Recently, we used Hadoop for a heavy batch processing job. The job itself was not very special, in fact,
the very same job is run on a daily basis to process some sort of data incrementally. The job instances
had run fine for several months. Now we wanted to process the data of some months at once and
all of a sudden, the processing job died with nasty (and somehow missleading) exceptions.
The reduce task logs were filled with lots of stacktraces like
java.io.EOFException
at java.io.DataInputStream.readByte(DataInputStream.java:250)
at org.apache.hadoop.io.WritableUtils.readVLong(WritableUtils.java:298)
at org.apache.hadoop.io.WritableUtils.readVInt(WritableUtils.java:319)
at org.apache.hadoop.io.Text.readString(Text.java:400)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.createBlockOutputStream(DFSClient.java:2901)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.nextBlockOutputStream(DFSClient.java:2826)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream.access$2000(DFSClient.java:2102)
at org.apache.hadoop.hdfs.DFSClient$DFSOutputStream$DataStreamer.run(DFSClient.java:2288)
This stacktrace alone did not provide a lot of information about the real exception cause.
However, together with the corresponding name node logs, it became clear that the corresponding reduce
processes could not open new files for output writing.
After realizing that the job is using
org.apache.hadoop.mapred.lib.MultipleOutputFormat
for output writing, the reason for the failing jobs became clear: file handle limitations.
The only question was: which ones? The Linux OS has these limitations and Hadoop's HDFS as well.
To make a long story short, we had to increase both of them.
Linux and Open File Limits
Linux limits the number of parallel open files on a per process basis. A given user might only start a certain
number of processes (type
ulimit -u to see your own limit) in parallel and each of these processes
is only allowed to write to a certain number of files in parallel (
ulimit -n). The default value for
open files is 1024 (at least in debian/ubuntu-flavored distributions).
To get around our problem from above, we increased this limit for the user running our Hadoop cluster by editing
the
/etc/security/limits.conf.
To make the machine recognize the new limits, it is necessary to log out and afterwards back in again. However,
in our case we do not login as the user
hadoop directly,
but using the
su command. Thus, no new login shell is started and the configuration option would not be recognized. In
his blog post,
Armin describes how to edit
/etc/pamd.d/su in this scenario.
Hadoop and Open File Limits
Hadoop (we use version 0.20.2) has configuration parameters for almost everything. The one to specify the number of
parallel files per datanode is named
dfs.datanode.max.xcievers. Unfortunately, if not set otherwise,
datanodes are at startup equipped with only 256 parallel file handles
(see
org.apache.hadoop.hdfs.server.datanode.DataXceiverServer class for details).
The
HBase documentation on the xcievers parameter
recommends a value of 4096.
As stated before, this parameter is evaluated during datanode startup time. Therefore, it is necessary to
configure this parameter in the
conf/hdfs-site.xml file on each datanode and restart the cluster afterwards.
Summary
We had to increase both the OS specific limits and the limits in the Hadoop configuration.
None of them alone was sufficient. To accomplish the configuration changes, we had to update setting on each
datanode machine and to restart the cluster. Afterwards, these exceptions from above were only to be seen on
cluster nodes which were not properly updated.
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