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From "Benedict (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CASSANDRA-8670) Large columns + NIO memory pooling causes excessive direct memory usage
Date Sun, 29 Mar 2015 23:14:53 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-8670?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14386007#comment-14386007
] 

Benedict commented on CASSANDRA-8670:
-------------------------------------

I've pushed some suggestions for further refactoring [here|https://github.com/belliottsmith/cassandra/tree/8670-suggestions].
I've only looked at the overall class hierarchy, I haven't focused yet on reviewing the method
implementation changes.

Mostly these changes flatten the class hierarchy; it's gotten deep enough I don't think there's
a good reason to maintain the distinction between DataStreamOutputPlus and DataStreamOutputPlusAndChannel,
especially since we often just mock up a Channel based off the OutputStream. I've also flattened
NIODataOutputStream and DataOutputStreamByteBufferPlus into BufferedDataOutputStreamPlus,
since we only write to the buffer if we don't exceed its size. At the same time, since we
are now refactoring this whole hierarchy, I made DataOutputBuffer extend BufferedDataOutputStreamPlus,
and just ensures the buffer grows as necessary, and have removed FastByteArrayOutputStream
since we no longer need it.

I've also stopped SequentialWriter implementing WritableByteChannel, and now pass in its internal
Channel, since that's the only way the operations will benefit. As a follow up ticket, we
should probably move SequentialWriter to utilising BufferedDataOutputStreamPlus directly,
so that it can benefit from faster encoding of primitives

Let me know what you think of the changes to the hierarchy, and once we've ironed that out
we can move on to the home stretch and confirm the code changes. One other thing we could
consider is dropping the "Plus" from everything except the interface, since it seems superfluous,
and it's all fairly verbose.


> Large columns + NIO memory pooling causes excessive direct memory usage
> -----------------------------------------------------------------------
>
>                 Key: CASSANDRA-8670
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-8670
>             Project: Cassandra
>          Issue Type: Bug
>          Components: Core
>            Reporter: Ariel Weisberg
>            Assignee: Ariel Weisberg
>             Fix For: 3.0
>
>         Attachments: largecolumn_test.py
>
>
> If you provide a large byte array to NIO and ask it to populate the byte array from a
socket it will allocate a thread local byte buffer that is the size of the requested read
no matter how large it is. Old IO wraps new IO for sockets (but not files) so old IO is effected
as well.
> Even If you are using Buffered{Input | Output}Stream you can end up passing a large byte
array to NIO. The byte array read method will pass the array to NIO directly if it is larger
than the internal buffer.  
> Passing large cells between nodes as part of intra-cluster messaging can cause the NIO
pooled buffers to quickly reach a high watermark and stay there. This ends up costing 2x the
largest cell size because there is a buffer for input and output since they are different
threads. This is further multiplied by the number of nodes in the cluster - 1 since each has
a dedicated thread pair with separate thread locals.
> Anecdotally it appears that the cost is doubled beyond that although it isn't clear why.
Possibly the control connections or possibly there is some way in which multiple 
> Need a workload in CI that tests the advertised limits of cells on a cluster. It would
be reasonable to ratchet down the max direct memory for the test to trigger failures if a
memory pooling issue is introduced. I don't think we need to test concurrently pulling in
a lot of them, but it should at least work serially.
> The obvious fix to address this issue would be to read in smaller chunks when dealing
with large values. I think small should still be relatively large (4 megabytes) so that code
that is reading from a disk can amortize the cost of a seek. It can be hard to tell what the
underlying thing being read from is going to be in some of the contexts where we might choose
to implement switching to reading chunks.



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