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https://issues.apache.org/jira/browse/HELIX-601?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14641815#comment-14641815
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ASF GitHub Bot commented on HELIX-601:
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Github user brandtg commented on the pull request:
https://github.com/apache/helix/pull/30#issuecomment-124923785
What's the specific use case for this feature? I'm imagining something
like, say, re-indexing multiple partitions of data. You want to just give it a
shot and let the chips fall as they may, then re-run the task on problem
partitions.
Maybe I'm not understanding it correctly, and there might be some gotcha
for this suggestion... But if you have a task that can be partitioned in to N
sequences of steps and require no synchronization between steps, why don't you
just model it as N tasks all with one partition (i.e. each has its own target
resource)?
Obviously this is more load on ZK, but I'm wondering if it'd be okay for
your use case. You could also leverage batch messaging to mitigate this I think.
> Allow work flow to schedule dependency jobs in parallel
> -------------------------------------------------------
>
> Key: HELIX-601
> URL: https://issues.apache.org/jira/browse/HELIX-601
> Project: Apache Helix
> Issue Type: New Feature
> Reporter: Congrui Ji
>
> Currently, Helix won't schedule dependency jobs in a same work flow. For
> example, if Job2 depends on Job1, Job2 won't be scheduled until every
> partition of Job1 is completed.
> However, if some participant is very slow, then all dependency jobs is
> waiting for that single participant.
> Helix should be able to schedule multiple jobs according to a parameter.
> A.C.
> 1. Introduce parallel count parameter in work flow and job queue.
> 2. Dependency jobs can be scheduled according to the parameter (Now the
> parameter is always 1, so no parallel)
> 3. If Job2 depends on Job1, Job1 is scheduled before Job2.
> 4. No parallel jobs on the same instance. If a instance is running Job1, it
> won't run Job2 until Job1 is finished.
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