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MapReduce >> mail # user >> Re: Hadoop 1.0.4 Performance Problem


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Jon Allen 2012-11-26, 21:49
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Jie Li 2012-12-14, 01:46
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Chris Smith 2012-12-17, 17:02
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Re: Hadoop 1.0.4 Performance Problem
Hi Jon,

FYI, this issue in the fair scheduler was fixed by
https://issues.apache.org/jira/browse/MAPREDUCE-2905 for 1.1.0.
Though it is present again in MR2:
https://issues.apache.org/jira/browse/MAPREDUCE-3268

-Todd

On Wed, Nov 28, 2012 at 2:32 PM, Jon Allen <[EMAIL PROTECTED]> wrote:
> Jie,
>
> Simple answer - I got lucky (though obviously there are thing you need to
> have in place to allow you to be lucky).
>
> Before running the upgrade I ran a set of tests to baseline the cluster
> performance, e.g. terasort, gridmix and some operational jobs.  Terasort by
> itself isn't very realistic as a cluster test but it's nice and simple to
> run and is good for regression testing things after a change.
>
> After the upgrade the intention was to run the same tests and show that the
> performance hadn't degraded (improved would have been nice but not worse was
> the minimum).  When we ran the terasort we found that performance was about
> 50% worse - execution time had gone from 40 minutes to 60 minutes.  As I've
> said, terasort doesn't provide a realistic view of operational performance
> but this showed that something major had changed and we needed to understand
> it before going further.  So how to go about diagnosing this ...
>
> First rule - understand what you're trying to achieve.  It's very easy to
> say performance isn't good enough but performance can always be better so
> you need to know what's realistic and at what point you're going to stop
> tuning things.  I had a previous baseline that I was trying to match so I
> knew what I was trying to achieve.
>
> Next thing to do is profile your job and identify where the problem is.  We
> had the full job history from the before and after jobs and comparing these
> we saw that map performance was fairly consistent as were the reduce sort
> and reduce phases.  The problem was with the shuffle, which had gone from 20
> minutes pre-upgrade to 40 minutes afterwards.  The important thing here is
> to make sure you've got as much information as possible.  If we'd just kept
> the overall job time then there would have been a lot more areas to look at
> but knowing the problem was with shuffle allowed me to focus effort in this
> area.
>
> So what had changed in the shuffle that may have slowed things down.  The
> first thing we thought of was that we'd moved from a tarball deployment to
> using the RPM so what effect might this have had on things.  Our operational
> configuration compresses the map output and in the past we've had problems
> with Java compression libraries being used rather than native ones and this
> has affected performance.  We knew the RPM deployment had moved the native
> library so spent some time confirming to ourselves that these were being
> used correctly (but this turned out to not be the problem).  We then spent
> time doing some process and server profiling - using dstat to look at the
> server bottlenecks and jstack/jmap to check what the task tracker and reduce
> processes were doing.  Although not directly relevant to this particular
> problem doing this was useful just to get my head around what Hadoop is
> doing at various points of the process.
>
> The next bit was one place where I got lucky - I happened to be logged onto
> one of the worker nodes when a test job was running and I noticed that there
> weren't any reduce tasks running on the server.  This was odd as we'd
> submitted more reducers than we have servers so I'd expected at least one
> task to be running on each server.  Checking the job tracker log file it
> turned out that since the upgrade the job tracker had been submitting reduce
> tasks to only 10% of the available nodes.  A different 10% each time the job
> was run so clearly the individual task trackers were working OK but there
> was something odd going on with the task allocation.  Checking the job
> tracker log file showed that before the upgrade tasks had been fairly evenly
> distributed so something had changed.  After that it was a case of digging

Todd Lipcon
Software Engineer, Cloudera
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anand sharma 2012-12-21, 01:21