[jira] [Commented] (AIRFLOW-1463) Scheduler does not reschedule tasks in QUEUED state

["Daniel Huang (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/AIRFLOW-1463?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16173844#comment-16173844 ] 

Daniel Huang commented on AIRFLOW-1463:
---------------------------------------

Hitting this as well due to deployments and one of my DAGs intermittently hitting the {{DAGBAG_IMPORT_TIMEOUT}}. Just restarting the scheduler does not get my tasks out of the QUEUED state. I've had to delete the task instance so it the scheduler re-queues the task with an increased timeout.

> Scheduler does not reschedule tasks in QUEUED state
> ---------------------------------------------------
>
>                 Key: AIRFLOW-1463
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-1463
>             Project: Apache Airflow
>          Issue Type: Improvement
>          Components: cli
>         Environment: Ubuntu 14.04
> Airflow 1.8.0
> SQS backed task queue, AWS RDS backed meta storage
> DAG folder is synced by script on code push: archive is downloaded from s3, unpacked, moved, install script is run. airflow executable is replaced with symlink pointing to the latest version of code, no airflow processes are restarted.
>            Reporter: Stanislav Pak
>            Priority: Minor
>   Original Estimate: 24h
>  Remaining Estimate: 24h
>
> Our pipelines related code is deployed almost simultaneously on all airflow boxes: scheduler+webserver box, workers boxes. Some common python package is deployed on those boxes on every other code push (3-5 deployments per hour). Due to installation specifics, a DAG that imports module from that package might fail. If DAG import fails when worker runs a task, the task is still removed from the queue but task state is not changed, so in this case the task stays in QUEUED state forever.
> Beside the described case, there is scenario when it happens because of DAG update lag in scheduler. A task can be scheduled with old DAG and worker can run the task with new DAG that fails to be imported.
> There might be other scenarios when it happens.
> Proposal:
> Catch errors when importing DAG on task run and clear task instance state if import fails. This should fix transient issues of this kind.



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