[jira] [Commented] (SPARK-11175) Concurrent execution of JobSet within a batch in Spark streaming

["Saisai Shao (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/SPARK-11175?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14962708#comment-14962708 ] 

Saisai Shao commented on SPARK-11175:
-------------------------------------

There's a configuration "spark.streaming.concurrentJobs" that may satisfy your needs, default number is 1, which means submitting jobs one by one.

> Concurrent execution of JobSet within a batch in Spark streaming
> ----------------------------------------------------------------
>
>                 Key: SPARK-11175
>                 URL: https://issues.apache.org/jira/browse/SPARK-11175
>             Project: Spark
>          Issue Type: Improvement
>          Components: Streaming
>            Reporter: Yongjia Wang
>
> Spark StreamingContext can register multiple independent Input DStreams (such as from different Kafka topics) that results in multiple independent jobs for each batch. These jobs should better be run concurrently to maximally take advantage of available resources. The current behavior is that these jobs end up in an invisible job queue to be submitted one by one.
> I went through a few hacks:
> 1.  launch the rdd action into a new thread from the function passed to foreachRDD. However, it will mess up with streaming statistics since the batch will finish immediately even the jobs it launched are still running in another thread. This can further affect resuming from checkpoint, since all batches are completed right away even the actual threaded jobs may fail and checkpoint only resume the last batch.
> 2. It's possible by just using foreachRDD and the available APIs to block the Jobset to wait for all threads to join, but doing this would mess up with closure serialization, and make checkpoint not usable.
> 3. Instead of running multiple Dstreams in one streaming context, just run them in separate streaming context (separate Spark applications). Putting aside the extra deployment overhead, when working with Spark standalone cluster which only has FIFO scheduler across applications, the resource has to be set in advance and it won't automatically adjust with resizing the cluster.
> Therefore, I think there is a good use case to make the default behavior just run all jobs of the current batch concurrently, and mark batch completion when all the jobs complete.



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