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HBase >> mail # user >> Poor HBase map-reduce scan performance


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Re: Poor HBase map-reduce scan performance
Interesting. If you can try 0.94.7 (but it'll probably not have changed that much from 0.94.4)
Do you have enabled one of the block encoders (FAST_DIFF, etc)? If so, try without. They currently need to reallocate a ByteBuffer for each single KV.
(Sine you see ScannerV2 rather than EncodedScannerV2 you probably have not enabled encoding, just checking).
And do you have a stack trace for the ByteBuffer.allocate(). That is a strange one since it never came up in my profiling (unless you enabled block encoding).
(You can get traces from VisualVM by creating a snapshot, but you'd have to drill in to find the allocate()).
During normal scanning (again, without encoding) there should be no allocation happening except for blocks read from disk (and they should all be the same size, thus allocation should be cheap).

-- Lars

________________________________
 From: Bryan Keller <[EMAIL PROTECTED]>
To: [EMAIL PROTECTED]
Sent: Thursday, May 2, 2013 10:54 AM
Subject: Re: Poor HBase map-reduce scan performance
 

I ran one of my regionservers through VisualVM. It looks like the top hot spots are HFileReaderV2$ScannerV2.getKeyValue() and ByteBuffer.allocate(). It appears at first glance that memory allocations may be an issue. Decompression was next below that but less of an issue it seems.

Would changing the block size, either HDFS or HBase, help here?

Also, if anyone has tips on how else to profile, that would be appreciated. VisualVM can produce a lot of noise that is hard to sift through.
On May 1, 2013, at 9:49 PM, Bryan Keller <[EMAIL PROTECTED]> wrote:

> I used exactly 0.94.4, pulled from the tag in subversion.
>
> On May 1, 2013, at 9:41 PM, lars hofhansl <[EMAIL PROTECTED]> wrote:
>
>> Hmm... Did you actually use exactly version 0.94.4, or the latest 0.94.7.
>> I would be very curious to see profiling data.
>>
>> -- Lars
>>
>>
>>
>> ----- Original Message -----
>> From: Bryan Keller <[EMAIL PROTECTED]>
>> To: "[EMAIL PROTECTED]" <[EMAIL PROTECTED]>
>> Cc:
>> Sent: Wednesday, May 1, 2013 6:01 PM
>> Subject: Re: Poor HBase map-reduce scan performance
>>
>> I tried running my test with 0.94.4, unfortunately performance was about the same. I'm planning on profiling the regionserver and trying some other things tonight and tomorrow and will report back.
>>
>> On May 1, 2013, at 8:00 AM, Bryan Keller <[EMAIL PROTECTED]> wrote:
>>
>>> Yes I would like to try this, if you can point me to the pom.xml patch that would save me some time.
>>>
>>> On Tuesday, April 30, 2013, lars hofhansl wrote:
>>> If you can, try 0.94.4+; it should significantly reduce the amount of bytes copied around in RAM during scanning, especially if you have wide rows and/or large key portions. That in turns makes scans scale better across cores, since RAM is shared resource between cores (much like disk).
>>>
>>>
>>> It's not hard to build the latest HBase against Cloudera's version of Hadoop. I can send along a simple patch to pom.xml to do that.
>>>
>>> -- Lars
>>>
>>>
>>>
>>> ________________________________
>>>  From: Bryan Keller <[EMAIL PROTECTED]>
>>> To: [EMAIL PROTECTED]
>>> Sent: Tuesday, April 30, 2013 11:02 PM
>>> Subject: Re: Poor HBase map-reduce scan performance
>>>
>>>
>>> The table has hashed keys so rows are evenly distributed amongst the regionservers, and load on each regionserver is pretty much the same. I also have per-table balancing turned on. I get mostly data local mappers with only a few rack local (maybe 10 of the 250 mappers).
>>>
>>> Currently the table is a wide table schema, with lists of data structures stored as columns with column prefixes grouping the data structures (e.g. 1_name, 1_address, 1_city, 2_name, 2_address, 2_city). I was thinking of moving those data structures to protobuf which would cut down on the number of columns. The downside is I can't filter on one value with that, but it is a tradeoff I would make for performance. I was also considering restructuring the table into a tall table.
NEW: Monitor These Apps!
elasticsearch, apache solr, apache hbase, hadoop, redis, casssandra, amazon cloudwatch, mysql, memcached, apache kafka, apache zookeeper, apache storm, ubuntu, centOS, red hat, debian, puppet labs, java, senseiDB