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From Todd Lipcon <t...@cloudera.com>
Subject Re: Questions about recommendation value of the "io.sort.mb" parameter
Date Wed, 23 Jun 2010 17:49:23 GMT
Plus there's some overhead for each record of map output. Specifically, 24
bytes. So if you output 64MB worth of data, but each of your objects is only
24 bytes long itself, you need more than 128MB worth of spill space for it.
Last, the map output buffer begins spilling when it is partially full so
that more records can be collected while spill proceeds.

200MB io.sort.mb has enough headroom for most 64M input splits that don't
blow up the data a lot. Expanding much above 200M for most jobs doesn't buy
you much. Good news is it's easy to tell by looking at the logs to see how
many times the map tasks are spilling. If you're only spilling once, more
io.sort.mb will not help.

-Todd

2010/6/23 李钰 <carp84@gmail.com>

> Hi Jeff,
>
> Thanks for your quick reply. Seems my thinking is stuck on the job style
> I'm
> running. Now I'm much clearer about it.
>
> Best Regards,
> Carp
>
> 2010/6/23 Jeff Zhang <zjffdu@gmail.com>
>
> > Hi 李钰
> >
> > The size of map output depends on your Mapper class. The Mapper class
> > will do processing on the input data.
> >
> >
> >
> > 2010/6/23 李钰 <carp84@gmail.com>:
> >  > Hi Sriguru,
> > >
> > > Thanks a lot for your comments and suggestions!
> > > Here I still have some questions: since map mainly do data preparation,
> > > say split input data into KVPs, sort and partition before spill, would
> > the
> > > size of map output KVPs be much larger than the input data size? If
> not,
> > > since one map task deals with one input split, and one input split is
> > > usually 64M, the map KVPs size would be proximately 64M. Could you
> please
> > > give me some example on map output much larger than the input split? It
> > > really confuse me for some time, thanks.
> > >
> > > Others,
> > >
> > > Also badly need your help if you know about this, thanks.
> > >
> > > Best Regards,
> > > Carp
> > >
> > > 在 2010年6月23日 下午5:11,Srigurunath Chakravarthi <sriguru@yahoo-inc.com
> >写道:
> > >
> > >> Hi Carp,
> > >>  Your assumption is right that this is a per-map-task setting.
> > >> However, this buffer stores map output KVPs, not input. Therefore the
> > >> optimal value depends on how much data your map task is generating.
> > >>
> > >> If your output per map is greater than io.sort.mb, these rules of
> thumb
> > >> that could work for you:
> > >>
> > >> 1) Increase max heap of map tasks to use RAM better, but not hit swap.
> > >> 2) Set io.sort.mb to ~70% of heap.
> > >>
> > >> Overall, causing extra "spills" (because of insufficient io.sort.mb)
> is
> > >> much better than risking swapping (by setting io.sort.mb and heap too
> > >> large), in terms of relative performance penalty you will pay.
> > >>
> > >> Cheers,
> > >> Sriguru
> > >>
> > >> >-----Original Message-----
> > >> >From: 李钰 [mailto:carp84@gmail.com]
> > >> >Sent: Wednesday, June 23, 2010 12:27 PM
> > >> >To: common-dev@hadoop.apache.org
> > >> >Subject: Questions about recommendation value of the "io.sort.mb"
> > >> >parameter
> > >> >
> > >> >Dear all,
> > >> >
> > >> >Here I've got a question about the "io.sort.mb" parameter. We can
> find
> > >> >material from Yahoo! or Cloudera which recommend setting this value
> to
> > >> >200
> > >> >if the job scale is large, but I'm confused about this. As I know,
> > >> >the tasktracker will launch a child-JVM for each task, and
> > >> >“*io.sort.mb*”
> > >> >presents the buffer size in memory inside *one map task child-JVM*,
> the
> > >> >default value 100MB should be large enough because the input split
of
> > >> >one
> > >> >map task is usually 64MB, as large as the block size we usually set.
> > >> >Then
> > >> >why the recommendation of “*io.sort.mb*” is 200MB for large jobs
(and
> > >> >it
> > >> >really works)? How could the job size affect the procedure?
> > >> >Is there any fault here of my understanding? Any comment/suggestion
> > >> >will be
> > >> >highly valued, thanks in advance.
> > >> >
> > >> >Best Regards,
> > >> >Carp
> > >>
> > >
> >
> >
> >
> > --
> > Best Regards
> >
> > Jeff Zhang
> >
>



-- 
Todd Lipcon
Software Engineer, Cloudera

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