Thanks for your inputs. Fortunately, our requirements for this job =
changed, allowing me to use a combiner that ended up reducing the data =
that was being pumped to the reducers and the problem went away.=20

@Arun -- You are probably right that the number of reducers is rather =
small, given the amount of data that is being reduced. We are currently =
using the Fair scheduler and it fits our other use cases very well. The =
newer versions of Fair scheduler appear to support limits on the reducer =
pools. So this solution has to wait until we can make a switch.=20

~ Niranjan.
On Dec 9, 2011, at 8:51 PM, Chandraprakash Bhagtani wrote:

Hi Niranjan,

Your issue looks similar to . Which hadoop version
are you using?

On Sat, Dec 10, 2011 at 12:17 AM, Prashant Kommireddi
Arun, I faced the same issue and increasing the # of reducers fixed the

I was initially under the impression MR framework spills to disk if data is
too huge to keep in memory, however on extraordinarily large reduce inputs
this was not the case and the job failed on trying to assign the in-memory

private MapOutput shuffleInMemory(MapOutputLocation mapOutputLoc,
URLConnection connection,
InputStream input,
int mapOutputLength,
int compressedLength)
throws IOException, InterruptedException {
// Reserve ram for the map-output

// Copy map-output into an in-memory buffer
byte[] shuffleData = new byte[mapOutputLength];

-Prahant Kommireddi

On Fri, Dec 9, 2011 at 10:29 AM, Arun C Murthy <>
Moving to mapreduce-user@, bcc common-user@. Please use project specific


If you average as 0.5G output per-map, it's 5000 maps *0.5G -> 2.5TB over
12 reduces i.e. nearly 250G per reduce - compressed!

If you think you have 4:1 compression you are doing nearly a Terabyte per
reducer... which is way too high!

I'd recommend you bump to somewhere along 1000 reduces to get to 2.5G
(compressed) per reducer for your job. If your compression ratio is 2:1,
try 500 reduces and so on.

If you are worried about other users, use the CapacityScheduler and submit
your job to a queue with a small capacity and max-capacity to restrict your
job to 10 or 20 concurrent reduces at a given point.

On Dec 7, 2011, at 10:51 AM, Niranjan Balasubramanian wrote:


I am encountering the following out-of-memory error during the reduce
phase of a large job.
Map output copy failure : java.lang.OutOfMemoryError: Java heap space
I tried increasing the memory available using
that only helps a little. The reduce task eventually fails again. Here are
some relevant job configuration details:
1. The input to the mappers is about 2.5 TB (LZO compressed). The
mappers filter out a small percentage of the input ( less than 1%).
2. I am currently using 12 reducers and I can't increase this count by
much to ensure availability of reduce slots for other users.
3. --> -Xms512M -Xmx1536M -XX:+UseSerialGC

4. mapred.job.shuffle.input.buffer.percent --> 0.70

5. mapred.job.shuffle.merge.percent --> 0.66

6. mapred.inmem.merge.threshold --> 1000

7. I have nearly 5000 mappers which are supposed to produce LZO
compressed outputs. The logs seem to indicate that the map outputs range
between 0.3G to 0.8GB.
Does anything here seem amiss? I'd appreciate any input of what
to try. I can try different reduced values for the input buffer percent and
the merge percent. Given that the job runs for about 7-8 hours before
crashing, I would like to make some informed choices if possible.
~ Niranjan.

Thanks & Regards,
Chandra Prakash Bhagtani,
Nokia India Pvt. Ltd.

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