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From Dan Stoner <dansto...@gmail.com>
Subject Re: 'Task Instance State' FAILED: Task is in the 'running' state which is not a valid state for execution. The task must be cleared in order to be run.
Date Thu, 14 Feb 2019 19:52:41 GMT
More info!

It appears that the Celery executor will silently fail if the
credentials to a postgres results_backend are not valid.

For example, we see:

[2019-02-13 20:45:21,132] {{models.py:1353}} INFO - Dependencies not
met for <TaskInstance: update_table_progress.update_table
2019-02-13T20:30:00+00:00 [running]>, dependency 'Task Instance Not
Already Running' FAILED: Task is already running, it started on
2019-02-13 20:45:09.088978+00:00.
[2019-02-13 20:45:21,132] {{models.py:1353}} INFO - Dependencies not
met for <TaskInstance: update_table_progress.update_table
2019-02-13T20:30:00+00:00 [running]>, dependency 'Task Instance State'
FAILED: Task is in the 'running' state which is not a valid state for
execution. The task must be cleared in order to be run.
[2019-02-13 20:45:21,135] {{logging_mixin.py:95}} INFO - [2019-02-13
20:45:21,134] {{jobs.py:2514}} INFO - Task is not able to be run


but no database connection failure anywhere in the logs.

After fixing our connection string (via
AIRFLOW__CELERY__RESULT_BACKEND or result_backend in airflow.cfg),
these issues went away.


Sorry I cannot produce a more solid bug report but hopefully this is a
breadcrumb for someone.

Dan Stoner

On Wed, Feb 13, 2019 at 10:16 PM Dan Stoner <danstoner@gmail.com> wrote:
>
> We saw this but the task instance state was generally "SUCCESS".
>
> In our case, we thought it was due to Redis being used as the results
> store. There is a WARNING against this right in the operational logs.
> Google Cloud Composer is surprisingly setup in this fashion.
>
> We went back to running our own infrastructure and using postgres as
> the results store, those issues have not occurred since.
>
> The real downside we saw to this error was that our workers were
> highly underutilized, we were getting terrible overall data
> throughput, and the workers kept trying to run these tasks they
> couldn't actually run.
>
> - Dan Stoner
>
>
> On Wed, Feb 13, 2019 at 4:16 PM Kevin Lam <kevin@fathomhealth.co> wrote:
> >
> > Friendly ping on the above! Has anyone encountered this by chance?
> >
> > We're still seeing it occasionally on longer running tasks.
> >
> > On Tue, Nov 20, 2018 at 10:31 AM Kevin Lam <kevin@fathomhealth.co> wrote:
> >
> > > Hi,
> > >
> > > We run Apache Airflow in Kubernetes in a manner very similar to what is
> > > outlined in puckel/docker-airflow [1] (Celery Executor, Redis for
> > > messaging, Postgres).
> > >
> > > Lately, we've encountered some of our Tasks getting stuck in a running
> > > state, and printing out the errors:
> > >
> > > [2018-11-20 05:31:23,009] {models.py:1329} INFO - Dependencies not met for
<TaskInstance: BLAH 2018-11-19T19:19:50.757184+00:00 [running]>, dependency 'Task Instance
Not Already Running' FAILED: Task is already running, it started on 2018-11-19 23:29:11.974497+00:00.
> > >> [2018-11-20 05:31:23,016] {models.py:1329} INFO - Dependencies not met
for <TaskInstance: BLAH 2018-11-19T19:19:50.757184+00:00 [running]>, dependency 'Task
Instance State' FAILED: Task is in the 'running' state which is not a valid state for execution.
The task must be cleared in order to be run.
> > >>
> > >>
> > > Is there anyway to avoid this? Does anyone know what causes this issue?
> > >
> > > This is quite problematic. The task is stuck in running state without
> > > making any progress when the above error occurs, and so turning on retries
> > > on doesn't help with getting our DAGs to reliably run to completion.
> > >
> > > Thanks!
> > >
> > > [1] https://github.com/puckel/docker-airflow
> > >

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