JD - apologies if that was unrelated to your email
On Sat, Feb 25, 2012 at 4:03 PM, Matt Corgan <[EMAIL PROTECTED]> wrote:
> 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 <[EMAIL PROTECTED]>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 <[EMAIL PROTECTED]>
>> To: [EMAIL PROTECTED]
>> 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
>> 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 the CompactionSelection that it held was terribly out of date.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
>> 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...
>> On Sat, Feb 25, 2012 at 10:05 AM, Jean-Daniel Cryans <[EMAIL PROTECTED]
>> > 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