airflow-commits mailing list archives

Site index · List index
Message view « Date » · « Thread »
Top « Date » · « Thread »
From "ASF subversion and git services (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (AIRFLOW-862) Add DaskExecutor
Date Sun, 19 Feb 2017 08:30:44 GMT

    [ https://issues.apache.org/jira/browse/AIRFLOW-862?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15873535#comment-15873535
] 

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

Commit fe7881656f3fbea341b91ed98c9cef5513accbc6 in incubator-airflow's branch refs/heads/master
from [~jlowin]
[ https://git-wip-us.apache.org/repos/asf?p=incubator-airflow.git;h=fe78816 ]

[AIRFLOW-862] Fix Unit Tests for DaskExecutor

Unit tests were inadvertently disabled for
DaskExecutor

Closes #2076 from jlowin/fix-dask-tests


> 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.



--
This message was sent by Atlassian JIRA
(v6.3.15#6346)

Mime
View raw message