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From Joey Echeverria <j...@cloudera.com>
Subject Re: Stability issue - dead DN's
Date Wed, 11 May 2011 18:51:09 GMT
Which version of hadoop are you running?

I'm pretty sure the problem is you're over committing your RAM. Hadoop
really doesn't like swapping. I would try setting your
mapred.child.java.opts to


On Wed, May 11, 2011 at 2:23 AM, Evert Lammerts <Evert.Lammerts@sara.nl> wrote:
> Hi list,
> I notice that whenever our Hadoop installation is put under a heavy load we lose one
or two (on a total of five) datanodes. This results in IOExceptions, and affects the overall
performance of the job being run. Can anybody give me advise or best practices on a different
configuration to increase the stability? Below I've included the specs of the cluster, the
hadoop related config and an example of when which things go wrong. Any help is very much
appreciated, and if I can provide any other info please let me know.
> Cheers,
> Evert
> == What goes wrong, and when ==
> See attached a screenshot of Ganglia when the cluster is under load of a single job.
This job:
> * reads ~1TB from HDFS
> * writes ~200GB to HDFS
> * runs 288 Mappers and 35 Reducers
> When the job runs it takes all available Map and Reduce slots. The system starts swapping
and there is a short time interval during which most cores are in WAIT. After that the job
really starts running. At around half way, one or two datanodes become unreachable and are
marked as dead nodes. The amount of under-replicated blocks becomes huge. Then some "java.io.IOException:
Could not obtain block" are thrown in Mappers. The job does manage to finish successfully
after around 3.5 hours, but my fear is that when we make the input much larger - which we
want - the system becomes too unstable to finish the job.
> Maybe worth mentioning - never know what might help diagnostics.  We notice that memory
usage becomes less when we switch our keys from Text to LongWritable. Also, the Mappers are
done in a fraction of the time. However, this for some reason results in much more network
traffic and makes Reducers extremely slow. We're working on figuring out what causes this.
> == The cluster ==
> We have a cluster that consists of 6 Sun Thumpers running Hadoop 0.20.2 on CentOS 5.5.
One of them acts as NN and JT, the other 5 run DN's and TT's. Each node has:
> * 16GB RAM
> * 32GB swapspace
> * 4 cores
> * 11 LVM's of 4 x 500GB disks (2TB in total) for HDFS
> * non-HDFS stuff on separate disks
> * a 2x1GE bonded network interface for interconnects
> * a 2x1GE bonded network interface for external access
> I realize that this is not a well balanced system, but it's what we had available for
a prototype environment. We're working on putting together a specification for a much larger
production environment.
> == Hadoop config ==
> Here some properties that I think might be relevant:
> fs.inmemory.size.mb: 200
> mapreduce.task.io.sort.factor: 100
> mapreduce.task.io.sort.mb: 200
> # 1024*1024*4 MB, blocksize of the LVM's
> io.file.buffer.size: 4194304
> # 1024*1024*4*32 MB, 32 times the blocksize of the LVM's
> dfs.block.size: 134217728
> # Only 5 DN's, but this shouldn't hurt
> dfs.namenode.handler.count: 40
> # This got rid of the occasional "Could not obtain block"'s
> dfs.datanode.max.xcievers: 4096
> mapred.tasktracker.map.tasks.maximum: 4
> mapred.tasktracker.reduce.tasks.maximum: 4
> mapred.child.java.opts: -Xmx2560m
> mapreduce.reduce.shuffle.parallelcopies: 20
> mapreduce.map.java.opts: -Xmx512m
> mapreduce.reduce.java.opts: -Xmx512m
> # Compression codecs are configured and seem to work fine
> mapred.compress.map.output: true
> mapred.map.output.compression.codec: com.hadoop.compression.lzo.LzoCodec

Joseph Echeverria
Cloudera, Inc.

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