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From Krishmin Rai <kr...@missionfoc.us>
Subject Re: Memory setting recommendations for Accumulo / Hadoop
Date Tue, 12 Mar 2013 17:49:50 GMT
Hi Mike,
  This could be related to the maximum number of processes or files allowed for your linux
user. You might try bumping these values up (e.g via /etc/security/limits.conf).

-Krishmin

On Mar 12, 2013, at 1:35 PM, Mike Hugo wrote:

> Hello,
> 
> I'm setting up accumulo on a small cluster where each node has 96GB of ram and 24 cores.
 Any recommendations on what memory settings to use for the accumulo processes, as well as
what to use for the hadoop processes (e.g. datanode, etc)?
> 
> I did a small test just to try some things standalone on a single node, setting the accumulo
processes to 2GB of ram and the HADOOP_HEAPSIZE=2000.  While running a map reduce job with
4 workers (each allocated 1GB of RAM), the datanode runs out of memory about 25% of the way
into the job and dies.  The job is basically building an index, iterating over data in one
table and applying mutations to another - nothing too fancy.
> 
> Since I'm dealing with a subset of data, I set the table split threshold to 128M for
testing purposes, there are currently about 170 tablets so we not dealing with a ton of data
here. Might this low split threshold be a contributing factor?
> 
> Should I increase the HADDOP_HEAPSIZE even further?  Or will that just delay the inevitable
OOM error? 
> 
> The exception we are seeing is below.
> 
> ERROR org.apache.hadoop.hdfs.server.datanode.DataNode: DatanodeRegistration(...):DataXceiveServer:
Exiting due to:java.lang.OutOfMemoryError: unable to create new native thread
>         at java.lang.Thread.start0(Native Method)
>         at java.lang.Thread.start(Unknown Source)
>         at org.apache.hadoop.hdfs.server.datanode.DataXceiverServer.run(DataXceiverServer.java:133)
>         at java.lang.Thread.run(Unknown Source)
> 
> 
> Thanks for your help!
> 
> Mike


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