[jira] [Issue Comment Deleted] (AIRFLOW-928) Same {task,execution_date} run multiple times in worker when using CeleryExecutor

["Uri Shamay (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/AIRFLOW-928?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Uri Shamay updated AIRFLOW-928:
-------------------------------
    Comment: was deleted

(was: Attached also:

1. scheduler.log to show that scheduler decided to send again and again and again infinitely the same {job,execution_date}
2. the dummy_dag.py of the test)

> Same {task,execution_date} run multiple times in worker when using CeleryExecutor
> ---------------------------------------------------------------------------------
>
>                 Key: AIRFLOW-928
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-928
>             Project: Apache Airflow
>          Issue Type: Bug
>          Components: celery
>    Affects Versions: Airflow 1.7.1.3
>         Environment: Docker
>            Reporter: Uri Shamay
>         Attachments: airflow.log, dag_runs.png, dummy_dag.py, rabbitmq.queue, scheduler.log, worker.log
>
>
> Hi,
> When using with Airflow with CeleryExecutor, both RabbitMQ && Redis I tested, I see that when workers are down, the scheduler run each period of time **append** to the same key of {task,execution_date} in the broker, the same {task,execution_date}, what means is that if workers are down/can't connect to broker for few hours, I got in the broker thousands of same executions.
> In my scenario I have just one dummy dag to run with dag_concurrency of 4,
> I expected in that scenario that broker will hold just 4 messages, and the scheduler shouldn't queuing another and another and another for same {task, execution_date}
> What happened is that when workers start to consume messages, they got thousands of tasks for just 4 tasks, and when they trying to write to database for task_instances - there are errors of integrity while such {task,execution_date} already exist.
> Attached files:
> 1. airflow.log - this is the task log, you can see that few instances processes of same {task,execution_date} write to the same log file.
> 2. worker.log - this is the worker log, you can see that worker trying to run same {task,execution_date} multiple times + the errors from the database integrity that said that those tasks on those dates already exists.
> 3. scheduler.log to show that scheduler decided to send again and again and again infinitely the same {job,execution_date}
> 4. the dummy_dag.py of the test
> 5. rabbitmq.queue - show that after 5 minutes the broker queue contains 40 messages of same 4 {job,execution_date}
> 6. dag_runs.png - show that there are only 4 jobs that need to be run, while there are much more messages in the queue
> Thanks.



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