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From Robert Evans <ev...@yahoo-inc.com>
Subject Re: Help me with architecture of a somewhat non-trivial mapreduce implementation
Date Fri, 20 Apr 2012 15:16:46 GMT
You could also use the NLineInputFormat which will launch 1 mapper for every N (configurable)
lines of input.


On 4/20/12 9:48 AM, "Sky" <skyusc@hotmail.com> wrote:

Thanks! That helped!



-----Original Message-----
From: Michael Segel
Sent: Thursday, April 19, 2012 9:38 PM
To: common-user@hadoop.apache.org
Subject: Re: Help me with architecture of a somewhat non-trivial mapreduce
implementation

If the file is small enough you could read it in to a java object like a
list and write your own input format that takes a list object as its input
and then lets you specify the number of mappers.

On Apr 19, 2012, at 11:34 PM, Sky wrote:

> My file for the input to mapper is very small - as all it has is urls to
> list of manifests. The task for mappers is to fetch each manifest, and
> then fetch files using urls from the manifests and then process them.
> Besides passing around lists of files, I am not really accessing the disk.
> It should be RAM, network, and CPU (unzip, parsexml,extract attributes).
>
> So is my only choice to break the input file and submit multiple files (if
> I have 15 cores, I should split the file with urls to 15 files? also how
> does it look in code?)? The two drawbacks are - some cores might finish
> early and stay idle, and I don't know how to deal with dynamically
> increasing/decreasing cores.
>
> Thx
> - Sky
>
> -----Original Message----- From: Michael Segel
> Sent: Thursday, April 19, 2012 8:49 PM
> To: common-user@hadoop.apache.org
> Subject: Re: Help me with architecture of a somewhat non-trivial mapreduce
> implementation
>
> How 'large' or rather in this case small is your file?
>
> If you're on a default system, the block sizes are 64MB. So if your file
> ~<= 64MB, you end up with 1 block, and you will only have 1 mapper.
>
>
> On Apr 19, 2012, at 10:10 PM, Sky wrote:
>
>> Thanks for your reply.  After I sent my email, I found a fundamental
>> defect - in my understanding of how MR is distributed. I discovered that
>> even though I was firing off 15 COREs, the map job - which is the most
>> expensive part of my processing was run only on 1 core.
>>
>> To start my map job, I was creating a single file with following data:
>> 1 storage:/root/1.manif.txt
>> 2 storage:/root/2.manif.txt
>> 3 storage:/root/3.manif.txt
>> ...
>> 4000 storage:/root/4000.manif.txt
>>
>> I thought that each of the available COREs will be assigned a map job
>> from top down from the same file one at a time, and as soon as one CORE
>> is done, it would get the next map job. However, it looks like I need to
>> split the file into the number of times. Now while that's clearly trivial
>> to do, I am not sure how I can detect at runtime how many splits I need
>> to do, and also to deal with adding new CORES at runtime. Any
>> suggestions? (it doesn't have to be a file, it can be a list, etc).
>>
>> This all would be much easier to debug, if somehow I could get my log4j
>> logs for my mappers and reducers. I can see log4j for my main launcher,
>> but not sure how to enable it for mappers and reducers.
>>
>> Thx
>> - Akash
>>
>>
>> -----Original Message----- From: Robert Evans
>> Sent: Thursday, April 19, 2012 2:08 PM
>> To: common-user@hadoop.apache.org
>> Subject: Re: Help me with architecture of a somewhat non-trivial
>> mapreduce implementation
>>
>> From what I can see your implementation seems OK, especially from a
>> performance perspective. Depending on what storage: is it is likely to be
>> your bottlekneck, not the hadoop computations.
>>
>> Because you are writing files directly instead of relying on Hadoop to do
>> it for you, you may need to deal with error cases that Hadoop will
>> normally hide from you, and you will not be able to turn on speculative
>> execution. Just be aware that a map or reduce task may have problems in
>> the middle, and be relaunched.  So when you are writing out your updated
>> manifest be careful to not replace the old one until the new one is
>> completely ready and will not fail, or you may lose data.  You may also
>> need to be careful in your reduce if you are writing directly to the file
>> there too, but because it is not a read modify write, but just a write it
>> is not as critical.
>>
>> --Bobby Evans
>>
>> On 4/18/12 4:56 PM, "Sky USC" <skyusc@hotmail.com> wrote:
>>
>>
>>
>>
>> Please help me architect the design of my first significant MR task
>> beyond "word count". My program works well. but I am trying to optimize
>> performance to maximize use of available computing resources. I have 3
>> questions at the bottom.
>>
>> Project description in an abstract sense (written in java):
>> * I have MM number of MANIFEST files available on
>> storage:/root/1.manif.txt to 4000.manif.txt
>>   * Each MANIFEST in turn contains varilable number "EE" of URLs to
>> EBOOKS (range could be 10000 - 50,000 EBOOKS urls per MANIFEST) -- stored
>> on storage:/root/1.manif/1223.folder/5443.Ebook.ebk
>> So we are talking about millions of ebooks
>>
>> My task is to:
>> 1. Fetch each ebook, and obtain a set of 3 attributes per ebook (example:
>> publisher, year, ebook-version).
>> 2. Update each of the EBOOK entry record in the manifest - with the 3
>> attributes (eg: ebook 1334 -> publisher=aaa year=bbb, ebook-version=2.01)
>> 3. Create a output file such that the named
>> "<publisher>_<year>_<ebook-version>"  contains a list of all "ebook
urls"
>> that met that criteria.
>> example:
>> File "storage:/root/summary/RANDOMHOUSE_1999_2.01.txt" contains:
>> storage:/root/1.manif/1223.folder/2143.Ebook.ebk
>> storage:/root/2.manif/2133.folder/5449.Ebook.ebk
>> storage:/root/2.manif/2133.folder/5450.Ebook.ebk
>> etc..
>>
>> and File "storage:/root/summary/PENGUIN_2001_3.12.txt" contains:
>> storage:/root/19.manif/2223.folder/4343.Ebook.ebk
>> storage:/root/13.manif/9733.folder/2149.Ebook.ebk
>> storage:/root/21.manif/3233.folder/1110.Ebook.ebk
>>
>> etc
>>
>> 4. finally, I also want to output statistics such that:
>> <publisher>_<year>_<ebook-version>  <COUNT_OF_URLs>
>> PENGUIN_2001_3.12     250,111
>> RANDOMHOUSE_1999_2.01  11,322
>> etc
>>
>> Here is how I implemented:
>> * My launcher gets list of MM manifests
>> * My Mapper gets one manifest.
>> --- It reads the manifest, within a WHILE loop,
>>  --- fetches each EBOOK,  and obtain attributes from each ebook,
>>  --- updates the manifest for that ebook
>>  --- context.write(new Text("RANDOMHOUSE_1999_2.01"), new
>> Text("storage:/root/1.manif/1223.folder/2143.Ebook.ebk"))
>> --- Once all ebooks in the manifest are read, it saves the updated
>> Manifest, and exits
>> * My Reducer gets the "RANDOMHOUSE_1999_2.01" and a list of ebooks urls.
>> --- It writes a new file
>> "storage:/root/summary/RANDOMHOUSE_1999_2.01.txt" with all the storage
>> urls for the ebooks
>> --- It also does a context.write(new Text("RANDOMHOUSE_1999_2.01"), new
>> IntWritable(SUM_OF_ALL_EBOOK_URLS_FROM_THE_LIST))
>>
>> As I mentioned, its working. I launch it on 15 elastic instances. I have
>> three questions:
>> 1. Is this the best way to implement the MR logic?
>> 2. I dont know if each of the instances is getting one task or multiple
>> tasks simultaneously for the MAP portion. If it is not getting multiple
>> MAP tasks, should I go with the route of "multithreaded" reading of
>> ebooks from each manifest? Its not efficient to read just one ebook at a
>> time per machine. Is "Context.write()" threadsafe?
>> 3. I can see log4j logs for main program, but no visibility into logs for
>> Mapper or Reducer. Any idea?
>>
>>
>>
>>
>>
>
>



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