FAQ
Arun, I faced the same issue and increasing the # of reducers fixed the
problem.

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
buffer

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 wrote:

Moving to mapreduce-user@, bcc common-user@. Please use project specific
lists.

Niranjan,

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.

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

All

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
at
org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.shuffleInMemory(ReduceTask.java:1669)
at
org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.getMapOutput(ReduceTask.java:1529)
at
org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.copyOutput(ReduceTask.java:1378)
at
org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.run(ReduceTask.java:1310)
I tried increasing the memory available using mapped.child.java.opts but
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. mapred.child.java.opts --> -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 settings
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.
Thanks.
~ Niranjan.

Search Discussions

Discussion Posts

Previous

Follow ups

Related Discussions

Discussion Navigation
viewthread | post
posts ‹ prev | 7 of 9 | next ›
Discussion Overview
groupcommon-user @
categorieshadoop
postedDec 7, '11 at 6:51p
activeDec 11, '11 at 1:32a
posts9
users5
websitehadoop.apache.org...
irc#hadoop

People

Translate

site design / logo © 2022 Grokbase