[jira] [Commented] (AIRFLOW-862) Add DaskExecutor

["ASF subversion and git services (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/AIRFLOW-862?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15862977#comment-15862977 ] 

ASF subversion and git services commented on AIRFLOW-862:
---------------------------------------------------------

Commit 6e2210278235d42bbc3a60e1e14bbf0f9127b54f in incubator-airflow's branch refs/heads/master from [~jlowin]
[ https://git-wip-us.apache.org/repos/asf?p=incubator-airflow.git;h=6e22102 ]

[AIRFLOW-862] Add DaskExecutor

Adds a DaskExecutor for running Airflow tasks
in Dask clusters.

Closes #2067 from jlowin/dask-executor


> 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: 1.8.1
>
>
> 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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