![]() ![]() If isinstance(dag._schedule_interval, six.string_types): Self.bag_dag(dag, parent_dag=dag, root_dag=dag) If not head and (ext = '.py' or ext = '.pyc'): Mod_name, ext = os.path.splitext(mod.filename) With timeout(( 'core', "DAGBAG_IMPORT_TIMEOUT")): Org_mod_name, _ = os.path.splitext(os.path.split(filepath)) Is_zipfile = zipfile.is_zipfile(filepath) ![]() ( "Creating / updating %s in ORM", ti)Īirflow/models/_init_.py#DagBag.process_fileĭef process_file(self, filepath, only_if_updated=True, safe_mode=True): # Also save this task instance to the DB. All tasks in the # scheduled state will be sent to the executor # Task starts out in the scheduled state. Ti.refresh_from_db(session=session, lock_for_update=True)ĭep_context = DepContext(deps=QUEUE_DEPS, ignore_task_deps=True) Self._process_dags(dagbag, dags, ti_keys_to_schedule) # Not using multiprocessing.Queue() since it's no longer a separate # process and due to some unusual behavior. Simple_dags.append(SimpleDag(dag, pickle_id=pickle_id))ĭags = [dag for dag in () # Only return DAGs that are not paused if dag_id not in paused_dag_ids: # Pickle the DAGs (if necessary) and put them into a SimpleDag for dag_id in dagbag.dags: # Save individual DAGs in the ORM and update DagModel.last_scheduled_time for dag in (): Send emails for tasks that have missed SLAs.ĭef process_file(self, file_path, pickle_dags=False, session=None):.If the dependencies of a TaskInstance is met, update the state of the TaskInstance to SCHEDULED. Find all active DagRuns for a dag and iterate over their unscheduled TaskInstances.Create appropriate TaskInstance(s) in the DB for new DagRuns.Calculate next_run_date for dag and create appropriate DagRun(s) in the DB.ScheduleJob iterates over all the un-paused dags and processes them. ScheduleJob then gets all dags from DagBag, sync their states db and collect those dags which are not paused. In the DagBag instance initiation, it loads the python modules in the file path using different std lib base on whether the path is a zip path.Īfter modules are loaded, DagBag collects all the DAGs in the modules. The scheduler_job.process_file method first creates a DagBag instance for the dag file path. Let’s keep diving into the scheduler_job.process_file method. heartbeat method.Īll the daemon service of Airflow are started by running airflow ".format(self._instance_id)) Eventually, the ScheduleJob will run a loop to invoke the processor_manager periodically. The ScheduleJob will save itself to the database, start the executor and create a DagFileProcessorManager. And then scheduler function creates a ScheduleJob and run its run method. Let’s look at how Airflow parses this command and start the process loop.įirstly, airflow use argparse to parse the command and invoke the scheduler function in. To kick it off, all you need to do is execute airflow scheduler command. The Airflow scheduler is designed to run as a persistent service in an Airflow production environment. There are three types of job in Airflow: ScheduleJob, LocalTaskJob, and BackfillJob. Job: Jobs are processing items with stateĪnd duration that aren’t task instances.Level configuration settings, like what database to use as a backend and DagBag: a collection of dags, parsed out of a folder tree and has high.TaskInstance: an object representing an instantiation of the Task in time.DagRun: an object representing an instantiation of the DAG in time.Task: a parameterized instance of an operator/sensor which represents a unit of actual work to be executed.Dag (Directed Acyclic Graph): a workflow which glues all the tasks with inter-dependencies.A DAG can be definite in a single python file or in multiple python files packaged into an egg file. Dag Files: Python files that could contain DAGs.We already told about the key concepts of Airflow, let’s recap some concepts use in airflow scheduler here. This article will talk about the detail of the scheduler by diving into some of the source code(version: 1.10.1). Last article we told about the basic concepts and architecture of Airflow, and we knew that Airflow has three major components: webserver, scheduler and executor. ![]()
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