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Date: Sun, 28 Sep 2014 20:59:33 +0000 (UTC)
From: "Mridul Muralidharan (JIRA)" <jira@apache.org>
To: issues@spark.apache.org
Message-ID: <JIRA.12744559.1411918836000.142432.1411937973637@Atlassian.JIRA>
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 <JIRA.12744559.1411918836136@arcas>
Subject: [jira] [Commented] (SPARK-3714) Spark workflow scheduler
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    [ https://issues.apache.org/jira/browse/SPARK-3714?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14151211#comment-14151211 ] 

Mridul Muralidharan commented on SPARK-3714:
--------------------------------------------

Have you tried using oozie for this ?
IIRC Tom has already gotten this working here quite a while back
/CC [~tgraves] 

> Spark workflow scheduler
> ------------------------
>
>                 Key: SPARK-3714
>                 URL: https://issues.apache.org/jira/browse/SPARK-3714
>             Project: Spark
>          Issue Type: New Feature
>          Components: Project Infra
>            Reporter: Egor Pakhomov
>            Priority: Minor
>
> [Design doc | https://docs.google.com/document/d/1q2Q8Ux-6uAkH7wtLJpc3jz-GfrDEjlbWlXtf20hvguk/edit?usp=sharing]
> Spark stack currently hard to use in the production processes due to the lack of next features:
> * Scheduling spark jobs
> * Retrying failed spark job in big pipeline
> * Share context among jobs in pipeline
> * Queue jobs
> Typical usecase for such platform would be - wait for new data, process new data, learn ML models on new data, compare model with previous one, in case of success - rewrite model in HDFS directory for current production model with new one.


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