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Posted to commits@airflow.apache.org by "Kaxil Naik (Jira)" <ji...@apache.org> on 2019/11/01 16:08:01 UTC

[jira] [Updated] (AIRFLOW-5660) Scheduler becomes unresponsive when processing large DAGs on kubernetes.

     [ https://issues.apache.org/jira/browse/AIRFLOW-5660?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Kaxil Naik updated AIRFLOW-5660:
--------------------------------
    Fix Version/s:     (was: 1.10.6)
                   1.10.7

> Scheduler becomes unresponsive when processing large DAGs on kubernetes.
> ------------------------------------------------------------------------
>
>                 Key: AIRFLOW-5660
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-5660
>             Project: Apache Airflow
>          Issue Type: Bug
>          Components: executor-kubernetes
>    Affects Versions: 1.10.5
>            Reporter: Aditya Vishwakarma
>            Assignee: Daniel Imberman
>            Priority: Major
>             Fix For: 1.10.7
>
>
> For very large dags( 10,000+) and high parallelism, the scheduling loop can take more 5-10 minutes. 
> It seems that `_labels_to_key` function in kubernetes_executor loads all tasks with a given execution date into memory. It does it for every task in progress. So, if 100 tasks are in progress of a dag with 10,000 tasks, it will load million tasks on every tick of the scheduler from db.
> [https://github.com/apache/airflow/blob/caf1f264b845153b9a61b00b1a57acb7c320e743/airflow/contrib/executors/kubernetes_executor.py#L598]
> A quick fix is to search for task in the db directly before regressing to full scan. I can submit a PR for it.
> A proper fix requires persisting a mapping of (safe_dag_id, safe_task_id, dag_id, task_id, execution_date) somewhere, probably in the metadatabase.
>  



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