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From Vladimir Rodionov <vrodio...@carrieriq.com>
Subject RE: Poor HBase random read performance
Date Mon, 01 Jul 2013 23:57:10 GMT
Varun,

LevelDB relies on Bloom filters to check only relevant sst-files. I think this is Bloom filters
for in HBase as well.
Am I wrong?

Best regards,
Vladimir Rodionov
Principal Platform Engineer
Carrier IQ, www.carrieriq.com
e-mail: vrodionov@carrieriq.com

________________________________________
From: Varun Sharma [varun@pinterest.com]
Sent: Monday, July 01, 2013 4:10 PM
To: dev@hbase.apache.org; lars hofhansl
Subject: Re: Poor HBase random read performance

Going back to leveldb vs hbase, I am not sure if we can come with a clean
way to identify HFiles containing more recent data in the wake of
compactions

I though wonder if this works with minor compactions, lets say you compact
a really old file with a new file. Now since this file's most recent
timestamp is very recent because of the new file, you look into this file,
but then retrieve something from the "old" portion of this file. So you end
with older data.

I guess one way would be just order the files by time ranges. Non
intersecting time range files can be ordered in reverse time order.
Intersecting stuff can be seeked together.

     File1
|-----------------|
                          File2
                     |---------------|
                                       File3
                             |-----------------------------|
                                                                     File4

 |--------------------|

So in this case, we seek

[File1], [File2, File3], [File4]

I think for random single key value looks (row, col)->key - this could lead
to good savings for time ordered clients (which are quite common). Unless
File1 and File4 get compacted, in which case, we always need to seek into
both.



On Mon, Jul 1, 2013 at 12:10 PM, lars hofhansl <larsh@apache.org> wrote:

> Sorry. Hit enter too early.
>
> Some discussion here:
> http://apache-hbase.679495.n3.nabble.com/keyvalue-cache-td3882628.html
> but no actionable outcome.
>
> -- Lars
> ________________________________
> From: lars hofhansl <larsh@apache.org>
> To: "dev@hbase.apache.org" <dev@hbase.apache.org>
> Sent: Monday, July 1, 2013 12:05 PM
> Subject: Re: Poor HBase random read performance
>
>
> This came up a few times before.
>
>
>
> ________________________________
> From: Vladimir Rodionov <vrodionov@carrieriq.com>
> To: "dev@hbase.apache.org" <dev@hbase.apache.org>; lars hofhansl <
> larsh@apache.org>
> Sent: Monday, July 1, 2013 11:08 AM
> Subject: RE: Poor HBase random read performance
>
>
> I would like to remind that in original BigTable's design  there is scan
> cache to take care of random reads and this
> important feature is still missing in HBase.
>
> Best regards,
> Vladimir Rodionov
> Principal Platform Engineer
> Carrier IQ, www.carrieriq.com
> e-mail: vrodionov@carrieriq.com
>
> ________________________________________
> From: lars hofhansl [larsh@apache.org]
> Sent: Saturday, June 29, 2013 3:24 PM
> To: dev@hbase.apache.org
> Subject: Re: Poor HBase random read performance
>
> Should also say that random reads this way are somewhat of a worst case
> scenario.
>
> If the working set is much larger than the block cache and the reads are
> random, then each read will likely have to bring in an entirely new block
> from the OS cache,
> even when the KVs are much smaller than a block.
>
> So in order to read a (say) 1k KV HBase needs to bring 64k (default block
> size) from the OS cache.
> As long as the dataset fits into the block cache this difference in size
> has no performance impact, but as soon as the dataset does not fit, we have
> to bring much more data from the OS cache than we're actually interested in.
>
> Indeed in my test I found that HBase brings in about 60x the data size
> from the OS cache (used PE with ~1k KVs). This can be improved with smaller
> block sizes; and with a more efficient way to instantiate HFile blocks in
> Java (which we need to work on).
>
>
> -- Lars
>
> ________________________________
> From: lars hofhansl <larsh@apache.org>
> To: "dev@hbase.apache.org" <dev@hbase.apache.org>
> Sent: Saturday, June 29, 2013 3:09 PM
> Subject: Re: Poor HBase random read performance
>
>
> I've seen the same bad performance behavior when I tested this on a real
> cluster. (I think it was in 0.94.6)
>
>
> Instead of en/disabling the blockcache, I tested sequential and random
> reads on a data set that does not fit into the (aggregate) block cache.
> Sequential reads were drastically faster than Random reads (7 vs 34
> minutes), which can really only be explained with the fact that the next
> get will with high probability hit an already cached block, whereas in the
> random read case it likely will not.
>
> In the RandomRead case I estimate that each RegionServer brings in between
> 100 and 200mb/s from the OS cache. Even at 200mb/s this would be quite
> slow.I understand that performance is bad when index/bloom blocks are not
> cached, but bringing in data blocks from the OS cache should be faster than
> it is.
>
>
> So this is something to debug.
>
> -- Lars
>
>
>
> ________________________________
> From: Varun Sharma <varun@pinterest.com>
> To: "dev@hbase.apache.org" <dev@hbase.apache.org>
> Sent: Saturday, June 29, 2013 12:13 PM
> Subject: Poor HBase random read performance
>
>
> Hi,
>
> I was doing some tests on how good HBase random reads are. The setup is
> consists of a 1 node cluster with dfs replication set to 1. Short circuit
> local reads and HBase checksums are enabled. The data set is small enough
> to be largely cached in the filesystem cache - 10G on a 60G machine.
>
> Client sends out multi-get operations in batches to 10 and I try to measure
> throughput.
>
> Test #1
>
> All Data was cached in the block cache.
>
> Test Time = 120 seconds
> Num Read Ops = 12M
>
> Throughput = 100K per second
>
> Test #2
>
> I disable block cache. But now all the data is in the file system cache. I
> verify this by making sure that IOPs on the disk drive are 0 during the
> test. I run the same test with batched ops.
>
> Test Time = 120 seconds
> Num Read Ops = 0.6M
> Throughput = 5K per second
>
> Test #3
>
> I saw all the threads are now stuck in idLock.lockEntry(). So I now run
> with the lock disabled and the block cache disabled.
>
> Test Time = 120 seconds
> Num Read Ops = 1.2M
> Throughput = 10K per second
>
> Test #4
>
> I re enable block cache and this time hack hbase to only cache Index and
> Bloom blocks but data blocks come from File System cache.
>
> Test Time = 120 seconds
> Num Read Ops = 1.6M
> Throughput = 13K per second
>
> So, I wonder how come such a massive drop in throughput. I know that HDFS
> code adds tremendous overhead but this seems pretty high to me. I use
> 0.94.7 and cdh 4.2.0
>
> Thanks
> Varun
>
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