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HDFS >> mail # user >> How to best decide mapper output/reducer input for a huge string?


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RE: How to best decide mapper output/reducer input for a huge string?
No, I'm pretty sure the job is executing fine.. Just that the time it takes
to complete the whole process, is too much that's all..

I didn't mean to say the mapper or the reducer doesn't work.. Just that
it's very slow and I'm trying to figure out where it's happening in my
code.

Regards,
Pavan
On Sep 23, 2013 11:49 PM, "John Lilley" <[EMAIL PROTECTED]> wrote:

>  You might try creating a “stub” MR job in which the mappers produce no
> output; that would isolate the time spent reading from HBase without the
> trouble of instrumenting your code.****
>
> John****
>
> ** **
>
> ** **
>
> *From:* Pavan Sudheendra [mailto:[EMAIL PROTECTED]]
> *Sent:* Monday, September 23, 2013 3:31 AM
> *To:* [EMAIL PROTECTED]
> *Subject:* Re: How to best decide mapper output/reducer input for a huge
> string?****
>
> ** **
>
> @John, to be really frank i don't know what the limiting factor is.. It
> might be all of them or a subset of them.. Cannot tell.. ****
>
> ** **
>
> On Mon, Sep 23, 2013 at 2:58 PM, Pavan Sudheendra <[EMAIL PROTECTED]>
> wrote:****
>
> @Rahul, Yes you are right. 21 mappers are spawned where all the 21 mappers
> are functional at the same time.. Although, @Pradeep, i should do the
> compression like you say.. I'll give it a shot.. As far as i can see, i
> think i'll need to implement Writable and write out the key of the mapper
> using the specific data types instead of writing it out as a string which
> might slow the operation down..****
>
> ** **
>
> On Mon, Sep 23, 2013 at 9:29 AM, Pradeep Gollakota <[EMAIL PROTECTED]>
> wrote:****
>
> Pavan,****
>
> ** **
>
> It's hard to tell whether there's anything wrong with your design or not
> since you haven't given us specific enough details. The best thing you can
> do is instrument your code and see what is taking a long time. Rahul
> mentioned a problem that I myself have seen before, with only one region
> (or a couple) having any data. So even if you have 21 regions, only mapper
> might be doing the heavy lifting.****
>
> ** **
>
> A combiner is hugely helpful in terms of reducing the data output of
> mappers. Writing a combiner is a best practice and you should almost always
> have one. Compression can be turned on by setting the following properties
> in your job config.****
>
> <property>****
>
>     <name> mapreduce.map.output.compress </name> ****
>
>     <value> true</value> ****
>
> </property>****
>
> <property>****
>
>     <name>mapreduce.map.output.compress.codec</name>****
>
>     <value>org.apache.hadoop.io.compress.GzipCodec</value>****
>
> </property>****
>
> You can also try other compression codes such as Lzo, Snappy, Bzip2, etc.
> depending on your use cases. Gzip is really slow but gets the best
> compression ratios. Snappy/Lzo are a lot faster but don't have as good of a
> compression ratio. If your computations are CPU bound, then you'd probably
> want to use Snappy/Lzo. If your computations are I/O bound, and your CPUs
> are idle, you can use Gzip. You'll have to experiment and find the best
> settings for you. There are a lot of other tweaks that you can try to get
> the best performance out of your cluster.****
>
> ** **
>
> One of the best things you can do is to install Ganglia (or some other
> similar tool) on your cluster and monitor usage of resources while your job
> is running. This will tell you if your job is I/O bound or CPU bound.****
>
> ** **
>
> Take a look at this paper by Intel about optimizing your Hadoop cluster
> and see if that fits your deployment.
> http://software.intel.com/sites/default/files/m/f/4/3/2/f/31124-Optimizing_Hadoop_2010_final.pdf
> ****
>
> ** **
>
> If your cluster is already optimized and your job is not I/O bound, then
> there might be a problem with your algorithm and might warrant a redesign.
> ****
>
> ** **
>
> Hope this helps!****
>
> - Pradeep****
>
> ** **
>
> On Sun, Sep 22, 2013 at 8:14 PM, Rahul Bhattacharjee <
> [EMAIL PROTECTED]> wrote:****
>
> One mapper is spawned per hbase table region. You can use the admin ui of