add logging statements for transformer matches

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

@@ -210,6 +210,10 @@ def populate_terms_in_schema_metadata(
     ) -> None:
         for schema_field in schema_metadata.fields:
             if schema_field.fieldPath in field_terms:
+                logger.info(
+                    f"Attaching glossary term '{field_terms[schema_field.fieldPath]}' "
+                    f"to field '{schema_field.fieldPath}'"
+                )
                 schema_field.glossaryTerms = GlossaryTerms(
                     terms=[
                         GlossaryTermAssociation(

metadata-ingestion/src/datahub/ingestion/transformer/add_dataset_schema_terms.py

@@ -1,3 +1,4 @@
+import logging
 from typing import Callable, Dict, List, Optional, cast
 
 import datahub.emitter.mce_builder as builder
@@ -14,11 +15,14 @@
 from datahub.metadata.schema_classes import (
     AuditStampClass,
     GlossaryTermAssociationClass,
+    GlossaryTermInfoClass,
     GlossaryTermsClass,
     SchemaFieldClass,
     SchemaMetadataClass,
 )
 
+logger: logging.Logger = logging.getLogger(__name__)
+
 
 class AddDatasetSchemaTermsConfig(TransformerSemanticsConfigModel):
     get_terms_to_add: Callable[[str], List[GlossaryTermAssociationClass]]
@@ -43,7 +47,10 @@ def create(cls, config_dict: dict, ctx: PipelineContext) -> "AddDatasetSchemaTer
         return cls(config, ctx)
 
     def extend_field(
-        self, schema_field: SchemaFieldClass, server_field: Optional[SchemaFieldClass]
+        self,
+        schema_field: SchemaFieldClass,
+        server_field: Optional[SchemaFieldClass],
+        entity_urn: str,
     ) -> SchemaFieldClass:
         all_terms = self.config.get_terms_to_add(schema_field.fieldPath)
         if len(all_terms) == 0:
@@ -76,9 +83,29 @@ def extend_field(
         new_glossary_terms.extend(terms_to_add)
 
         unique_gloseary_terms = []
+        term_log_entries = []
         for term in new_glossary_terms:
             if term not in unique_gloseary_terms:
                 unique_gloseary_terms.append(term)
+                if self.ctx.graph:
+                    term_aspect = self.ctx.graph.get_aspect(
+                        entity_urn=term.urn, aspect_type=GlossaryTermInfoClass
+                    )
+                    if term_aspect:
+                        term_name = term_aspect.name
+                        term_log_entries.append(
+                            f"Term URN: {term.urn}, Term Name: {term_name}, Field Path: {schema_field.fieldPath}, Entity URN: {entity_urn}"
+                        )
+                    else:
+                        term_log_entries.append(
+                            f"Term URN: {term.urn}, Term Name: Not Found, Field Path: {schema_field.fieldPath}, Entity URN: {entity_urn}"
+                        )
+
+        # Log the collected term details together
+        if term_log_entries:
+            logger.debug("Adding below terms to fields:")
+            for entry in term_log_entries:
+                logger.debug(entry)
 
         new_glossary_term = GlossaryTermsClass(
             terms=[],
@@ -130,7 +157,11 @@ def transform_aspect(
             return None
 
         schema_metadata_aspect.fields = [
-            self.extend_field(field, server_field=server_field_map.get(field.fieldPath))
+            self.extend_field(
+                field,
+                server_field=server_field_map.get(field.fieldPath),
+                entity_urn=entity_urn,
+            )
             for field in schema_metadata_aspect.fields
         ]