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From "Vincent (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-24917) Sending a partition over netty results in 2x memory usage
Date Wed, 25 Jul 2018 12:09:00 GMT

     [ 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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