Eventually it will be 4 nodes, this particular test was running on a single
hadoop version is 1.0.4
we already upped the limits in /etc/security/limits.conf to:
usernamehere hard nofile 16384
On Tue, Mar 12, 2013 at 12:49 PM, Krishmin Rai <[EMAIL PROTECTED]> 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
> 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
> > at java.lang.Thread.run(Unknown Source)
> > Thanks for your help!
> > Mike