def _register_dagrun(self, dagrun, execution_date):
self.log.debug(f"self.task_dict: {self.task_dict}")
# Register each task in the DAG
for task_id, task in self.task_dict.items():
t = self._now_ms()
try:
step = self._extract_metadata(dagrun, task)
job_name = self._marquez_job_name(self.dag_id, task.task_id)
run_id = self._marquez_run_id(dagrun.run_id, task.task_id)
task_run_id = _MARQUEZ.start_task(
run_id,
job_name,
self.description,
DagUtils.to_iso_8601(self._now_ms()),
None, # TODO: add parent hierarchy
self._get_location(task),
DagUtils.get_start_time(execution_date),
DagUtils.get_end_time(
execution_date,
self.following_schedule(execution_date)),
step)
JobIdMapping.set(job_name, dagrun.run_id, task_run_id)
except Exception as e:
self.log.error(
f'Failed to record task {task_id}: {e} '
f'{self._timed_log_message(t)}',
exc_info=True)
def _register_dagrun(self, dagrun, execution_date, run_args):
self.log.debug(f"self.task_dict: {self.task_dict}")
# Register each task in the DAG
for task_id, task in self.task_dict.items():
t = self._now_ms()
try:
steps = self._extract_metadata(dagrun, task)
[
_MARQUEZ.create_job(step, self._get_location(task),
self.description) for step in steps
]
marquez_jobrun_ids = [
_MARQUEZ.create_run(
self.new_run_id(), step, run_args,
DagUtils.get_start_time(execution_date),
DagUtils.get_end_time(
execution_date,
self.following_schedule(execution_date)))
for step in steps
]
JobIdMapping.set(
self._marquez_job_name(self.dag_id, task.task_id),
dagrun.run_id, marquez_jobrun_ids)
except Exception as e:
self.log.error(
f'Failed to record task {task_id}: {e} '
f'{self._timed_log_message(t)}',
exc_info=True)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._marquez_dataset_cache = {}
self._marquez_source_cache = {}
self.marquez_namespace = os.getenv('MARQUEZ_NAMESPACE',
DAG.DEFAULT_NAMESPACE)
self._job_id_mapping = JobIdMapping()
def _register_dagrun(self, dagrun: DagRun, is_external_trigger: bool, execution_date: str):
self.log.debug(f"self.task_dict: {self.task_dict}")
# Register each task in the DAG
for task_id, task in self.task_dict.items():
t = self._now_ms()
try:
step = self._extract_metadata(dagrun, task)
job_name = self._marquez_job_name(self.dag_id, task.task_id)
run_id = self._marquez_run_id(dagrun.run_id, task.task_id)
task_run_id = _MARQUEZ.start_task(
run_id,
job_name,
self.description,
DagUtils.to_iso_8601(self._now_ms()),
dagrun.run_id,
self._get_location(task),
DagUtils.get_start_time(execution_date),
DagUtils.get_end_time(execution_date, self.following_schedule(execution_date)),
step,
{**step.run_facets, **get_custom_facets(task, is_external_trigger)}
)
JobIdMapping.set(
job_name,
dagrun.run_id,
task_run_id
)
except Exception as e:
self.log.error(
f'Failed to record task {task_id}: {e} '
f'{self._timed_log_message(t)}',
exc_info=True)
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.marquez_namespace = os.environ.get('MARQUEZ_NAMESPACE') or \
DAG.DEFAULT_NAMESPACE
self.marquez_location = kwargs['default_args'].get(
'marquez_location', 'unknown')
self.marquez_input_urns = kwargs['default_args'].get(
'marquez_input_urns', [])
self.marquez_output_urns = kwargs['default_args'].get(
'marquez_output_urns', [])
self._job_id_mapping = JobIdMapping()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._job_id_mapping = JobIdMapping()
self._marquez = Marquez()
# TODO: Manually define operator->extractor mappings for now,
# but we'll want to encapsulate this logic in an 'Extractors' class
# with more convenient methods (ex: 'Extractors.extractor_for_task()')
self._extractors = {
PostgresOperator: PostgresExtractor,
BigQueryOperator: BigQueryExtractor
# Append new extractors here
}
self.log.debug(
f"DAG successfully created with extractors: {self._extractors}")
def create_dagrun(self, *args, **kwargs):
run_args = "{}" # TODO extract the run Args from the tasks
marquez_jobrun_id = None
try:
marquez_jobrun_id = self.report_jobrun(run_args,
kwargs['execution_date'])
log.info(f'Successfully recorded job run.',
airflow_dag_id=self.dag_id,
marquez_run_id=marquez_jobrun_id,
marquez_namespace=self.marquez_namespace)
except Exception as e:
log.error(f'Failed to record job run: {e}',
airflow_dag_id=self.dag_id,
marquez_namespace=self.marquez_namespace)
pass
run = super(DAG, self).create_dagrun(*args, **kwargs)
if marquez_jobrun_id:
try:
self._job_id_mapping.set(
JobIdMapping.make_key(run.dag_id, run.run_id),
marquez_jobrun_id)
except Exception as e:
log.error(f'Failed job run lookup: {e}',
airflow_dag_id=self.dag_id,
airflow_run_id=run.run_id,
marquez_run_id=marquez_jobrun_id,
marquez_namespace=self.marquez_namespace)
pass
return run
def report_jobrun_change(self, job_name, run_id, **kwargs):
session = kwargs.get('session')
marquez_job_run_ids = self._job_id_mapping.pop(
JobIdMapping.make_key(job_name, run_id), session)
if marquez_job_run_ids:
log.info('Found job runs.',
airflow_dag_id=self.dag_id,
airflow_job_id=job_name,
airflow_run_id=run_id,
marquez_run_ids=marquez_job_run_ids,
marquez_namespace=self.marquez_namespace)
ids = json.loads(marquez_job_run_ids)
if kwargs.get('success'):
for marquez_job_run_id in ids:
self.get_marquez_client().mark_job_run_as_completed(
marquez_job_run_id)
else:
for marquez_job_run_id in ids:
self.get_marquez_client().mark_job_run_as_failed(
marquez_job_run_id)
state = 'COMPLETED' if kwargs.get('success') else 'FAILED'
log.info(f'Marked job run(s) as {state}.',
airflow_dag_id=self.dag_id,
airflow_job_id=job_name,
airflow_run_id=run_id,
marquez_run_id=marquez_job_run_ids,
marquez_namespace=self.marquez_namespace)
def _report_task_instance(self, task_instance, dagrun, session):
task = self.get_task(task_instance.task_id)
# Note: task_run_id could be missing if it was removed from airflow
# or the job could not be registered.
task_run_id = JobIdMapping.pop(
self._marquez_job_name_from_task_instance(task_instance), dagrun.run_id, session)
step = self._extract_metadata(dagrun, task, task_instance)
job_name = self._marquez_job_name(self.dag_id, task.task_id)
run_id = self._marquez_run_id(dagrun.run_id, task.task_id)
if not task_run_id:
task_run_id = _MARQUEZ.start_task(
run_id,
job_name,
self.description,
DagUtils.to_iso_8601(task_instance.start_date),
dagrun.run_id,
self._get_location(task),
DagUtils.to_iso_8601(task_instance.start_date),
DagUtils.to_iso_8601(task_instance.end_date),
step,
{**step.run_facets, **get_custom_facets(task, False)}
)
if not task_run_id:
self.log.warning('Could not emit lineage')
self.log.debug(f'Setting task state: {task_instance.state}'
f' for {task_instance.task_id}')
if task_instance.state in {State.SUCCESS, State.SKIPPED}:
_MARQUEZ.complete_task(
task_run_id,
job_name,
DagUtils.to_iso_8601(task_instance.end_date),
step
)
else:
_MARQUEZ.fail_task(
task_run_id,
job_name,
DagUtils.to_iso_8601(task_instance.end_date),
step
)
def _report_task_instance(self, ti, dagrun, run_args, session):
task = self.get_task(ti.task_id)
run_ids = JobIdMapping.pop(self._marquez_job_name_from_ti(ti),
dagrun.run_id, session)
steps = self._extract_metadata(dagrun, task, ti)
# Note: run_ids could be missing if it was removed from airflow
# or the job could not be registered.
if not run_ids:
[
_MARQUEZ.create_job(step, self._get_location(task),
self.description) for step in steps
]
run_ids = [
_MARQUEZ.create_run(self.new_run_id(), step, run_args,
DagUtils.to_iso_8601(ti.start_date),
DagUtils.to_iso_8601(ti.end_date))
for step in steps
]
if not run_ids:
self.log.warn('Could not emit lineage')
for step in steps:
for run_id in run_ids:
_MARQUEZ.create_job(step, self._get_location(task),
self.description, ti.state, run_id)
_MARQUEZ.start_run(run_id, DagUtils.to_iso_8601(ti.start_date))
self.log.debug(f'Setting task state: {ti.state}'
f' for {ti.task_id}')
if ti.state in {State.SUCCESS, State.SKIPPED}:
_MARQUEZ.complete_run(run_id,
DagUtils.to_iso_8601(ti.end_date))
else:
_MARQUEZ.fail_run(run_id,
DagUtils.to_iso_8601(ti.end_date))
def report_jobrun_change(self, dagrun, **kwargs):
session = kwargs.get('session')
marquez_job_run_id = self._job_id_mapping.pop(
JobIdMapping.make_key(dagrun.dag_id, dagrun.run_id), session)
if marquez_job_run_id:
log.info(f'Found job run.',
airflow_dag_id=dagrun.dag_id,
airflow_run_id=dagrun.run_id,
marquez_run_id=marquez_job_run_id,
marquez_namespace=self.marquez_namespace)
if kwargs.get('success'):
self.get_marquez_client().mark_job_run_as_completed(
marquez_job_run_id)
else:
self.get_marquez_client().mark_job_run_as_failed(
marquez_job_run_id)
state = 'COMPLETED' if kwargs.get('success') else 'FAILED'
log.info(f'Marked job run as {state}.',
airflow_dag_id=dagrun.dag_id,
airflow_run_id=dagrun.run_id,
marquez_run_id=marquez_job_run_id,
marquez_namespace=self.marquez_namespace)
class DAG(airflow.models.DAG, LoggingMixin):
_job_id_mapping = None
_marquez = None
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._job_id_mapping = JobIdMapping()
self._marquez = Marquez()
# TODO: Manually define operator->extractor mappings for now,
# but we'll want to encapsulate this logic in an 'Extractors' class
# with more convenient methods (ex: 'Extractors.extractor_for_task()')
self._extractors = {
PostgresOperator: PostgresExtractor,
BigQueryOperator: BigQueryExtractor
# Append new extractors here
}
self.log.debug(
f"DAG successfully created with extractors: {self._extractors}")
def create_dagrun(self, *args, **kwargs):
# run Airflow's create_dagrun() first
dagrun = super(DAG, self).create_dagrun(*args, **kwargs)
create_dag_start_ms = self._now_ms()
try:
self._marquez.create_namespace()
self._register_dagrun(dagrun,
DagUtils.get_execution_date(**kwargs),
DagUtils.get_run_args(**kwargs))
except Exception as e:
self.log.error(
f'Failed to record metadata: {e} '
f'{self._timed_log_message(create_dag_start_ms)}',
exc_info=True)
return dagrun
def _register_dagrun(self, dagrun, execution_date, run_args):
self.log.debug(f"self.task_dict: {self.task_dict}")
# Register each task in the DAG
for task_id, task in self.task_dict.items():
t = self._now_ms()
try:
steps = self._extract_metadata(dagrun, task)
[
self._marquez.create_job(step, self._get_location(task),
self.description)
for step in steps
]
marquez_jobrun_ids = [
self._marquez.create_run(
self.new_run_id(), step, run_args,
DagUtils.get_start_time(execution_date),
DagUtils.get_end_time(
execution_date,
self.following_schedule(execution_date)))
for step in steps
]
self._job_id_mapping.set(
self._marquez_job_name(self.dag_id, task.task_id),
dagrun.run_id, marquez_jobrun_ids)
except Exception as e:
self.log.error(
f'Failed to record task {task_id}: {e} '
f'{self._timed_log_message(t)}',
exc_info=True)
def handle_callback(self, *args, **kwargs):
self.log.debug(f"handle_callback({args}, {kwargs})")
try:
dagrun = args[0]
self.log.debug(f"handle_callback() dagrun : {dagrun}")
self._marquez.create_namespace()
self._report_task_instances(dagrun,
DagUtils.get_run_args(**kwargs),
kwargs.get('session'))
except Exception as e:
self.log.error(
f'Failed to record dagrun callback: {e} '
f'dag_id={self.dag_id}',
exc_info=True)
return super().handle_callback(*args)
def _report_task_instances(self, dagrun, run_args, session):
task_instances = dagrun.get_task_instances()
for ti in task_instances:
try:
self._report_task_instance(ti, dagrun, run_args, session)
except Exception as e:
self.log.error(
f'Failed to record task instance: {e} '
f'dag_id={self.dag_id}',
exc_info=True)
def _report_task_instance(self, ti, dagrun, run_args, session):
task = self.get_task(ti.task_id)
run_ids = self._job_id_mapping.pop(self._marquez_job_name_from_ti(ti),
dagrun.run_id, session)
steps = self._extract_metadata(dagrun, task, ti)
# Note: run_ids could be missing if it was removed from airflow
# or the job could not be registered.
if not run_ids:
[
self._marquez.create_job(step, self._get_location(task),
self.description) for step in steps
]
run_ids = [
self._marquez.create_run(self.new_run_id(), step, run_args,
DagUtils.to_iso_8601(ti.start_date),
DagUtils.to_iso_8601(ti.end_date))
for step in steps
]
if not run_ids:
self.log.warn('Could not emit lineage')
for step in steps:
for run_id in run_ids:
self._marquez.create_job(step, self._get_location(task),
self.description, ti.state, run_id)
self._marquez.start_run(run_id,
DagUtils.to_iso_8601(ti.start_date))
self.log.debug(f'Setting task state: {ti.state}'
f' for {ti.task_id}')
if ti.state in {State.SUCCESS, State.SKIPPED}:
self._marquez.complete_run(
run_id, DagUtils.to_iso_8601(ti.end_date))
else:
self._marquez.fail_run(run_id,
DagUtils.to_iso_8601(ti.end_date))
def _extract_metadata(self, dagrun, task, ti=None):
extractor = self._get_extractor(task)
task_info = f'task_type={task.__class__.__name__} ' \
f'airflow_dag_id={self.dag_id} ' \
f'task_id={task.task_id} ' \
f'airflow_run_id={dagrun.run_id} '
if extractor:
try:
self.log.debug(
f'Using extractor {extractor.__name__} {task_info}')
steps = self._extract(extractor, task, ti)
return add_airflow_info_to(task, steps)
except Exception as e:
self.log.error(f'Failed to extract metadata {e} {task_info}',
exc_info=True)
else:
self.log.warning(f'Unable to find an extractor. {task_info}')
return add_airflow_info_to(task, [
StepMetadata(
name=self._marquez_job_name(self.dag_id, task.task_id))
])
def _extract(self, extractor, task, ti):
if ti:
steps = extractor(task).extract_on_complete(ti)
if steps:
return steps
return extractor(task).extract()
def _get_extractor(self, task):
extractor = self._extractors.get(task.__class__)
log.debug(f'extractor for {task.__class__} is {extractor}')
return extractor
def _timed_log_message(self, start_time):
return f'airflow_dag_id={self.dag_id} ' \
f'duration_ms={(self._now_ms() - start_time)}'
def new_run_id(self) -> str:
return str(uuid4())
@staticmethod
def _now_ms():
return int(round(time.time() * 1000))
@staticmethod
def _get_location(task):
try:
if hasattr(task, 'file_path') and task.file_path:
return get_location(task.file_path)
else:
return get_location(task.dag.fileloc)
except Exception:
log.warning(f"Failed to get location for task '{task.task_id}'.",
exc_info=True)
return None
@staticmethod
def _marquez_job_name_from_ti(ti):
return DAG._marquez_job_name(ti.dag_id, ti.task_id)
@staticmethod
def _marquez_job_name(dag_id, task_id):
return f'{dag_id}.{task_id}'
class DAG(airflow.models.DAG):
DEFAULT_NAMESPACE = 'default'
_job_id_mapping = None
_marquez_client = None
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.marquez_namespace = os.environ.get('MARQUEZ_NAMESPACE') or \
DAG.DEFAULT_NAMESPACE
self.marquez_location = kwargs['default_args'].get(
'marquez_location', 'unknown')
self.marquez_input_urns = kwargs['default_args'].get(
'marquez_input_urns', [])
self.marquez_output_urns = kwargs['default_args'].get(
'marquez_output_urns', [])
self._job_id_mapping = JobIdMapping()
def create_dagrun(self, *args, **kwargs):
run_args = "{}" # TODO extract the run Args from the tasks
marquez_jobrun_id = None
try:
marquez_jobrun_id = self.report_jobrun(run_args,
kwargs['execution_date'])
log.info(f'Successfully recorded job run.',
airflow_dag_id=self.dag_id,
marquez_run_id=marquez_jobrun_id,
marquez_namespace=self.marquez_namespace)
except Exception as e:
log.error(f'Failed to record job run: {e}',
airflow_dag_id=self.dag_id,
marquez_namespace=self.marquez_namespace)
pass
run = super(DAG, self).create_dagrun(*args, **kwargs)
if marquez_jobrun_id:
try:
self._job_id_mapping.set(
JobIdMapping.make_key(run.dag_id, run.run_id),
marquez_jobrun_id)
except Exception as e:
log.error(f'Failed job run lookup: {e}',
airflow_dag_id=self.dag_id,
airflow_run_id=run.run_id,
marquez_run_id=marquez_jobrun_id,
marquez_namespace=self.marquez_namespace)
pass
return run
def handle_callback(self, *args, **kwargs):
try:
self.report_jobrun_change(args[0], **kwargs)
except Exception as e:
log.error(f'Failed to record job run state change: {e}',
dag_id=self.dag_id)
return super().handle_callback(*args, **kwargs)
def report_jobrun(self, run_args, execution_date):
now_ms = self._now_ms()
job_name = self.dag_id
start_time = execution_date.format("%Y-%m-%dT%H:%M:%SZ")
end_time = self.compute_endtime(execution_date)
if end_time:
end_time = end_time.strftime("%Y-%m-%dT%H:%M:%SZ")
marquez_client = self.get_marquez_client()
marquez_client.create_job(job_name,
self.marquez_location,
self.marquez_input_urns,
self.marquez_output_urns,
description=self.description)
log.info(f'Successfully recorded job: {job_name}',
airflow_dag_id=self.dag_id,
marquez_namespace=self.marquez_namespace)
marquez_jobrun = marquez_client.create_job_run(
job_name,
run_args=run_args,
nominal_start_time=start_time,
nominal_end_time=end_time)
marquez_jobrun_id = marquez_jobrun.get('runId')
if marquez_jobrun_id:
marquez_client.mark_job_run_as_running(marquez_jobrun_id)
log.info(f'Successfully recorded job run: {job_name}',
airflow_dag_id=self.dag_id,
airflow_dag_execution_time=start_time,
marquez_run_id=marquez_jobrun_id,
marquez_namespace=self.marquez_namespace,
duration_ms=(self._now_ms() - now_ms))
else:
log.warn(f'Run id found not found: {job_name}',
airflow_dag_id=self.dag_id,
airflow_dag_execution_time=start_time,
marquez_run_id=marquez_jobrun_id,
marquez_namespace=self.marquez_namespace,
duration_ms=(self._now_ms() - now_ms))
return marquez_jobrun_id
def compute_endtime(self, execution_date):
return self.following_schedule(execution_date)
def report_jobrun_change(self, dagrun, **kwargs):
session = kwargs.get('session')
marquez_job_run_id = self._job_id_mapping.pop(
JobIdMapping.make_key(dagrun.dag_id, dagrun.run_id), session)
if marquez_job_run_id:
log.info(f'Found job run.',
airflow_dag_id=dagrun.dag_id,
airflow_run_id=dagrun.run_id,
marquez_run_id=marquez_job_run_id,
marquez_namespace=self.marquez_namespace)
if kwargs.get('success'):
self.get_marquez_client().mark_job_run_as_completed(
marquez_job_run_id)
else:
self.get_marquez_client().mark_job_run_as_failed(
marquez_job_run_id)
state = 'COMPLETED' if kwargs.get('success') else 'FAILED'
log.info(f'Marked job run as {state}.',
airflow_dag_id=dagrun.dag_id,
airflow_run_id=dagrun.run_id,
marquez_run_id=marquez_job_run_id,
marquez_namespace=self.marquez_namespace)
def get_marquez_client(self):
if not self._marquez_client:
self._marquez_client = MarquezClient(
namespace_name=self.marquez_namespace)
self._marquez_client.create_namespace(self.marquez_namespace,
"default_owner")
return self._marquez_client
@staticmethod
def _now_ms():
return int(round(time.time() * 1000))
class DAG(airflow.models.DAG):
DEFAULT_NAMESPACE = 'default'
_job_id_mapping = None
_marquez_client = None
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._marquez_dataset_cache = {}
self._marquez_source_cache = {}
self.marquez_namespace = os.getenv('MARQUEZ_NAMESPACE',
DAG.DEFAULT_NAMESPACE)
self._job_id_mapping = JobIdMapping()
def create_dagrun(self, *args, **kwargs):
# run Airflow's create_dagrun() first
dagrun = super(DAG, self).create_dagrun(*args, **kwargs)
create_dag_start_ms = self._now_ms()
execution_date = kwargs.get('execution_date')
run_args = {'external_trigger': kwargs.get('external_trigger', False)}
extractors = {}
try:
extractors = get_extractors()
except Exception as e:
log.warn(f'Failed retrieve extractors: {e}',
airflow_dag_id=self.dag_id,
marquez_namespace=self.marquez_namespace)
# Marquez metadata collection
try:
marquez_client = self.get_marquez_client()
# Create the Namespace
marquez_client.create_namespace(self.marquez_namespace,
"default_owner")
# Register each task in the DAG
for task_id, task in self.task_dict.items():
t = self._now_ms()
try:
self.report_task(dagrun.run_id, execution_date, run_args,
task,
extractors.get(task.__class__.__name__))
except Exception as e:
log.error(f'Failed to record task: {e}',
airflow_dag_id=self.dag_id,
task_id=task_id,
marquez_namespace=self.marquez_namespace,
duration_ms=(self._now_ms() - t))
log.info('Successfully recorded metadata',
airflow_dag_id=self.dag_id,
marquez_namespace=self.marquez_namespace,
duration_ms=(self._now_ms() - create_dag_start_ms))
except Exception as e:
log.error(f'Failed to record metadata: {e}',
airflow_dag_id=self.dag_id,
marquez_namespace=self.marquez_namespace,
duration_ms=(self._now_ms() - create_dag_start_ms))
return dagrun
def handle_callback(self, *args, **kwargs):
try:
dagrun = args[0]
task_instances = dagrun.get_task_instances()
for ti in task_instances:
try:
job_name = f'{ti.dag_id}.{ti.task_id}'
self.report_jobrun_change(job_name, dagrun.run_id,
**kwargs)
except Exception as e:
log.error(f'Failed to record task run state change: {e}',
dag_id=self.dag_id)
except Exception as e:
log.error(f'Failed to record dagrun state change: {e}',
dag_id=self.dag_id)
return super().handle_callback(*args, **kwargs)
def report_task(self, dag_run_id, execution_date, run_args, task,
extractor):
report_job_start_ms = self._now_ms()
marquez_client = self.get_marquez_client()
if execution_date:
start_time = self._to_iso_8601(execution_date)
end_time = self.compute_endtime(execution_date)
else:
start_time = None
end_time = None
if end_time:
end_time = self._to_iso_8601(end_time)
task_location = None
try:
if hasattr(task, 'file_path') and task.file_path:
task_location = get_location(task.file_path)
else:
task_location = get_location(task.dag.fileloc)
except Exception:
log.warn('Unable to fetch the location')
steps_metadata = []
if extractor:
try:
log.info(f'Using extractor {extractor.__name__}',
task_type=task.__class__.__name__,
airflow_dag_id=self.dag_id,
task_id=task.task_id,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
steps_metadata = extractor(task).extract()
except Exception as e:
log.error(f'Failed to extract metadata {e}',
airflow_dag_id=self.dag_id,
task_id=task.task_id,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
else:
log.warn('Unable to find an extractor.',
task_type=task.__class__.__name__,
airflow_dag_id=self.dag_id,
task_id=task.task_id,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
task_name = f'{self.dag_id}.{task.task_id}'
# If no extractor found or failed to extract metadata,
# report the task metadata
if not steps_metadata:
steps_metadata = [StepMetadata(task_name)]
# store all the JobRuns associated with a task
marquez_jobrun_ids = []
for step in steps_metadata:
input_datasets = []
output_datasets = []
try:
input_datasets = self.register_datasets(step.inputs)
except Exception as e:
log.error(f'Failed to register inputs: {e}',
inputs=str(step.inputs),
airflow_dag_id=self.dag_id,
task_id=task.task_id,
step=step.name,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
try:
output_datasets = self.register_datasets(step.outputs)
except Exception as e:
log.error(f'Failed to register outputs: {e}',
outputs=str(step.outputs),
airflow_dag_id=self.dag_id,
task_id=task.task_id,
step=step.name,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
marquez_client.create_job(
job_name=step.name,
job_type='BATCH', # job type
location=(step.location or task_location),
input_dataset=input_datasets,
output_dataset=output_datasets,
context=step.context,
description=self.description,
namespace_name=self.marquez_namespace)
log.info(f'Successfully recorded job: {step.name}',
airflow_dag_id=self.dag_id,
marquez_namespace=self.marquez_namespace)
marquez_jobrun_id = marquez_client.create_job_run(
step.name,
run_args=run_args,
nominal_start_time=start_time,
nominal_end_time=end_time).get('runId')
if marquez_jobrun_id:
marquez_jobrun_ids.append(marquez_jobrun_id)
marquez_client.mark_job_run_as_started(marquez_jobrun_id)
else:
log.error(f'Failed to get run id: {step.name}',
airflow_dag_id=self.dag_id,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
log.info(f'Successfully recorded job run: {step.name}',
airflow_dag_id=self.dag_id,
airflow_dag_execution_time=start_time,
marquez_run_id=marquez_jobrun_id,
marquez_namespace=self.marquez_namespace,
duration_ms=(self._now_ms() - report_job_start_ms))
# Store the mapping for all the steps associated with a task
try:
self._job_id_mapping.set(
JobIdMapping.make_key(task_name, dag_run_id),
json.dumps(marquez_jobrun_ids))
except Exception as e:
log.error(f'Failed to set id mapping : {e}',
airflow_dag_id=self.dag_id,
task_id=task.task_id,
airflow_run_id=dag_run_id,
marquez_run_id=marquez_jobrun_ids,
marquez_namespace=self.marquez_namespace)
def compute_endtime(self, execution_date):
return self.following_schedule(execution_date)
def report_jobrun_change(self, job_name, run_id, **kwargs):
session = kwargs.get('session')
marquez_job_run_ids = self._job_id_mapping.pop(
JobIdMapping.make_key(job_name, run_id), session)
if marquez_job_run_ids:
log.info('Found job runs.',
airflow_dag_id=self.dag_id,
airflow_job_id=job_name,
airflow_run_id=run_id,
marquez_run_ids=marquez_job_run_ids,
marquez_namespace=self.marquez_namespace)
ids = json.loads(marquez_job_run_ids)
if kwargs.get('success'):
for marquez_job_run_id in ids:
self.get_marquez_client().mark_job_run_as_completed(
marquez_job_run_id)
else:
for marquez_job_run_id in ids:
self.get_marquez_client().mark_job_run_as_failed(
marquez_job_run_id)
state = 'COMPLETED' if kwargs.get('success') else 'FAILED'
log.info(f'Marked job run(s) as {state}.',
airflow_dag_id=self.dag_id,
airflow_job_id=job_name,
airflow_run_id=run_id,
marquez_run_id=marquez_job_run_ids,
marquez_namespace=self.marquez_namespace)
def get_marquez_client(self):
if not self._marquez_client:
self._marquez_client = MarquezClient()
return self._marquez_client
@staticmethod
def _now_ms():
return int(round(time.time() * 1000))
def register_datasets(self, datasets):
dataset_names = []
if not datasets:
return dataset_names
client = self.get_marquez_client()
for dataset in datasets:
if isinstance(dataset, Dataset):
_key = str(dataset)
if _key not in self._marquez_dataset_cache:
source_name = self.register_source(dataset.source)
if source_name:
dataset = client.create_dataset(
dataset.name,
dataset.type,
dataset.name, # physical_name the same for now
source_name,
namespace_name=self.marquez_namespace)
dataset_name = dataset.get('name')
if dataset_name:
self._marquez_dataset_cache[_key] = dataset_name
dataset_names.append(dataset_name)
else:
dataset_names.append(self._marquez_dataset_cache[_key])
return dataset_names
def register_source(self, source):
if isinstance(source, Source):
_key = str(source)
if _key in self._marquez_source_cache:
return self._marquez_source_cache[_key]
client = self.get_marquez_client()
ds = client.create_source(source.name, source.type,
source.connection_url)
source_name = ds.get('name')
self._marquez_source_cache[_key] = source_name
return source_name
@staticmethod
def _to_iso_8601(dt):
if isinstance(dt, Pendulum):
return dt.format(_NOMINAL_TIME_FORMAT)
else:
return dt.strftime(_NOMINAL_TIME_FORMAT)
def report_task(self, dag_run_id, execution_date, run_args, task,
extractor):
report_job_start_ms = self._now_ms()
marquez_client = self.get_marquez_client()
if execution_date:
start_time = self._to_iso_8601(execution_date)
end_time = self.compute_endtime(execution_date)
else:
start_time = None
end_time = None
if end_time:
end_time = self._to_iso_8601(end_time)
task_location = None
try:
if hasattr(task, 'file_path') and task.file_path:
task_location = get_location(task.file_path)
else:
task_location = get_location(task.dag.fileloc)
except Exception:
log.warn('Unable to fetch the location')
steps_metadata = []
if extractor:
try:
log.info(f'Using extractor {extractor.__name__}',
task_type=task.__class__.__name__,
airflow_dag_id=self.dag_id,
task_id=task.task_id,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
steps_metadata = extractor(task).extract()
except Exception as e:
log.error(f'Failed to extract metadata {e}',
airflow_dag_id=self.dag_id,
task_id=task.task_id,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
else:
log.warn('Unable to find an extractor.',
task_type=task.__class__.__name__,
airflow_dag_id=self.dag_id,
task_id=task.task_id,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
task_name = f'{self.dag_id}.{task.task_id}'
# If no extractor found or failed to extract metadata,
# report the task metadata
if not steps_metadata:
steps_metadata = [StepMetadata(task_name)]
# store all the JobRuns associated with a task
marquez_jobrun_ids = []
for step in steps_metadata:
input_datasets = []
output_datasets = []
try:
input_datasets = self.register_datasets(step.inputs)
except Exception as e:
log.error(f'Failed to register inputs: {e}',
inputs=str(step.inputs),
airflow_dag_id=self.dag_id,
task_id=task.task_id,
step=step.name,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
try:
output_datasets = self.register_datasets(step.outputs)
except Exception as e:
log.error(f'Failed to register outputs: {e}',
outputs=str(step.outputs),
airflow_dag_id=self.dag_id,
task_id=task.task_id,
step=step.name,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
marquez_client.create_job(
job_name=step.name,
job_type='BATCH', # job type
location=(step.location or task_location),
input_dataset=input_datasets,
output_dataset=output_datasets,
context=step.context,
description=self.description,
namespace_name=self.marquez_namespace)
log.info(f'Successfully recorded job: {step.name}',
airflow_dag_id=self.dag_id,
marquez_namespace=self.marquez_namespace)
marquez_jobrun_id = marquez_client.create_job_run(
step.name,
run_args=run_args,
nominal_start_time=start_time,
nominal_end_time=end_time).get('runId')
if marquez_jobrun_id:
marquez_jobrun_ids.append(marquez_jobrun_id)
marquez_client.mark_job_run_as_started(marquez_jobrun_id)
else:
log.error(f'Failed to get run id: {step.name}',
airflow_dag_id=self.dag_id,
airflow_run_id=dag_run_id,
marquez_namespace=self.marquez_namespace)
log.info(f'Successfully recorded job run: {step.name}',
airflow_dag_id=self.dag_id,
airflow_dag_execution_time=start_time,
marquez_run_id=marquez_jobrun_id,
marquez_namespace=self.marquez_namespace,
duration_ms=(self._now_ms() - report_job_start_ms))
# Store the mapping for all the steps associated with a task
try:
self._job_id_mapping.set(
JobIdMapping.make_key(task_name, dag_run_id),
json.dumps(marquez_jobrun_ids))
except Exception as e:
log.error(f'Failed to set id mapping : {e}',
airflow_dag_id=self.dag_id,
task_id=task.task_id,
airflow_run_id=dag_run_id,
marquez_run_id=marquez_jobrun_ids,
marquez_namespace=self.marquez_namespace)
