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https://issues.apache.org/jira/browse/SPARK-17606?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15571870#comment-15571870
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etienne edited comment on SPARK-17606 at 10/13/16 1:17 PM:
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Theses string are log as DEBUG or in upper logs level ? ( because there is a
lot of log to process )
edit : I found that
log4j.logger.org.apache.spark.streaming.scheduler.JobGenerator=DEBUG I think
this will help
was (Author: crakjie):
Theses string are log as DEBUG or in upper logs level ? ( because there is a
lot of log to process )
> New batches are not created when there are 1000 created after restarting
> streaming from checkpoint.
> ---------------------------------------------------------------------------------------------------
>
> Key: SPARK-17606
> URL: https://issues.apache.org/jira/browse/SPARK-17606
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Affects Versions: 1.6.1
> Reporter: etienne
>
> When spark restarts from a checkpoint after being down for a while.
> It recreates missing batch since the down time.
> When there are few missing batches, spark creates new incoming batch every
> batchTime, but when there is enough missing time to create 1000 batches no
> new batch is created.
> So when all these batch are completed the stream is idle ...
> I think there is a rigid limit set somewhere.
> I was expecting that spark continue to recreate missed batches, maybe not all
> at once ( because it's look like it's cause driver memory problem ), and then
> recreate batches each batchTime.
> Another solution would be to not create missing batches but still restart the
> direct input.
> Right know for me the only solution to restart a stream after a long break it
> to remove the checkpoint to allow the creation of a new stream. But losing
> all my states.
> ps : I'm speaking about direct Kafka input because it's the source I'm
> currently using, I don't know what happens with other sources.
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