[jira] [Closed] (AIRFLOW-163) Running multiple LocalExecutor schedulers makes system load skyrocket

["Ash Berlin-Taylor (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/AIRFLOW-163?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Ash Berlin-Taylor closed AIRFLOW-163.
-------------------------------------
    Resolution: Cannot Reproduce

Closing as a stale issue for an old version that possibly isn't an issue anymore - if anyone comes across similar issues on the latest version of Airflow please create a new issue

> Running multiple LocalExecutor schedulers makes system load skyrocket
> ---------------------------------------------------------------------
>
>                 Key: AIRFLOW-163
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-163
>             Project: Apache Airflow
>          Issue Type: Bug
>    Affects Versions: 1.7.1
>         Environment: EC2 t2.medium instance, 
> Docker `version 1.11.1, build 5604cbe`, 
> Host is `Linux ip-172-31-44-140 3.13.0-85-generic #129-Ubuntu SMP Thu Mar 17 20:50:15 UTC 2016 x86_64 x86_64 x86_64 GNU/Linux`, 
> Docker containers are built upon the `python:3.5` image, 
> LocalExecutor is used with two scheduler containers running
>            Reporter: Bence Nagy
>            Priority: Minor
>              Labels: scheduler
>
> I've been told on Gitter that this is expected currently, but thought I'd create an issue for it anyway.
> See this screenshot of a task duration chart — I launched a second scheduler for the 8:50 execution. The orange line represents a PostgresOperator task (i.e. processing happens independent of airflow), while the other lines represent data copying tasks that go through a temp file on the airflow host https://i.imgur.com/2tDKgKj.png
> I'm seeing a system load of around 4.0-5.0 when processing tasks when one scheduler is running, and 20.0-30.0 with two.
> Running {{airflow scheduler --num_runs 3}} under yappi got me these results when ordered by total time: http://pastebin.com/8TiEG4P3. I still have the raw profiling data, let me know if another data extract would be useful.



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