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From "ASF subversion and git services (JIRA)" <>
Subject [jira] [Commented] (AIRFLOW-862) Add DaskExecutor
Date Sun, 19 Feb 2017 08:30:44 GMT


ASF subversion and git services commented on AIRFLOW-862:

Commit fe7881656f3fbea341b91ed98c9cef5513accbc6 in incubator-airflow's branch refs/heads/master
from [~jlowin]
[;h=fe78816 ]

[AIRFLOW-862] Fix Unit Tests for DaskExecutor

Unit tests were inadvertently disabled for

Closes #2076 from jlowin/fix-dask-tests

> Add DaskExecutor
> ----------------
>                 Key: AIRFLOW-862
>                 URL:
>             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
>   - 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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