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From Krishmin Rai <kr...@missionfoc.us>
Subject Re: Memory setting recommendations for Accumulo / Hadoop
Date Tue, 12 Mar 2013 18:21:16 GMT
Have you also increased the maximum number of processes ("nproc" in the same file)? I have
definitely seen this kind of error as a result of in insufficiently large process limit.

Some more details, maybe, on these pages:

http://ww2.cs.fsu.edu/~czhang/errors.html
http://incubator.apache.org/ambari/1.2.0/installing-hadoop-using-ambari/content/ambari-chap5-3-1.html

-Krishmin

On Mar 12, 2013, at 1:52 PM, Mike Hugo wrote:

> Eventually it will be 4 nodes, this particular test was running on a single node
> 
> hadoop version is 1.0.4
> 
> we already upped the limits in /etc/security/limits.conf to:
> 
> usernamehere    hard    nofile           16384
> 
> Mike
> 
> 
> On Tue, Mar 12, 2013 at 12:49 PM, Krishmin Rai <krrai@missionfoc.us> wrote:
> 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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