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Apache Spark commented on SPARK-17386: -------------------------------------- User 'frreiss' has created a pull request for this issue: https://github.com/apache/spark/pull/15162 > 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 > Priority: Minor > > 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 sit in a loop calling {{getOffset()}} every 10 msec > (the default value of STREAMING_POLLING_DELAY) on every {{Source}} until new > data arrives. > In test cases, where most of the sources are {{MemoryStream}} or > {{TextSocketSource}}, this rapid polling leads to excessive CPU usage. > In a production environment, this overhead could disrupt 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 no > data has arrived, Spark will list the directory's contents up to 100 times > per second. 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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org