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https://issues.apache.org/jira/browse/AIRFLOW-862?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15862977#comment-15862977
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ASF subversion and git services commented on AIRFLOW-862:
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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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