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" <sky...@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?
