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https://issues.apache.org/jira/browse/AIRFLOW-5071?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17359937#comment-17359937
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ASF GitHub Bot commented on AIRFLOW-5071:
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anitakar commented on issue #10790:
URL: https://github.com/apache/airflow/issues/10790#issuecomment-857597589
> Hello. I am using airflow 2.0 and just ran into this error.
>
> How can I fix it??
Just summarising what others have reported worked for them:
1. Set worker_concurrency to match the CPU number and worker_autoscale to
"4,2".
2. Increase the poke_interval value for the sensors to 5 minutes (from the
default 1 minute).
3. Reduce the scheduler looping time(dag processing time, etc) or increase
the sensor task reschedule.
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> Thousand os Executor reports task instance X finished (success) although the
> task says its queued. Was the task killed externally?
> ----------------------------------------------------------------------------------------------------------------------------------
>
> Key: AIRFLOW-5071
> URL: https://issues.apache.org/jira/browse/AIRFLOW-5071
> Project: Apache Airflow
> Issue Type: Bug
> Components: DAG, scheduler
> Affects Versions: 1.10.3
> Reporter: msempere
> Priority: Critical
> Fix For: 1.10.12
>
> Attachments: image-2020-01-27-18-10-29-124.png,
> image-2020-07-08-07-58-42-972.png
>
>
> I'm opening this issue because since I update to 1.10.3 I'm seeing thousands
> of daily messages like the following in the logs:
>
> ```
> {{__init__.py:1580}} ERROR - Executor reports task instance <TaskInstance: X
> 2019-07-29 00:00:00+00:00 [queued]> finished (success) although the task says
> its queued. Was the task killed externally?
> {{jobs.py:1484}} ERROR - Executor reports task instance <TaskInstance: X
> 2019-07-29 00:00:00+00:00 [queued]> finished (success) although the task says
> its queued. Was the task killed externally?
> ```
> -And looks like this is triggering also thousand of daily emails because the
> flag to send email in case of failure is set to True.-
> I have Airflow setup to use Celery and Redis as a backend queue service.
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