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https://issues.apache.org/jira/browse/SPARK-25034?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Dongjoon Hyun updated SPARK-25034:
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Affects Version/s: (was: 3.0.0)
3.1.0
> possible triple memory consumption in fetchBlockSync()
> ------------------------------------------------------
>
> Key: SPARK-25034
> URL: https://issues.apache.org/jira/browse/SPARK-25034
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 3.1.0
> Reporter: Vincent
> Priority: Major
>
> Hello
> in the code of _fetchBlockSync_() in _blockTransferService_, we have:
>
> {code:java}
> val ret = ByteBuffer.allocate(data.size.toInt)
> ret.put(data.nioByteBuffer())
> ret.flip()
> result.success(new NioManagedBuffer(ret))
> {code}
> In some cases, the _data_ variable is a _NettyManagedBuffer_, whose
> underlying netty representation is a _CompositeByteBuffer_.
> Going through the code above in this configuration, assuming that the
> variable _data_ holds N bytes:
> 1) we allocate a full buffer of N bytes in _ret_
> 2) calling _data.nioByteBuffer()_ on a _CompositeByteBuffer_ will trigger a
> full merge of all the composite buffers, which will allocate *again* a full
> buffer of N bytes
> 3) we copy to _ret_ the data byte by byte
> This means that at some point the N bytes of data are located 3 times in
> memory.
> Is this really necessary?
> It seems unclear to me why we have to process at all the data, given that we
> receive a _ManagedBuffer_ and we want to return a _ManagedBuffer_
> Is there something I'm missing here? It seems this whole operation could be
> done with 0 copies.
> The only upside here is that the new buffer will have merged all the
> composite buffer's arrays, but it is really not clear if this is intended. In
> any case this could be done with peak memory of 2N and not 3N
> Cheers!
>
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