fix(ingest/ruff): added flake8-comprehensions to ruff

metadata-ingestion/examples/airflow/circuit_breaker/long_tail_companion/01-operation/ecommerce/01_snowflake_load.py

@@ -24,7 +24,9 @@ def report_operation(context):
     for outlet in task._outlets:
         print(f"Reporting insert operation for {outlet.urn}")
         reporter.report_operation(
-            urn=outlet.urn, operation_type="INSERT", num_affected_rows=123
+            urn=outlet.urn,
+            operation_type="INSERT",
+            num_affected_rows=123,
         )
 
 

metadata-ingestion/examples/airflow/circuit_breaker/long_tail_companion/01-operation/marketing/01_send_emails.py

@@ -21,7 +21,7 @@
     task_id="pet_profiles_operation_sensor",
     datahub_rest_conn_id="datahub_longtail",
     urn=[
-        "urn:li:dataset:(urn:li:dataPlatform:snowflake,long_tail_companions.adoption.pet_profiles,PROD)"
+        "urn:li:dataset:(urn:li:dataPlatform:snowflake,long_tail_companions.adoption.pet_profiles,PROD)",
     ],
     time_delta=datetime.timedelta(minutes=10),
 )

metadata-ingestion/examples/airflow/circuit_breaker/long_tail_companion/02-assertion/marketing/02_send_emails.py

@@ -24,7 +24,7 @@
     task_id="pet_profiles_operation_sensor",
     datahub_rest_conn_id="datahub_longtail",
     urn=[
-        "urn:li:dataset:(urn:li:dataPlatform:snowflake,long_tail_companions.adoption.pet_profiles,PROD)"
+        "urn:li:dataset:(urn:li:dataPlatform:snowflake,long_tail_companions.adoption.pet_profiles,PROD)",
     ],
     time_delta=datetime.timedelta(days=1),
 )
@@ -36,7 +36,7 @@
     task_id="pet_profiles_assertion_circuit_breaker",
     datahub_rest_conn_id="datahub_longtail",
     urn=[
-        "urn:li:dataset:(urn:li:dataPlatform:snowflake,long_tail_companions.adoption.pet_profiles,PROD)"
+        "urn:li:dataset:(urn:li:dataPlatform:snowflake,long_tail_companions.adoption.pet_profiles,PROD)",
     ],
     check_last_assertion_time=True,
 )

metadata-ingestion/examples/demo_data/enrich.py

@@ -88,9 +88,9 @@ def create_owner_entity_mce(owner: str) -> MetadataChangeEventClass:
                     displayName=owner,
                     fullName=owner,
                     email=f"{clean_name}[email protected]",
-                )
+                ),
             ],
-        )
+        ),
     )
 
 
@@ -111,9 +111,9 @@ def create_ownership_aspect_mce(directive: Directive) -> MetadataChangeEventClas
                         time=int(time.time() * 1000),
                         actor="urn:li:corpuser:datahub",
                     ),
-                )
+                ),
             ],
-        )
+        ),
     )
 
 
@@ -133,10 +133,10 @@ def create_lineage_aspect_mce(directive: Directive) -> MetadataChangeEventClass:
                             ),
                         )
                         for upstream in directive.depends_on
-                    ]
-                )
+                    ],
+                ),
             ],
-        )
+        ),
     )
 
 
@@ -145,7 +145,7 @@ def create_global_tags_aspect_mce(directive: Directive) -> MetadataChangeEventCl
         proposedSnapshot=DatasetSnapshotClass(
             urn=dataset_name_to_urn(directive.table),
             aspects=[GlobalTagsClass(tags=[])],
-        )
+        ),
     )
 
 
@@ -166,9 +166,9 @@ def create_editable_schema_info_aspect_mce(
                         actor="urn:li:corpuser:datahub",
                     ),
                     editableSchemaFieldInfo=[],
-                )
+                ),
             ],
-        )
+        ),
     )
 
 

metadata-ingestion/examples/library/add_mlfeature_to_mlfeature_table.py

@@ -10,19 +10,22 @@
 emitter = DatahubRestEmitter(gms_server=gms_endpoint, extra_headers={})
 
 feature_table_urn = builder.make_ml_feature_table_urn(
-    feature_table_name="my-feature-table", platform="feast"
+    feature_table_name="my-feature-table",
+    platform="feast",
 )
 feature_urns = [
     builder.make_ml_feature_urn(
-        feature_name="my-feature2", feature_table_name="my-feature-table"
+        feature_name="my-feature2",
+        feature_table_name="my-feature-table",
     ),
 ]
 
 # This code concatenates the new features with the existing features in the feature table.
 # If you want to replace all existing features with only the new ones, you can comment out this line.
 graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
 feature_table_properties = graph.get_aspect(
-    entity_urn=feature_table_urn, aspect_type=MLFeatureTablePropertiesClass
+    entity_urn=feature_table_urn,
+    aspect_type=MLFeatureTablePropertiesClass,
 )
 if feature_table_properties:
     current_features = feature_table_properties.mlFeatures

metadata-ingestion/examples/library/add_mlfeature_to_mlmodel.py

@@ -10,19 +10,23 @@
 emitter = DatahubRestEmitter(gms_server=gms_endpoint, extra_headers={})
 
 model_urn = builder.make_ml_model_urn(
-    model_name="my-test-model", platform="science", env="PROD"
+    model_name="my-test-model",
+    platform="science",
+    env="PROD",
 )
 feature_urns = [
     builder.make_ml_feature_urn(
-        feature_name="my-feature3", feature_table_name="my-feature-table"
+        feature_name="my-feature3",
+        feature_table_name="my-feature-table",
     ),
 ]
 
 # This code concatenates the new features with the existing features in the model
 # If you want to replace all existing features with only the new ones, you can comment out this line.
 graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
 model_properties = graph.get_aspect(
-    entity_urn=model_urn, aspect_type=MLModelPropertiesClass
+    entity_urn=model_urn,
+    aspect_type=MLModelPropertiesClass,
 )
 if model_properties:
     current_features = model_properties.mlFeatures

metadata-ingestion/examples/library/add_mlgroup_to_mlmodel.py

@@ -10,19 +10,24 @@
 
 model_group_urns = [
     builder.make_ml_model_group_urn(
-        group_name="my-model-group", platform="science", env="PROD"
-    )
+        group_name="my-model-group",
+        platform="science",
+        env="PROD",
+    ),
 ]
 model_urn = builder.make_ml_model_urn(
-    model_name="science-model", platform="science", env="PROD"
+    model_name="science-model",
+    platform="science",
+    env="PROD",
 )
 
 # This code concatenates the new features with the existing features in the feature table.
 # If you want to replace all existing features with only the new ones, you can comment out this line.
 graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
 
 target_model_properties = graph.get_aspect(
-    entity_urn=model_urn, aspect_type=models.MLModelPropertiesClass
+    entity_urn=model_urn,
+    aspect_type=models.MLModelPropertiesClass,
 )
 if target_model_properties:
     current_model_groups = target_model_properties.groups

metadata-ingestion/examples/library/create_domain.py

@@ -10,7 +10,8 @@
 
 domain_urn = make_domain_urn("marketing")
 domain_properties_aspect = DomainPropertiesClass(
-    name="Marketing", description="Entities related to the marketing department"
+    name="Marketing",
+    description="Entities related to the marketing department",
 )
 
 event: MetadataChangeProposalWrapper = MetadataChangeProposalWrapper(

metadata-ingestion/examples/library/create_form.py

@@ -23,7 +23,7 @@
     title="First Prompt",
     type=FormPromptTypeClass.STRUCTURED_PROPERTY,  # structured property type prompt
     structuredPropertyParams=StructuredPropertyParamsClass(
-        urn="urn:li:structuredProperty:property1"
+        urn="urn:li:structuredProperty:property1",
     ),  # reference existing structured property
     required=True,
 )
@@ -32,7 +32,7 @@
     title="Second Prompt",
     type=FormPromptTypeClass.FIELDS_STRUCTURED_PROPERTY,  # structured property prompt on dataset schema fields
     structuredPropertyParams=StructuredPropertyParamsClass(
-        urn="urn:li:structuredProperty:property1"
+        urn="urn:li:structuredProperty:property1",
     ),
     required=False,  # dataset schema fields prompts should not be required
 )

metadata-ingestion/examples/library/create_mlfeature.py

@@ -7,7 +7,9 @@
 emitter = DatahubRestEmitter(gms_server="http://localhost:8080", extra_headers={})
 
 dataset_urn = builder.make_dataset_urn(
-    name="fct_users_created", platform="hive", env="PROD"
+    name="fct_users_created",
+    platform="hive",
+    env="PROD",
 )
 feature_urn = builder.make_ml_feature_urn(
     feature_table_name="users_feature_table",

metadata-ingestion/examples/library/create_mlfeature_table.py

@@ -7,23 +7,26 @@
 emitter = DatahubRestEmitter(gms_server="http://localhost:8080", extra_headers={})
 
 feature_table_urn = builder.make_ml_feature_table_urn(
-    feature_table_name="users_feature_table", platform="feast"
+    feature_table_name="users_feature_table",
+    platform="feast",
 )
 
 feature_urns = [
     builder.make_ml_feature_urn(
-        feature_name="user_signup_date", feature_table_name="users_feature_table"
+        feature_name="user_signup_date",
+        feature_table_name="users_feature_table",
     ),
     builder.make_ml_feature_urn(
-        feature_name="user_last_active_date", feature_table_name="users_feature_table"
+        feature_name="user_last_active_date",
+        feature_table_name="users_feature_table",
     ),
 ]
 
 primary_key_urns = [
     builder.make_ml_primary_key_urn(
         feature_table_name="users_feature_table",
         primary_key_name="user_id",
-    )
+    ),
 ]
 
 feature_table_properties = models.MLFeatureTablePropertiesClass(

metadata-ingestion/examples/library/create_mlmodel.py

@@ -6,19 +6,25 @@
 # Create an emitter to DataHub over REST
 emitter = DatahubRestEmitter(gms_server="http://localhost:8080", extra_headers={})
 model_urn = builder.make_ml_model_urn(
-    model_name="my-recommendations-model-run-1", platform="science", env="PROD"
+    model_name="my-recommendations-model-run-1",
+    platform="science",
+    env="PROD",
 )
 model_group_urns = [
     builder.make_ml_model_group_urn(
-        group_name="my-recommendations-model-group", platform="science", env="PROD"
-    )
+        group_name="my-recommendations-model-group",
+        platform="science",
+        env="PROD",
+    ),
 ]
 feature_urns = [
     builder.make_ml_feature_urn(
-        feature_name="user_signup_date", feature_table_name="users_feature_table"
+        feature_name="user_signup_date",
+        feature_table_name="users_feature_table",
     ),
     builder.make_ml_feature_urn(
-        feature_name="user_last_active_date", feature_table_name="users_feature_table"
+        feature_name="user_last_active_date",
+        feature_table_name="users_feature_table",
     ),
 ]
 
@@ -33,13 +39,17 @@
         mlFeatures=feature_urns,
         trainingMetrics=[
             models.MLMetricClass(
-                name="accuracy", description="accuracy of the model", value="1.0"
-            )
+                name="accuracy",
+                description="accuracy of the model",
+                value="1.0",
+            ),
         ],
         hyperParams=[
             models.MLHyperParamClass(
-                name="hyper_1", description="hyper_1", value="0.102"
-            )
+                name="hyper_1",
+                description="hyper_1",
+                value="0.102",
+            ),
         ],
     ),
 )

metadata-ingestion/examples/library/create_mlmodel_group.py

@@ -6,7 +6,9 @@
 # Create an emitter to DataHub over REST
 emitter = DatahubRestEmitter(gms_server="http://localhost:8080", extra_headers={})
 model_group_urn = builder.make_ml_model_group_urn(
-    group_name="my-recommendations-model-group", platform="science", env="PROD"
+    group_name="my-recommendations-model-group",
+    platform="science",
+    env="PROD",
 )
 
 

metadata-ingestion/examples/library/create_mlprimarykey.py

@@ -7,7 +7,9 @@
 emitter = DatahubRestEmitter(gms_server="http://localhost:8080", extra_headers={})
 
 dataset_urn = builder.make_dataset_urn(
-    name="fct_users_created", platform="hive", env="PROD"
+    name="fct_users_created",
+    platform="hive",
+    env="PROD",
 )
 primary_key_urn = builder.make_ml_primary_key_urn(
     feature_table_name="users_feature_table",