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https://issues.apache.org/jira/browse/SPARK-11175?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Yongjia Wang updated SPARK-11175:
---------------------------------
Description:
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.
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.
Therefore, I would propose to make the default behavior to just run all jobs of
the current batch concurrently, and mark batch completion when all the jobs
complete.
was:
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.
I went through a few hacks:
1. launch the rdd action into a new thread from the function passed to
foreachRDD to achieve. 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.
Therefore, I would propose to make the default behavior to just run all jobs of
the current batch concurrently, and mark batch completion when all the jobs
complete.
> 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.
> 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.
> Therefore, I would propose to make the default behavior to just run all jobs
> of the current batch concurrently, and mark batch completion when all the
> jobs complete.
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