subject:"OOM Error Map output copy."

Re: OOM Error Map output copy.

2011-12-10 Thread Niranjan Balasubramanian

All

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
https://issues.apache.org/jira/browse/MAPREDUCE-1182 . Which hadoop version
are you using?

On Sat, Dec 10, 2011 at 12:17 AM, Prashant Kommireddi
prash1...@gmail.comwrote:

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 a...@hortonworks.com
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.

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

RE: OOM Error Map output copy.

2011-12-09 Thread Devaraj K

Can you try increasing the max heap memory whether still you face the
problem.



Devaraj K 

-Original Message-
From: Niranjan Balasubramanian [mailto:niran...@cs.washington.edu] 
Sent: Thursday, December 08, 2011 11:09 PM
To: common-user@hadoop.apache.org
Subject: Re: OOM Error Map output copy.

I am using version 0.20.203.

Thanks
~ Niranjan.
On Dec 8, 2011, at 9:26 AM, Niranjan Balasubramanian wrote:

 Devaraj
 
 These are indeed the actual settings I copied over from the job.xml. 
 
 ~ Niranjan.
 On Dec 8, 2011, at 12:10 AM, Devaraj K wrote:
 
 Hi Niranjan,
 
  Every thing looks ok as per the info you have given. Can you check
 in the job.xml file whether these child opts reflecting or any thing else
is
 overwriting this config.
  
 3. mapred.child.java.opts -- -Xms512M -Xmx1536M -XX:+UseSerialGC
 
 
 and also can you tell me which version of hadoop using?
 
 
 Devaraj K 
 
 -Original Message-
 From: Niranjan Balasubramanian [mailto:niran...@cs.washington.edu] 
 Sent: Thursday, December 08, 2011 12:21 AM
 To: common-user@hadoop.apache.org
 Subject: OOM Error Map output copy.
 
 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.shuffleInMe
 mory(ReduceTask.java:1669)
  at

org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.getMapOutpu
 t(ReduceTask.java:1529)
  at

org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.copyOutput(
 ReduceTask.java:1378)
  at

org.apache.hadoop.mapred.ReduceTask$ReduceCopier$MapOutputCopier.run(ReduceT
 ask.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.

Re: OOM Error Map output copy.

2011-12-09 Thread Arun C Murthy

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.

Re: OOM Error Map output copy.

2011-12-09 Thread Prashant Kommireddi

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

// 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 a...@hortonworks.com 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.

Re: OOM Error Map output copy.

2011-12-09 Thread Chandraprakash Bhagtani