![]() ![]() python_operator import BranchPythonOperator from airflow. This is almost certainly something specific to your deployment (others do not experience it). There must be something in your DAGs or task that simply causes the Airflow scheduler to lock up. Learn how to use the secret key for an Apache Airflow variable ( test-variable) in Using a secret key in AWS Secrets Manager for an Apache Airflow variable. bash_operator import BashOperator from airflow. It certainly looks like your long running task blocks some resources that blocks scheduler somehow (but there is no indication how it can be blocked). We recommend the following steps: Learn how to create secret keys for your Apache Airflow connection and variables in Configuring an Apache Airflow connection using a Secrets Manager secret. dummy_operator import DummyOperator from airflow. We also checked server resources for those cases - but there are a lot of free RAM and CPU in that time, so it shoudn't be the cause.įrom airflow. This how LocalExecutor should work according to documentation. We expect all other DAGs to start according to their schedule when long-running task is running. Manual triggering also doesn't help - triggered tasks are not started until long-running task is finished. But with long-running task there are no log messages like this. ![]() When there is no long-running task running we see that scheduler tries to check whether any task could run and check parallelism/concurrency limitation for them. We examined Airflow scheduler logs and figured out that scheduler just doesn't try to grab new tasks while long-running task is running. The DAGs list may not update, and new tasks will not be scheduled. Last heartbeat was received XX minutes ago. We are looking now for a number of days now to get this solved but unfortunately no success.The scheduler does not appear to be running. We also looked at several configuration option as stated here It seems like the scheduler cannot pick up / detect a finished task after like a couple of tasks have run. Data included with the heartbeat event fails user defined assertions. Also the AKS node pool we are using does not show any sign of stress. The rhythm (schedule) of the heartbeats changes. The metadata database (Azure managed postgreSQL) is not overloading. This issue is also described in this post: Where in the post a link to Stackoverflow does not work anymore. Is the server running on host ".com" (13.49.105.208) and accepting Recently, we duplicated the same DAG multiple times to test the schedules and ended up with the issue where the scheduler heartbeat keeps failing. We are using Airflow 2.2.1 in HA mode with MySQL as the backend. Psycopg2.OperationalError: could not connect to server: Connection timed out Hello all, I am facing an issue with the Airflow scheduler heartbeat not getting updated regularly. If youre using greater than 50 of your environments capacity you may start overwhelming the Apache Airflow Scheduler. In the end this leads up to this error message and marking the already finished task as a fail: ERROR - LocalTaskJob heartbeat got an exception Airflow parses DAGs whether they are enabled or not. The tasks after that are also successfully executed on AKS, but Airflow not marked as completed in Airflow as such. If DAG files are heavy and a lot of top-level codes are present in them, the scheduler. Here are some of the common causes: Does your script compile, can the Airflow engine parse it and find your DAG object To test this, you can run airflow dags list and confirm that your DAG shows up in the list. Usually the first like 5 tasks execute successfully (taking like 1 or 2 minute each) and get marked as done (and colored green) in the Airflow UI. The Airflow scheduler scans and compiles DAG files at each heartbeat. There are very many reasons why your task might not be getting scheduled. Airflow is configured with the Airflow official HELM chart. We have a DAG that has like 30 tasks, each task use a KubernetesPodOperator (using the apache-airflow-providers-cncf-kubernetes=2.2.0) to execute some container logic. The Airflow metadata database is an Azure managed postgreSQL(8 cpu). ![]()
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