummaryTypeDef,
)
@@ -147,7 +147,7 @@ def get_feature_type(self, aws_type: str, feature_name: str) -> str:
def get_feature_wu(
self,
feature_group_details: "DescribeFeatureGroupResponseTypeDef",
- feature: "FeatureDefinitionOutputTypeDef",
+ feature: "FeatureDefinitionTypeDef",
) -> MetadataWorkUnit:
"""
Generate an MLFeature workunit for a SageMaker feature.
diff --git a/metadata-ingestion/src/datahub/ingestion/source/sql/vertica.py b/metadata-ingestion/src/datahub/ingestion/source/sql/vertica.py
index 37ccc1d1fedb5..3b70c476639a9 100644
--- a/metadata-ingestion/src/datahub/ingestion/source/sql/vertica.py
+++ b/metadata-ingestion/src/datahub/ingestion/source/sql/vertica.py
@@ -1,15 +1,11 @@
import logging
import traceback
-from collections import defaultdict
from dataclasses import dataclass
-from textwrap import dedent
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Set, Tuple, Union
import pydantic
from pydantic.class_validators import validator
-from sqlalchemy import create_engine, sql
-from sqlalchemy.engine.reflection import Inspector
-from sqlalchemy.exc import ProgrammingError
+from vertica_sqlalchemy_dialect.base import VerticaInspector
from datahub.configuration.common import AllowDenyPattern
from datahub.emitter.mce_builder import (
@@ -63,24 +59,21 @@
class VerticaSourceReport(SQLSourceReport):
projection_scanned: int = 0
models_scanned: int = 0
- oauth_scanned: int = 0
def report_entity_scanned(self, name: str, ent_type: str = "table") -> None:
"""
- Entity could be a projection or a models or Oauth .
+ Entity could be a projection or a model.
"""
if ent_type == "projection":
self.projection_scanned += 1
elif ent_type == "models":
self.models_scanned += 1
- elif ent_type == "oauth":
- self.oauth_scanned += 1
else:
super().report_entity_scanned(name, ent_type)
-# Extended BasicSQLAlchemyConfig to config for projections,models and oauth metadata.
+# Extended BasicSQLAlchemyConfig to config for projections,models metadata.
class VerticaConfig(BasicSQLAlchemyConfig):
models_pattern: AllowDenyPattern = pydantic.Field(
default=AllowDenyPattern.allow_all(),
@@ -92,13 +85,12 @@ class VerticaConfig(BasicSQLAlchemyConfig):
include_models: Optional[bool] = pydantic.Field(
default=True, description="Whether Models should be ingested."
)
- include_oauth: Optional[bool] = pydantic.Field(
- default=True, description="Whether Oauth should be ingested."
- )
+
include_view_lineage: Optional[bool] = pydantic.Field(
default=True,
description="If the source supports it, include view lineage to the underlying storage location.",
)
+
include_projection_lineage: Optional[bool] = pydantic.Field(
default=True,
description="If the source supports it, include view lineage to the underlying storage location.",
@@ -132,10 +124,6 @@ def __init__(self, config: VerticaConfig, ctx: PipelineContext):
# self.platform = platform
super(VerticaSource, self).__init__(config, ctx, "vertica")
self.report: SQLSourceReport = VerticaSourceReport()
- self.view_lineage_map: Optional[Dict[str, List[Tuple[str, str, str]]]] = None
- self.projection_lineage_map: Optional[
- Dict[str, List[Tuple[str, str, str]]]
- ] = None
self.config: VerticaConfig = config
@classmethod
@@ -201,16 +189,13 @@ def get_workunits_internal(self) -> Iterable[Union[MetadataWorkUnit, SqlWorkUnit
profile_requests, profiler, platform=self.platform
)
- oauth_schema = "Entities"
- if sql_config.include_oauth:
- yield from self.loop_oauth(inspector, oauth_schema, sql_config)
-
def get_database_properties(
- self, inspector: Inspector, database: str
+ self, inspector: VerticaInspector, database: str
) -> Optional[Dict[str, str]]:
try:
- custom_properties = inspector._get_database_properties(database) # type: ignore
+ custom_properties = inspector._get_database_properties(database)
return custom_properties
+
except Exception as ex:
self.report.report_failure(
f"{database}", f"unable to get extra_properties : {ex}"
@@ -218,10 +203,10 @@ def get_database_properties(
return None
def get_schema_properties(
- self, inspector: Inspector, database: str, schema: str
+ self, inspector: VerticaInspector, database: str, schema: str
) -> Optional[Dict[str, str]]:
try:
- custom_properties = inspector._get_schema_properties(schema) # type: ignore
+ custom_properties = inspector._get_schema_properties(schema)
return custom_properties
except Exception as ex:
self.report.report_failure(
@@ -232,7 +217,7 @@ def get_schema_properties(
def _process_table(
self,
dataset_name: str,
- inspector: Inspector,
+ inspector: VerticaInspector,
schema: str,
table: str,
sql_config: SQLAlchemyConfig,
@@ -243,7 +228,7 @@ def _process_table(
self.config.platform_instance,
self.config.env,
)
- table_owner = self._get_owner_information(table, "table")
+ table_owner = inspector.get_table_owner(table, schema)
yield from add_owner_to_entity_wu(
entity_type="dataset",
entity_urn=dataset_urn,
@@ -255,7 +240,7 @@ def _process_table(
def loop_views(
self,
- inspector: Inspector,
+ inspector: VerticaInspector,
schema: str,
sql_config: SQLAlchemyConfig,
) -> Iterable[Union[SqlWorkUnit, MetadataWorkUnit]]:
@@ -286,7 +271,7 @@ def loop_views(
self.report.report_warning(
f"{schema}.{view}", f"Ingestion error: {e}"
)
- if sql_config.include_view_lineage: # type: ignore
+ if self.config.include_view_lineage:
try:
dataset_urn = make_dataset_urn_with_platform_instance(
self.platform,
@@ -294,19 +279,22 @@ def loop_views(
self.config.platform_instance,
self.config.env,
)
+
dataset_snapshot = DatasetSnapshot(
urn=dataset_urn, aspects=[StatusClass(removed=False)]
)
+
lineage_info = self._get_upstream_lineage_info(
- dataset_urn, view
+ dataset_urn,
+ inspector,
+ view,
+ schema,
)
+
if lineage_info is not None:
# upstream_column_props = []
yield MetadataChangeProposalWrapper(
- entityType="dataset",
- changeType=ChangeTypeClass.UPSERT,
entityUrn=dataset_snapshot.urn,
- aspectName="upstreamLineage",
aspect=lineage_info,
).as_workunit()
@@ -323,7 +311,7 @@ def loop_views(
def _process_view(
self,
dataset_name: str,
- inspector: Inspector,
+ inspector: VerticaInspector,
schema: str,
view: str,
sql_config: SQLAlchemyConfig,
@@ -334,7 +322,7 @@ def _process_view(
Args:
dataset_name (str)
inspector (Inspector)
- schema (str): schame name
+ schema (str): schema name
view (str): name of the view to inspect
sql_config (SQLAlchemyConfig)
table_tags (Dict[str, str], optional) Defaults to dict().
@@ -345,25 +333,28 @@ def _process_view(
Yields:
Iterator[Iterable[Union[SqlWorkUnit, MetadataWorkUnit]]]
"""
+
dataset_urn = make_dataset_urn_with_platform_instance(
self.platform,
dataset_name,
self.config.platform_instance,
self.config.env,
)
- view_owner = self._get_owner_information(view, "view")
+
+ view_owner = inspector.get_view_owner(view, schema)
yield from add_owner_to_entity_wu(
entity_type="dataset",
entity_urn=dataset_urn,
owner_urn=f"urn:li:corpuser:{view_owner}",
)
+
yield from super()._process_view(
dataset_name, inspector, schema, view, sql_config
)
def loop_projections(
self,
- inspector: Inspector,
+ inspector: VerticaInspector,
schema: str,
sql_config: SQLAlchemyConfig,
) -> Iterable[Union[SqlWorkUnit, MetadataWorkUnit]]:
@@ -386,8 +377,7 @@ def loop_projections(
projections_seen: Set[str] = set()
try:
- # table_tags = self.get_extra_tags(inspector, schema, "projection")
- for projection in inspector.get_projection_names(schema): # type: ignore
+ for projection in inspector.get_projection_names(schema):
dataset_name = self.get_identifier(
schema=schema, entity=projection, inspector=inspector
)
@@ -412,7 +402,7 @@ def loop_projections(
self.report.report_warning(
f"{schema}.{projection}", f"Ingestion error: {ex}"
)
- if sql_config.include_projection_lineage: # type: ignore
+ if self.config.include_projection_lineage:
try:
dataset_urn = make_dataset_urn_with_platform_instance(
self.platform,
@@ -422,37 +412,35 @@ def loop_projections(
)
dataset_snapshot = DatasetSnapshot(
- urn=dataset_urn,
- aspects=[StatusClass(removed=False)],
+ urn=dataset_urn, aspects=[StatusClass(removed=False)]
)
+
lineage_info = self._get_upstream_lineage_info_projection(
- dataset_urn, projection
+ dataset_urn, inspector, projection, schema
)
if lineage_info is not None:
yield MetadataChangeProposalWrapper(
entityUrn=dataset_snapshot.urn, aspect=lineage_info
).as_workunit()
- except Exception as ex:
+
+ except Exception as e:
logger.warning(
- f"Unable to get lineage of Projection {schema}.{projection} due to an exception %s",
- ex,
- )
- self.report.report_warning(
- f"{schema}.{projection}", f"Ingestion error: {ex}"
+ f"Unable to get lineage of projection {projection} due to an exception.\n {traceback.format_exc()}"
)
+ self.report.report_warning(f"{schema}", f"Ingestion error: {e}")
except Exception as ex:
self.report.report_failure(f"{schema}", f"Projection error: {ex}")
def _process_projections(
self,
dataset_name: str,
- inspector: Inspector,
+ inspector: VerticaInspector,
schema: str,
projection: str,
sql_config: SQLAlchemyConfig,
) -> Iterable[Union[SqlWorkUnit, MetadataWorkUnit]]:
- columns = self._get_columns(dataset_name, inspector, schema, projection)
+ columns = inspector.get_projection_columns(projection, schema)
dataset_urn = make_dataset_urn_with_platform_instance(
self.platform,
dataset_name,
@@ -474,20 +462,7 @@ def _process_projections(
)
dataset_snapshot.aspects.append(dataset_properties)
- if location_urn:
- external_upstream_table = UpstreamClass(
- dataset=location_urn,
- type=DatasetLineageTypeClass.COPY,
- )
- yield MetadataChangeProposalWrapper(
- entityType="dataset",
- changeType=ChangeTypeClass.UPSERT,
- entityUrn=dataset_snapshot.urn,
- aspectName="upstreamLineage",
- aspect=UpstreamLineage(upstreams=[external_upstream_table]),
- ).as_workunit()
-
- projection_owner = self._get_owner_information(projection, "projection")
+ projection_owner = inspector.get_projection_owner(projection, schema)
yield from add_owner_to_entity_wu(
entity_type="dataset",
entity_urn=dataset_urn,
@@ -533,49 +508,87 @@ def _process_projections(
domain_registry=self.domain_registry,
)
- def get_projection_properties(
- self, inspector: Inspector, schema: str, projection: str
- ) -> Tuple[Optional[str], Dict[str, str], Optional[str]]:
- """
- Returns projection related metadata information to show in properties tab
- eg. projection type like super, segmented etc
- partition key, segmentation and so on
+ def loop_profiler_requests(
+ self,
+ inspector: VerticaInspector,
+ schema: str,
+ sql_config: SQLAlchemyConfig,
+ ) -> Iterable["GEProfilerRequest"]:
+ """Function is used for collecting profiling related information for every projections
+ inside an schema.
+
+ We have extended original loop_profiler_requests and added functionality for projection
+ profiling . All the logics are same only change is we have ran the loop to get projection name from inspector.get_projection_name .
+
+ Args: schema: schema name
- Args:
- inspector (Inspector): inspector obj from reflection engine
- schema (str): schema name
- projection (str): projection name
- Returns:
- Tuple[Optional[str], Dict[str, str], Optional[str]]: [description]
"""
- description: Optional[str] = None
- properties: Dict[str, str] = {}
- # The location cannot be fetched generically, but subclasses may override
- # this method and provide a location.
- location: Optional[str] = None
- try:
- # SQLAlchemy stubs are incomplete and missing this method.
- # PR: https://github.com/dropbox/sqlalchemy-stubs/pull/223.
- projection_info: dict = inspector.get_projection_comment(projection, schema) # type: ignore
- except NotImplementedError:
- return description, properties, location
- except ProgrammingError as error:
+ from datahub.ingestion.source.ge_data_profiler import GEProfilerRequest
+
+ tables_seen: Set[str] = set()
+ profile_candidates = None # Default value if profile candidates not available.
+ yield from super().loop_profiler_requests(inspector, schema, sql_config)
+
+ for projection in inspector.get_projection_names(schema):
+ dataset_name = self.get_identifier(
+ schema=schema, entity=projection, inspector=inspector
+ )
+
+ if not self.is_dataset_eligible_for_profiling(
+ dataset_name, sql_config, inspector, profile_candidates
+ ):
+ if self.config.profiling.report_dropped_profiles:
+ self.report.report_dropped(f"profile of {dataset_name}")
+ continue
+ if dataset_name not in tables_seen:
+ tables_seen.add(dataset_name)
+ else:
+ logger.debug(f"{dataset_name} has already been seen, skipping...")
+ continue
+ missing_column_info_warn = self.report.warnings.get(MISSING_COLUMN_INFO)
+ if (
+ missing_column_info_warn is not None
+ and dataset_name in missing_column_info_warn
+ ):
+ continue
+ (partition, custom_sql) = self.generate_partition_profiler_query(
+ schema, projection, self.config.profiling.partition_datetime
+ )
+ if partition is None and self.is_table_partitioned(
+ database=None, schema=schema, table=projection
+ ):
+ self.report.report_warning(
+ "profile skipped as partitioned table is empty or partition id was invalid",
+ dataset_name,
+ )
+ continue
+ if (
+ partition is not None
+ and not self.config.profiling.partition_profiling_enabled
+ ):
+ logger.debug(
+ f"{dataset_name} and partition {partition} is skipped because profiling.partition_profiling_enabled property is disabled"
+ )
+ continue
+ self.report.report_entity_profiled(dataset_name)
logger.debug(
- f"Encountered ProgrammingError. Retrying with quoted schema name for schema {schema} and table {properties} %s",
- error,
+ f"Preparing profiling request for {schema}, {projection}, {partition}"
+ )
+
+ yield GEProfilerRequest(
+ pretty_name=dataset_name,
+ batch_kwargs=self.prepare_profiler_args(
+ inspector=inspector,
+ schema=schema,
+ table=projection,
+ partition=partition,
+ custom_sql=custom_sql,
+ ),
)
- projection_info: dict = inspector.get_projection_comment(properties, f'"{schema}"') # type: ignore
- description = projection_info.get("text")
- if isinstance(description, tuple):
- # Handling for value type tuple which is coming for dialect 'db2+ibm_db'
- description = projection_info["text"][0]
- # The "properties" field is a non-standard addition to SQLAlchemy's interface.
- properties = projection_info.get("properties", {})
- return description, properties, location
def loop_models(
self,
- inspector: Inspector,
+ inspector: VerticaInspector,
schema: str,
sql_config: SQLAlchemyConfig,
) -> Iterable[Union[SqlWorkUnit, MetadataWorkUnit]]:
@@ -595,7 +608,7 @@ def loop_models(
"""
models_seen: Set[str] = set()
try:
- for models in inspector.get_models_names(schema): # type: ignore
+ for models in inspector.get_models_names(schema):
dataset_name = self.get_identifier(
schema="Entities", entity=models, inspector=inspector
)
@@ -630,7 +643,7 @@ def loop_models(
def _process_models(
self,
dataset_name: str,
- inspector: Inspector,
+ inspector: VerticaInspector,
schema: str,
table: str,
sql_config: SQLAlchemyConfig,
@@ -689,10 +702,7 @@ def _process_models(
if dpi_aspect:
yield dpi_aspect
yield MetadataChangeProposalWrapper(
- entityType="dataset",
- changeType=ChangeTypeClass.UPSERT,
entityUrn=dataset_urn,
- aspectName="subTypes",
aspect=SubTypesClass(typeNames=["ML Models"]),
).as_workunit()
if self.config.domain:
@@ -704,19 +714,20 @@ def _process_models(
domain_registry=self.domain_registry,
)
- def get_model_properties(
- self, inspector: Inspector, schema: str, model: str
+ def get_projection_properties(
+ self, inspector: VerticaInspector, schema: str, projection: str
) -> Tuple[Optional[str], Dict[str, str], Optional[str]]:
"""
- Returns ml models related metadata information to show in properties tab
- eg. ml model attribute and ml model specification information.
+ Returns projection related metadata information to show in properties tab
+ eg. projection type like super, segmented etc
+ partition key, segmentation and so on
Args:
inspector (Inspector): inspector obj from reflection engine
schema (str): schema name
- model (str): ml model name
+ projection (str): projection name
Returns:
- Tuple[Optional[str], Dict[str, str], Optional[str]]
+ Tuple[Optional[str], Dict[str, str], Optional[str]]: [description]
"""
description: Optional[str] = None
properties: Dict[str, str] = {}
@@ -724,154 +735,28 @@ def get_model_properties(
# this method and provide a location.
location: Optional[str] = None
try:
- table_info: dict = inspector.get_model_comment(model, schema) # type: ignore
+ # SQLAlchemy stubs are incomplete and missing this method.
+ # PR: https://github.com/dropbox/sqlalchemy-stubs/pull/223.
+ projection_info: dict = inspector.get_projection_comment(projection, schema)
except NotImplementedError:
return description, properties, location
- except ProgrammingError as error:
- logger.debug(
- f"Encountered ProgrammingError. Retrying with quoted schema name for schema {schema} and {model} %s",
- error,
- )
- table_info: dict = inspector.get_model_comment(model, f'"{schema}"') # type: ignore
- description = table_info.get("text")
- if type(description) is tuple:
+ description = projection_info.get("text")
+ if isinstance(description, tuple):
# Handling for value type tuple which is coming for dialect 'db2+ibm_db'
- description = table_info["text"][0]
+ description = projection_info["text"][0]
# The "properties" field is a non-standard addition to SQLAlchemy's interface.
- properties = table_info.get("properties", {})
+ properties = projection_info.get("properties", {})
return description, properties, location
- def loop_oauth(
- self,
- inspector: Inspector,
- schema: str,
- sql_config: SQLAlchemyConfig,
- ) -> Iterable[Union[SqlWorkUnit, MetadataWorkUnit]]:
- """
- This function is for iterating over the oauth in vertica db
- Args:
- inspector (Inspector):
- schema (str): schema name
- sql_config (SQLAlchemyConfig): configuration
-
- Returns:
- Iterable[Union[SqlWorkUnit, MetadataWorkUnit]]: [description]
- """
- oauth_seen: Set[str] = set()
- try:
- for oauth in inspector.get_Oauth_names(schema): # type: ignore
- dataset_name = self.get_identifier(
- schema=schema, entity=oauth, inspector=inspector
- )
-
- if dataset_name not in oauth_seen:
- oauth_seen.add(dataset_name)
- else:
- logger.debug("has already been seen, skipping %s", dataset_name)
- continue
- self.report.report_entity_scanned(dataset_name, ent_type="oauth")
- if not sql_config.table_pattern.allowed(dataset_name):
- self.report.report_dropped(dataset_name)
- continue
- try:
- yield from self._process_oauth(
- dataset_name,
- inspector,
- schema,
- oauth,
- sql_config,
- )
- except Exception as error:
- self.report.report_warning(
- f"{schema}.{oauth}", f"Ingestion error: {error}"
- )
- except Exception as error:
- self.report.report_failure(f"{schema}", f"oauth error: {error}")
-
- def _process_oauth(
- self,
- dataset_name: str,
- inspector: Inspector,
- schema: str,
- oauth: str,
- sql_config: SQLAlchemyConfig,
- ) -> Iterable[Union[SqlWorkUnit, MetadataWorkUnit]]:
- """
- To fetch oauth related metadata of oauth from vertica db
- Args:
- dataset_name (str): dataset name
- inspector (Inspector): inspector object from reflection
- schema (str): schema name
- Oauth (str): oauth name
- sql_config (SQLAlchemyConfig): configuration
- Returns:
- Iterable[Union[SqlWorkUnit, MetadataWorkUnit]]
- """
- columns: List[Dict[Any, Any]] = []
- dataset_urn = make_dataset_urn_with_platform_instance(
- self.platform,
- dataset_name,
- self.config.platform_instance,
- self.config.env,
- )
- dataset_snapshot = DatasetSnapshot(
- urn=dataset_urn,
- aspects=[StatusClass(removed=False)],
- )
- description, properties, location_urn = self.get_oauth_properties(
- inspector, schema, oauth
- )
-
- dataset_properties = DatasetPropertiesClass(
- name=oauth,
- description=description,
- customProperties=properties,
- )
- dataset_snapshot.aspects.append(dataset_properties)
-
- schema_fields = self.get_schema_fields(dataset_name, columns)
- schema_metadata = get_schema_metadata(
- self.report,
- dataset_name,
- self.platform,
- columns,
- schema_fields, # type: ignore
- )
- dataset_snapshot.aspects.append(schema_metadata)
- db_name = self.get_db_name(inspector)
-
- yield from self.add_table_to_schema_container(dataset_urn, db_name, schema)
- mce = MetadataChangeEvent(proposedSnapshot=dataset_snapshot)
- yield SqlWorkUnit(id=dataset_name, mce=mce)
- dpi_aspect = self.get_dataplatform_instance_aspect(dataset_urn=dataset_urn)
- if dpi_aspect:
- yield dpi_aspect
- yield MetadataChangeProposalWrapper(
- entityType="dataset",
- changeType=ChangeTypeClass.UPSERT,
- entityUrn=dataset_urn,
- aspectName="subTypes",
- aspect=SubTypesClass(typeNames=["oauth"]),
- ).as_workunit()
-
- if self.config.domain:
- assert self.domain_registry
- yield from get_domain_wu(
- dataset_name=dataset_name,
- entity_urn=dataset_urn,
- domain_config=self.config.domain,
- domain_registry=self.domain_registry,
- )
-
- def get_oauth_properties(
- self, inspector: Inspector, schema: str, model: str
+ def get_model_properties(
+ self, inspector: VerticaInspector, schema: str, model: str
) -> Tuple[Optional[str], Dict[str, str], Optional[str]]:
"""
- Returns oauth related metadata information to show in properties tab
- eg. is_auth_enabled , auth_priority and all auth related info.
+ Returns ml models related metadata information to show in properties tab
+ eg. ml model attribute and ml model specification information.
Args:
- inspector (Inspector): inspector obj from reflection engine
+ inspector (VerticaInspector): inspector obj from reflection engine
schema (str): schema name
model (str): ml model name
Returns:
@@ -883,110 +768,39 @@ def get_oauth_properties(
# this method and provide a location.
location: Optional[str] = None
try:
- table_info: dict = inspector.get_oauth_comment(model, schema) # type: ignore
+ table_info: dict = inspector.get_model_comment(model, schema)
except NotImplementedError:
return description, properties, location
- except ProgrammingError as error:
- logger.debug(
- f"Encountered ProgrammingError. Retrying with quoted schema name for schema {schema} and oauth {model} %s",
- error,
- )
- table_info: dict = inspector.get_oauth_comment(model, f'"{schema}"') # type: ignore
description = table_info.get("text")
- if isinstance(description, tuple):
- # Handling for value type tuple which is coming for dialect 'db2+ibm_db'
- description = table_info["text"][0]
+
# The "properties" field is a non-standard addition to SQLAlchemy's interface.
properties = table_info.get("properties", {})
return description, properties, location
- def loop_profiler_requests(
+ def _get_upstream_lineage_info(
self,
- inspector: Inspector,
+ dataset_urn: str,
+ inspector: VerticaInspector,
+ view: str,
schema: str,
- sql_config: SQLAlchemyConfig,
- ) -> Iterable["GEProfilerRequest"]:
- """Function is used for collecting profiling related information for every projections
- inside an schema.
-
- Args: schema: schema name
-
- """
- from datahub.ingestion.source.ge_data_profiler import GEProfilerRequest
-
- tables_seen: Set[str] = set()
- profile_candidates = None # Default value if profile candidates not available.
- yield from super().loop_profiler_requests(inspector, schema, sql_config)
- for projection in inspector.get_projection_names(schema): # type: ignore
- dataset_name = self.get_identifier(
- schema=schema, entity=projection, inspector=inspector
- )
-
- if not self.is_dataset_eligible_for_profiling(
- dataset_name, sql_config, inspector, profile_candidates
- ):
- if self.config.profiling.report_dropped_profiles:
- self.report.report_dropped(f"profile of {dataset_name}")
- continue
- if dataset_name not in tables_seen:
- tables_seen.add(dataset_name)
- else:
- logger.debug(f"{dataset_name} has already been seen, skipping...")
- continue
- missing_column_info_warn = self.report.warnings.get(MISSING_COLUMN_INFO)
- if (
- missing_column_info_warn is not None
- and dataset_name in missing_column_info_warn
- ):
- continue
- (partition, custom_sql) = self.generate_partition_profiler_query(
- schema, projection, self.config.profiling.partition_datetime
- )
- if partition is None and self.is_table_partitioned(
- database=None, schema=schema, table=projection
- ):
- self.report.report_warning(
- "profile skipped as partitioned table is empty or partition id was invalid",
- dataset_name,
- )
- continue
- if (
- partition is not None
- and not self.config.profiling.partition_profiling_enabled
- ):
- logger.debug(
- f"{dataset_name} and partition {partition} is skipped because profiling.partition_profiling_enabled property is disabled"
- )
- continue
- self.report.report_entity_profiled(dataset_name)
- logger.debug(
- f"Preparing profiling request for {schema}, {projection}, {partition}"
- )
-
- yield GEProfilerRequest(
- pretty_name=dataset_name,
- batch_kwargs=self.prepare_profiler_args(
- inspector=inspector,
- schema=schema,
- table=projection,
- partition=partition,
- custom_sql=custom_sql,
- ),
- )
-
- def _get_upstream_lineage_info(
- self, dataset_urn: str, view: str
) -> Optional[_Aspect]:
dataset_key = dataset_urn_to_key(dataset_urn)
if dataset_key is None:
logger.warning(f"Invalid dataset urn {dataset_urn}. Could not get key!")
return None
- self._populate_view_lineage(view)
- dataset_name = dataset_key.name
- lineage = self.view_lineage_map[dataset_name] # type: ignore
+ view_lineage_map = inspector._populate_view_lineage(view, schema)
+ if dataset_key.name is not None:
+ dataset_name = dataset_key.name
- if not (lineage):
+ else:
+ # Handle the case when dataset_key.name is None
+ # You can raise an exception, log a warning, or take any other appropriate action
+ logger.warning("Invalid dataset name")
+
+ lineage = view_lineage_map[dataset_name]
+
+ if lineage is None:
logger.debug(f"No lineage found for {dataset_name}")
return None
upstream_tables: List[UpstreamClass] = []
@@ -1015,87 +829,21 @@ def _get_upstream_lineage_info(
return None
- def _populate_view_lineage(self, view: str) -> None:
- """Collects upstream and downstream lineage information for views .
-
- Args:
- view (str): name of the view
-
- """
-
- url = self.config.get_sql_alchemy_url()
- engine = create_engine(url, **self.config.options)
-
- get_refrence_table = sql.text(
- dedent(
- """ select reference_table_name from v_catalog.view_tables where table_name = '%(view)s' """
- % {"view": view}
- )
- )
-
- refrence_table = ""
- for data in engine.execute(get_refrence_table):
- # refrence_table.append(data)
- refrence_table = data["reference_table_name"]
-
- view_upstream_lineage_query = sql.text(
- dedent(
- """
- select reference_table_name ,reference_table_schema from v_catalog.view_tables where table_name = '%(view)s' """
- % {"view": view}
- )
- )
-
- view_downstream_query = sql.text(
- dedent(
- """
- select table_name ,table_schema from v_catalog.view_tables where reference_table_name = '%(view)s'
- """
- % {"view": refrence_table}
- )
- )
- num_edges: int = 0
-
- try:
- self.view_lineage_map = defaultdict(list)
- for db_row_key in engine.execute(view_downstream_query):
- downstream = f"{db_row_key['table_schema']}.{db_row_key['table_name']}"
-
- for db_row_value in engine.execute(view_upstream_lineage_query):
- upstream = f"{db_row_value['reference_table_schema']}.{db_row_value['reference_table_name']}"
-
- view_upstream: str = upstream
- view_name: str = downstream
- self.view_lineage_map[view_name].append(
- # (
, , )
- (view_upstream, "[]", "[]")
- )
-
- num_edges += 1
-
- except Exception as e:
- self.warn(
- logger,
- "view_upstream_lineage",
- "Extracting the upstream & Downstream view lineage from vertica failed."
- + f"Please check your permissions. Continuing...\nError was {e}.",
- )
-
- logger.info(
- f"A total of {num_edges} View upstream edges found found for {view}"
- )
-
def _get_upstream_lineage_info_projection(
- self, dataset_urn: str, projection: str
+ self,
+ dataset_urn: str,
+ inspector: VerticaInspector,
+ projection: str,
+ schema: str,
) -> Optional[_Aspect]:
dataset_key = dataset_urn_to_key(dataset_urn)
if dataset_key is None:
logger.warning(f"Invalid dataset urn {dataset_urn}. Could not get key!")
return None
- self._populate_projection_lineage(projection)
+ projection_lineage = inspector._populate_projection_lineage(projection, schema)
dataset_name = dataset_key.name
- lineage = self.projection_lineage_map[dataset_name] # type: ignore
+ lineage = projection_lineage[dataset_name]
if not (lineage):
logger.debug(f"No lineage found for {dataset_name}")
@@ -1103,7 +851,7 @@ def _get_upstream_lineage_info_projection(
upstream_tables: List[UpstreamClass] = []
for lineage_entry in lineage:
- # Update the projection-lineage
+ # Update the view-lineage
upstream_table_name = lineage_entry[0]
upstream_table = UpstreamClass(
@@ -1119,115 +867,9 @@ def _get_upstream_lineage_info_projection(
if upstream_tables:
logger.debug(
- f"lineage of Projection '{dataset_name}': {[u.dataset for u in upstream_tables]}"
- )
- return UpstreamLineage(upstreams=upstream_tables)
- return None
-
- def _populate_projection_lineage(self, projection: str) -> None:
- """
- Collects upstream and downstream lineage information for views .
-
- Args:
- projection (str): name of the projection
-
-
- """
- url = self.config.get_sql_alchemy_url()
- logger.debug(f"sql_alchemy_url={url}")
- engine = create_engine(url, **self.config.options)
-
- view_upstream_lineage_query = sql.text(
- dedent(
- """ select basename , schemaname from vs_projections where name ='%(projection)s'
- """
- % {"projection": projection}
- )
- )
-
- num_edges: int = 0
-
- try:
- self.projection_lineage_map = defaultdict(list)
- for db_row_key in engine.execute(view_upstream_lineage_query):
- basename = db_row_key["basename"]
- upstream = f"{db_row_key['schemaname']}.{db_row_key['basename']}"
-
- view_downstream_query = sql.text(
- dedent(
- """
- select name,schemaname
- from vs_projections
- where basename='%(basename)s'
- """
- % {"basename": basename}
- )
- )
- for db_row_value in engine.execute(view_downstream_query):
- downstream = f"{db_row_value['schemaname']}.{db_row_value['name']}"
- projection_upstream: str = upstream
- projection_name: str = downstream
- self.projection_lineage_map[projection_name].append(
- # (, , )
- (projection_upstream, "[]", "[]")
- )
- num_edges += 1
-
- except Exception as error:
- logger.warning(
- "Extracting the upstream & downstream Projection lineage from Vertica failed %s",
- error,
- )
-
- logger.info(
- f"A total of {num_edges} Projection lineage edges found for {projection}."
- )
-
- def _get_owner_information(self, table: str, label: str) -> Optional[str]:
- url = self.config.get_sql_alchemy_url()
- engine = create_engine(url, **self.config.options)
- if label == "table":
- get_owner_query = sql.text(
- dedent(
- """
- SELECT owner_name
- FROM v_catalog.tables
- WHERE table_name = '%(table)s'
- """
- % {"table": table}
- )
- )
-
- for each in engine.execute(get_owner_query):
- return each["owner_name"]
- elif label == "view":
- get_owner_query = sql.text(
- dedent(
- """
- SELECT owner_name
- FROM v_catalog.views
- WHERE table_name = '%(view)s'
- """
- % {"view": table}
- )
- )
-
- for each in engine.execute(get_owner_query):
- return each["owner_name"]
-
- elif label == "projection":
- get_owner_query = sql.text(
- dedent(
- """
- SELECT owner_name
- FROM v_catalog.projections
- WHERE projection_name = '%(projection)s'
- """
- % {"projection": table}
- )
+ f" lineage of '{dataset_name}': {[u.dataset for u in upstream_tables]}"
)
- for each in engine.execute(get_owner_query):
- return each["owner_name"]
+ return UpstreamLineage(upstreams=upstream_tables)
return None
diff --git a/metadata-ingestion/src/datahub/ingestion/source/tableau.py b/metadata-ingestion/src/datahub/ingestion/source/tableau.py
index e923727a990f4..6752bdf519830 100644
--- a/metadata-ingestion/src/datahub/ingestion/source/tableau.py
+++ b/metadata-ingestion/src/datahub/ingestion/source/tableau.py
@@ -4,14 +4,24 @@
from dataclasses import dataclass
from datetime import datetime
from functools import lru_cache
-from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Union, cast
+from typing import (
+ Any,
+ Callable,
+ Dict,
+ Iterable,
+ List,
+ Optional,
+ Set,
+ Tuple,
+ Union,
+ cast,
+)
import dateutil.parser as dp
import tableauserverclient as TSC
from pydantic import root_validator, validator
from pydantic.fields import Field
from requests.adapters import ConnectionError
-from sqllineage.runner import LineageRunner
from tableauserverclient import (
PersonalAccessTokenAuth,
Server,
@@ -71,8 +81,11 @@
dashboard_graphql_query,
database_tables_graphql_query,
embedded_datasource_graphql_query,
+ get_overridden_info,
get_unique_custom_sql,
+ make_fine_grained_lineage_class,
make_table_urn,
+ make_upstream_class,
published_datasource_graphql_query,
query_metadata,
sheet_graphql_query,
@@ -120,10 +133,15 @@
OwnershipClass,
OwnershipTypeClass,
SubTypesClass,
- UpstreamClass,
ViewPropertiesClass,
)
from datahub.utilities import config_clean
+from datahub.utilities.sqlglot_lineage import (
+ ColumnLineageInfo,
+ SchemaResolver,
+ SqlParsingResult,
+ sqlglot_lineage,
+)
logger: logging.Logger = logging.getLogger(__name__)
@@ -870,14 +888,14 @@ def _create_upstream_table_lineage(
f"A total of {len(upstream_tables)} upstream table edges found for datasource {datasource[tableau_constant.ID]}"
)
- if datasource.get(tableau_constant.FIELDS):
- datasource_urn = builder.make_dataset_urn_with_platform_instance(
- platform=self.platform,
- name=datasource[tableau_constant.ID],
- platform_instance=self.config.platform_instance,
- env=self.config.env,
- )
+ datasource_urn = builder.make_dataset_urn_with_platform_instance(
+ platform=self.platform,
+ name=datasource[tableau_constant.ID],
+ platform_instance=self.config.platform_instance,
+ env=self.config.env,
+ )
+ if datasource.get(tableau_constant.FIELDS):
if self.config.extract_column_level_lineage:
# Find fine grained lineage for datasource column to datasource column edge,
# upstream columns may be from same datasource
@@ -1140,6 +1158,57 @@ def get_upstream_fields_of_field_in_datasource(self, datasource, datasource_urn)
)
return fine_grained_lineages
+ def get_upstream_fields_from_custom_sql(
+ self, datasource: dict, datasource_urn: str
+ ) -> List[FineGrainedLineage]:
+ fine_grained_lineages: List[FineGrainedLineage] = []
+
+ parsed_result = self.parse_custom_sql(
+ datasource=datasource,
+ datasource_urn=datasource_urn,
+ env=self.config.env,
+ platform=self.platform,
+ platform_instance=self.config.platform_instance,
+ func_overridden_info=None, # Here we don't want to override any information from configuration
+ )
+
+ if parsed_result is None:
+ logger.info(
+ f"Failed to extract column level lineage from datasource {datasource_urn}"
+ )
+ return fine_grained_lineages
+
+ cll: List[ColumnLineageInfo] = (
+ parsed_result.column_lineage
+ if parsed_result.column_lineage is not None
+ else []
+ )
+ for cll_info in cll:
+ downstream = (
+ [
+ builder.make_schema_field_urn(
+ datasource_urn, cll_info.downstream.column
+ )
+ ]
+ if cll_info.downstream is not None
+ and cll_info.downstream.column is not None
+ else []
+ )
+ upstreams = [
+ builder.make_schema_field_urn(column_ref.table, column_ref.column)
+ for column_ref in cll_info.upstreams
+ ]
+ fine_grained_lineages.append(
+ FineGrainedLineage(
+ downstreamType=FineGrainedLineageDownstreamType.FIELD,
+ downstreams=downstream,
+ upstreamType=FineGrainedLineageUpstreamType.FIELD_SET,
+ upstreams=upstreams,
+ )
+ )
+
+ return fine_grained_lineages
+
def get_transform_operation(self, field):
field_type = field[tableau_constant.TYPE_NAME]
if field_type in (
@@ -1176,6 +1245,7 @@ def emit_custom_sql_datasources(self) -> Iterable[MetadataWorkUnit]:
platform_instance=self.config.platform_instance,
env=self.config.env,
)
+
dataset_snapshot = DatasetSnapshot(
urn=csql_urn,
aspects=[self.get_data_platform_instance()],
@@ -1225,14 +1295,20 @@ def emit_custom_sql_datasources(self) -> Iterable[MetadataWorkUnit]:
csql_urn, tables, datasource
)
elif self.config.extract_lineage_from_unsupported_custom_sql_queries:
+ logger.debug("Extracting TLL & CLL from custom sql")
# custom sql tables may contain unsupported sql, causing incomplete lineage
# we extract the lineage from the raw queries
yield from self._create_lineage_from_unsupported_csql(
csql_urn, csql
)
-
# Schema Metadata
- columns = csql.get(tableau_constant.COLUMNS, [])
+ # if condition is needed as graphQL return "cloumns": None
+ columns: List[Dict[Any, Any]] = (
+ cast(List[Dict[Any, Any]], csql.get(tableau_constant.COLUMNS))
+ if tableau_constant.COLUMNS in csql
+ and csql.get(tableau_constant.COLUMNS) is not None
+ else []
+ )
schema_metadata = self.get_schema_metadata_for_custom_sql(columns)
if schema_metadata is not None:
dataset_snapshot.aspects.append(schema_metadata)
@@ -1450,53 +1526,143 @@ def _create_lineage_to_upstream_tables(
aspect=upstream_lineage,
)
- def _create_lineage_from_unsupported_csql(
- self, csql_urn: str, csql: dict
- ) -> Iterable[MetadataWorkUnit]:
- database = csql.get(tableau_constant.DATABASE) or {}
+ def parse_custom_sql(
+ self,
+ datasource: dict,
+ datasource_urn: str,
+ platform: str,
+ env: str,
+ platform_instance: Optional[str],
+ func_overridden_info: Optional[
+ Callable[
+ [
+ str,
+ Optional[str],
+ Optional[Dict[str, str]],
+ Optional[TableauLineageOverrides],
+ ],
+ Tuple[Optional[str], Optional[str], str, str],
+ ]
+ ],
+ ) -> Optional["SqlParsingResult"]:
+
+ database_info = datasource.get(tableau_constant.DATABASE) or {}
+
+ if datasource.get(tableau_constant.IS_UNSUPPORTED_CUSTOM_SQL) in (None, False):
+ logger.debug(f"datasource {datasource_urn} is not created from custom sql")
+ return None
+
if (
- csql.get(tableau_constant.IS_UNSUPPORTED_CUSTOM_SQL, False)
- and tableau_constant.NAME in database
- and tableau_constant.CONNECTION_TYPE in database
+ tableau_constant.NAME not in database_info
+ or tableau_constant.CONNECTION_TYPE not in database_info
):
- upstream_tables = []
- query = csql.get(tableau_constant.QUERY)
- parser = LineageRunner(query)
-
- try:
- for table in parser.source_tables:
- split_table = str(table).split(".")
- if len(split_table) == 2:
- datset = make_table_urn(
- env=self.config.env,
- upstream_db=database.get(tableau_constant.NAME),
- connection_type=database.get(
- tableau_constant.CONNECTION_TYPE, ""
- ),
- schema=split_table[0],
- full_name=split_table[1],
- platform_instance_map=self.config.platform_instance_map,
- lineage_overrides=self.config.lineage_overrides,
- )
- upstream_tables.append(
- UpstreamClass(
- type=DatasetLineageType.TRANSFORMED, dataset=datset
- )
- )
- except Exception as e:
- self.report.report_warning(
- key="csql-lineage",
- reason=f"Unable to retrieve lineage from query. "
- f"Query: {query} "
- f"Reason: {str(e)} ",
+ logger.debug(
+ f"database information is missing from datasource {datasource_urn}"
+ )
+ return None
+
+ query = datasource.get(tableau_constant.QUERY)
+ if query is None:
+ logger.debug(
+ f"raw sql query is not available for datasource {datasource_urn}"
+ )
+ return None
+
+ logger.debug(f"Parsing sql={query}")
+
+ upstream_db = database_info.get(tableau_constant.NAME)
+
+ if func_overridden_info is not None:
+ # Override the information as per configuration
+ upstream_db, platform_instance, platform, _ = func_overridden_info(
+ database_info[tableau_constant.CONNECTION_TYPE],
+ database_info.get(tableau_constant.NAME),
+ self.config.platform_instance_map,
+ self.config.lineage_overrides,
+ )
+
+ logger.debug(
+ f"Overridden info upstream_db={upstream_db}, platform_instance={platform_instance}, platform={platform}"
+ )
+
+ parsed_result: Optional["SqlParsingResult"] = None
+ try:
+ schema_resolver = (
+ self.ctx.graph._make_schema_resolver(
+ platform=platform,
+ platform_instance=platform_instance,
+ env=env,
+ )
+ if self.ctx.graph is not None
+ else SchemaResolver(
+ platform=platform,
+ platform_instance=platform_instance,
+ env=env,
+ graph=None,
)
- upstream_lineage = UpstreamLineage(upstreams=upstream_tables)
- yield self.get_metadata_change_proposal(
- csql_urn,
- aspect_name=tableau_constant.UPSTREAM_LINEAGE,
- aspect=upstream_lineage,
)
+ if schema_resolver.graph is None:
+ logger.warning(
+ "Column Level Lineage extraction would not work as DataHub graph client is None."
+ )
+
+ parsed_result = sqlglot_lineage(
+ query,
+ schema_resolver=schema_resolver,
+ default_db=upstream_db,
+ )
+ except Exception as e:
+ self.report.report_warning(
+ key="csql-lineage",
+ reason=f"Unable to retrieve lineage from query. "
+ f"Query: {query} "
+ f"Reason: {str(e)} ",
+ )
+
+ return parsed_result
+
+ def _create_lineage_from_unsupported_csql(
+ self, csql_urn: str, csql: dict
+ ) -> Iterable[MetadataWorkUnit]:
+
+ parsed_result = self.parse_custom_sql(
+ datasource=csql,
+ datasource_urn=csql_urn,
+ env=self.config.env,
+ platform=self.platform,
+ platform_instance=self.config.platform_instance,
+ func_overridden_info=get_overridden_info,
+ )
+
+ if parsed_result is None:
+ logger.info(
+ f"Failed to extract table level lineage for datasource {csql_urn}"
+ )
+ return
+
+ upstream_tables = make_upstream_class(parsed_result)
+
+ logger.debug(f"Upstream tables = {upstream_tables}")
+
+ fine_grained_lineages: List[FineGrainedLineage] = []
+ if self.config.extract_column_level_lineage:
+ logger.info("Extracting CLL from custom sql")
+ fine_grained_lineages = make_fine_grained_lineage_class(
+ parsed_result, csql_urn
+ )
+
+ upstream_lineage = UpstreamLineage(
+ upstreams=upstream_tables,
+ fineGrainedLineages=fine_grained_lineages,
+ )
+
+ yield self.get_metadata_change_proposal(
+ csql_urn,
+ aspect_name=tableau_constant.UPSTREAM_LINEAGE,
+ aspect=upstream_lineage,
+ )
+
def _get_schema_metadata_for_datasource(
self, datasource_fields: List[dict]
) -> Optional[SchemaMetadata]:
diff --git a/metadata-ingestion/src/datahub/ingestion/source/tableau_common.py b/metadata-ingestion/src/datahub/ingestion/source/tableau_common.py
index e573f1d924d1a..2c92285fdba77 100644
--- a/metadata-ingestion/src/datahub/ingestion/source/tableau_common.py
+++ b/metadata-ingestion/src/datahub/ingestion/source/tableau_common.py
@@ -1,11 +1,17 @@
import html
from functools import lru_cache
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
from pydantic.fields import Field
import datahub.emitter.mce_builder as builder
from datahub.configuration.common import ConfigModel
+from datahub.metadata.com.linkedin.pegasus2avro.dataset import (
+ DatasetLineageType,
+ FineGrainedLineage,
+ FineGrainedLineageDownstreamType,
+ FineGrainedLineageUpstreamType,
+)
from datahub.metadata.com.linkedin.pegasus2avro.schema import (
ArrayTypeClass,
BooleanTypeClass,
@@ -21,7 +27,9 @@
BytesTypeClass,
GlobalTagsClass,
TagAssociationClass,
+ UpstreamClass,
)
+from datahub.utilities.sqlglot_lineage import ColumnLineageInfo, SqlParsingResult
class TableauLineageOverrides(ConfigModel):
@@ -574,15 +582,13 @@ def get_platform_instance(
return None
-def make_table_urn(
- env: str,
- upstream_db: Optional[str],
+def get_overridden_info(
connection_type: str,
- schema: str,
- full_name: str,
+ upstream_db: Optional[str],
platform_instance_map: Optional[Dict[str, str]],
lineage_overrides: Optional[TableauLineageOverrides] = None,
-) -> str:
+) -> Tuple[Optional[str], Optional[str], str, str]:
+
original_platform = platform = get_platform(connection_type)
if (
lineage_overrides is not None
@@ -599,10 +605,38 @@ def make_table_urn(
):
upstream_db = lineage_overrides.database_override_map[upstream_db]
+ platform_instance = get_platform_instance(original_platform, platform_instance_map)
+
+ if original_platform in ("athena", "hive", "mysql"): # Two tier databases
+ upstream_db = None
+
+ return upstream_db, platform_instance, platform, original_platform
+
+
+def make_table_urn(
+ env: str,
+ upstream_db: Optional[str],
+ connection_type: str,
+ schema: str,
+ full_name: str,
+ platform_instance_map: Optional[Dict[str, str]],
+ lineage_overrides: Optional[TableauLineageOverrides] = None,
+) -> str:
+
+ upstream_db, platform_instance, platform, original_platform = get_overridden_info(
+ connection_type=connection_type,
+ upstream_db=upstream_db,
+ lineage_overrides=lineage_overrides,
+ platform_instance_map=platform_instance_map,
+ )
+
table_name = get_fully_qualified_table_name(
- original_platform, upstream_db, schema, full_name
+ original_platform,
+ upstream_db if upstream_db is not None else "",
+ schema,
+ full_name,
)
- platform_instance = get_platform_instance(original_platform, platform_instance_map)
+
return builder.make_dataset_urn_with_platform_instance(
platform, table_name, platform_instance, env
)
@@ -620,6 +654,57 @@ def make_description_from_params(description, formula):
return final_description
+def make_upstream_class(
+ parsed_result: Optional[SqlParsingResult],
+) -> List[UpstreamClass]:
+ upstream_tables: List[UpstreamClass] = []
+
+ if parsed_result is None:
+ return upstream_tables
+
+ for dataset_urn in parsed_result.in_tables:
+ upstream_tables.append(
+ UpstreamClass(type=DatasetLineageType.TRANSFORMED, dataset=dataset_urn)
+ )
+ return upstream_tables
+
+
+def make_fine_grained_lineage_class(
+ parsed_result: Optional[SqlParsingResult], dataset_urn: str
+) -> List[FineGrainedLineage]:
+ fine_grained_lineages: List[FineGrainedLineage] = []
+
+ if parsed_result is None:
+ return fine_grained_lineages
+
+ cll: List[ColumnLineageInfo] = (
+ parsed_result.column_lineage if parsed_result.column_lineage is not None else []
+ )
+
+ for cll_info in cll:
+ downstream = (
+ [builder.make_schema_field_urn(dataset_urn, cll_info.downstream.column)]
+ if cll_info.downstream is not None
+ and cll_info.downstream.column is not None
+ else []
+ )
+ upstreams = [
+ builder.make_schema_field_urn(column_ref.table, column_ref.column)
+ for column_ref in cll_info.upstreams
+ ]
+
+ fine_grained_lineages.append(
+ FineGrainedLineage(
+ downstreamType=FineGrainedLineageDownstreamType.FIELD,
+ downstreams=downstream,
+ upstreamType=FineGrainedLineageUpstreamType.FIELD_SET,
+ upstreams=upstreams,
+ )
+ )
+
+ return fine_grained_lineages
+
+
def get_unique_custom_sql(custom_sql_list: List[dict]) -> List[dict]:
unique_custom_sql = []
for custom_sql in custom_sql_list:
diff --git a/metadata-ingestion/src/datahub/ingestion/transformer/dataset_transformer.py b/metadata-ingestion/src/datahub/ingestion/transformer/dataset_transformer.py
index 0753d6c3fd830..0b2433c3a1fe2 100644
--- a/metadata-ingestion/src/datahub/ingestion/transformer/dataset_transformer.py
+++ b/metadata-ingestion/src/datahub/ingestion/transformer/dataset_transformer.py
@@ -75,7 +75,11 @@ def update_if_keep_existing(
) -> None:
"""Check if user want to keep existing tags"""
if in_global_tags_aspect is not None and config.replace_existing is False:
- out_global_tags_aspect.tags.extend(in_global_tags_aspect.tags)
+ tags_seen = set()
+ for item in in_global_tags_aspect.tags:
+ if item.tag not in tags_seen:
+ out_global_tags_aspect.tags.append(item)
+ tags_seen.add(item.tag)
@staticmethod
def get_result_semantics(
diff --git a/metadata-ingestion/src/datahub/utilities/mapping.py b/metadata-ingestion/src/datahub/utilities/mapping.py
index e7c60435e13f8..32666ceecdf85 100644
--- a/metadata-ingestion/src/datahub/utilities/mapping.py
+++ b/metadata-ingestion/src/datahub/utilities/mapping.py
@@ -252,11 +252,11 @@ def sanitize_owner_ids(self, owner_id: str) -> str:
def get_match(self, match_clause: Any, raw_props_value: Any) -> Optional[Match]:
# function to check if a match clause is satisfied to a value.
- if type(raw_props_value) not in Constants.OPERAND_DATATYPE_SUPPORTED or type(
- raw_props_value
- ) != type(match_clause):
+ if not any(
+ isinstance(raw_props_value, t) for t in Constants.OPERAND_DATATYPE_SUPPORTED
+ ) or not isinstance(raw_props_value, type(match_clause)):
return None
- elif type(raw_props_value) == str:
+ elif isinstance(raw_props_value, str):
return re.match(match_clause, raw_props_value)
else:
return re.match(str(match_clause), str(raw_props_value))
diff --git a/metadata-ingestion/src/datahub_provider/_plugin.py b/metadata-ingestion/src/datahub_provider/_plugin.py
index b4ac5a36c6eae..6f6c7c9ab71b7 100644
--- a/metadata-ingestion/src/datahub_provider/_plugin.py
+++ b/metadata-ingestion/src/datahub_provider/_plugin.py
@@ -107,7 +107,7 @@ def get_inlets_from_task(task: BaseOperator, context: Any) -> Iterable[Any]:
]
for inlet in task_inlets:
- if type(inlet) != str:
+ if isinstance(inlet, str):
inlets.append(inlet)
return inlets
diff --git a/metadata-ingestion/src/datahub_provider/operators/datahub_assertion_operator.py b/metadata-ingestion/src/datahub_provider/operators/datahub_assertion_operator.py
index 89a037324e7cb..28be8ad860179 100644
--- a/metadata-ingestion/src/datahub_provider/operators/datahub_assertion_operator.py
+++ b/metadata-ingestion/src/datahub_provider/operators/datahub_assertion_operator.py
@@ -62,9 +62,9 @@ def execute(self, context: Any) -> bool:
return True
self.log.info(f"Checking if dataset {self.urn} is ready to be consumed")
- if type(self.urn) == str:
+ if isinstance(self.urn, str):
urns = [self.urn]
- elif type(self.urn) == list:
+ elif isinstance(self.urn, list):
urns = self.urn
else:
raise Exception(f"urn parameter has invalid type {type(self.urn)}")
diff --git a/metadata-ingestion/src/datahub_provider/operators/datahub_assertion_sensor.py b/metadata-ingestion/src/datahub_provider/operators/datahub_assertion_sensor.py
index 55a3492f9c8d6..ceb970dd8dc7f 100644
--- a/metadata-ingestion/src/datahub_provider/operators/datahub_assertion_sensor.py
+++ b/metadata-ingestion/src/datahub_provider/operators/datahub_assertion_sensor.py
@@ -61,9 +61,9 @@ def poke(self, context: Any) -> bool:
return True
self.log.info(f"Checking if dataset {self.urn} is ready to be consumed")
- if type(self.urn) == str:
+ if isinstance(self.urn, str):
urns = [self.urn]
- elif type(self.urn) == list:
+ elif isinstance(self.urn, list):
urns = self.urn
else:
raise Exception(f"urn parameter has invalid type {type(self.urn)}")
diff --git a/metadata-ingestion/src/datahub_provider/operators/datahub_operation_operator.py b/metadata-ingestion/src/datahub_provider/operators/datahub_operation_operator.py
index e5e45c2bf4694..6b2535994c101 100644
--- a/metadata-ingestion/src/datahub_provider/operators/datahub_operation_operator.py
+++ b/metadata-ingestion/src/datahub_provider/operators/datahub_operation_operator.py
@@ -76,9 +76,9 @@ def execute(self, context: Any) -> bool:
return True
self.log.info(f"Checking if dataset {self.urn} is ready to be consumed")
- if type(self.urn) == str:
+ if isinstance(self.urn, str):
urns = [self.urn]
- elif type(self.urn) == list:
+ elif isinstance(self.urn, list):
urns = self.urn
else:
raise Exception(f"urn parameter has invalid type {type(self.urn)}")
diff --git a/metadata-ingestion/src/datahub_provider/operators/datahub_operation_sensor.py b/metadata-ingestion/src/datahub_provider/operators/datahub_operation_sensor.py
index 31b387a7e65b0..8796215453500 100644
--- a/metadata-ingestion/src/datahub_provider/operators/datahub_operation_sensor.py
+++ b/metadata-ingestion/src/datahub_provider/operators/datahub_operation_sensor.py
@@ -78,9 +78,9 @@ def poke(self, context: Any) -> bool:
return True
self.log.info(f"Checking if dataset {self.urn} is ready to be consumed")
- if type(self.urn) == str:
+ if isinstance(self.urn, str):
urns = [self.urn]
- elif type(self.urn) == list:
+ elif isinstance(self.urn, list):
urns = self.urn
else:
raise Exception(f"urn parameter has invalid type {type(self.urn)}")
diff --git a/metadata-ingestion/tests/integration/tableau/setup/customSQLTablesConnection_all.json b/metadata-ingestion/tests/integration/tableau/setup/customSQLTablesConnection_all.json
index 5c01adbd8a673..e0ef5a8e0b5a5 100644
--- a/metadata-ingestion/tests/integration/tableau/setup/customSQLTablesConnection_all.json
+++ b/metadata-ingestion/tests/integration/tableau/setup/customSQLTablesConnection_all.json
@@ -754,13 +754,73 @@
"connectionType": "postgres"
}
]
- }
+ },
+ {
+ "id": "10c6297d-0dbd-44f1-b1ba-458bea446513",
+ "name": "Custom SQL Query",
+ "isUnsupportedCustomSql": true,
+ "query": "select seller_city, price from demo-custom-323403.bigquery_demo.sellers sell LEFT JOIN (\nselect * from demo-custom-323403.bigquery_demo.order_items\n) items on items.seller_id=sell.seller_id",
+ "columns": [
+ {
+ "id": "2cf31227-900f-7ac8-88c9-07404c3ff832",
+ "name": "price",
+ "remoteType": "R8",
+ "description": null,
+ "referencedByFields": [
+ {
+ "datasource": {
+ "__typename": "PublishedDatasource",
+ "id": "bda518f0-0867-679d-2390-891a05972f9e",
+ "name": "AbcJoinWorkbook",
+ "upstreamTables": [],
+ "projectName": "SubProject1",
+ "luid": "830c29b1-9d28-4e76-922e-65c0061a4b82"
+ }
+ }
+ ]
+ },
+ {
+ "id": "d94ba54b-3c1b-410a-7109-ce0c5f82a955",
+ "name": "seller_city",
+ "remoteType": "WSTR",
+ "description": null,
+ "referencedByFields": [
+ {
+ "datasource": {
+ "__typename": "PublishedDatasource",
+ "id": "bda518f0-0867-679d-2390-891a05972f9e",
+ "name": "AbcJoinWorkbook",
+ "upstreamTables": [],
+ "projectName": "SubProject1",
+ "luid": "830c29b1-9d28-4e76-922e-65c0061a4b82"
+ }
+ }
+ ]
+ }
+ ],
+ "tables": [],
+ "database": {
+ "name": "My Project 42084",
+ "connectionType": "bigquery"
+ },
+ "datasources": [
+ {
+ "__typename": "PublishedDatasource",
+ "id": "bda518f0-0867-679d-2390-891a05972f9e",
+ "name": "AbcJoinWorkbook",
+ "upstreamTables": [],
+ "projectName": "SubProject1",
+ "luid": "830c29b1-9d28-4e76-922e-65c0061a4b82"
+ }
+ ]
+ }
],
+
"pageInfo": {
"hasNextPage": false,
"endCursor": null
},
- "totalCount": 2
+ "totalCount": 3
}
}
}
\ No newline at end of file
diff --git a/metadata-ingestion/tests/integration/tableau/setup/embeddedDatasourcesConnection_all.json b/metadata-ingestion/tests/integration/tableau/setup/embeddedDatasourcesConnection_all.json
index 665076bd8affb..bdb5013c2ea48 100644
--- a/metadata-ingestion/tests/integration/tableau/setup/embeddedDatasourcesConnection_all.json
+++ b/metadata-ingestion/tests/integration/tableau/setup/embeddedDatasourcesConnection_all.json
@@ -1625,7 +1625,7 @@
"name": "last_name",
"table": {
"__typename": "CustomSQLTable",
- "id": "22b0b4c3-6b85-713d-a161-5a87fdd78f40"
+ "id": "10c6297d-0dbd-44f1-b1ba-458bea446513"
}
}
]
diff --git a/metadata-ingestion/tests/integration/tableau/tableau_cll_mces_golden.json b/metadata-ingestion/tests/integration/tableau/tableau_cll_mces_golden.json
new file mode 100644
index 0000000000000..4575144c6f844
--- /dev/null
+++ b/metadata-ingestion/tests/integration/tableau/tableau_cll_mces_golden.json
@@ -0,0 +1,43283 @@
+[
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:5ec314b9630974ec084f5dfd3849f87b",
+ "changeType": "UPSERT",
+ "aspectName": "containerProperties",
+ "aspect": {
+ "json": {
+ "customProperties": {
+ "platform": "tableau",
+ "project_id": "190a6a5c-63ed-4de1-8045-faeae5df5b01"
+ },
+ "name": "default"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:5ec314b9630974ec084f5dfd3849f87b",
+ "changeType": "UPSERT",
+ "aspectName": "status",
+ "aspect": {
+ "json": {
+ "removed": false
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:5ec314b9630974ec084f5dfd3849f87b",
+ "changeType": "UPSERT",
+ "aspectName": "dataPlatformInstance",
+ "aspect": {
+ "json": {
+ "platform": "urn:li:dataPlatform:tableau"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:5ec314b9630974ec084f5dfd3849f87b",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "Project"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:5ec314b9630974ec084f5dfd3849f87b",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": []
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:252a054d4dd93cd657735aa46dd71370",
+ "changeType": "UPSERT",
+ "aspectName": "containerProperties",
+ "aspect": {
+ "json": {
+ "customProperties": {
+ "platform": "tableau",
+ "project_id": "c30aafe5-44f4-4f28-80d3-d181010a263c"
+ },
+ "name": "Project 2"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:252a054d4dd93cd657735aa46dd71370",
+ "changeType": "UPSERT",
+ "aspectName": "status",
+ "aspect": {
+ "json": {
+ "removed": false
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:252a054d4dd93cd657735aa46dd71370",
+ "changeType": "UPSERT",
+ "aspectName": "dataPlatformInstance",
+ "aspect": {
+ "json": {
+ "platform": "urn:li:dataPlatform:tableau"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:252a054d4dd93cd657735aa46dd71370",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "Project"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:252a054d4dd93cd657735aa46dd71370",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": []
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:d2dcd6bd1bb954d62f1cfc68332ee873",
+ "changeType": "UPSERT",
+ "aspectName": "containerProperties",
+ "aspect": {
+ "json": {
+ "customProperties": {
+ "platform": "tableau",
+ "project_id": "910733aa-2e95-4ac3-a2e8-71570751099d"
+ },
+ "name": "Samples"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:d2dcd6bd1bb954d62f1cfc68332ee873",
+ "changeType": "UPSERT",
+ "aspectName": "status",
+ "aspect": {
+ "json": {
+ "removed": false
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:d2dcd6bd1bb954d62f1cfc68332ee873",
+ "changeType": "UPSERT",
+ "aspectName": "dataPlatformInstance",
+ "aspect": {
+ "json": {
+ "platform": "urn:li:dataPlatform:tableau"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:d2dcd6bd1bb954d62f1cfc68332ee873",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "Project"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:d2dcd6bd1bb954d62f1cfc68332ee873",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": []
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:008e111aa1d250dd52e0fd5d4b307b1a",
+ "changeType": "UPSERT",
+ "aspectName": "containerProperties",
+ "aspect": {
+ "json": {
+ "customProperties": {
+ "platform": "tableau",
+ "workbook_id": "1f15d897-7f0c-7c59-037a-afa6a9b7c9a9"
+ },
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+ "fieldPath": "Id (Activity - Open Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Activity Date (Activity - Open Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.TimeType": {}
+ }
+ },
+ "nativeDataType": "DATETIME",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Description",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Campaign Run ID (Activity - Click Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Created At",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.TimeType": {}
+ }
+ },
+ "nativeDataType": "DATETIME",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Clickthrough Emails",
+ "nullable": false,
+ "description": "formula: COUNTD([Id (Activity - Click Email)])",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Is Mobile Device (Activity - Click Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Mailing ID",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Device (Activity - Click Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ },
+ {
+ "tag": "urn:li:tag:COUNT"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Mailing ID (Activity - Click Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Choice Number (Activity - Click Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Test Variant",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Link ID",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Click-to-Open",
+ "nullable": false,
+ "description": "formula: ZN([Clickthrough Emails]\r\n/ \r\n[Opened Email])",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "REAL",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Opened Email",
+ "nullable": false,
+ "description": "formula: COUNTD([Id (Activity - Open Email)])",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Link",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ },
+ {
+ "tag": "urn:li:tag:COUNT"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Lead ID",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Campaign Run ID (Activity - Open Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Opened Non Clicked Emails",
+ "nullable": false,
+ "description": "formula: [Opened Email]-[Clickthrough Emails]",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Test Variant (Activity - Open Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Step ID (Activity - Email Delivered)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Non Clickthrough Email",
+ "nullable": false,
+ "description": "formula: [Delivered Email]-[Clickthrough Emails]",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Device",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ },
+ {
+ "tag": "urn:li:tag:COUNT"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Lead ID (Activity - Email Delivered)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Id (Activity - Email Delivered)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Program Name",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Campaign ID (Activity - Open Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Activity Date (Activity - Email Delivered)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.TimeType": {}
+ }
+ },
+ "nativeDataType": "DATETIME",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Test Variant (Activity - Click Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Has Predictive",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Non Opened Email",
+ "nullable": false,
+ "description": "formula: [Delivered Email]-[Opened Email]",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Measure Values",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "REAL",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Has Predictive (Activity - Email Delivered)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "User Agent",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Active",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Step ID",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Bounceback",
+ "nullable": false,
+ "description": "formula: [Sent Email] - [Delivered Email]",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Choice Number (Activity - Open Email)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Id",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ }
+ ]
+ }
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,801c95e3-b07e-7bfe-3789-a561c7beccd3,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "Embedded Data Source"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,801c95e3-b07e-7bfe-3789-a561c7beccd3,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "container",
+ "aspect": {
+ "json": {
+ "container": "urn:li:container:008e111aa1d250dd52e0fd5d4b307b1a"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,801c95e3-b07e-7bfe-3789-a561c7beccd3,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": [
+ {
+ "id": "urn:li:container:5ec314b9630974ec084f5dfd3849f87b",
+ "urn": "urn:li:container:5ec314b9630974ec084f5dfd3849f87b"
+ },
+ {
+ "id": "urn:li:container:008e111aa1d250dd52e0fd5d4b307b1a",
+ "urn": "urn:li:container:008e111aa1d250dd52e0fd5d4b307b1a"
+ }
+ ]
+ }
+ },
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+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD),Overdue)"
+ ],
+ "transformOperation": "CalculatedFieldformula: // This is a calculated field\r\n// It shows if an accident is overdue\r\n\r\n//Check overdue cases among inactive incidents\r\nIF [Active]=FALSE \r\nAND\r\n[Closed]>[Due date]\r\n\r\nOR\r\n//Check overdue cases among active incidents\r\n[Active]=TRUE \r\nAND NOW()>[Due date]\r\n\r\nTHEN \"Overdue\"\r\nELSE \"Non Overdue\"\r\nEND",
+ "confidenceScore": 1.0
+ },
+ {
+ "upstreamType": "FIELD_SET",
+ "upstreams": [
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD),Opened)",
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD),Number)"
+ ],
+ "downstreamType": "FIELD",
+ "downstreams": [
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD),Total # Request)"
+ ],
+ "transformOperation": "CalculatedFieldformula: // This is a calculated field\r\n// It shows the total number of problems ignoring opened date\r\n\r\n{ EXCLUDE [Opened]: COUNTD([Number])}",
+ "confidenceScore": 1.0
+ },
+ {
+ "upstreamType": "FIELD_SET",
+ "upstreams": [
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD),Opened)",
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD),Overdue)",
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD),Active)",
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD),Max Year?)",
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD),Number)"
+ ],
+ "downstreamType": "FIELD",
+ "downstreams": [
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD),Total Active Requests)"
+ ],
+ "transformOperation": "CalculatedFieldformula: // This is a calculated field\r\n// It counts each distinct active request. The \"exclude\" is used to avoid filters interfering with the final result \r\n\r\n{EXCLUDE [Opened], [Overdue], [Max Year?]: \r\nCOUNTD(IF [Active]=TRUE\r\nTHEN [Number]\r\nEND)\r\n}",
+ "confidenceScore": 1.0
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "proposedSnapshot": {
+ "com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
+ "urn": "urn:li:dataset:(urn:li:dataPlatform:tableau,06c3e060-8133-4b58-9b53-a0fced25e056,PROD)",
+ "aspects": [
+ {
+ "com.linkedin.pegasus2avro.common.DataPlatformInstance": {
+ "platform": "urn:li:dataPlatform:tableau"
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.common.BrowsePaths": {
+ "paths": [
+ "/prod/tableau/default/Executive Dashboard"
+ ]
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.common.Ownership": {
+ "owners": [
+ {
+ "owner": "urn:li:corpuser:jawadqu@gmail.com",
+ "type": "DATAOWNER"
+ }
+ ],
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ }
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.DatasetProperties": {
+ "customProperties": {
+ "hasExtracts": "True",
+ "extractLastRefreshTime": "2018-01-18T19:35:39Z",
+ "extractLastUpdateTime": "2018-01-18T19:35:39Z"
+ },
+ "name": "Requests",
+ "tags": []
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.schema.SchemaMetadata": {
+ "schemaName": "test",
+ "platform": "urn:li:dataPlatform:tableau",
+ "version": 0,
+ "created": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "hash": "",
+ "platformSchema": {
+ "com.linkedin.pegasus2avro.schema.OtherSchema": {
+ "rawSchema": ""
+ }
+ },
+ "fields": [
+ {
+ "fieldPath": "Closed by (Request)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Location",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Opened",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.TimeType": {}
+ }
+ },
+ "nativeDataType": "DATETIME",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Mobile Picture",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Delivery task",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "% made SLA",
+ "nullable": false,
+ "description": "formula: // This is a calculated field\r\n// It shows the percentage of requests which made SLA\r\n\r\nCOUNTD(IF [Made SLA]= TRUE\r\nTHEN [Number]\r\nEND)\r\n/\r\nCOUNTD([Number])",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "REAL",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
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+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Meta",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Correlation ID (Request)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Domain Path (Requested Item)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Work notes (Requested Item)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
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+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Domain",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Short description",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Domain Path (Request)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Updated by (Requested Item)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Additional comments (Requested Item)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Recurring price",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
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+ {
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+ {
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+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.TimeType": {}
+ }
+ },
+ "nativeDataType": "DATETIME",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Contact type",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
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+ }
+ },
+ "nativeDataType": "STRING",
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+ "globalTags": {
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+ {
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+ {
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+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Total # Request",
+ "nullable": false,
+ "description": "formula: // This is a calculated field\r\n// It shows the total number of problems ignoring opened date\r\n\r\n{ EXCLUDE [Opened]: COUNTD([Number])}",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
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+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
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+ {
+ "tag": "urn:li:tag:ATTRIBUTE"
+ }
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+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Requested for date",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.DateType": {}
+ }
+ },
+ "nativeDataType": "DATE",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Upon approval",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
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+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Ordered item link",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
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+ {
+ "fieldPath": "Visible elsewhere",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
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+ }
+ },
+ "nativeDataType": "BOOLEAN",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
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+ {
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+ "nullable": false,
+ "description": "",
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+ "nullable": false,
+ "description": "",
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+ "globalTags": {
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+ {
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+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Recurring Price Frequency (Catalog Item)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
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+ "globalTags": {
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+ "description": "",
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+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
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+ },
+ "isPartOfKey": false
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+ {
+ "fieldPath": "Order (Request)",
+ "nullable": false,
+ "description": "",
+ "type": {
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+ "com.linkedin.pegasus2avro.schema.NumberType": {}
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+ },
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+ "confidenceScore": 1.0
+ },
+ {
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+ "confidenceScore": 1.0
+ },
+ {
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+ "upstreams": [
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,3ade7817-ae27-259e-8e48-1570e7f932f6,PROD),Active)",
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,3ade7817-ae27-259e-8e48-1570e7f932f6,PROD),Closed)",
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,3ade7817-ae27-259e-8e48-1570e7f932f6,PROD),Due date)"
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+ "confidenceScore": 1.0
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+ {
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+ {
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+ "fieldPath": "Source",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Closed (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.TimeType": {}
+ }
+ },
+ "nativeDataType": "DATETIME",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Opened by",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Work start",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.TimeType": {}
+ }
+ },
+ "nativeDataType": "DATETIME",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Additional assignee list (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "% of known error",
+ "nullable": false,
+ "description": "formula: // This is a calculated field\r\n// It shows the percentage of problems that are known errors\r\n\r\nCOUNTD(IF [Known error]=TRUE\r\nTHEN [Number] END)\r\n/\r\nCOUNTD([Number])",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "REAL",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Work end",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.TimeType": {}
+ }
+ },
+ "nativeDataType": "DATETIME",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Problem state",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Upon approval",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Domain",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Reassignment count (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Contact type (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Upon reject (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Close notes",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Max Year?",
+ "nullable": false,
+ "description": "formula: // This is a calculated field\r\n// It shows TRUE/FALSE if the year of opened is equal the max year of the dataset \r\n\r\nDATEPART(\"year\", [Opened]) = DATEPART(\"year\", {MAX([Opened])})",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.BooleanType": {}
+ }
+ },
+ "nativeDataType": "BOOLEAN",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Configuration item (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Short description",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Company (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Work notes",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Delivery plan",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Additional comments (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Knowledge (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.BooleanType": {}
+ }
+ },
+ "nativeDataType": "BOOLEAN",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Known error",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.BooleanType": {}
+ }
+ },
+ "nativeDataType": "BOOLEAN",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Group email",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Age of Problems",
+ "nullable": false,
+ "description": "formula: //Calculates the age of active problems since opened until now\r\n\r\nDATEDIFF('second', [Opened], NOW())",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Priority",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ },
+ {
+ "tag": "urn:li:tag:SUM"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Comments and Work notes",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Measure Values",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "REAL",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Approval history",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Assigned to",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Impact (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Updated (Group)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.TimeType": {}
+ }
+ },
+ "nativeDataType": "DATETIME",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "% of critical and high priority",
+ "nullable": false,
+ "description": "formula: // This is a calculated field\r\n// It shows the percentage of critical or high priority problems\r\n\r\nCOUNTD(IF [Priority]=1\r\nOR [Priority]=2\r\nTHEN [Number]\r\nEND)\r\n/\r\nCOUNTD([Number])",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "REAL",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:CALCULATEDFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Closed",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.TimeType": {}
+ }
+ },
+ "nativeDataType": "DATETIME",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Configuration item",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Business service (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Additional assignee list",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Include members",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.BooleanType": {}
+ }
+ },
+ "nativeDataType": "BOOLEAN",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Order",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:MEASURE"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Measure Names",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Location",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Description (Problem)",
+ "nullable": false,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "STRING",
+ "recursive": false,
+ "globalTags": {
+ "tags": [
+ {
+ "tag": "urn:li:tag:DIMENSION"
+ },
+ {
+ "tag": "urn:li:tag:COLUMNFIELD"
+ }
+ ]
+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Current Year Total Cases",
+ "nullable": false,
+ "description": "formula: // This is a calculated field\r\n// It counts each disctinct problem. The \"exclude\" is used to avoid filters interfering with the result\r\n\r\n{EXCLUDE [Opened], [Overdue], [Max Year?]: \r\nCOUNTD(IF [Max Year?]=TRUE\r\nTHEN [Number]\r\nEND)\r\n}",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
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+ {
+ "fieldPath": "Lease contract",
+ "nullable": false,
+ "description": "",
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+ },
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "Overdue",
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+ {
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+ "nativeDataType": "BOOLEAN",
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+ },
+ "nativeDataType": "STRING",
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+ {
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+ "description": "",
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+ "changeType": "UPSERT",
+ "aspectName": "tagKey",
+ "aspect": {
+ "json": {
+ "name": "COUNT"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "tag",
+ "entityUrn": "urn:li:tag:DATASOURCEFIELD",
+ "changeType": "UPSERT",
+ "aspectName": "tagKey",
+ "aspect": {
+ "json": {
+ "name": "DATASOURCEFIELD"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "tag",
+ "entityUrn": "urn:li:tag:DIMENSION",
+ "changeType": "UPSERT",
+ "aspectName": "tagKey",
+ "aspect": {
+ "json": {
+ "name": "DIMENSION"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "tag",
+ "entityUrn": "urn:li:tag:GROUPFIELD",
+ "changeType": "UPSERT",
+ "aspectName": "tagKey",
+ "aspect": {
+ "json": {
+ "name": "GROUPFIELD"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "tag",
+ "entityUrn": "urn:li:tag:HIERARCHYFIELD",
+ "changeType": "UPSERT",
+ "aspectName": "tagKey",
+ "aspect": {
+ "json": {
+ "name": "HIERARCHYFIELD"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "tag",
+ "entityUrn": "urn:li:tag:MEASURE",
+ "changeType": "UPSERT",
+ "aspectName": "tagKey",
+ "aspect": {
+ "json": {
+ "name": "MEASURE"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "tag",
+ "entityUrn": "urn:li:tag:SETFIELD",
+ "changeType": "UPSERT",
+ "aspectName": "tagKey",
+ "aspect": {
+ "json": {
+ "name": "SETFIELD"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "tag",
+ "entityUrn": "urn:li:tag:SUM",
+ "changeType": "UPSERT",
+ "aspectName": "tagKey",
+ "aspect": {
+ "json": {
+ "name": "SUM"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "tag",
+ "entityUrn": "urn:li:tag:TagSheet3",
+ "changeType": "UPSERT",
+ "aspectName": "tagKey",
+ "aspect": {
+ "json": {
+ "name": "TagSheet3"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "tag",
+ "entityUrn": "urn:li:tag:YEAR",
+ "changeType": "UPSERT",
+ "aspectName": "tagKey",
+ "aspect": {
+ "json": {
+ "name": "YEAR"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+}
+]
\ No newline at end of file
diff --git a/metadata-ingestion/tests/integration/tableau/tableau_extract_all_project_mces_golden.json b/metadata-ingestion/tests/integration/tableau/tableau_extract_all_project_mces_golden.json
index e6782c1c9f5f3..12eb780447c18 100644
--- a/metadata-ingestion/tests/integration/tableau/tableau_extract_all_project_mces_golden.json
+++ b/metadata-ingestion/tests/integration/tableau/tableau_extract_all_project_mces_golden.json
@@ -12779,6 +12779,14 @@
},
"dataset": "urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD)",
"type": "TRANSFORMED"
+ },
+ {
+ "auditStamp": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "dataset": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "type": "TRANSFORMED"
}
],
"fineGrainedLineages": [
@@ -12796,7 +12804,7 @@
{
"upstreamType": "FIELD_SET",
"upstreams": [
- "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD),last_name)"
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD),last_name)"
],
"downstreamType": "FIELD",
"downstreams": [
@@ -33581,6 +33589,132 @@
"runId": "tableau-test"
}
},
+{
+ "proposedSnapshot": {
+ "com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
+ "urn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "aspects": [
+ {
+ "com.linkedin.pegasus2avro.common.DataPlatformInstance": {
+ "platform": "urn:li:dataPlatform:tableau"
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.schema.SchemaMetadata": {
+ "schemaName": "test",
+ "platform": "urn:li:dataPlatform:tableau",
+ "version": 0,
+ "created": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "hash": "",
+ "platformSchema": {
+ "com.linkedin.pegasus2avro.schema.OtherSchema": {
+ "rawSchema": ""
+ }
+ },
+ "fields": [
+ {
+ "fieldPath": "price",
+ "nullable": false,
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "R8",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "seller_city",
+ "nullable": false,
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "WSTR",
+ "recursive": false,
+ "isPartOfKey": false
+ }
+ ]
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.common.BrowsePaths": {
+ "paths": [
+ "/prod/tableau/SubProject1/AbcJoinWorkbook"
+ ]
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.DatasetProperties": {
+ "customProperties": {},
+ "name": "Custom SQL Query",
+ "tags": []
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.ViewProperties": {
+ "materialized": false,
+ "viewLogic": "select seller_city, price from demo-custom-323403.bigquery_demo.sellers sell LEFT JOIN (\nselect * from demo-custom-323403.bigquery_demo.order_items\n) items on items.seller_id=sell.seller_id",
+ "viewLanguage": "SQL"
+ }
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "View",
+ "Custom SQL"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": [
+ {
+ "id": "SubProject1"
+ },
+ {
+ "id": "AbcJoinWorkbook"
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"proposedSnapshot": {
"com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
@@ -42756,6 +42890,21 @@
"runId": "tableau-test"
}
},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "status",
+ "aspect": {
+ "json": {
+ "removed": false
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"entityType": "dataset",
"entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD)",
diff --git a/metadata-ingestion/tests/integration/tableau/tableau_mces_golden.json b/metadata-ingestion/tests/integration/tableau/tableau_mces_golden.json
index ea15a2ac56b58..77ea484abfa0e 100644
--- a/metadata-ingestion/tests/integration/tableau/tableau_mces_golden.json
+++ b/metadata-ingestion/tests/integration/tableau/tableau_mces_golden.json
@@ -12547,6 +12547,14 @@
},
"dataset": "urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD)",
"type": "TRANSFORMED"
+ },
+ {
+ "auditStamp": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "dataset": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "type": "TRANSFORMED"
}
],
"fineGrainedLineages": [
@@ -12564,7 +12572,7 @@
{
"upstreamType": "FIELD_SET",
"upstreams": [
- "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD),last_name)"
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD),last_name)"
],
"downstreamType": "FIELD",
"downstreams": [
@@ -33349,6 +33357,132 @@
"runId": "tableau-test"
}
},
+{
+ "proposedSnapshot": {
+ "com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
+ "urn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "aspects": [
+ {
+ "com.linkedin.pegasus2avro.common.DataPlatformInstance": {
+ "platform": "urn:li:dataPlatform:tableau"
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.schema.SchemaMetadata": {
+ "schemaName": "test",
+ "platform": "urn:li:dataPlatform:tableau",
+ "version": 0,
+ "created": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "hash": "",
+ "platformSchema": {
+ "com.linkedin.pegasus2avro.schema.OtherSchema": {
+ "rawSchema": ""
+ }
+ },
+ "fields": [
+ {
+ "fieldPath": "price",
+ "nullable": false,
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "R8",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "seller_city",
+ "nullable": false,
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "WSTR",
+ "recursive": false,
+ "isPartOfKey": false
+ }
+ ]
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.common.BrowsePaths": {
+ "paths": [
+ "/prod/tableau/SubProject1/AbcJoinWorkbook"
+ ]
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.DatasetProperties": {
+ "customProperties": {},
+ "name": "Custom SQL Query",
+ "tags": []
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.ViewProperties": {
+ "materialized": false,
+ "viewLogic": "select seller_city, price from demo-custom-323403.bigquery_demo.sellers sell LEFT JOIN (\nselect * from demo-custom-323403.bigquery_demo.order_items\n) items on items.seller_id=sell.seller_id",
+ "viewLanguage": "SQL"
+ }
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "View",
+ "Custom SQL"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": [
+ {
+ "id": "SubProject1"
+ },
+ {
+ "id": "AbcJoinWorkbook"
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"proposedSnapshot": {
"com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
@@ -42524,6 +42658,21 @@
"runId": "tableau-test"
}
},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "status",
+ "aspect": {
+ "json": {
+ "removed": false
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"entityType": "dataset",
"entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD)",
diff --git a/metadata-ingestion/tests/integration/tableau/tableau_mces_golden_deleted_stateful.json b/metadata-ingestion/tests/integration/tableau/tableau_mces_golden_deleted_stateful.json
index 08d5127b18041..fb9cca0b6a544 100644
--- a/metadata-ingestion/tests/integration/tableau/tableau_mces_golden_deleted_stateful.json
+++ b/metadata-ingestion/tests/integration/tableau/tableau_mces_golden_deleted_stateful.json
@@ -482,6 +482,21 @@
"runId": "tableau-test"
}
},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "status",
+ "aspect": {
+ "json": {
+ "removed": true
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"entityType": "chart",
"entityUrn": "urn:li:chart:(tableau,f4317efd-c3e6-6ace-8fe6-e71b590bbbcc)",
diff --git a/metadata-ingestion/tests/integration/tableau/tableau_nested_project_mces_golden.json b/metadata-ingestion/tests/integration/tableau/tableau_nested_project_mces_golden.json
index e6782c1c9f5f3..87cba090cee77 100644
--- a/metadata-ingestion/tests/integration/tableau/tableau_nested_project_mces_golden.json
+++ b/metadata-ingestion/tests/integration/tableau/tableau_nested_project_mces_golden.json
@@ -12779,6 +12779,14 @@
},
"dataset": "urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD)",
"type": "TRANSFORMED"
+ },
+ {
+ "auditStamp": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "dataset": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "type": "TRANSFORMED"
}
],
"fineGrainedLineages": [
@@ -12796,7 +12804,7 @@
{
"upstreamType": "FIELD_SET",
"upstreams": [
- "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD),last_name)"
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD),last_name)"
],
"downstreamType": "FIELD",
"downstreams": [
@@ -33581,6 +33589,103 @@
"runId": "tableau-test"
}
},
+{
+ "proposedSnapshot": {
+ "com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
+ "urn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "aspects": [
+ {
+ "com.linkedin.pegasus2avro.common.DataPlatformInstance": {
+ "platform": "urn:li:dataPlatform:tableau"
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.schema.SchemaMetadata": {
+ "schemaName": "test",
+ "platform": "urn:li:dataPlatform:tableau",
+ "version": 0,
+ "created": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "hash": "",
+ "platformSchema": {
+ "com.linkedin.pegasus2avro.schema.OtherSchema": {
+ "rawSchema": ""
+ }
+ },
+ "fields": [
+ {
+ "fieldPath": "price",
+ "nullable": false,
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "R8",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "seller_city",
+ "nullable": false,
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "WSTR",
+ "recursive": false,
+ "isPartOfKey": false
+ }
+ ]
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.DatasetProperties": {
+ "customProperties": {},
+ "name": "Custom SQL Query",
+ "tags": []
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.ViewProperties": {
+ "materialized": false,
+ "viewLogic": "select seller_city, price from demo-custom-323403.bigquery_demo.sellers sell LEFT JOIN (\nselect * from demo-custom-323403.bigquery_demo.order_items\n) items on items.seller_id=sell.seller_id",
+ "viewLanguage": "SQL"
+ }
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "View",
+ "Custom SQL"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"proposedSnapshot": {
"com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
@@ -42756,6 +42861,21 @@
"runId": "tableau-test"
}
},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "status",
+ "aspect": {
+ "json": {
+ "removed": false
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"entityType": "dataset",
"entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD)",
diff --git a/metadata-ingestion/tests/integration/tableau/tableau_signout_timeout_mces_golden.json b/metadata-ingestion/tests/integration/tableau/tableau_signout_timeout_mces_golden.json
index ea15a2ac56b58..77ea484abfa0e 100644
--- a/metadata-ingestion/tests/integration/tableau/tableau_signout_timeout_mces_golden.json
+++ b/metadata-ingestion/tests/integration/tableau/tableau_signout_timeout_mces_golden.json
@@ -12547,6 +12547,14 @@
},
"dataset": "urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD)",
"type": "TRANSFORMED"
+ },
+ {
+ "auditStamp": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "dataset": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "type": "TRANSFORMED"
}
],
"fineGrainedLineages": [
@@ -12564,7 +12572,7 @@
{
"upstreamType": "FIELD_SET",
"upstreams": [
- "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD),last_name)"
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD),last_name)"
],
"downstreamType": "FIELD",
"downstreams": [
@@ -33349,6 +33357,132 @@
"runId": "tableau-test"
}
},
+{
+ "proposedSnapshot": {
+ "com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
+ "urn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "aspects": [
+ {
+ "com.linkedin.pegasus2avro.common.DataPlatformInstance": {
+ "platform": "urn:li:dataPlatform:tableau"
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.schema.SchemaMetadata": {
+ "schemaName": "test",
+ "platform": "urn:li:dataPlatform:tableau",
+ "version": 0,
+ "created": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "hash": "",
+ "platformSchema": {
+ "com.linkedin.pegasus2avro.schema.OtherSchema": {
+ "rawSchema": ""
+ }
+ },
+ "fields": [
+ {
+ "fieldPath": "price",
+ "nullable": false,
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "R8",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "seller_city",
+ "nullable": false,
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "WSTR",
+ "recursive": false,
+ "isPartOfKey": false
+ }
+ ]
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.common.BrowsePaths": {
+ "paths": [
+ "/prod/tableau/SubProject1/AbcJoinWorkbook"
+ ]
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.DatasetProperties": {
+ "customProperties": {},
+ "name": "Custom SQL Query",
+ "tags": []
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.ViewProperties": {
+ "materialized": false,
+ "viewLogic": "select seller_city, price from demo-custom-323403.bigquery_demo.sellers sell LEFT JOIN (\nselect * from demo-custom-323403.bigquery_demo.order_items\n) items on items.seller_id=sell.seller_id",
+ "viewLanguage": "SQL"
+ }
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "View",
+ "Custom SQL"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": [
+ {
+ "id": "SubProject1"
+ },
+ {
+ "id": "AbcJoinWorkbook"
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"proposedSnapshot": {
"com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
@@ -42524,6 +42658,21 @@
"runId": "tableau-test"
}
},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "status",
+ "aspect": {
+ "json": {
+ "removed": false
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"entityType": "dataset",
"entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD)",
diff --git a/metadata-ingestion/tests/integration/tableau/tableau_with_platform_instance_mces_golden.json b/metadata-ingestion/tests/integration/tableau/tableau_with_platform_instance_mces_golden.json
index e94ff0118e04c..7408e481a9411 100644
--- a/metadata-ingestion/tests/integration/tableau/tableau_with_platform_instance_mces_golden.json
+++ b/metadata-ingestion/tests/integration/tableau/tableau_with_platform_instance_mces_golden.json
@@ -12649,6 +12649,14 @@
},
"dataset": "urn:li:dataset:(urn:li:dataPlatform:tableau,acryl_site1.22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD)",
"type": "TRANSFORMED"
+ },
+ {
+ "auditStamp": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "dataset": "urn:li:dataset:(urn:li:dataPlatform:tableau,acryl_site1.10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "type": "TRANSFORMED"
}
],
"fineGrainedLineages": [
@@ -12666,7 +12674,7 @@
{
"upstreamType": "FIELD_SET",
"upstreams": [
- "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,acryl_site1.22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD),last_name)"
+ "urn:li:schemaField:(urn:li:dataset:(urn:li:dataPlatform:tableau,acryl_site1.10c6297d-0dbd-44f1-b1ba-458bea446513,PROD),last_name)"
],
"downstreamType": "FIELD",
"downstreams": [
@@ -33490,6 +33498,137 @@
"runId": "tableau-test"
}
},
+{
+ "proposedSnapshot": {
+ "com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
+ "urn": "urn:li:dataset:(urn:li:dataPlatform:tableau,acryl_site1.10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "aspects": [
+ {
+ "com.linkedin.pegasus2avro.common.DataPlatformInstance": {
+ "platform": "urn:li:dataPlatform:tableau",
+ "instance": "urn:li:dataPlatformInstance:(urn:li:dataPlatform:tableau,acryl_site1)"
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.schema.SchemaMetadata": {
+ "schemaName": "test",
+ "platform": "urn:li:dataPlatform:tableau",
+ "version": 0,
+ "created": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "hash": "",
+ "platformSchema": {
+ "com.linkedin.pegasus2avro.schema.OtherSchema": {
+ "rawSchema": ""
+ }
+ },
+ "fields": [
+ {
+ "fieldPath": "price",
+ "nullable": false,
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "R8",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "seller_city",
+ "nullable": false,
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "WSTR",
+ "recursive": false,
+ "isPartOfKey": false
+ }
+ ]
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.common.BrowsePaths": {
+ "paths": [
+ "/prod/tableau/SubProject1/AbcJoinWorkbook"
+ ]
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.DatasetProperties": {
+ "customProperties": {},
+ "name": "Custom SQL Query",
+ "tags": []
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.ViewProperties": {
+ "materialized": false,
+ "viewLogic": "select seller_city, price from demo-custom-323403.bigquery_demo.sellers sell LEFT JOIN (\nselect * from demo-custom-323403.bigquery_demo.order_items\n) items on items.seller_id=sell.seller_id",
+ "viewLanguage": "SQL"
+ }
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,acryl_site1.10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "View",
+ "Custom SQL"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,acryl_site1.10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": [
+ {
+ "id": "urn:li:dataPlatformInstance:(urn:li:dataPlatform:tableau,acryl_site1)",
+ "urn": "urn:li:dataPlatformInstance:(urn:li:dataPlatform:tableau,acryl_site1)"
+ },
+ {
+ "id": "SubProject1"
+ },
+ {
+ "id": "AbcJoinWorkbook"
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"proposedSnapshot": {
"com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
@@ -42749,6 +42888,21 @@
"runId": "tableau-test"
}
},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,acryl_site1.10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "status",
+ "aspect": {
+ "json": {
+ "removed": false
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1638860400000,
+ "runId": "tableau-test"
+ }
+},
{
"entityType": "dataset",
"entityUrn": "urn:li:dataset:(urn:li:dataPlatform:tableau,acryl_site1.22b0b4c3-6b85-713d-a161-5a87fdd78f40,PROD)",
diff --git a/metadata-ingestion/tests/integration/tableau/test_tableau_ingest.py b/metadata-ingestion/tests/integration/tableau/test_tableau_ingest.py
index 8615545ffbd57..d04c8d905b439 100644
--- a/metadata-ingestion/tests/integration/tableau/test_tableau_ingest.py
+++ b/metadata-ingestion/tests/integration/tableau/test_tableau_ingest.py
@@ -2,7 +2,7 @@
import logging
import pathlib
import sys
-from typing import cast
+from typing import Any, Dict, cast
from unittest import mock
import pytest
@@ -27,6 +27,7 @@
UpstreamLineage,
)
from datahub.metadata.schema_classes import MetadataChangeProposalClass, UpstreamClass
+from datahub.utilities.sqlglot_lineage import SqlParsingResult
from tests.test_helpers import mce_helpers
from tests.test_helpers.state_helpers import (
get_current_checkpoint_from_pipeline,
@@ -289,6 +290,39 @@ def test_tableau_ingest(pytestconfig, tmp_path, mock_datahub_graph):
)
+@freeze_time(FROZEN_TIME)
+@pytest.mark.integration
+def test_tableau_cll_ingest(pytestconfig, tmp_path, mock_datahub_graph):
+ enable_logging()
+ output_file_name: str = "tableau_mces_cll.json"
+ golden_file_name: str = "tableau_cll_mces_golden.json"
+
+ new_pipeline_config: Dict[Any, Any] = {
+ **config_source_default,
+ "extract_lineage_from_unsupported_custom_sql_queries": True,
+ "extract_column_level_lineage": True,
+ }
+
+ tableau_ingest_common(
+ pytestconfig=pytestconfig,
+ tmp_path=tmp_path,
+ side_effect_query_metadata_response=[
+ read_response(pytestconfig, "workbooksConnection_all.json"),
+ read_response(pytestconfig, "sheetsConnection_all.json"),
+ read_response(pytestconfig, "dashboardsConnection_all.json"),
+ read_response(pytestconfig, "embeddedDatasourcesConnection_all.json"),
+ read_response(pytestconfig, "publishedDatasourcesConnection_all.json"),
+ read_response(pytestconfig, "customSQLTablesConnection_all.json"),
+ read_response(pytestconfig, "databaseTablesConnection_all.json"),
+ ],
+ golden_file_name=golden_file_name,
+ output_file_name=output_file_name,
+ mock_datahub_graph=mock_datahub_graph,
+ pipeline_name="test_tableau_cll_ingest",
+ pipeline_config=new_pipeline_config,
+ )
+
+
@freeze_time(FROZEN_TIME)
@pytest.mark.integration
def test_project_pattern(pytestconfig, tmp_path, mock_datahub_graph):
@@ -615,7 +649,7 @@ def test_tableau_stateful(pytestconfig, tmp_path, mock_time, mock_datahub_graph)
state1.get_urns_not_in(type="dataset", other_checkpoint_state=state2)
)
- assert len(difference_dataset_urns) == 33
+ assert len(difference_dataset_urns) == 34
deleted_dataset_urns = [
"urn:li:dataset:(urn:li:dataPlatform:tableau,dfe2c02a-54b7-f7a2-39fc-c651da2f6ad8,PROD)",
"urn:li:dataset:(urn:li:dataPlatform:tableau,d00f4ba6-707e-4684-20af-69eb47587cc2,PROD)",
@@ -650,6 +684,7 @@ def test_tableau_stateful(pytestconfig, tmp_path, mock_time, mock_datahub_graph)
"urn:li:dataset:(urn:li:dataPlatform:webdata-direct:marketo-marketo,marketo.campaignstable,PROD)",
"urn:li:dataset:(urn:li:dataPlatform:external,sample - superstore%2C %28new%29.xls.people,PROD)",
"urn:li:dataset:(urn:li:dataPlatform:webdata-direct:servicenowitsm-servicenowitsm,ven01911.sc_cat_item,PROD)",
+ "urn:li:dataset:(urn:li:dataPlatform:tableau,10c6297d-0dbd-44f1-b1ba-458bea446513,PROD)",
]
assert sorted(deleted_dataset_urns) == sorted(difference_dataset_urns)
@@ -755,30 +790,48 @@ def test_tableau_unsupported_csql(mock_datahub_graph):
config.lineage_overrides = TableauLineageOverrides(
database_override_map={"production database": "prod"}
)
- source = TableauSource(config=config, ctx=context)
- lineage = source._create_lineage_from_unsupported_csql(
- csql_urn="urn:li:dataset:(urn:li:dataPlatform:tableau,09988088-05ad-173c-a2f1-f33ba3a13d1a,PROD)",
- csql={
- "query": "SELECT user_id, source, user_source FROM (SELECT *, ROW_NUMBER() OVER (partition BY user_id ORDER BY __partition_day DESC) AS rank_ FROM invent_dw.UserDetail ) source_user WHERE rank_ = 1",
- "isUnsupportedCustomSql": "true",
- "database": {"name": "production database", "connectionType": "bigquery"},
- },
- )
- mcp = cast(MetadataChangeProposalClass, next(iter(lineage)).metadata)
+ with mock.patch(
+ "datahub.ingestion.source.tableau.sqlglot_lineage"
+ ) as sqlglot_lineage:
- assert mcp.aspect == UpstreamLineage(
- upstreams=[
- UpstreamClass(
- dataset="urn:li:dataset:(urn:li:dataPlatform:bigquery,prod.invent_dw.userdetail,PROD)",
- type=DatasetLineageType.TRANSFORMED,
- )
- ]
- )
- assert (
- mcp.entityUrn
- == "urn:li:dataset:(urn:li:dataPlatform:tableau,09988088-05ad-173c-a2f1-f33ba3a13d1a,PROD)"
- )
+ sqlglot_lineage.return_value = SqlParsingResult( # type:ignore
+ in_tables=[
+ "urn:li:dataset:(urn:li:dataPlatform:bigquery,my_bigquery_project.invent_dw.userdetail,PROD)"
+ ],
+ out_tables=[],
+ column_lineage=None,
+ )
+
+ source = TableauSource(config=config, ctx=context)
+
+ lineage = source._create_lineage_from_unsupported_csql(
+ csql_urn="urn:li:dataset:(urn:li:dataPlatform:tableau,09988088-05ad-173c-a2f1-f33ba3a13d1a,PROD)",
+ csql={
+ "query": "SELECT user_id, source, user_source FROM (SELECT *, ROW_NUMBER() OVER (partition BY user_id ORDER BY __partition_day DESC) AS rank_ FROM invent_dw.UserDetail ) source_user WHERE rank_ = 1",
+ "isUnsupportedCustomSql": "true",
+ "database": {
+ "name": "my-bigquery-project",
+ "connectionType": "bigquery",
+ },
+ },
+ )
+
+ mcp = cast(MetadataChangeProposalClass, next(iter(lineage)).metadata)
+
+ assert mcp.aspect == UpstreamLineage(
+ upstreams=[
+ UpstreamClass(
+ dataset="urn:li:dataset:(urn:li:dataPlatform:bigquery,my_bigquery_project.invent_dw.userdetail,PROD)",
+ type=DatasetLineageType.TRANSFORMED,
+ )
+ ],
+ fineGrainedLineages=[],
+ )
+ assert (
+ mcp.entityUrn
+ == "urn:li:dataset:(urn:li:dataPlatform:tableau,09988088-05ad-173c-a2f1-f33ba3a13d1a,PROD)"
+ )
@freeze_time(FROZEN_TIME)
diff --git a/metadata-ingestion/tests/integration/vertica/ddl.sql b/metadata-ingestion/tests/integration/vertica/ddl.sql
new file mode 100644
index 0000000000000..59a71a1a1f7b5
--- /dev/null
+++ b/metadata-ingestion/tests/integration/vertica/ddl.sql
@@ -0,0 +1,46 @@
+\set AUTOCOMMIT on
+ALTER USER dbadmin IDENTIFIED BY 'abc123';
+
+-- Create a Top-k projection
+CREATE TABLE readings (meter_id INT, reading_date TIMESTAMP, reading_value FLOAT);
+CREATE PROJECTION readings_topk (meter_id, recent_date, recent_value) AS SELECT meter_id, reading_date, reading_value FROM readings LIMIT 5 OVER (PARTITION BY meter_id ORDER BY reading_date DESC);
+
+-- Create a live agg projs
+CREATE TABLE clicks(user_id IDENTITY(1,1), page_id INTEGER, click_time TIMESTAMP NOT NULL);
+CREATE PROJECTION clicks_agg AS SELECT page_id, click_time::DATE click_date, COUNT(*) num_clicks FROM clicks GROUP BY page_id, click_time::DATE;
+
+
+-- Create a VIEW
+CREATE VIEW sampleview AS SELECT SUM(annual_income), customer_state
+FROM public.customer_dimension
+WHERE customer_key IN (SELECT customer_key FROM store.store_sales_fact)
+GROUP BY customer_state ORDER BY customer_state ASC;
+
+-- Step 1: Create library
+\set libfile '\''/opt/sqlalchemy-vertica-dialect'/python/TransformFunctions.py\''
+CREATE LIBRARY TransformFunctions AS '/opt/vertica/sdk/examples/python/TransformFunctions.py' LANGUAGE 'Python';
+
+
+-- Step 2: Create functions
+CREATE TRANSFORM FUNCTION tokenize AS NAME 'StringTokenizerFactory' LIBRARY TransformFunctions;
+CREATE TRANSFORM FUNCTION topk AS NAME 'TopKPerPartitionFactory' LIBRARY TransformFunctions;
+
+CREATE TABLE phrases (phrase VARCHAR(128));
+COPY phrases FROM STDIN;
+Word
+The quick brown fox jumped over the lazy dog
+\.
+
+SELECT tokenize(phrase) OVER () FROM phrases;
+
+-- Create a temp table
+
+CREATE TEMPORARY TABLE sampletemp (a int, b int) ON COMMIT PRESERVE ROWS;
+INSERT INTO sampletemp VALUES(1,2);
+
+-- Create partition key
+ALTER TABLE store.store_orders_fact PARTITION BY date_ordered::DATE GROUP BY DATE_TRUNC('month', (date_ordered)::DATE);
+SELECT PARTITION_TABLE('store.store_orders_fact');
+CREATE PROJECTION ytd_orders AS SELECT * FROM store.store_orders_fact ORDER BY date_ordered
+ ON PARTITION RANGE BETWEEN date_trunc('year',now())::date AND NULL;
+SELECT start_refresh();
\ No newline at end of file
diff --git a/metadata-ingestion/tests/integration/vertica/docker-compose.yml b/metadata-ingestion/tests/integration/vertica/docker-compose.yml
index 1b22d804b0a37..ddaf206f236cf 100644
--- a/metadata-ingestion/tests/integration/vertica/docker-compose.yml
+++ b/metadata-ingestion/tests/integration/vertica/docker-compose.yml
@@ -3,7 +3,7 @@ services:
vertica:
environment:
APP_DB_USER: "dbadmin"
- APP_DB_PASSWORD: "vertica"
+ APP_DB_PASSWORD: "abc123"
container_name: vertica-ce
image: vertica/vertica-ce:12.0.2-0
ports:
@@ -20,3 +20,4 @@ volumes:
vertica-data:
+
diff --git a/metadata-ingestion/tests/integration/vertica/test_vertica.py b/metadata-ingestion/tests/integration/vertica/test_vertica.py
index 384dac9b8d0c0..db8bfd247313b 100644
--- a/metadata-ingestion/tests/integration/vertica/test_vertica.py
+++ b/metadata-ingestion/tests/integration/vertica/test_vertica.py
@@ -1,24 +1,34 @@
+import subprocess
+import time
+from typing import List, Optional
+
import pytest
from freezegun import freeze_time
from tests.test_helpers import mce_helpers
from tests.test_helpers.click_helpers import run_datahub_cmd
-from tests.test_helpers.docker_helpers import is_responsive, wait_for_port
+from tests.test_helpers.docker_helpers import wait_for_port
FROZEN_TIME = "2020-04-14 07:00:00"
-pytestmark = pytest.mark.skip(
- reason="Vertica tests are disabled due to a dependency conflict with SQLAlchemy 1.3.24"
-)
-
@pytest.fixture(scope="module")
def test_resources_dir(pytestconfig):
return pytestconfig.rootpath / "tests/integration/vertica"
+def is_vertica_responsive(
+ container_name: str, port: int, hostname: Optional[str]
+) -> bool:
+ if hostname:
+ cmd = f"docker logs {container_name} 2>&1 | grep 'Vertica is now running' "
+ ret = subprocess.run(cmd, shell=True, stdout=subprocess.DEVNULL)
+
+ return ret.returncode == 0
+
+
@pytest.fixture(scope="module")
-def vertica_runner(docker_compose_runner, pytestconfig, test_resources_dir):
+def vertica_runner(docker_compose_runner, test_resources_dir):
with docker_compose_runner(
test_resources_dir / "docker-compose.yml", "vertica"
) as docker_services:
@@ -27,26 +37,49 @@ def vertica_runner(docker_compose_runner, pytestconfig, test_resources_dir):
"vertica-ce",
5433,
timeout=120,
- checker=lambda: is_responsive("vertica-ce", 5433, hostname="vertica-ce"),
+ checker=lambda: is_vertica_responsive(
+ "vertica-ce", 5433, hostname="vertica-ce"
+ ),
)
+
+ commands = """
+ docker cp tests/integration/vertica/ddl.sql vertica-ce:/home/dbadmin/ &&
+ docker exec vertica-ce sh -c "/opt/vertica/bin/vsql -w abc123 -f /home/dbadmin/ddl.sql
+ """
+
+ ret = subprocess.run(commands, shell=True, stdout=subprocess.DEVNULL)
+ # waiting for vertica to create default table and system table and ml models
+ time.sleep(60)
+
+ assert ret.returncode >= 1
+
yield docker_services
# Test needs more work to be done , currently it is working fine.
@freeze_time(FROZEN_TIME)
@pytest.mark.integration
-@pytest.mark.skip("This does not work yet and needs to be fixed.")
def test_vertica_ingest_with_db(vertica_runner, pytestconfig, tmp_path):
test_resources_dir = pytestconfig.rootpath / "tests/integration/vertica"
+
# Run the metadata ingestion pipeline.
config_file = (test_resources_dir / "vertica_to_file.yml").resolve()
run_datahub_cmd(
["ingest", "--strict-warnings", "-c", f"{config_file}"], tmp_path=tmp_path
)
+ ignore_paths: List[str] = [
+ r"root\[\d+\]\['proposedSnapshot'\].+\['aspects'\].+\['customProperties'\]\['create_time'\]",
+ r"root\[\d+\]\['proposedSnapshot'\].+\['aspects'\].+\['customProperties'\]\['table_size'\]",
+ r"root\[\d+\]\['proposedSnapshot'\].+\['aspects'\].+\['customProperties'\]\['projection_size'\]",
+ r"root\[\d+\]\['proposedSnapshot'\].+\['aspects'\].+\['customProperties'\]\['ROS_Count'\]",
+ r"root\[\d+\]\['aspect'\].+\['customProperties'\]\['cluster_size'\]",
+ r"root\[\d+\]\['aspect'\].+\['customProperties'\]\['udx_language'\]",
+ ]
# Verify the output.
mce_helpers.check_golden_file(
pytestconfig,
+ ignore_paths=ignore_paths,
output_path=tmp_path / "vertica.json",
golden_path=test_resources_dir / "vertica_mces_with_db_golden.json",
)
diff --git a/metadata-ingestion/tests/integration/vertica/vertica_mces_with_db_golden.json b/metadata-ingestion/tests/integration/vertica/vertica_mces_with_db_golden.json
index 7c2de60372b67..44a5e07d7b996 100644
--- a/metadata-ingestion/tests/integration/vertica/vertica_mces_with_db_golden.json
+++ b/metadata-ingestion/tests/integration/vertica/vertica_mces_with_db_golden.json
@@ -8,10 +8,10 @@
"json": {
"customProperties": {
"platform": "vertica",
- "instance": "PROD",
+ "env": "PROD",
"database": "vmart",
"cluster_type": "Enterprise",
- "cluster_size": "87 GB",
+ "cluster_size": "122 GB",
"subcluster": " ",
"communal_storage_path": ""
},
@@ -70,6 +70,21 @@
"runId": "vertica-2020_04_14-07_00_00"
}
},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:343f520ad0fb3259b298736800bb1385",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": []
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
{
"entityType": "container",
"entityUrn": "urn:li:container:eb682025a9113b5543ec7ed26bfa21e4",
@@ -79,12 +94,12 @@
"json": {
"customProperties": {
"platform": "vertica",
- "instance": "PROD",
+ "env": "PROD",
"database": "vmart",
"schema": "public",
- "projection_count": "0",
- "udx_list": "",
- "Udx_langauge": ""
+ "projection_count": "9",
+ "udx_list": "APPROXIMATE_COUNT_DISTINCT_SYNOPSIS_INFO, APPROXIMATE_MEDIAN, APPROXIMATE_PERCENTILE, AcdDataToCount, AcdDataToLongSyn, AcdDataToSyn, AcdSynToCount, AcdSynToSyn, DelimitedExport, DelimitedExportMulti, EmptyMap, Explode, FAvroParser, FCefParser, FCsvParser, FDelimitedPairParser, FDelimitedParser, FIDXParser, FJSONParser, FRegexParser, FlexTokenizer, JsonExport, JsonExportMulti, KafkaAvroParser, KafkaCheckBrokers, KafkaExport, KafkaInsertDelimiters, KafkaInsertLengths, KafkaJsonParser, KafkaListManyTopics, KafkaListTopics, KafkaOffsets, KafkaParser, KafkaSource, KafkaTopicDetails, MSE, MapAggregate, MapAggregate, MapContainsKey, MapContainsKey, MapContainsValue, MapContainsValue, MapDelimitedExtractor, MapItems, MapItems, MapJSONExtractor, MapKeys, MapKeys, MapKeysInfo, MapKeysInfo, MapLookup, MapLookup, MapLookup, MapPut, MapRegexExtractor, MapSize, MapSize, MapToString, MapToString, MapValues, MapValues, MapValuesOrField, MapVersion, MapVersion, OrcExport, OrcExportMulti, PRC, ParquetExport, ParquetExportMulti, PickBestType, PickBestType, PickBestType, ROC, STV_AsGeoJSON, STV_AsGeoJSON, STV_AsGeoJSON, STV_Create_Index, STV_Create_Index, STV_Create_Index, STV_DWithin, STV_DWithin, STV_DWithin, STV_Describe_Index, STV_Drop_Index, STV_Export2Shapefile, STV_Extent, STV_Extent, STV_ForceLHR, STV_Geography, STV_Geography, STV_GeographyPoint, STV_Geometry, STV_Geometry, STV_GeometryPoint, STV_GeometryPoint, STV_GetExportShapefileDirectory, STV_Intersect, STV_Intersect, STV_Intersect, STV_Intersect, STV_Intersect, STV_Intersect, STV_Intersect, STV_Intersect, STV_IsValidReason, STV_IsValidReason, STV_IsValidReason, STV_LineStringPoint, STV_LineStringPoint, STV_LineStringPoint, STV_MemSize, STV_MemSize, STV_MemSize, STV_NN, STV_NN, STV_NN, STV_PolygonPoint, STV_PolygonPoint, STV_PolygonPoint, STV_Refresh_Index, STV_Refresh_Index, STV_Refresh_Index, STV_Rename_Index, STV_Reverse, STV_SetExportShapefileDirectory, STV_ShpCreateTable, STV_ShpParser, STV_ShpSource, ST_Area, ST_Area, ST_Area, ST_AsBinary, ST_AsBinary, ST_AsBinary, ST_AsText, ST_AsText, ST_AsText, ST_Boundary, ST_Buffer, ST_Centroid, ST_Contains, ST_Contains, ST_Contains, ST_ConvexHull, ST_Crosses, ST_Difference, ST_Disjoint, ST_Disjoint, ST_Disjoint, ST_Distance, ST_Distance, ST_Distance, ST_Envelope, ST_Equals, ST_Equals, ST_Equals, ST_GeoHash, ST_GeoHash, ST_GeoHash, ST_GeographyFromText, ST_GeographyFromWKB, ST_GeomFromGeoHash, ST_GeomFromGeoJSON, ST_GeomFromGeoJSON, ST_GeomFromText, ST_GeomFromText, ST_GeomFromWKB, ST_GeomFromWKB, ST_GeometryN, ST_GeometryN, ST_GeometryN, ST_GeometryType, ST_GeometryType, ST_GeometryType, ST_Intersection, ST_Intersects, ST_Intersects, ST_IsEmpty, ST_IsEmpty, ST_IsEmpty, ST_IsSimple, ST_IsSimple, ST_IsSimple, ST_IsValid, ST_IsValid, ST_IsValid, ST_Length, ST_Length, ST_Length, ST_NumGeometries, ST_NumGeometries, ST_NumGeometries, ST_NumPoints, ST_NumPoints, ST_NumPoints, ST_Overlaps, ST_PointFromGeoHash, ST_PointN, ST_PointN, ST_PointN, ST_Relate, ST_SRID, ST_SRID, ST_SRID, ST_Simplify, ST_SimplifyPreserveTopology, ST_SymDifference, ST_Touches, ST_Touches, ST_Touches, ST_Transform, ST_Union, ST_Union, ST_Within, ST_Within, ST_Within, ST_X, ST_X, ST_X, ST_XMax, ST_XMax, ST_XMax, ST_XMin, ST_XMin, ST_XMin, ST_Y, ST_Y, ST_Y, ST_YMax, ST_YMax, ST_YMax, ST_YMin, ST_YMin, ST_YMin, ST_intersects, SetMapKeys, Summarize_CatCol, Summarize_CatCol, Summarize_CatCol, Summarize_CatCol, Summarize_CatCol, Summarize_NumCol, VoltageSecureAccess, VoltageSecureAccess, VoltageSecureConfigure, VoltageSecureConfigureGlobal, VoltageSecureProtect, VoltageSecureProtect, VoltageSecureProtectAllKeys, VoltageSecureRefreshPolicy, VoltageSecureVersion, append_centers, apply_bisecting_kmeans, apply_iforest, apply_inverse_pca, apply_inverse_svd, apply_kmeans, apply_normalize, apply_one_hot_encoder, apply_pca, apply_svd, approximate_quantiles, ar_create_blobs, ar_final_newton, ar_save_model, ar_transition_newton, avg_all_columns_local, bisecting_kmeans_init_model, bk_apply_best_kmeans_results, bk_compute_totss_local, bk_finalize_model, bk_get_rows_in_active_cluster, bk_kmeans_compute_local_centers, bk_kmeans_compute_withinss, bk_kmeans_fast_random_init, bk_kmeans_slow_random_init, bk_kmeanspp_init_cur_cluster, bk_kmeanspp_reset_blob, bk_kmeanspp_select_new_centers, bk_kmeanspp_within_chunk_sum, bk_save_final_model, bk_write_new_cluster_level, blob_to_table, bufUdx, bufUdx, calc_pseudo_centers, calculate_alpha_linear, calculate_hessian_linear1, calculate_hessian_linear2, cleanup_kmeans_files, compute_and_save_global_center, compute_and_save_new_centers, compute_local_totss, compute_local_withinss, compute_new_local_centers, confusion_matrix, coordinate_descent_covariance, corr_matrix, count_rows_in_blob, create_aggregator_blob, error_rate, evaluate_naive_bayes_model, evaluate_reg_model, evaluate_svm_model, export_model_files, finalize_blob_resource_group, get_attr_minmax, get_attr_robust_zscore, get_attr_zscore, get_model_attribute, get_model_summary, get_robust_zscore_median, iforest_create_blobs, iforest_phase0_udf1, iforest_phase0_udf2, iforest_phase1_udf1, iforest_phase1_udf2, iforest_phase1_udf3, iforest_phase1_udf4, iforest_phase2_udf1, iforest_phase2_udf2, iforest_phase2_udf3, iforest_phase2_udf4, iforest_save_model, import_model_files, isOrContains, kmeansAddMetricsToModel, kmeans_init_blobs, kmeans_to_write_final_centers, lift_table, line_search_logistic1, line_search_logistic2, load_rows_into_blocks, map_factor, math_op, matrix_global_xtx, matrix_local_xtx, mode_finder, model_converter, naive_bayes_phase1, naive_bayes_phase1_blob, naive_bayes_phase2, pca_prep1_global, pca_prep1_local, pca_prep2, pmml_parser, predict_autoregressor, predict_linear_reg, predict_logistic_reg, predict_moving_average, predict_naive_bayes, predict_naive_bayes_classes, predict_pmml, predict_rf_classifier, predict_rf_classifier_classes, predict_rf_regressor, predict_svm_classifier, predict_svm_regressor, predict_xgb_classifier, predict_xgb_classifier_classes, predict_xgb_regressor, random_init, random_init_write, read_from_dfblob, read_map_factor, read_ptree, read_tree, reg_final_bfgs, reg_final_newton, reg_transition_bfgs, reg_transition_newton, reg_write_model, remove_blob, reverse_normalize, rf_blob, rf_clean, rf_phase0_udf1, rf_phase0_udf2, rf_phase1_udf1, rf_phase1_udf2, rf_phase1_udf3, rf_phase1_udf4, rf_phase2_udf1, rf_phase2_udf2, rf_phase2_udf3, rf_phase2_udf4, rf_predictor_importance, rf_save_model, rsquared, save_cv_result, save_pca_model, save_svd_model, save_svm_model, select_new_centers, store_minmax_model, store_one_hot_encoder_model, store_robust_zscore_model, store_zscore_model, table_to_blob, table_to_dfblob, update_and_return_sum_of_squared_distances, upgrade_model_format, writeInitialKmeansModelToDfs, xgb_create_blobs, xgb_phase0_udf1, xgb_phase0_udf2, xgb_phase1_udf1, xgb_phase1_udf2, xgb_phase1_udf3, xgb_phase2_udf1, xgb_phase2_udf2, xgb_phase2_udf3, xgb_prune, xgb_save_model, yule_walker, ",
+ "udx_language": "ComplexTypesLib -- Functions for Complex Types | DelimitedExportLib -- Delimited data export package | JsonExportLib -- Json data export package | MachineLearningLib -- Machine learning package | OrcExportLib -- Orc export package | ParquetExportLib -- Parquet export package | ApproximateLib -- Approximate package | FlexTableLib -- Flexible Tables Data Load and Query | KafkaLib -- Kafka streaming load and export | PlaceLib -- Geospatial package | VoltageSecureLib -- Voltage SecureData Connector | "
},
"name": "public"
}
@@ -155,5 +170,10210 @@
"lastObserved": 1586847600000,
"runId": "vertica-2020_04_14-07_00_00"
}
+},
+{
+ "entityType": "container",
+ "entityUrn": "urn:li:container:eb682025a9113b5543ec7ed26bfa21e4",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": [
+ {
+ "id": "urn:li:container:343f520ad0fb3259b298736800bb1385",
+ "urn": "urn:li:container:343f520ad0fb3259b298736800bb1385"
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,public.customer_dimension,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "ownership",
+ "aspect": {
+ "json": {
+ "owners": [
+ {
+ "owner": "urn:li:corpuser:dbadmin",
+ "type": "DATAOWNER"
+ }
+ ],
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ }
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,public.customer_dimension,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "container",
+ "aspect": {
+ "json": {
+ "container": "urn:li:container:eb682025a9113b5543ec7ed26bfa21e4"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "proposedSnapshot": {
+ "com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
+ "urn": "urn:li:dataset:(urn:li:dataPlatform:vertica,public.customer_dimension,PROD)",
+ "aspects": [
+ {
+ "com.linkedin.pegasus2avro.common.Status": {
+ "removed": false
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.DatasetProperties": {
+ "customProperties": {
+ "create_time": "2023-07-26 06:37:53.358215+00:00"
+ },
+ "name": "customer_dimension",
+ "description": "References the properties of a native table in Vertica. Vertica physically stores table data in projections, which are collections of table columns. Projections store data in a format that optimizes query execution. In order to query or perform any operation on a Vertica table, the table must have one or more projections associated with it. ",
+ "tags": []
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.schema.SchemaMetadata": {
+ "schemaName": "public.customer_dimension",
+ "platform": "urn:li:dataPlatform:vertica",
+ "version": 0,
+ "created": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "hash": "",
+ "platformSchema": {
+ "com.linkedin.pegasus2avro.schema.MySqlDDL": {
+ "tableSchema": ""
+ }
+ },
+ "fields": [
+ {
+ "fieldPath": "customer_key",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": true
+ },
+ {
+ "fieldPath": "customer_type",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=16)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "customer_name",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=256)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "customer_gender",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=8)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "title",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=8)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "household_id",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "customer_address",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=256)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "customer_city",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=64)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "customer_state",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "CHAR(length=2)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "customer_region",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=64)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "marital_status",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=32)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "customer_age",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "number_of_children",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "annual_income",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "occupation",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=64)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "largest_bill_amount",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "store_membership_card",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "customer_since",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.DateType": {}
+ }
+ },
+ "nativeDataType": "DATE()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "deal_stage",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=32)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "deal_size",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "last_deal_update",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.DateType": {}
+ }
+ },
+ "nativeDataType": "DATE()",
+ "recursive": false,
+ "isPartOfKey": false
+ }
+ ]
+ }
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,public.customer_dimension,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "Table"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,public.customer_dimension,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": [
+ {
+ "id": "urn:li:container:343f520ad0fb3259b298736800bb1385",
+ "urn": "urn:li:container:343f520ad0fb3259b298736800bb1385"
+ },
+ {
+ "id": "urn:li:container:eb682025a9113b5543ec7ed26bfa21e4",
+ "urn": "urn:li:container:eb682025a9113b5543ec7ed26bfa21e4"
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,public.date_dimension,PROD)",
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+ "fieldPath": "cc_open_date",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.DateType": {}
+ }
+ },
+ "nativeDataType": "DATE()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "cc_name",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=50)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "cc_class",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=50)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "cc_employees",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "cc_hours",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "CHAR(length=20)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "cc_manager",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=40)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "cc_address",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=256)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "cc_city",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=64)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "cc_state",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "CHAR(length=2)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "cc_region",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=64)",
+ "recursive": false,
+ "isPartOfKey": false
+ }
+ ]
+ }
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.call_center_dimension_super,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "Projections"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.call_center_dimension_super,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "upstreamLineage",
+ "aspect": {
+ "json": {
+ "upstreams": [
+ {
+ "auditStamp": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "dataset": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.call_center_dimension,PROD)",
+ "type": "TRANSFORMED"
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.call_center_dimension_super,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": [
+ {
+ "id": "urn:li:container:343f520ad0fb3259b298736800bb1385",
+ "urn": "urn:li:container:343f520ad0fb3259b298736800bb1385"
+ },
+ {
+ "id": "urn:li:container:ae8df3182db1bb8b3a612998126beae7",
+ "urn": "urn:li:container:ae8df3182db1bb8b3a612998126beae7"
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.online_sales_fact_super,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "ownership",
+ "aspect": {
+ "json": {
+ "owners": [
+ {
+ "owner": "urn:li:corpuser:dbadmin",
+ "type": "DATAOWNER"
+ }
+ ],
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ }
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.online_sales_fact_super,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "container",
+ "aspect": {
+ "json": {
+ "container": "urn:li:container:ae8df3182db1bb8b3a612998126beae7"
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "proposedSnapshot": {
+ "com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot": {
+ "urn": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.online_sales_fact_super,PROD)",
+ "aspects": [
+ {
+ "com.linkedin.pegasus2avro.common.Status": {
+ "removed": false
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.dataset.DatasetProperties": {
+ "customProperties": {
+ "ROS_Count": "1",
+ "Projection_Type": "is_super_projection",
+ "is_segmented": "True",
+ "Segmentation_key": "hash(online_sales_fact.sale_date_key, online_sales_fact.ship_date_key, online_sales_fact.product_key, online_sales_fact.product_version, online_sales_fact.customer_key, online_sales_fact.call_center_key, online_sales_fact.online_page_key, online_sales_fact.shipping_key)",
+ "projection_size": "182385 KB",
+ "Partition_Key": "Not Available",
+ "Partition_Size": "0",
+ "Projection_Cached": "False"
+ },
+ "name": "online_sales_fact_super",
+ "description": "Vertica physically stores table data in projections, which are collections of table columns. Projections store data in a format that optimizes query execution For more info on projections and corresponding properties check out the Vertica Docs: https://www.vertica.com/docs",
+ "tags": []
+ }
+ },
+ {
+ "com.linkedin.pegasus2avro.schema.SchemaMetadata": {
+ "schemaName": "online_sales.online_sales_fact_super",
+ "platform": "urn:li:dataPlatform:vertica",
+ "version": 0,
+ "created": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "lastModified": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "hash": "",
+ "platformSchema": {
+ "com.linkedin.pegasus2avro.schema.MySqlDDL": {
+ "tableSchema": ""
+ }
+ },
+ "fields": [
+ {
+ "fieldPath": "sale_date_key",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "ship_date_key",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "product_key",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "product_version",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "customer_key",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "call_center_key",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "online_page_key",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "shipping_key",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "warehouse_key",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "promotion_key",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "pos_transaction_number",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "sales_quantity",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "INTEGER()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "sales_dollar_amount",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "FLOAT()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "ship_dollar_amount",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "FLOAT()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "net_dollar_amount",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "FLOAT()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "cost_dollar_amount",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "FLOAT()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "gross_profit_dollar_amount",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.NumberType": {}
+ }
+ },
+ "nativeDataType": "FLOAT()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "transaction_type",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.StringType": {}
+ }
+ },
+ "nativeDataType": "VARCHAR(length=16)",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "online_sales_saledate",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.DateType": {}
+ }
+ },
+ "nativeDataType": "DATE()",
+ "recursive": false,
+ "isPartOfKey": false
+ },
+ {
+ "fieldPath": "online_sales_shipdate",
+ "nullable": true,
+ "description": "",
+ "type": {
+ "type": {
+ "com.linkedin.pegasus2avro.schema.DateType": {}
+ }
+ },
+ "nativeDataType": "DATE()",
+ "recursive": false,
+ "isPartOfKey": false
+ }
+ ]
+ }
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.online_sales_fact_super,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "subTypes",
+ "aspect": {
+ "json": {
+ "typeNames": [
+ "Projections"
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.online_sales_fact_super,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "upstreamLineage",
+ "aspect": {
+ "json": {
+ "upstreams": [
+ {
+ "auditStamp": {
+ "time": 0,
+ "actor": "urn:li:corpuser:unknown"
+ },
+ "dataset": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.online_sales_fact,PROD)",
+ "type": "TRANSFORMED"
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
+},
+{
+ "entityType": "dataset",
+ "entityUrn": "urn:li:dataset:(urn:li:dataPlatform:vertica,online_sales.online_sales_fact_super,PROD)",
+ "changeType": "UPSERT",
+ "aspectName": "browsePathsV2",
+ "aspect": {
+ "json": {
+ "path": [
+ {
+ "id": "urn:li:container:343f520ad0fb3259b298736800bb1385",
+ "urn": "urn:li:container:343f520ad0fb3259b298736800bb1385"
+ },
+ {
+ "id": "urn:li:container:ae8df3182db1bb8b3a612998126beae7",
+ "urn": "urn:li:container:ae8df3182db1bb8b3a612998126beae7"
+ }
+ ]
+ }
+ },
+ "systemMetadata": {
+ "lastObserved": 1586847600000,
+ "runId": "vertica-2020_04_14-07_00_00"
+ }
}
]
\ No newline at end of file
diff --git a/metadata-ingestion/tests/integration/vertica/vertica_to_file.yml b/metadata-ingestion/tests/integration/vertica/vertica_to_file.yml
index da1fcc2b5304f..ebd800ee09ff5 100644
--- a/metadata-ingestion/tests/integration/vertica/vertica_to_file.yml
+++ b/metadata-ingestion/tests/integration/vertica/vertica_to_file.yml
@@ -4,7 +4,7 @@ source:
host_port: 127.0.0.1:5433
database: Vmart
username: dbadmin
- password: vertica
+ password: abc123
sink:
type: file
diff --git a/metadata-ingestion/tests/unit/schema/test_json_schema_util.py b/metadata-ingestion/tests/unit/schema/test_json_schema_util.py
index 731ee336621fd..2635363ed8d2e 100644
--- a/metadata-ingestion/tests/unit/schema/test_json_schema_util.py
+++ b/metadata-ingestion/tests/unit/schema/test_json_schema_util.py
@@ -712,6 +712,19 @@ def test_required_field():
assert json.loads(fields[0].jsonProps or "{}")["required"] is False
+def test_non_str_enums():
+ schema = {
+ "$id": "test",
+ "$schema": "http://json-schema.org/draft-06/schema#",
+ "properties": {"bar": {"description": "Mixed enum", "enum": ["baz", 1, None]}},
+ }
+
+ fields = list(JsonSchemaTranslator.get_fields_from_schema(schema))
+ expected_field_paths: List[str] = ["[version=2.0].[type=object].[type=enum].bar"]
+ assert_field_paths_match(fields, expected_field_paths)
+ assert fields[0].description == 'One of: "baz", 1, null'
+
+
def test_anyof_with_properties():
# We expect the event / timestamp fields to be included in both branches of the anyOf.
diff --git a/metadata-ingestion/tests/unit/sql_parsing/goldens/test_snowflake_case_statement.json b/metadata-ingestion/tests/unit/sql_parsing/goldens/test_snowflake_case_statement.json
new file mode 100644
index 0000000000000..7d1a4f2039b10
--- /dev/null
+++ b/metadata-ingestion/tests/unit/sql_parsing/goldens/test_snowflake_case_statement.json
@@ -0,0 +1,37 @@
+{
+ "query_type": "SELECT",
+ "in_tables": [
+ "urn:li:dataset:(urn:li:dataPlatform:snowflake,snowflake_sample_data.tpch_sf1.orders,PROD)"
+ ],
+ "out_tables": [],
+ "column_lineage": [
+ {
+ "downstream": {
+ "table": null,
+ "column": "TOTAL_PRICE_CATEGORY"
+ },
+ "upstreams": [
+ {
+ "table": "urn:li:dataset:(urn:li:dataPlatform:snowflake,snowflake_sample_data.tpch_sf1.orders,PROD)",
+ "column": "totalprice"
+ }
+ ]
+ },
+ {
+ "downstream": {
+ "table": null,
+ "column": "TOTAL_PRICE_SUCCESS"
+ },
+ "upstreams": [
+ {
+ "table": "urn:li:dataset:(urn:li:dataPlatform:snowflake,snowflake_sample_data.tpch_sf1.orders,PROD)",
+ "column": "is_payment_successful"
+ },
+ {
+ "table": "urn:li:dataset:(urn:li:dataPlatform:snowflake,snowflake_sample_data.tpch_sf1.orders,PROD)",
+ "column": "totalprice"
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/metadata-ingestion/tests/unit/sql_parsing/test_sqlglot_lineage.py b/metadata-ingestion/tests/unit/sql_parsing/test_sqlglot_lineage.py
index 0d8471291a505..5a294be150fa0 100644
--- a/metadata-ingestion/tests/unit/sql_parsing/test_sqlglot_lineage.py
+++ b/metadata-ingestion/tests/unit/sql_parsing/test_sqlglot_lineage.py
@@ -287,6 +287,34 @@ def test_snowflake_column_normalization():
)
+def test_snowflake_case_statement():
+ assert_sql_result(
+ """
+SELECT
+ CASE
+ WHEN o."totalprice" > 1000 THEN 'high'
+ WHEN o."totalprice" > 100 THEN 'medium'
+ ELSE 'low'
+ END as total_price_category,
+ -- Also add a case where the column is in the THEN clause.
+ CASE
+ WHEN o."is_payment_successful" THEN o."totalprice"
+ ELSE 0
+ END as total_price_success
+FROM snowflake_sample_data.tpch_sf1.orders o
+""",
+ dialect="snowflake",
+ schemas={
+ "urn:li:dataset:(urn:li:dataPlatform:snowflake,snowflake_sample_data.tpch_sf1.orders,PROD)": {
+ "orderkey": "NUMBER",
+ "totalprice": "FLOAT",
+ "is_payment_successful": "BOOLEAN",
+ },
+ },
+ expected_file=RESOURCE_DIR / "test_snowflake_case_statement.json",
+ )
+
+
@pytest.mark.skip(reason="We don't handle the unnest lineage correctly")
def test_bigquery_unnest_columns():
assert_sql_result(
diff --git a/metadata-ingestion/tests/unit/test_glue_source.py b/metadata-ingestion/tests/unit/test_glue_source.py
index 23dc3b97e09b5..8fb840ee003c7 100644
--- a/metadata-ingestion/tests/unit/test_glue_source.py
+++ b/metadata-ingestion/tests/unit/test_glue_source.py
@@ -89,7 +89,7 @@ def test_column_type(hive_column_type: str, expected_type: Type) -> None:
)
schema_fields = avro_schema_to_mce_fields(json.dumps(avro_schema))
actual_schema_field_type = schema_fields[0].type
- assert type(actual_schema_field_type.type) == expected_type
+ assert isinstance(actual_schema_field_type.type, expected_type)
@pytest.mark.parametrize(
diff --git a/metadata-ingestion/tests/unit/test_kafka_source.py b/metadata-ingestion/tests/unit/test_kafka_source.py
index 33496b868adb7..b48ebf12ee37a 100644
--- a/metadata-ingestion/tests/unit/test_kafka_source.py
+++ b/metadata-ingestion/tests/unit/test_kafka_source.py
@@ -132,7 +132,9 @@ def test_kafka_source_workunits_with_platform_instance(mock_kafka, mock_admin_cl
# DataPlatform aspect should be present when platform_instance is configured
data_platform_aspects = [
- asp for asp in proposed_snap.aspects if type(asp) == DataPlatformInstanceClass
+ asp
+ for asp in proposed_snap.aspects
+ if isinstance(asp, DataPlatformInstanceClass)
]
assert len(data_platform_aspects) == 1
assert data_platform_aspects[0].instance == make_dataplatform_instance_urn(
@@ -141,7 +143,7 @@ def test_kafka_source_workunits_with_platform_instance(mock_kafka, mock_admin_cl
# The default browse path should include the platform_instance value
browse_path_aspects = [
- asp for asp in proposed_snap.aspects if type(asp) == BrowsePathsClass
+ asp for asp in proposed_snap.aspects if isinstance(asp, BrowsePathsClass)
]
assert len(browse_path_aspects) == 1
assert f"/prod/{PLATFORM}/{PLATFORM_INSTANCE}" in browse_path_aspects[0].paths
@@ -177,13 +179,15 @@ def test_kafka_source_workunits_no_platform_instance(mock_kafka, mock_admin_clie
# DataPlatform aspect should not be present when platform_instance is not configured
data_platform_aspects = [
- asp for asp in proposed_snap.aspects if type(asp) == DataPlatformInstanceClass
+ asp
+ for asp in proposed_snap.aspects
+ if isinstance(asp, DataPlatformInstanceClass)
]
assert len(data_platform_aspects) == 0
# The default browse path should include the platform_instance value
browse_path_aspects = [
- asp for asp in proposed_snap.aspects if type(asp) == BrowsePathsClass
+ asp for asp in proposed_snap.aspects if isinstance(asp, BrowsePathsClass)
]
assert len(browse_path_aspects) == 1
assert f"/prod/{PLATFORM}" in browse_path_aspects[0].paths
diff --git a/metadata-ingestion/tests/unit/utilities/test_file_backed_collections.py b/metadata-ingestion/tests/unit/utilities/test_file_backed_collections.py
index 582e3814c4f6d..2d7556eb88341 100644
--- a/metadata-ingestion/tests/unit/utilities/test_file_backed_collections.py
+++ b/metadata-ingestion/tests/unit/utilities/test_file_backed_collections.py
@@ -256,7 +256,7 @@ def test_shared_connection() -> None:
iterator = cache2.sql_query_iterator(
f"SELECT y, sum(x) FROM {cache2.tablename} GROUP BY y ORDER BY y"
)
- assert type(iterator) == sqlite3.Cursor
+ assert isinstance(iterator, sqlite3.Cursor)
assert [tuple(r) for r in iterator] == [("a", 15), ("b", 11)]
# Test joining between the two tables.