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Date: Mon, 17 Dec 2012 19:28:14 -0800
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Subject: Re: HBase Map/Reduce Data Ingest Performance
From: Ted Yu <yuzhihong@gmail.com>
To: dev@hbase.apache.org
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Experts from Cloudera would be more familiar with security in
hadoop-0.20.2-cdh3u

If you can show us the exception (using pastebin e.g.), that would help
find the root cause.

Cheers

On Mon, Dec 17, 2012 at 6:30 PM, Upender K. Nimbekar <
upender.kumar@gmail.com> wrote:

> Thanks ! I'm calling doBulkLoad() from mapper cleanup() method. But running
> into permission issues while hbase user tries to import Hfile into Hbase.
> Not sure, if there is way to change the target HDFS file permission via
> HFileOutputFormat.
>
>
> On Mon, Dec 17, 2012 at 7:52 PM, Ted Yu <yuzhihong@gmail.com> wrote:
>
> > I think second approach is better.
> >
> > Cheers
> >
> > On Mon, Dec 17, 2012 at 11:11 AM, Upender K. Nimbekar <
> > upender.kumar@gmail.com> wrote:
> >
> > > Sure. I can try that. Just curious, out of these 2 strategies, which
> one
> > do
> > > you thin is better ? Do you have any experience of trying one or the
> > other
> > > ?
> > >
> > > Thanks
> > > Upen
> > >
> > > On Mon, Dec 17, 2012 at 12:45 PM, Ted Yu <yuzhihong@gmail.com> wrote:
> > >
> > > > Thanks for sharing your experiences.
> > > >
> > > > Have you considered upgrading to HBase 0.92 or 0.94 ?
> > > > There have been several bug fixes / enhancements
> > > > to LoadIncrementHFiles.bulkLoad() API in newer HBase releases.
> > > >
> > > > Cheers
> > > >
> > > > On Mon, Dec 17, 2012 at 7:34 AM, Upender K. Nimbekar <
> > > > upender.kumar@gmail.com> wrote:
> > > >
> > > > > Hi All,
> > > > > I have question about improving the Map / Reduce job performance
> > while
> > > > > ingesting huge amount of data into Hbase using HFileOutputFormat.
> > Here
> > > is
> > > > > what we are using:
> > > > >
> > > > > 1) *Cloudera hadoop-0.20.2-cdh3u*
> > > > > 2) *hbase-0.90.40cdh3u2*
> > > > >
> > > > > I've used 2 different strategies as described below:
> > > > >
> > > > > *Strategy#1:* PreSplit the number of regions with 10 regions per
> > region
> > > > > server. And then subsequently kick off the hadoop job with
> > > > > HFileOutputFormat.configureIncrementLoad. This mchanism does create
> > > > reduce
> > > > > tasks equal to the number of regions * 10. We used the "hash" of
> each
> > > > > record as the Key to Mapoutput. This process resulted in each
> mapper
> > > > finish
> > > > > process in accepetable amount of time. But the reduce task takes
> > > forever
> > > > to
> > > > > finish. We found that first the copy/shuffle process too
> condierable
> > > > amoun
> > > > > of time and then the sort process took foreever to finish.
> > > > > We tried to address this issue by constructing the key as
> > > > > "fixedhash1"_"hash2" where "fixedhash1" is fixed for all the
> records
> > > of a
> > > > > gven mapper. The idea was to reduce shuffling / copying from each
> > > mapper.
> > > > > But even this solution didn't save us anytime and the reduce step
> > took
> > > > > significant amount to finish. I played with adjusting the number of
> > > > > pre-split regions in both dierctions but to no avail.
> > > > > This led us to move to Strategy#2 we got rid of the reduce step.
> > > > >
> > > > > *QUESTION:* Is there anything I could've done better in this
> strategy
> > > to
> > > > > make reduce step finish faster ? Do I need to produce Row Keys
> > > > differently
> > > > > than "hash1"_"hash2" of the text ? Is it a known issue with CDH3 or
> > > > > Hbase0.90 ? Please help me troubleshoot.
> > > > >
> > > > > Strategy#2: PreSplit the number of regions with 10 regions per
> region
> > > > > server. And then subsequently kick off the hadoop job with
> > > > > HFileOutputFormat.configureIncrementLoad. But set the number of
> > > reducer =
> > > > > 0. In this strategy (current), I pre-sorted all the mapper input
> > using
> > > > > Treeset before writing to output. With No. of reducers = 0, this
> > > resulted
> > > > > the mapper to write directly to HFiles. This was cool because
> > > map/reduce
> > > > > (no reduce phase actually) finished very fast and we noticed the
> > HFiles
> > > > got
> > > > > written very quickly. Then I used *
> > > > > hbase.utils.LoadIncrementHFiles.bulkLoad()* API to move HFiles into
> > > > Hbase.
> > > > > I called this method on successful completon of the job in the
> > > > > driver class. This is working much better than the Strategy#1 in
> > terms
> > > of
> > > > > performance. But the bulkLoad() call in the driver sometimes takes
> > > longer
> > > > > if there is huge amount of data.
> > > > >
> > > > > *QUESTION:* Is there anyway to make the bulkLoad() run faster ?
> Can I
> > > > call
> > > > > this api from Mapper directly, instead of waiting the whole job to
> > > finish
> > > > > first?  I've used used habse "completebulkload" utilty but it has
> two
> > > > > issues with it. First, I do not see any performance improvement
> with
> > > it.
> > > > > Second, it needs to be run separately from Hadoop Job driver class
> > and
> > > we
> > > > > wanted to integrate both the piece. So we used
> > > > > *hbase.utils.LoadIncrementHFiles.bulkLoad().
> > > >  > *
> > > > > Also, we used Hbase RegionSplitter to pre-split the regions. But
> > hbase
> > > > 0.90
> > > > > version doesn't have the option to pass ALGORITHM. Is that
> something
> > we
> > > > > need to worry about?
> > > > >
> > > > > Please help me point in the right direction to address this
> problem.
> > > > >
> > > > > Thanks
> > > > > Upen
> > > > >
> > > >
> > >
> >
>

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