Hello!
The advices that I gave were based on my experience. They helped me to
solve my issues when I was sending a lot of data to reducers.
I can give you just two advices, and hope that the really expert guys from
Hadoop and Cloudera to help you. I'm particular interested in this subject too.
1. Set up the speculative task execution to false
(mapred.reduce.tasks.speculative.execution=false)
2. Check your if you have enough space on the HDD (hadoop uses a some temporary
folders to write data - please check the documentation about it)
3. Check if you have memory leaks on your process algorithm. Run the process
algorithm in a separate program and profile it with JProfiler or other tool for
profiling. It seems to me that you have an OutOfMemory Error.
Hope that these helps.
Good luck,
Florin
--- On Mon, 11/7/11, Steve Lewis <lordjoe2...@gmail.com> wrote:
From: Steve Lewis <lordjoe2...@gmail.com>
Subject: Re: Problems with MR Job running really slowly
To: mapreduce-user@hadoop.apache.org
Date: Monday, November 7, 2011, 12:13 AM
1) I am varying both the number of mappers and reducers trying to determine
three things a) What are the options I need reducers and mappers to
- Not have mappers or reducers killed with GC overhead limit exceeded
- Minimize execution time for the cluster I use a custom
Splitter and can adjust the block size for anywhere from 1 Mapper to hundreds
of mappers -
For an 8 node cluster I am trying 8,16 and 24 reducers
2) I have been playing with io,sort,factor - using 100 for now
io.sort.mb is 400 - use mich higher values and the job will not run
3) I set child,vm.opts to Xmx3000m ( getting simulat results to using 1300
4) My mappreads a single file about 1 GB in size. Each item the splitter
delivers (about 1KB) generates tens of thousands of Key Value pair (<100bytes
per value) I can do all the work of generating the output on one machine (but
not the shuffle and sort) in about an hour on one box but my job is running
for a many hours without completing. I also got a lot of
after seeing in other tasksLost task tracker:
tracker_glados4.systemsbiology.net:localhost.localdomain/127.0.0.1:32790
Caused by: java.lang.NullPointerException
at
org.apache.hadoop.io.compress.BZip2Codec$BZip2CompressionOutputStream.write(BZip2Codec.java:200)
at
org.apache.hadoop.fs.FSDataOutputStream$PositionCache.write(FSDataOutputStream.java:41)
at java.io.DataOutputStream.writeByte(DataOutputStream.java:136)
at org.apache.hadoop.io.WritableUtils.writeVLong(WritableUtils.java:263)
at org.apache.hadoop.io.WritableUtils.writeVInt(WritableUtils.java:243)
at org.apache.hadoop.mapred.IFile$Writer.close(IFile.java:126)
at
org.apache.hadoop.mapred.MapTask$MapOutputBuffer.sortAndSpill(MapTask.java:1242)
at
org.apache.hadoop.mapred.MapTask$MapOutputBuffer.access$1800(MapTask.java:648)
at
org.apache.hadoop.mapred.MapTask$MapOutputBuffer$SpillThread.run(MapTask.java:1135)
The same job runs well with a smaller data set. Most of the reason for moving
to hadoop is to allow solutions to scale and I am very concerned at howbadly my
larger cases are doing. The documentation does not say about about
tuning parameters for my larger jobs without running into swap hell or
GC overhead limit exceeeded.
On Sun, Nov 6, 2011 at 7:11 AM, Florin P <florinp...@yahoo.com> wrote:
Hello!
How many reducers you are using?
Regarding the performance parameters, fist you can increase the size of the
io.sort.mb parameter.
It seems that you are sending a lot of amount of data to the reducer. By
increasing the value of this parameter, in the shuffle phase, the framework
will not be forced to write/spill data on the HDD that could be a reason for
slowing the process.
If you are using one reducer, then the whole data is sent over HTTP to that
reducer. Another thing that you have to think about it.
Just for a curiosity, try increase also the dfs.block.size to 128 MB. It seems
that you are using the default 64 MB. You'll get less mapper tasks.
Also, depending what configuration you have on the machine how many cores do
you have on CPU, you can increase the values for
mapred.tasktracker.{map|reduce}.tasks.maximum The maximum number of
Map/Reduce tasks, which are run simultaneously on a given TaskTracker,
individually. Defaults to 2 (2 maps and 2 reduces), but vary it depending
on your hardware
You can have a look at
http://hadoop.apache.org/common/docs/r0.20.2/cluster_setup.html.
A good book for understanding tuning parameters is Hadoop Definitive Guide by
Tom White.
Hope that the above helps.
Regards,
Florin
--- On Thu, 11/3/11, Steve Lewis <lordjoe2...@gmail.com> wrote:
From: Steve Lewis <lordjoe2...@gmail.com>
Subject: Problems with MR Job running really slowly
To: "mapreduce-user" <mapreduce-user@hadoop.apache.org>
Date: Thursday, November 3, 2011, 11:07 PM
I have a job which takes an xml file - the splitter breaks the file into tags,
the mapper parses each tag and sends the data to the reducer. I am using a
custom splitter which reads
the file looking for start and end tags.
When I run the code in the splitter and the mapper - generating separate tags
and parsing them I can read a file sized at about 500MB containing 12000 tags
on my local system in 23 seconds
When I read a file on HDFS on a local cluster I can read and parse the file in
38 seconds
When I run the same code on a eight node cluster I get 7 map tasks. The mappers
are taking 190 seconds to handle 100 tags of
which 200 millisec is parsing and almost all of the rest of the time is
in context.write. A mapper handling 1600 tags takes about 3 hours -These are
the statistics for a map task - it it true that one tag well be sent to about
300 keys but still 3 hours to write 1,5 million records and 5Gb seems
way excessive
FileSystemCountersFILE_BYTES_READ 816,935,457HDFS_BYTES_READ
439,554,860FILE_BYTES_WRITTEN 1,667,745,197
PerformanceTotalScoredScans 1,660
Map-Reduce FrameworkCombine output records0Map input records 6,134Spilled
Records 1,690,063Map output bytes 5,517,423,780Combine input records 0
Map output records 571,475
Anyone want to offer suggestions on how to tune the job better
--
Steven M. Lewis PhD4221 105th Ave NEKirkland, WA 98033
206-384-1340 (cell)
Skype lordjoe_com
--
Steven M. Lewis PhD4221 105th Ave NEKirkland, WA 98033206-384-1340 (cell)
Skype lordjoe_com
