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https://issues.apache.org/jira/browse/AIRFLOW-5071?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17192002#comment-17192002
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ASF GitHub Bot commented on AIRFLOW-5071:
-----------------------------------------

turbaszek commented on issue #10790:
URL: https://github.com/apache/airflow/issues/10790#issuecomment-688671892


   Thanks @dmariassy for bringing this issue to Github! I think this one is 
quite important to fix but as long we don't know how to replicate it we are 
going blind. 
   
   I spent some time trying to reproduce it on 2.0 and 1.10.9 but to no effect 
:<


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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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