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. Check airflow webserver or scheduler logs for more details, as stderr or stdout goes there. Learn more about Teams Python script scheduling in airflow. Scheduler, might not parse all your DAGs. Connect and share knowledge within a single location that is structured and easy to search. 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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