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From Matt Corgan <mcor...@hotpads.com>
Subject Re: Follow-up to my HBASE-4365 testing
Date Sun, 26 Feb 2012 00:03:01 GMT
Yeah.  You would also want a mechanism to prevent queuing the same CF
multiple times, and probably want the completion of one compaction to
trigger a check to see if it should queue another.

A possibly different architecture than the current style of queues would be
to have each Store (all open in memory) keep a compactionPriority score up
to date after events like flushes, compactions, schema changes, etc.  Then
you create a "CompactionPriorityComparator implements Comparator<Store>"
and stick all the Stores into a PriorityQueue.  The async compaction
threads would keep pulling off the head of that queue as long as the head
has compactionPriority > X.


On Sat, Feb 25, 2012 at 3:44 PM, lars hofhansl <lhofhansl@yahoo.com> wrote:

> Interesting. So a compaction request would hold no information beyond the
> CF, really,
> but is just a promise to do a compaction as soon as possible.
> I agree with Ted, we should explore this in a jira.
>
> -- Lars
>
>
> ----- Original Message -----
> From: Matt Corgan <mcorgan@hotpads.com>
> To: dev@hbase.apache.org
> Cc:
> Sent: Saturday, February 25, 2012 3:18 PM
> Subject: Re: Follow-up to my HBASE-4365 testing
>
> I've been meaning to look into something regarding compactions for a while
> now that may be relevant here.  It could be that this is already how it
> works, but just to be sure I'll spell out my suspicions...
>
> I did a lot of large uploads when we moved to .92.  Our biggest dataset is
> time series data (partitioned 16 ways with a row prefix).  The actual
> inserting and flushing went extremely quickly, and the parallel compactions
> were churning away.  However, when the compactions inevitably started
> falling behind I noticed a potential problem.  The compaction queue would
> get up to, say, 40, which represented, say, an hour's worth of requests.
> The problem was that by the time a compaction request started executing,
> the CompactionSelection that it held was terribly out of date.  It was
> compacting a small selection (3-5) of the 50 files that were now there.
> Then the next request would compact another (3-5), etc, etc, until the
> queue was empty.  It would have been much better if a CompactionRequest
> decided what files to compact when it got to the head of the queue.  Then
> it could see that there are now 50 files needing compacting and to possibly
> compact the 30 smallest ones, not just 5.  When the insertions were done
> after many hours, I would have preferred it to do one giant major
> compaction, but it sat there and worked through it's compaction queue
> compacting all sorts of different combinations of files.
>
> Said differently, it looks like .92 picks the files to compact at
> compaction request time rather than compaction execution time which is
> problematic when these times grow far apart.  Is that the case?  Maybe
> there are some other effects that are mitigating it...
>
> Matt
>
> On Sat, Feb 25, 2012 at 10:05 AM, Jean-Daniel Cryans <jdcryans@apache.org
> >wrote:
>
> > Hey guys,
> >
> > So in HBASE-4365 I ran multiple uploads and the latest one I reported
> > was a 5TB import on 14 RS and it took 18h with Stack's patch. Now one
> > thing we can see is that apart from some splitting, there's a lot of
> > compacting going on. Stack was wondering exactly how much that IO
> > costs us, so we devised a test where we could upload 5TB with 0
> > compactions. Here are the results:
> >
> > The table was pre-split with 14 regions, 1 per region server.
> > hbase.hstore.compactionThreshold=100
> > hbase.hstore.blockingStoreFiles=110
> > hbase.regionserver.maxlogs=64  (the block size is 128MB)
> > hfile.block.cache.size=0.05
> > hbase.regionserver.global.memstore.lowerLimit=0.40
> > hbase.regionserver.global.memstore.upperLimit=0.74
> > export HBASE_REGIONSERVER_OPTS="$HBASE_JMX_BASE -Xmx14G
> > -XX:CMSInitiatingOccupancyFraction=75 -XX:NewSize=256m
> > -XX:MaxNewSize=256m"
> >
> > The table had:
> >  MAX_FILESIZE => '549755813888', MEMSTORE_FLUSHSIZE => '549755813888'
> >
> > Basically what I'm trying to do is to never block and almost always be
> > flushing. You'll probably notice the big difference between the lower
> > and upper barriers and think "le hell?", it's because it takes so long
> > to flush that you have to have enough room to take on more data while
> > this is happening (and we are able to flush faster than we take on
> > write).
> >
> > The test reports the following:
> > Wall time: 34984.083 s
> > Aggregate Throughput: 156893.07 queries/s
> > Aggregate Throughput: 160030935.29 bytes/s
> >
> > That's 2x faster than when we wait for compactions and splits, not too
> > bad but I'm pretty sure we can do better:
> >
> >  - The QPS was very uneven, it seems that when it's flushing it takes
> > a big toll and queries drop to ~100k/s while the rest of the time it's
> > more like 200k/s. Need to figure out what's going there and if it's
> > really just caused by flush-related IO.
> >  - The logs were rolling every 6 seconds and since this takes a global
> > write lock, I can see how we could be slowing down a lot across 14
> > machines.
> >  - The load was a bit uneven, I miscalculated my split points and the
> > last region always had 2-3k more queries per second.
> >
> > Stay tuned for more.
> >
> > J-D
> >
>
>

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