fix: PR Comments

metadata-ingestion/docs/transformer/dataset_transformer.md

@@ -679,11 +679,11 @@ We can add glossary terms to datasets based on a regex filter.
 
 <br/>
 
-The `tags_to_term` transformer is designed to map specific tags to glossary terms within DataHub. It takes a configuration of tags should be translated into corresponding glossaryTerm. This transformer can apply these mappings to any tags found either at column level of dataset or dataset top level.
+The `tags_to_term` transformer is designed to map specific tags to glossary terms within DataHub. It takes a configuration of tags that should be translated into corresponding glossary terms. This transformer can apply these mappings to any tags found either at the column level of a dataset or at the dataset top level.
 
 When specifying tags in the configuration, use the tag's simple name rather than the full tag URN.
 
-For example, instead of using the tag URN `urn:li:tag:snowflakedb.snowflakeschema.tag_name:tag_value`, you should specify just the tag name `tag_name` in the mapping configuration
+For example, instead of using the tag URN `urn:li:tag:snowflakedb.snowflakeschema.tag_name:tag_value`, you should specify just the tag name `tag_name` in the mapping configuration.
 
 ```yaml
 transformers:
@@ -694,28 +694,29 @@ transformers:
  - "tag_name"
 ```
 
-`tags_to_term` can be configured in below different way
+The `tags_to_term` transformer can be configured in the following ways:
 
-- Add domains based on tags, however overwrite the domains available for the dataset on DataHub GMS
+- Add terms based on tags, however overwrite the terms available for the dataset on DataHub GMS
 ```yaml
  transformers:
- - type: "domain_mapping_based_on_tags"
+ - type: "tags_to_term"
  config:
  semantics: OVERWRITE # OVERWRITE is default behaviour
  tags:
  - "example1"
  - "example2"
  - "example3"
  ```
-- Add domains based on tags, however keep the domains available for the dataset on DataHub GMS
+- Add terms based on tags, however keep the terms available for the dataset on DataHub GMS
 ```yaml
  transformers:
- - type: "domain_mapping_based_on_tags"
+ - type: "tags_to_term"
  config:
  semantics: PATCH
- domain_mapping:
- 'example1': "urn:li:domain:engineering"
- 'example2': "urn:li:domain:hr"
+ tags:
+ - "example1"
+ - "example2"
+ - "example3"
  ```
 
 ## Pattern Add Dataset Schema Field glossaryTerms

metadata-ingestion/src/datahub/ingestion/graph/client.py

@@ -1279,17 +1279,15 @@ def create_tag(self, tag_name: str) -> str:
  return res["createTag"]
 
  def remove_tag(self, tag_urn: str, resource_urn: str) -> bool:
- graph_query = """
+ graph_query = f"""
  mutation removeTag {{
  removeTag(
  input: {{
  tagUrn: "{tag_urn}",
  resourceUrn: "{resource_urn}"
  }})
  }}
- """.format(
- tag_urn=tag_urn, resource_urn=resource_urn
- )
+ """
 
  res = self.execute_graphql(query=graph_query)
  return res["removeTag"]

metadata-ingestion/tests/unit/test_transform_dataset.py

@@ -1898,9 +1898,11 @@ def run_dataset_transformer_pipeline(
  transformer_type: Type[Union[DatasetTransformer, TagTransformer]],
  aspect: Optional[builder.Aspect],
  config: dict,
- pipeline_context: PipelineContext = PipelineContext(run_id="transformer_pipe_line"),
+ pipeline_context: Optional[PipelineContext] = None,
  use_mce: bool = False,
 ) -> List[RecordEnvelope]:
+ if pipeline_context is None:
+ pipeline_context = PipelineContext(run_id="transformer_pipe_line")
  transformer: DatasetTransformer = cast(
  DatasetTransformer, transformer_type.create(config, pipeline_context)
  )
@@ -3665,8 +3667,8 @@ def test_tags_to_terms_transformation(mock_datahub_graph):
  def fake_get_tags(entity_urn: str) -> models.GlobalTagsClass:
  return models.GlobalTagsClass(
  tags=[
- TagAssociationClass(tag=builder.make_tag_urn("exmaple1")),
- TagAssociationClass(tag=builder.make_tag_urn("exmaple2")),
+ TagAssociationClass(tag=builder.make_tag_urn("example1")),
+ TagAssociationClass(tag=builder.make_tag_urn("example2")),
  ]
  )
 
@@ -3729,7 +3731,7 @@ def fake_schema_metadata(entity_urn: str) -> models.SchemaMetadataClass:
  pipeline_context.graph.get_schema_metadata = fake_schema_metadata # type: ignore
 
  # Configuring the transformer
- config = {"tags": ["exmaple1", "exmaple2"]}
+ config = {"tags": ["example1", "example2"]}
 
  # Running the transformer within a test pipeline
  output = run_dataset_transformer_pipeline(
@@ -3753,8 +3755,8 @@ def fake_schema_metadata(entity_urn: str) -> models.SchemaMetadataClass:
  assert isinstance(terms_aspect, models.GlossaryTermsClass)
  assert len(terms_aspect.terms) == len(expected_terms)
  assert set(term.urn for term in terms_aspect.terms) == {
- "urn:li:glossaryTerm:exmaple1",
- "urn:li:glossaryTerm:exmaple2",
+ "urn:li:glossaryTerm:example1",
+ "urn:li:glossaryTerm:example2",
  }