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https://issues.apache.org/jira/browse/SPARK-18857?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Apache Spark reassigned SPARK-18857:
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Assignee: Apache Spark
> SparkSQL ThriftServer hangs while extracting huge data volumes in incremental
> collect mode
> ------------------------------------------------------------------------------------------
>
> Key: SPARK-18857
> URL: https://issues.apache.org/jira/browse/SPARK-18857
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.2
> Reporter: vishal agrawal
> Assignee: Apache Spark
> Attachments: GC-spark-1.6.3, GC-spark-2.0.2
>
>
> We are trying to run a sql query on our spark cluster and extracting around
> 200 million records through SparkSQL ThriftServer interface. This query works
> fine for Spark 1.6.3 version, however for spark 2.0.2, thrift server hangs
> after fetching data from a few partitions (we are using incremental collect
> mode with 400 partitions). As per documentation max memory taken up by thrift
> server should be what is required by the biggest data partition. But we
> observed that Thrift server is not releasing the old partitions memory
> whenever the GC occurs even though it has moved to next partition data
> fetches. which is not the case with 1.6.3 version.
> On further investigation we found that SparkExecuteStatementOperation.scala
> was modified for "[SPARK-16563][SQL] fix spark sql thrift server FetchResults
> bug" and result set iterator was duplicated to keep a reference to the first
> set.
> + val (itra, itrb) = iter.duplicate
> + iterHeader = itra
> + iter = itrb
> We suspect that this is resulting in the memory not being cleared on GC. To
> confirm this we created an iterator in our test class and fetched the data
> once without duplicating and second time with creating a duplicate. we could
> see that in first instance it ran fine and fetched the entire data set while
> in second instance driver hanged after fetching data from a few partitions.
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