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https://issues.apache.org/jira/browse/SPARK-17386?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15459940#comment-15459940
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Apache Spark commented on SPARK-17386:
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User 'frreiss' has created a pull request for this issue:
https://github.com/apache/spark/pull/14945
> Default trigger interval causes excessive RPC calls
> ---------------------------------------------------
>
> Key: SPARK-17386
> URL: https://issues.apache.org/jira/browse/SPARK-17386
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Reporter: Frederick Reiss
>
> The default trigger interval for a Structured Streaming query is
> {{ProcessingTime(0)}}, i.e. "trigger new microbatches as fast as possible".
> When the trigger is set to this default value, the scheduler in
> {{StreamExecution}} will spin in a tight loop calling {{getOffset()}} on
> every {{Source}} until new data arrives.
> In test cases, where most of the sources are {{MemoryStream}} or
> {{TextSocketSource}}, this spinning leads to excessive CPU usage.
> In a production environment, this spinning could take down critical
> infrastructure. Most sources in Spark clusters will be {{FileStreamSource}}
> or the not-yet-written Kafka 0.10 Source. The {{getOffset()}} method of
> {{FileStreamSource}} performs a directory listing of an HDFS directory. If
> the scheduler calls {{FileStreamSource.getOffset()}} in a tight loop, Spark
> will make hundreds of RPC calls per second to the HDFS NameNode. This
> overhead could disrupt service to other systems using HDFS, including Spark
> itself. A similar situation will exist with the Kafka source, the
> {{getOffset()}} method of which will presumably call Kafka's
> {{Consumer.poll()}} method.
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