Is the right thing to do here to wrap the tasks in try / finally block? Or should I suppress the exception raising? Or maybe I’m still thinking about this wrong and the five future does exist, but in that case I’m confused why clean_up_task does not run, since it allows failure. The problem (as I understand it) is that if task_4 fails non-deterministically, when the future is resolved in task_5 that raises an exception and no five future is returned / set at all (?) In any event, in this modified form, the cleanup task will not run when there’s an error. Print("Cleaning up name="Cleanup task may not get executed")įour = task_4.submit(wait_for=)Ĭlean_up_task.submit(wait_for=) Raise ValueError("Non-deterministic error has occured.") ![]() Ok, I’ve spent some more time understanding Results and failures and I think I’m starting to think of what I’ll need to do here, but here’s a simple example of the problem I’m facing, modifying this last task_1(): I’m sure I’m just using the wrong terms for Airflow. I’m sure I’m missing something obvious, but searching hasn’t turned up an answer for me yet. ![]() However, it seems that if it errors out before some of those futures are even created that this does not get run. I tried to do something like this in Prefect close_out_search.submit( are just the sentinel values that set up the task dependencies so that this always happens after those other tasks have completed (successfully or not, including the case where they didn’t run at all). Task_id="close_out_search", python_callable=close_out_search, trigger_rule=TriggerRule.ALL_DONE In airflow this is a little janky (like most things), but handled roughly like: close_out_search = PythonOperator( In our DAG we have some bookkeeping code (a task) that always needs to run at the end of the flow. In the list of environments, click the name of your environment.I’m new to Prefect, working on porting over an Airflow DAG as a proof-of concept. In Google Cloud console, go to the Environments page. Inspect DAG parse times with the Cloud Composer Monitoring page: In Google Cloud console you can use the Monitoring page and To verify if the issue happens at DAG parse time, follow these steps. This value must beĬorrect or remove DAGs that cause problems to the DAG processor. To at least 180 seconds (or more, if required). To at least 120 seconds (or more, if required). Increase parameters related to DAG parsing: For example: airflow-scheduler Failed to get task '' for dag ![]() Processor for /home/airflow/gcs/dags/dag-example.py exited with returnĪirflow schedulers experience issues which lead to scheduler restarts.Īirflow tasks that are scheduled for execution are cancelled and DAG runsįor DAGs that failed to be parsed might be marked as failed. There are errors in the DAG processor logs, for example: dag-processor-manager ERROR. If DAGs are generated dynamically, these issues might be more impactful compared to static DAGs.ĭAGs are not visible in Airflow UI and DAG UI. If the DAG Processor encounters problems when parsing your DAGs, then it might lead to a combination of the issues listed below. Scheduler, might not parse all your DAGs. ![]() If you have complex DAGs then the DAG Processor, which is run by the Or while processing tasks at execution time.įor more information about parse time and execution time, readĭifference between DAG parse time and DAG execution time. To begin troubleshooting, identify if the issue happens at DAG parse time This page provides troubleshooting steps and information for common Save money with our transparent approach to pricing Rapid Assessment & Migration Program (RAMP) Migrate from PaaS: Cloud Foundry, OpenshiftĬOVID-19 Solutions for the Healthcare Industry
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