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https://issues.apache.org/jira/browse/SPARK-22170?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Xiao Li resolved SPARK-22170.
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Resolution: Fixed
Assignee: Ryan Blue
Fix Version/s: 2.3.0
> Broadcast join holds an extra copy of rows in driver memory
> -----------------------------------------------------------
>
> Key: SPARK-22170
> URL: https://issues.apache.org/jira/browse/SPARK-22170
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.2, 2.1.1, 2.2.0
> Reporter: Ryan Blue
> Assignee: Ryan Blue
> Fix For: 2.3.0
>
>
> I investigated a driver OOM that was building a large broadcast table with a
> memory profiler and found that a huge amount of memory is used while building
> a broadcast table. This is because [BroadcastExchangeExec uses
> {{executeCollect}}|https://github.com/apache/spark/blob/master/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/BroadcastExchangeExec.scala#L76].
> In {{executeCollect}}, all of the partitions are fetched as compressed
> blocks, then each block is decompressed (with a stream), and each row is
> copied to a new byte buffer and added to an ArrayBuffer, which is copied to
> an Array. This results in a huge amount of allocation: a buffer for each row
> in the broadcast. Those rows are only used to get copied into a
> {{BytesToBytesMap}} that will be broadcasted, so there is no need to keep
> them in memory.
> Replacing the array buffer step with an iterator reduces the amount of memory
> held while creating the map by not requiring all rows to be in memory. It
> also avoids allocating a large Array for the rows. In practice, a 16MB
> broadcast table used 100MB less memory with this approach, but the reduction
> depends on the size of rows and compression (16MB was in Parquet format).
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