[
https://issues.apache.org/jira/browse/SPARK-24917?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Vincent updated SPARK-24917:
----------------------------
Description:
Hello
while investigating some OOM errors in Spark 2.2 [(here's my call
stack)|https://image.ibb.co/hHa2R8/sparkOOM.png], I find the following behavior
happening, which I think is weird:
* a request happens to send a partition over network
* this partition is 1.9 GB and is persisted in memory
* this partition is apparently stored in a ByteBufferBlockData, that is made
of a ChunkedByteBuffer, which is a list of (lots of) ByteBuffer of 4 MB each.
* the call to toNetty() is supposed to only wrap all the arrays and not
allocate any memory
* yet the call stack shows that netty is allocating memory and is trying to
consolidate all the chunks into one big 1.9GB array
* this means that at this point the memory footprint is 2x the size of the
actual partition (which is huge when the partition is 1.9GB)
Is this transient allocation expected?
After digging, it turns out that the actual copy is due to [this
method|https://github.com/netty/netty/blob/4.0/buffer/src/main/java/io/netty/buffer/Unpooled.java#L260]
in netty. If my initial buffer is made of more than DEFAULT_MAX_COMPONENTS
(16) components it will trigger a re-allocation of all the buffer. This netty
issue was fixed in this recent change :
[https://github.com/netty/netty/commit/9b95b8ee628983e3e4434da93fffb893edff4aa2]
As a result, is it possible to benefit from this change somehow in spark 2.2
and above? I don't know how the netty dependencies are handled for spark
NB: it seems this ticket: [https://jira.apache.org/jira/browse/SPARK-24307]
kinda changed the approach for spark 2.4 by bypassing netty buffer altogether.
However as it is written in the ticket, this approach *still* needs to have the
*entire* block serialized in memory, so this would be a downgrade from fixing
the netty issue when your buffer in < 2GB
Thanks!
was:
Hello
while investigating some OOM errors in Spark 2.2 [(here's my call
stack)|https://image.ibb.co/hHa2R8/sparkOOM.png], I find the following behavior
happening, which I think is weird:
* a request happens to send a partition over network
* this partition is 1.9 GB and is persisted in memory
* this partition is apparently stored in a ByteBufferBlockData, that is made
of a ChunkedByteBuffer, which is a list of (lots of) ByteBuffer of 4 MB each.
* the call to toNetty() is supposed to only wrap all the arrays and not
allocate any memory
* yet the call stack shows that netty is allocating memory and is trying to
consolidate all the chunks into one big 1.9GB array
* this means that at this point the memory footprint is 2x the size of the
actual partition (which is huge when the partition is 1.9GB)
Is this transient allocation expected?
After digging, it turns out that the actual copy is due to [this
method|https://github.com/netty/netty/blob/4.0/buffer/src/main/java/io/netty/buffer/Unpooled.java#L260]
in netty. If my initial buffer is made of more than DEFAULT_MAX_COMPONENTS
(16) components it will trigger a re-allocation of all the buffer. This netty
issue was fixed in this recent change :
[https://github.com/netty/netty/commit/9b95b8ee628983e3e4434da93fffb893edff4aa2]
As a result, is it possible to benefit from this change somehow in spark 2.2
and above? I don't know how the netty dependencies are handled for spark
NB: it seems this ticket: [https://jira.apache.org/jira/browse/SPARK-24307]
fixes the issue for spark 2.4 by bypassing netty buffer altogether
Thanks!
> Sending a partition over netty results in 2x memory usage
> ---------------------------------------------------------
>
> Key: SPARK-24917
> URL: https://issues.apache.org/jira/browse/SPARK-24917
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Affects Versions: 2.2.2
> Reporter: Vincent
> Priority: Major
>
> Hello
> while investigating some OOM errors in Spark 2.2 [(here's my call
> stack)|https://image.ibb.co/hHa2R8/sparkOOM.png], I find the following
> behavior happening, which I think is weird:
> * a request happens to send a partition over network
> * this partition is 1.9 GB and is persisted in memory
> * this partition is apparently stored in a ByteBufferBlockData, that is made
> of a ChunkedByteBuffer, which is a list of (lots of) ByteBuffer of 4 MB each.
> * the call to toNetty() is supposed to only wrap all the arrays and not
> allocate any memory
> * yet the call stack shows that netty is allocating memory and is trying to
> consolidate all the chunks into one big 1.9GB array
> * this means that at this point the memory footprint is 2x the size of the
> actual partition (which is huge when the partition is 1.9GB)
> Is this transient allocation expected?
> After digging, it turns out that the actual copy is due to [this
> method|https://github.com/netty/netty/blob/4.0/buffer/src/main/java/io/netty/buffer/Unpooled.java#L260]
> in netty. If my initial buffer is made of more than DEFAULT_MAX_COMPONENTS
> (16) components it will trigger a re-allocation of all the buffer. This netty
> issue was fixed in this recent change :
> [https://github.com/netty/netty/commit/9b95b8ee628983e3e4434da93fffb893edff4aa2]
>
> As a result, is it possible to benefit from this change somehow in spark 2.2
> and above? I don't know how the netty dependencies are handled for spark
>
> NB: it seems this ticket: [https://jira.apache.org/jira/browse/SPARK-24307]
> kinda changed the approach for spark 2.4 by bypassing netty buffer
> altogether. However as it is written in the ticket, this approach *still*
> needs to have the *entire* block serialized in memory, so this would be a
> downgrade from fixing the netty issue when your buffer in < 2GB
>
> Thanks!
>
>
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