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


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Re: Poor HBase map-reduce scan performance
Yes I have monitored GC, CPU, disk and network IO, anything else I could think of. Only the CPU usage by the regionserver is on the high side.

I mentioned data local jobs make up generally 240 of the 250 mappers (96%) - I get this information from the jobtracker. Does the JMX console give more accurate information?

On May 1, 2013, at 3:56 AM, Jean-Marc Spaggiari <[EMAIL PROTECTED]> wrote:

> @Lars, how have your calculated the 35K/row size? I'm not able to find the
> same number.
>
> @Bryan, Matt's idea below is good. With the hadoop test you always had data
> locality. Which your HBase test, maybe not. Can you take a look at the JMX
> console and tell us your locality % ? Also, over those 45 minutes, have you
> monitored the CPWIO, GC activities, etc. to see if any of those might have
> impacted the performances?
>
> JM
>
> 2013/5/1 Matt Corgan <[EMAIL PROTECTED]>
>
>> Not that it's a long-term solution, but try major-compacting before running
>> the benchmark.  If the LSM tree is CPU bound in merging HFiles/KeyValues
>> through the PriorityQueue, then reducing to a single file per region should
>> help.  The merging of HFiles during a scan is not heavily optimized yet.
>>
>>
>> On Tue, Apr 30, 2013 at 11:21 PM, lars hofhansl <[EMAIL PROTECTED]> 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.
>>>
>>> Something interesting is that my old regionserver machines had five 15k
>>> SCSI drives instead of 2 SSDs, and performance was about the same. Also,
>> my
>>> old network was 1gbit, now it is 10gbit. So neither network nor disk I/O
>>> appear to be the bottleneck. The CPU is rather high for the regionserver
>> so
>>> it seems like the best candidate to investigate. I will try profiling it
>>> tomorrow and will report back. I may revisit compression on vs off since
>>> that is adding load to the CPU.
>>>
>>> I'll also come up with a sample program that generates data similar to my
>>> table.
>>>
>>>
>>> On Apr 30, 2013, at 10:01 PM, lars hofhansl <[EMAIL PROTECTED]> wrote:
>>>
>>>> Your average row is 35k so scanner caching would not make a huge
>>> difference, although I would have expected some improvements by setting
>> it
>>> to 10 or 50 since you have a wide 10ge pipe.
>>>>
>>>> I assume your table is split sufficiently to touch all RegionServer...
>>> Do you see the same load/IO on all region servers?
>>>>
>>>> A bunch of scan improvements went into HBase since 0.94.2.
>>>> I blogged about some of these changes here:
>>> http://hadoop-hbase.blogspot.com/2012/12/hbase-profiling.html