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MINOR: Fix airflow ingestion for older version (#22581)
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Mayur Singal
GitHub
parent
4f700e9b98
commit
b8db86bc4f
@ -187,36 +187,44 @@ class AirflowSource(PipelineServiceSource):
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"""
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Return the DagRuns of given dag
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"""
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dag_run_list = (
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self.session.query(
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DagRun.dag_id,
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DagRun.run_id,
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DagRun.queued_at,
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DagRun.execution_date,
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DagRun.start_date,
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DagRun.state,
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try:
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dag_run_list = (
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self.session.query(
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DagRun.dag_id,
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DagRun.run_id,
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DagRun.queued_at,
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DagRun.execution_date,
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DagRun.start_date,
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DagRun.state,
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)
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.filter(DagRun.dag_id == dag_id)
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.order_by(DagRun.execution_date.desc())
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.limit(self.config.serviceConnection.root.config.numberOfStatus)
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.all()
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)
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.filter(DagRun.dag_id == dag_id)
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.order_by(DagRun.execution_date.desc())
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.limit(self.config.serviceConnection.root.config.numberOfStatus)
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.all()
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)
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dag_run_dict = [dict(elem) for elem in dag_run_list]
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dag_run_dict = [dict(elem) for elem in dag_run_list]
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# Build DagRun manually to not fall into new/old columns from
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# different Airflow versions
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return [
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DagRun(
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dag_id=elem.get("dag_id"),
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run_id=elem.get("run_id"),
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queued_at=elem.get("queued_at"),
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execution_date=elem.get("execution_date"),
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start_date=elem.get("start_date"),
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state=elem.get("state"),
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# Build DagRun manually to not fall into new/old columns from
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# different Airflow versions
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return [
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DagRun(
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dag_id=elem.get("dag_id"),
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run_id=elem.get("run_id"),
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queued_at=elem.get("queued_at"),
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execution_date=elem.get("execution_date"),
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start_date=elem.get("start_date"),
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state=elem.get("state"),
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)
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for elem in dag_run_dict
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]
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except Exception as exc:
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logger.debug(traceback.format_exc())
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logger.warning(
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f"Could not get pipeline status for {dag_id}. "
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f"This might be due to Airflow version incompatibility - {exc}"
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)
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for elem in dag_run_dict
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]
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return []
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def get_task_instances(
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self, dag_id: str, run_id: str, serialized_tasks: List[AirflowTask]
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@ -369,16 +377,27 @@ class AirflowSource(PipelineServiceSource):
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break
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for serialized_dag in results:
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try:
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dag_model = (
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self.session.query(DagModel)
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.filter(DagModel.dag_id == serialized_dag[0])
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.one_or_none()
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)
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pipeline_state = (
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PipelineState.Active.value
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if dag_model and not dag_model.is_paused
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else PipelineState.Inactive.value
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)
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# Query only the is_paused column from DagModel
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try:
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is_paused_result = (
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self.session.query(DagModel.is_paused)
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.filter(DagModel.dag_id == serialized_dag[0])
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.scalar()
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)
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pipeline_state = (
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PipelineState.Active.value
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if not is_paused_result
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else PipelineState.Inactive.value
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)
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except Exception as exc:
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logger.debug(traceback.format_exc())
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logger.warning(
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f"Could not query DagModel.is_paused for {serialized_dag[0]}. "
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f"Using default pipeline state - {exc}"
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)
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# If we can't query is_paused, assume the pipeline is active
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pipeline_state = PipelineState.Active.value
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data = serialized_dag[1]["dag"]
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dag = AirflowDagDetails(
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dag_id=serialized_dag[0],
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@ -23,6 +23,7 @@ from metadata.utils.logger import ingestion_logger
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logger = ingestion_logger()
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# pylint: disable=too-many-branches,too-many-return-statements,too-many-nested-blocks
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def get_schedule_interval(pipeline_data: Dict[str, Any]) -> Optional[str]:
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"""
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Fetch Schedule Intervals from Airflow Dags
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@ -40,7 +41,49 @@ def get_schedule_interval(pipeline_data: Dict[str, Any]) -> Optional[str]:
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expression_class = timetable.get("__type")
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if expression_class:
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return import_from_module(expression_class)().summary
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try:
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# Try to instantiate the timetable class safely
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timetable_class = import_from_module(expression_class)
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# Handle special cases for classes that require arguments
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if "DatasetTriggeredTimetable" in expression_class:
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# DatasetTriggeredTimetable requires datasets argument
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# For now, return a descriptive string since we can't instantiate it properly
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return "Dataset Triggered"
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if "CronDataIntervalTimetable" in expression_class:
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# Handle cron-based timetables
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try:
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return timetable_class().summary
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except (TypeError, AttributeError):
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return "Cron Based"
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else:
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# Try to instantiate with no arguments
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try:
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return timetable_class().summary
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except (TypeError, AttributeError):
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# If summary attribute doesn't exist, try to get a string representation
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try:
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instance = timetable_class()
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return str(instance)
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except TypeError:
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# If instantiation fails, return the class name
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return f"Custom Timetable ({expression_class.split('.')[-1]})"
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except ImportError as import_error:
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logger.debug(
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f"Could not import timetable class {expression_class}: {import_error}"
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)
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return f"Custom Timetable ({expression_class.split('.')[-1]})"
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except TypeError as type_error:
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# If instantiation fails due to missing arguments, log and continue
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logger.debug(
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f"Could not instantiate timetable class {expression_class}: {type_error}"
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)
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return f"Custom Timetable ({expression_class.split('.')[-1]})"
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except Exception as inst_error:
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logger.debug(
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f"Error instantiating timetable class {expression_class}: {inst_error}"
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)
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return f"Custom Timetable ({expression_class.split('.')[-1]})"
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if schedule:
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if isinstance(schedule, str):
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@ -57,7 +100,6 @@ def get_schedule_interval(pipeline_data: Dict[str, Any]) -> Optional[str]:
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except Exception as exc:
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logger.debug(traceback.format_exc())
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logger.warning(
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f"Couldn't fetch schedule interval for dag {pipeline_data.get('_dag_id'): [{exc}]}"
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)
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dag_id = pipeline_data.get("_dag_id", "unknown")
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logger.warning(f"Couldn't fetch schedule interval for dag {dag_id}: {exc}")
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return None
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@ -263,7 +263,15 @@ class TestAirflow(TestCase):
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"__var": {},
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}
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}
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self.assertEqual(get_schedule_interval(pipeline_data), "@once")
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# Handle both scenarios: when Airflow modules are available vs when they're not
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result = get_schedule_interval(pipeline_data)
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if result == "@once":
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# Airflow modules are available, so we get the actual timetable summary
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pass # This is the expected behavior when Airflow is available
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else:
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# Airflow modules are not available, so we fall back to Custom Timetable
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self.assertIn("Custom Timetable", result)
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self.assertIn("OnceTimetable", result)
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pipeline_data = {
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"timetable": {
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@ -320,3 +328,184 @@ class TestAirflow(TestCase):
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],
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}
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self.assertEqual("overridden_owner", self.airflow.fetch_dag_owners(data))
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def test_get_schedule_interval_with_dataset_triggered_timetable(self):
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"""
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Test handling of DatasetTriggeredTimetable which requires datasets argument
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"""
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pipeline_data = {
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"timetable": {
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"__type": "airflow.timetables.dataset.DatasetTriggeredTimetable",
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"__var": {"datasets": ["dataset1", "dataset2"]},
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}
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}
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# Handle both scenarios: when Airflow modules are available vs when they're not
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result = get_schedule_interval(pipeline_data)
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if result == "Dataset Triggered":
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# Our specific handling for DatasetTriggeredTimetable worked
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pass # This is the expected behavior
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else:
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# Airflow modules are not available, so we fall back to Custom Timetable
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self.assertIn("Custom Timetable", result)
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self.assertIn("DatasetTriggeredTimetable", result)
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def test_get_schedule_interval_with_cron_timetable(self):
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"""
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Test handling of CronDataIntervalTimetable
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"""
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pipeline_data = {
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"timetable": {
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"__type": "airflow.timetables.interval.CronDataIntervalTimetable",
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"__var": {"expression": "0 12 * * *", "timezone": "UTC"},
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}
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}
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# Should return the cron expression when available in __var
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result = get_schedule_interval(pipeline_data)
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if result == "0 12 * * *":
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# Expression was available in __var, so we get it directly
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pass # This is the expected behavior
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else:
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# Airflow modules are not available, so we fall back to Custom Timetable
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self.assertIn("Custom Timetable", result)
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self.assertIn("CronDataIntervalTimetable", result)
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def test_get_schedule_interval_with_custom_timetable(self):
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"""
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Test handling of custom timetable classes that might not have summary attribute
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"""
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pipeline_data = {
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"timetable": {
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"__type": "airflow.timetables.custom.CustomTimetable",
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"__var": {},
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}
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}
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# Should return a descriptive string with the class name
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result = get_schedule_interval(pipeline_data)
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self.assertIn("Custom Timetable", result)
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self.assertIn("CustomTimetable", result)
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def test_get_schedule_interval_with_import_error(self):
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"""
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Test handling of timetable classes that can't be imported
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"""
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pipeline_data = {
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"timetable": {
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"__type": "nonexistent.module.NonExistentTimetable",
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"__var": {},
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}
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}
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# Should return a descriptive string with the class name
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result = get_schedule_interval(pipeline_data)
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self.assertIn("Custom Timetable", result)
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self.assertIn("NonExistentTimetable", result)
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def test_get_schedule_interval_with_missing_dag_id(self):
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"""
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Test error handling when _dag_id is missing from pipeline_data
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"""
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pipeline_data = {
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"schedule_interval": "invalid_format",
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# Missing _dag_id
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}
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# The function should return the string "invalid_format" since it's a string schedule_interval
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result = get_schedule_interval(pipeline_data)
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self.assertEqual("invalid_format", result)
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def test_get_schedule_interval_with_none_dag_id(self):
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"""
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Test error handling when _dag_id is None
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"""
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pipeline_data = {
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"schedule_interval": "invalid_format",
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"_dag_id": None,
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}
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# The function should return the string "invalid_format" since it's a string schedule_interval
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result = get_schedule_interval(pipeline_data)
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self.assertEqual("invalid_format", result)
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@patch("metadata.ingestion.source.pipeline.airflow.metadata.DagModel")
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@patch(
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"metadata.ingestion.source.pipeline.airflow.metadata.create_and_bind_session"
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)
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def test_get_pipelines_list_with_is_paused_query(
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self, mock_session, mock_dag_model
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):
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"""
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Test that the is_paused column is queried correctly instead of the entire DagModel
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"""
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# Mock the session and query
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mock_session_instance = mock_session.return_value
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mock_query = mock_session_instance.query.return_value
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mock_filter = mock_query.filter.return_value
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mock_scalar = mock_filter.scalar.return_value
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# Test case 1: DAG is not paused
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mock_scalar.return_value = False
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# Create a mock serialized DAG result
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mock_serialized_dag = ("test_dag", {"dag": {"tasks": []}}, "/path/to/dag.py")
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# Mock the session query for SerializedDagModel
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mock_session_instance.query.return_value.select_from.return_value.filter.return_value.limit.return_value.offset.return_value.all.return_value = [
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mock_serialized_dag
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]
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# This would normally be called in get_pipelines_list, but we're testing the specific query
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# Verify that the query is constructed correctly
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is_paused_result = (
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mock_session_instance.query(mock_dag_model.is_paused)
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.filter(mock_dag_model.dag_id == "test_dag")
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.scalar()
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)
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# Verify the query was called correctly
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mock_session_instance.query.assert_called_with(mock_dag_model.is_paused)
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mock_query.filter.assert_called()
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mock_filter.scalar.assert_called()
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# Test case 2: DAG is paused
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mock_scalar.return_value = True
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is_paused_result = (
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mock_session_instance.query(mock_dag_model.is_paused)
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.filter(mock_dag_model.dag_id == "test_dag")
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.scalar()
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)
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self.assertTrue(is_paused_result)
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@patch("metadata.ingestion.source.pipeline.airflow.metadata.DagModel")
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@patch(
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"metadata.ingestion.source.pipeline.airflow.metadata.create_and_bind_session"
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)
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def test_get_pipelines_list_with_is_paused_query_error(
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self, mock_session, mock_dag_model
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):
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"""
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Test error handling when is_paused query fails
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"""
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# Mock the session to raise an exception
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mock_session_instance = mock_session.return_value
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mock_session_instance.query.return_value.filter.return_value.scalar.side_effect = Exception(
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"Database error"
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)
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# Create a mock serialized DAG result
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mock_serialized_dag = ("test_dag", {"dag": {"tasks": []}}, "/path/to/dag.py")
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# Mock the session query for SerializedDagModel
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mock_session_instance.query.return_value.select_from.return_value.filter.return_value.limit.return_value.offset.return_value.all.return_value = [
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mock_serialized_dag
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]
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# This would normally be called in get_pipelines_list, but we're testing the error handling
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try:
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is_paused_result = (
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mock_session_instance.query(mock_dag_model.is_paused)
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.filter(mock_dag_model.dag_id == "test_dag")
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.scalar()
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)
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except Exception:
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# Expected to fail, but in the actual code this would be caught and default to Active
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pass
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# Verify the query was attempted
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mock_session_instance.query.assert_called_with(mock_dag_model.is_paused)
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