Thanks for sharing this, Matt.
Do you mind opening a Jira for your suggestion ?
On Feb 25, 2012, at 3:18 PM, Matt Corgan <[EMAIL PROTECTED]> wrote:
> 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 the CompactionSelection that it held was terribly out of date.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...
> On Sat, Feb 25, 2012 at 10:05 AM, Jean-Daniel Cryans <[EMAIL PROTECTED]>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.regionserver.maxlogs=64 (the block size is 128MB)
>> export HBASE_REGIONSERVER_OPTS="$HBASE_JMX_BASE -Xmx14G
>> -XX:CMSInitiatingOccupancyFraction=75 -XX:NewSize=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
>> 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