[jira] [Updated] (SPARK-17386) Default polling and trigger intervals cause excessive RPC calls

["Frederick Reiss (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-17386?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Frederick Reiss updated SPARK-17386:
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    Summary: Default polling and trigger intervals cause excessive RPC calls  (was: Default trigger interval causes excessive RPC calls)

> Default polling and trigger intervals cause 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()}} every 10 msec 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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