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From "Sylvain Lebresne (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CASSANDRA-2901) Allow taking advantage of multiple cores while compacting a single CF
Date Mon, 01 Aug 2011 09:46:09 GMT

    [ https://issues.apache.org/jira/browse/CASSANDRA-2901?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13073477#comment-13073477
] 

Sylvain Lebresne commented on CASSANDRA-2901:
---------------------------------------------

Comments:
* PCI.Reducer.getCompactedRow unwraps NotifyingSSTableIterators, so their close() function
won't be called (as a side note, it doesn't seem like we ever call close() on the SSTableIdentityIterator).
* The MergeTask executor has a bounded queue (and number of threads), so tasks can be rejected.
If we want submitters to block when the queue is full and all threads are occupied, we need
to reuse the trick of DebuggableThreadPoolExecutor.
* Deserializer uses a queue of size 1 to queue up to 1 row while it deserialize the next one.
However, we already queue up rows in the MergeTask executor, so it feels like it would be
simple to use direct handoff here. It would make it easier to reason about how many rows are
in memory at any given time for instance.
* More generally, the memory blow up is (potentially) much more than the 2x (compared to mono-threaded)
in the description of this ticket. I think that right now we may have:
  ** 1 for the row being deserialized
  ** 1 for the row in the Deserialized queue
  ** nbAvailProcessor's for the row in the MergeTask executor queue (each mergeTask can contain
up to 'InMemoryCompactionLimit' worth of data)
  ** 1 for the row being merged
  Note that if we really want to get to the (roughly) 2x like in the description of this ticket,
we need direct hand-off for both the Deserializer queue *and* the merge executor. I would
be fine queuing a few tasks in the merge executor though if that can help with throughput,
but I'm not even sure it will.
* MergeTask calls removeDeleted and removeOldShards on the compacted cf, but it is also called
in the constructor of PreCompactedRow a little bit later (we should probably remove the occurrence
in PreCompactedRow as it's still multi-threaded while in the MergeTask). 
* In PCI.Reducer.getCompactedRow, in the case where inMemory == false, it seems we use the
SSTI even for rows that were already read by the Deserializer, we should use the row instead
to avoid deserializing twice.

Nitpick:
* In the CompactionIterable (and PCI), we create one Comparator<IColumnIterator> each
time instead of having a private static final one (as it is the case prior to this patch).
Granted, we don't create compaction tasks quickly enough that it would really matter much,
but it seems like a good habit to be nice with the GC :)
* This is due to this patch, but there is a "race" when updating the bytesRead, such that
a user could get a 0 bytesRead temporarily in the middle of a big compaction (and bytesRead
should probably be volatile since it won't be read for the same thread that write it).

> Allow taking advantage of multiple cores while compacting a single CF
> ---------------------------------------------------------------------
>
>                 Key: CASSANDRA-2901
>                 URL: https://issues.apache.org/jira/browse/CASSANDRA-2901
>             Project: Cassandra
>          Issue Type: Improvement
>          Components: Core
>            Reporter: Jonathan Ellis
>            Assignee: Jonathan Ellis
>            Priority: Minor
>             Fix For: 0.8.3
>
>         Attachments: 2901-v2.txt, 2901.patch
>
>
> Moved from CASSANDRA-1876:
> There are five stages: read, deserialize, merge, serialize, and write. We probably want
to continue doing read+deserialize and serialize+write together, or you waste a lot copying
to/from buffers.
> So, what I would suggest is: one thread per input sstable doing read + deserialize (a
row at a time). A thread pool (one per core?) merging corresponding rows from each input sstable.
One thread doing serialize + writing the output (this has to wait for the merge threads to
complete in-order, obviously). This should take us from being CPU bound on SSDs (since only
one core is compacting) to being I/O bound.
> This will require roughly 2x the memory, to allow the reader threads to work ahead of
the merge stage. (I.e. for each input sstable you will have up to one row in a queue waiting
to be merged, and the reader thread working on the next.) Seems quite reasonable on that front.
 You'll also want a small queue size for the serialize-merged-rows executor.
> Multithreaded compaction should be either on or off. It doesn't make sense to try to
do things halfway (by doing the reads with a
> threadpool whose size you can grow/shrink, for instance): we still have compaction threads
tuned to low priority, by default, so the impact on the rest of the system won't be very different.
Nor do we expect to have so many input sstables that we lose a lot in context switching between
reader threads.
> IMO it's acceptable to punt completely on rows that are larger than memory, and fall
back to the old non-parallel code there. I don't see any sane way to parallelize large-row
compactions.

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