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https://issues.apache.org/jira/browse/CASSANDRA-8670?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14390561#comment-14390561
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Benedict commented on CASSANDRA-8670:
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bq. In most cases we opt to use the channel when available. ... Where we could
really do better is when using compression
You're right. I thought we had more paths leftover, but it is just compression
really. That's a much cleaner state of affairs!
It looks like compression will typically be under the stack buffer size, so we
don't have such a problem, but it would still be nice to move to a
DirectByteBuffer compressed output stream. It should be quite viable since
there are now compression methods for working over these directly, but that
should probably be a follow up ticket. Do you want to file, or shall I?
bq. BufferedDataOutputStreamPlus.close() will clean the buffer even if it might
not own the buffer such as when it is provided to the constructor. It also
doesn't check if the channel is null.
We don't call close in situations where this is a problem
bq. When used from the commit log without a channel it will throw an NPE if it
exceeds the capacity of the buffer when it goes to flush
We presize the buffer correctly
Still, on both these counts we could offer better safety if we wanted, without
much cost. If we used a DataOutputBuffer it would solve these problems, and we
could assert that the final buffer is == the provided buffer...
bq. It almost seems like there is a case for a version just for wrapping fixed
size bytebuffers.
Possibly. I'm on the fence, since it's extra code cache and class hierarchy
pollution, with limited positive impact. We would need to duplicate the whole
of BufferedDataOutputStreamPlus. You could benchmark to see if there's an
appreciable difference? If the difference is small, I would prefer to avoid the
pollution.
> 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: OutputStreamBench.java, 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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