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From "Bolke de Bruin (JIRA)" <j...@apache.org>
Subject [jira] [Resolved] (AIRFLOW-862) Add DaskExecutor
Date Sun, 19 Feb 2017 08:30:45 GMT

     [ https://issues.apache.org/jira/browse/AIRFLOW-862?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Bolke de Bruin resolved AIRFLOW-862.
------------------------------------
       Resolution: Fixed
    Fix Version/s:     (was: 1.8.1)
                   1.9.0

Issue resolved by pull request #2076
[https://github.com/apache/incubator-airflow/pull/2076]

> Add DaskExecutor
> ----------------
>
>                 Key: AIRFLOW-862
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-862
>             Project: Apache Airflow
>          Issue Type: New Feature
>          Components: executor
>            Reporter: Jeremiah Lowin
>            Assignee: Jeremiah Lowin
>             Fix For: 1.9.0
>
>
> The Dask Distributed sub-project makes it very easy to create pure-python clusters of
Dask workers ranging from a personal laptop to thousands of networked cores. The workers can
execute arbitrary functions submitted to the Dask scheduler node. A full Dask app would involve
multiple tasks with data-dependencies (similar in philosophy to an Airflow DAG) but it will
happily run single functions as well.
> The DaskExecutor is configured by supplying the IP address of the Dask Scheduler. It
submits Airflow commands to the cluster for execution (note: the cluster should have access
to any Airflow dependencies, including Airflow itself!) and checks the resulting futures to
see if the tasks completed successfully.
> Some advantages of using Dask for parallel execution over LocalExecutor or CeleryExecutor
are:
>   - simple scaling, from local machines to remote clusters
>   - pure python implementation (minimal dependencies and no need to run additional databases)
>   - built in live-updating web UI for monitoring the cluster
>   
> ** Note: This does NOT replace the Airflow scheduler or DAG engine with the analogous
Dask versions; it just uses the Dask cluster to run Airflow tasks.



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