fix(ingest/bigquery): Fix performance issue with column profiling ignore (#11807)

metadata-ingestion/src/datahub/ingestion/source/bigquery_v2/bigquery_schema.py

@@ -118,7 +118,6 @@ class BigqueryTable(BaseTable):
     active_billable_bytes: Optional[int] = None
     long_term_billable_bytes: Optional[int] = None
     partition_info: Optional[PartitionInfo] = None
-    columns_ignore_from_profiling: List[str] = field(default_factory=list)
     external: bool = False
     constraints: List[BigqueryTableConstraint] = field(default_factory=list)
     table_type: Optional[str] = None

metadata-ingestion/src/datahub/ingestion/source/bigquery_v2/bigquery_schema_gen.py

@@ -598,18 +598,6 @@ class BigQuerySchemaGenerator:
                     dataset_name=dataset_name,
                 )
 
-    # This method is used to generate the ignore list for datatypes the profiler doesn't support we have to do it here
-    # because the profiler doesn't have access to columns
-    def generate_profile_ignore_list(self, columns: List[BigqueryColumn]) -> List[str]:
-        ignore_list: List[str] = []
-        for column in columns:
-            if not column.data_type or any(
-                word in column.data_type.lower()
-                for word in ["array", "struct", "geography", "json"]
-            ):
-                ignore_list.append(column.field_path)
-        return ignore_list
-
     def _process_table(
         self,
         table: BigqueryTable,
@@ -631,15 +619,6 @@ class BigQuerySchemaGenerator:
             )
         table.column_count = len(columns)
 
-        # We only collect profile ignore list if profiling is enabled and profile_table_level_only is false
-        if (
-            self.config.is_profiling_enabled()
-            and not self.config.profiling.profile_table_level_only
-        ):
-            table.columns_ignore_from_profiling = self.generate_profile_ignore_list(
-                columns
-            )
-
         if not table.column_count:
             logger.warning(
                 f"Table doesn't have any column or unable to get columns for table: {table_identifier}"

metadata-ingestion/src/datahub/ingestion/source/bigquery_v2/profiler.py

@@ -166,12 +166,6 @@ WHERE
                 normalized_table_name = BigqueryTableIdentifier(
                     project_id=project_id, dataset=dataset, table=table.name
                 ).get_table_name()
-                for column in table.columns_ignore_from_profiling:
-                    # Profiler has issues with complex types (array, struct, geography, json), so we deny those types from profiling
-                    # We also filter columns without data type as it means that column is part of a complex type.
-                    self.config.profile_pattern.deny.append(
-                        f"^{normalized_table_name}.{column}$"
-                    )
 
                 if table.external and not self.config.profiling.profile_external_tables:
                     self.report.profiling_skipped_other[f"{project_id}.{dataset}"] += 1

metadata-ingestion/src/datahub/ingestion/source/ge_data_profiler.py

@@ -7,6 +7,7 @@ import dataclasses
 import functools
 import json
 import logging
+import re
 import threading
 import traceback
 import unittest.mock
@@ -123,6 +124,8 @@ ProfilerTypeMapping.BINARY_TYPE_NAMES.append("LargeBinary")
 
 _datasource_connection_injection_lock = threading.Lock()
 
+NORMALIZE_TYPE_PATTERN = re.compile(r"^(.*?)(?:[\[<(].*)?$")
+
 
 @contextlib.contextmanager
 def _inject_connection_into_datasource(conn: Connection) -> Iterator[None]:
@@ -165,11 +168,9 @@ def get_column_unique_count_dh_patch(self: SqlAlchemyDataset, column: str) -> in
         return convert_to_json_serializable(element_values.fetchone()[0])
     elif self.engine.dialect.name.lower() == BIGQUERY:
         element_values = self.engine.execute(
-            sa.select(
+            sa.select(sa.func.APPROX_COUNT_DISTINCT(sa.column(column))).select_from(
-                [
+                self._table
-                    sa.func.coalesce(sa.text(f"APPROX_COUNT_DISTINCT(`{column}`)")),
+            )
-                ]
-            ).select_from(self._table)
         )
         return convert_to_json_serializable(element_values.fetchone()[0])
     elif self.engine.dialect.name.lower() == SNOWFLAKE:
@@ -378,6 +379,9 @@ class _SingleDatasetProfiler(BasicDatasetProfilerBase):
                 f"{self.dataset_name}.{col}"
             ):
                 ignored_columns_by_pattern.append(col)
+            # We try to ignore nested columns as well
+            elif not self.config.profile_nested_fields and "." in col:
+                ignored_columns_by_pattern.append(col)
             elif col_dict.get("type") and self._should_ignore_column(col_dict["type"]):
                 ignored_columns_by_type.append(col)
             else:
@@ -407,9 +411,18 @@ class _SingleDatasetProfiler(BasicDatasetProfilerBase):
         return columns_to_profile
 
     def _should_ignore_column(self, sqlalchemy_type: sa.types.TypeEngine) -> bool:
-        return str(sqlalchemy_type) in _get_column_types_to_ignore(
+        # We don't profiles columns with None types
-            self.dataset.engine.dialect.name
+        if str(sqlalchemy_type) == "NULL":
-        )
+            return True
+
+        sql_type = str(sqlalchemy_type)
+
+        match = re.match(NORMALIZE_TYPE_PATTERN, sql_type)
+
+        if match:
+            sql_type = match.group(1)
+
+        return sql_type in _get_column_types_to_ignore(self.dataset.engine.dialect.name)
 
     @_run_with_query_combiner
     def _get_column_type(self, column_spec: _SingleColumnSpec, column: str) -> None:
@@ -1397,6 +1410,8 @@ class DatahubGEProfiler:
 def _get_column_types_to_ignore(dialect_name: str) -> List[str]:
     if dialect_name.lower() == POSTGRESQL:
         return ["JSON"]
+    elif dialect_name.lower() == BIGQUERY:
+        return ["ARRAY", "STRUCT", "GEOGRAPHY", "JSON"]
 
     return []
 

metadata-ingestion/src/datahub/ingestion/source/ge_profiling_config.py

@@ -188,6 +188,11 @@ class GEProfilingConfig(GEProfilingBaseConfig):
         ),
     )
 
+    profile_nested_fields: bool = Field(
+        default=False,
+        description="Whether to profile complex types like structs, arrays and maps. ",
+    )
+
     @pydantic.root_validator(pre=True)
     def deprecate_bigquery_temp_table_schema(cls, values):
         # TODO: Update docs to remove mention of this field.