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From "Stefania (JIRA)" <>
Subject [jira] [Commented] (CASSANDRA-8630) Faster sequential IO (on compaction, streaming, etc)
Date Tue, 25 Aug 2015 08:55:47 GMT


Stefania commented on CASSANDRA-8630:

bq. DataInputBuffer line 25, NIODataInputStream no longer has the bytes shuffling behavior
so that comment should go away.

Not sure if I found the comment you are referring to, you mean the description of the class
{{Input stream around a fixed ByteBuffer}}? I can change it to {{Input stream around a single
ByteBuffer}}, is this what you meant?

bq. RebufferingInputStream copy constructor appears unused (or Eclipse is lying). It's also
looks suspicious since it doesn't inherit the rebuffering behavior of whatever it is copying?

Left over from previous attempts, definitely not needed, thanks.

bq. Does ChecksummedDataInput handle files larger than 2 gigabytes? Seems like we could end
up with large hint files? The way the file based hints loop is written it seems like it could
do it. Possibly unintentionally.

Each single hint cannot be more than 2GB because its size is encoded as an integer (existing
behavior). {{ChecksummedDataInput}} should be fine except for the handling of limits, which
are used only for single hints. I will change {{ChecksummedDataInput.limit}} to a long for
future safety and move the checked cast to {{readHint}}. {{crcPosition}} is an integer but
this is fine as it caches the last buffer position, which is also an integer. It is reset
each time we rebuffer.

bq. The CoW idiom used for MmappedRegions seems a little off. It's making a copy on read so
every SSTableReader (they aren't shared globally I believe) will have a separate deep copy
of the entire MmappedRegions. I know this is tricky and you probably get it better than I
do, but can you get it so that the same array is shared? Ideally both the arrays and the State
object will be shared. Looking at how the refcounting is supposed to work

The readers are potentially used in different threads. Once Lifecycletransaction.checkpoint()
publishes a new view in the tracker, we can potentially use the tracker in different threads,
due to various asynchronous tasks. At least this is what I understand. The builder lives in
the BigTableWriter, each time we open an sstable early it will create a new sstable reader
which shares the same builder with the previous readers and the final reader - hence they
share the channel and the mmapped regions if any. The readers created in open early are published
and obsoleted by the lifecycle transaction so MT access is possible. This is my understanding,
perhaps [~benedict] can fill any gaps?

The whole idea was to ensure copies do not modify the arrays. It's true that we could share
the arrays as we enforce the non-modification via the {{isCopy}} flag, so I am happy to avoid
the deep copy. Once we implement compaction of mmapped regions, this will become trickier
and sharing arrays may not be possible any longer. However, in this case we'll also have bigger
problems like tracking the old non-compacted regions that are still used.

bq. The fact that MmappedRegions and it's owning MmappedSegmentedFile both are SharedClosables
seems odd to me. Seems like only one of them needs to determine the lifetime of the whole

The segmented files and the builders own the MmappedRegions, just like for the channel. Sometimes
the builders are closed before the segmented files, and the mmapped regions need to survive.
It's exactly the same as for the channel. These two resources, channel and mmapped regions,
need to have their own resource management independent of their owners.

bq. For rate limiting. It seems like we acquire buffer size from the rate limiter at a time.
What is the potential distribution of buffer sizes and how reasonable are they? It seems like
they can vary with the statistics of a file. Since we got into trouble with rate limiting
once I just want to be sure there isn't a corner case where it can be a problem again.

Buffer sizes can vary with the statistics of the files, since CASSANDRA-8894. For data files
they are a multiple of the page size and approximately equal to the 95th percentile of the
partition size. So far we've throttled based on the buffer size and not at all for mmapped
segments, we also had fixed buffer sizes of 64k up to CASSANDRA-8894. What alternatives would
we have if we did not throttle based on the buffer size? We cannot throttle for every read
method in {{RebufferingInputStream}} or can we?

I will work on the missing tests and submit the remaining code fixes tomorrow, thanks!

> Faster sequential IO (on compaction, streaming, etc)
> ----------------------------------------------------
>                 Key: CASSANDRA-8630
>                 URL:
>             Project: Cassandra
>          Issue Type: Improvement
>          Components: Core, Tools
>            Reporter: Oleg Anastasyev
>            Assignee: Stefania
>              Labels: compaction, performance
>             Fix For: 3.x
>         Attachments: 8630-FasterSequencialReadsAndWrites.txt, cpu_load.png, flight_recorder_001_files.tar.gz,
flight_recorder_002_files.tar.gz, mmaped_uncomp_hotspot.png
> When node is doing a lot of sequencial IO (streaming, compacting, etc) a lot of CPU is
lost in calls to RAF's int read() and DataOutputStream's write(int).
> This is because default implementations of readShort,readLong, etc as well as their matching
write* are implemented with numerous calls of byte by byte read and write. 
> This makes a lot of syscalls as well.
> A quick microbench shows than just reimplementation of these methods in either way gives
8x speed increase.
> A patch attached implements<Type> and SequencialWriter.write<Type>
methods in more efficient way.
> I also eliminated some extra byte copies in CompositeType.split and ColumnNameHelper.maxComponents,
which were on my profiler's hotspot method list during tests.
> A stress tests on my laptop show that this patch makes compaction 25-30% faster  on uncompressed
sstables and 15% faster for compressed ones.
> A deployment to production shows much less CPU load for compaction. 
> (I attached a cpu load graph from one of our production, orange is niced CPU load - i.e.
compaction; yellow is user - i.e. not compaction related tasks)

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