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https://issues.apache.org/jira/browse/SPARK-25224?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17441131#comment-17441131
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ramakrishna chilaka commented on SPARK-25224:
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can anyone please confirm, if there are any plans to revive this ? Thanks.
> Improvement of Spark SQL ThriftServer memory management
> -------------------------------------------------------
>
> Key: SPARK-25224
> URL: https://issues.apache.org/jira/browse/SPARK-25224
> Project: Spark
> Issue Type: Improvement
> Components: SQL
> Affects Versions: 2.3.1
> Reporter: Dooyoung Hwang
> Priority: Major
> Labels: bulk-closed
>
> Spark SQL just have two options for managing thriftserver memory - enable
> spark.sql.thriftServer.incrementalCollect or not
> *1. The case of enabling spark.sql.thriftServer.incrementalCollects*
> *1) Pros :* thriftserver can handle large output without OOM.
> *2) Cons*
> * Performance degradation because of executing task partition by partition.
> * Handle queries with count-limit inefficiently because of executing all
> partitions. (executeTake stop scanning after collecting count-limit.)
> * Cannot cache result for FETCH_FIRST
> *2. The case of disabling spark.sql.thriftServer.incrementalCollects*
> *1) Pros :* Good performance for small output
> *2) Cons*
> * Memory peak usage is too large because allocating decompressed &
> deserialized rows in "batch" manner, and OOM could occur for large output.
> * It is difficult to measure memory peak usage of Query, so configuring
> spark.driver.maxResultSize is very difficult.
> * If decompressed & deserialized rows fills up eden area of JVM Heap, they
> moves to old Gen and could increase possibility of "Full GC" that stops the
> world.
>
> The improvement idea is below:
> # *DataSet does not decompress & deserialize result, and just return total
> row count & iterator to SQL-Executor.* By doing that, only uncompressed data
> reside in memory, so that the memory usage is not only much lower than before
> but is configurable with using spark.driver.maxResultSize.
> # *After SQL-Executor get total row count & iterator from DataSet, it could
> decide whether collecting them as batch manner(appropriate for small row
> count) or deserializing and sending them iteratively (appropriate for large
> row count) with considering returned row count.*
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