feat(ingest/airflow): support BigQueryInsertJobOperator

docs/lineage/airflow.md

@@ -8,7 +8,7 @@ If you're looking to schedule DataHub ingestion using Airflow, see the guide on
 
 The DataHub Airflow plugin supports:
 
-- Automatic column-level lineage extraction from various operators e.g. SQL operators (including `MySqlOperator`, `PostgresOperator`, `SnowflakeOperator`, and more), `S3FileTransformOperator`, and more.
+- Automatic column-level lineage extraction from various operators e.g. SQL operators (including `MySqlOperator`, `PostgresOperator`, `SnowflakeOperator`, `BigQueryInsertJobOperator`, and more), `S3FileTransformOperator`, and more.
 - Airflow DAG and tasks, including properties, ownership, and tags.
 - Task run information, including task successes and failures.
 - Manual lineage annotations using `inlets` and `outlets` on Airflow operators.
@@ -166,6 +166,7 @@ Supported operators:
 - `SQLExecuteQueryOperator`, including any subclasses. Note that in newer versions of Airflow (generally Airflow 2.5+), most SQL operators inherit from this class.
 - `AthenaOperator` and `AWSAthenaOperator`
 - `BigQueryOperator` and `BigQueryExecuteQueryOperator`
+- `BigQueryInsertJobOperator` (incubating)
 - `MySqlOperator`
 - `PostgresOperator`
 - `RedshiftSQLOperator`
@@ -224,6 +225,14 @@ class DbtOperator(BaseOperator):
 
 If you override the `pre_execute` and `post_execute` function, ensure they include the `@prepare_lineage` and `@apply_lineage` decorators respectively. Reference the [Airflow docs](https://airflow.apache.org/docs/apache-airflow/stable/administration-and-deployment/lineage.html#lineage) for more details.
 
+### Custom Extractors
+
+Note: these are only supported in the v2 plugin.
+
+You can also create a custom extractor to extract lineage from any operator. This is useful if you're using a built-in Airflow operator for which we don't support automatic lineage extraction.
+
+See this [example PR](https://github.com/datahub-project/datahub/pull/10452) which adds a custom extractor for the `BigQueryInsertJobOperator` operator.
+
 ## Emit Lineage Directly
 
 If you can't use the plugin or annotate inlets/outlets, you can also emit lineage using the `DatahubEmitterOperator`.

metadata-ingestion-modules/airflow-plugin/src/datahub_airflow_plugin/_extractors.py

@@ -59,6 +59,10 @@ def __init__(self):
         for operator in _sql_operator_overrides:
             self.task_to_extractor.extractors[operator] = GenericSqlExtractor
 
+        self.task_to_extractor.extractors[
+            "BigQueryInsertJobOperator"
+        ] = BigQueryInsertJobOperatorExtractor
+
         self._graph: Optional["DataHubGraph"] = None
 
     @contextlib.contextmanager
@@ -78,7 +82,7 @@ def _patch_extractors(self):
                 unittest.mock.patch.object(
                     SnowflakeExtractor,
                     "default_schema",
-                    property(snowflake_default_schema),
+                    property(_snowflake_default_schema),
                 )
             )
 
@@ -166,12 +170,6 @@ def _sql_extractor_extract(self: "SqlExtractor") -> TaskMetadata:
     task_name = f"{self.operator.dag_id}.{self.operator.task_id}"
     sql = self.operator.sql
 
-    run_facets = {}
-    job_facets = {"sql": SqlJobFacet(query=self._normalize_sql(sql))}
-
-    # Prepare to run the SQL parser.
-    graph = self.context.get(_DATAHUB_GRAPH_CONTEXT_KEY, None)
-
     default_database = getattr(self.operator, "database", None)
     if not default_database:
         default_database = self.database
@@ -185,6 +183,31 @@ def _sql_extractor_extract(self: "SqlExtractor") -> TaskMetadata:
     # Run the SQL parser.
     scheme = self.scheme
     platform = OL_SCHEME_TWEAKS.get(scheme, scheme)
+
+    return _parse_sql_into_task_metadata(
+        self,
+        sql,
+        platform=platform,
+        default_database=default_database,
+        default_schema=default_schema,
+    )
+
+
+def _parse_sql_into_task_metadata(
+    self: "BaseExtractor",
+    sql: str,
+    platform: str,
+    default_database: Optional[str],
+    default_schema: Optional[str],
+) -> TaskMetadata:
+    task_name = f"{self.operator.dag_id}.{self.operator.task_id}"
+
+    run_facets = {}
+    job_facets = {"sql": SqlJobFacet(query=self._normalize_sql(sql))}
+
+    # Prepare to run the SQL parser.
+    graph = self.context.get(_DATAHUB_GRAPH_CONTEXT_KEY, None)
+
     self.log.debug(
         "Running the SQL parser %s (platform=%s, default db=%s, schema=%s): %s",
         "with graph client" if graph else "in offline mode",
@@ -232,7 +255,28 @@ def _sql_extractor_extract(self: "SqlExtractor") -> TaskMetadata:
     )
 
 
-def snowflake_default_schema(self: "SnowflakeExtractor") -> Optional[str]:
+class BigQueryInsertJobOperatorExtractor(BaseExtractor):
+    def extract(self) -> Optional[TaskMetadata]:
+        from airflow.providers.google.cloud.operators.bigquery import (
+            BigQueryInsertJobOperator,  # type: ignore
+        )
+
+        operator: "BigQueryInsertJobOperator" = self.operator
+        sql = operator.configuration.get("query")
+        if not sql:
+            self.log.warning("No query found in BigQueryInsertJobOperator")
+            return None
+
+        return _parse_sql_into_task_metadata(
+            self,
+            sql,
+            platform="bigquery",
+            default_database=operator.project_id,
+            default_schema=None,
+        )
+
+
+def _snowflake_default_schema(self: "SnowflakeExtractor") -> Optional[str]:
     if hasattr(self.operator, "schema") and self.operator.schema is not None:
         return self.operator.schema
     return (