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https://issues.apache.org/jira/browse/SPARK-3714?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14151486#comment-14151486
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Mridul Muralidharan commented on SPARK-3714:
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Most of the drawbacks mentioned are not severe imo - at best, they are
unfamiliarity with oozie platform (points 2, 3, 4, 5).
Point 1 is interesting (sharing spark context) - though from a fault tolerance
point of view, it makes supporting it challenging; ofcourse oozie was not,
probably, designed with something like spark in mind - so there might be
changes to oozie which might benefit spark; we could engage with oozie dev for
that.
But discarding it to reinvent something when oozie already does everything
mentioned in requirements section seems counterintutive.
I have seen multiple attempts to 'simplify' workflow management, and at
production scale almost everything ends up being similar ...
Note that most production jobs have to depend on a variety of jobs - not just
spark or MR - so you will end up converigng on a variant of oozie anyway :-)
Having said that, if you want to take a crack at solving this with spark
specific idioms in mind, it would be interesting to see the result - I dont
want to dissuade from doing so !
We might end up with something quite interesting.
> 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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