Service as a Job: Hadoop MapReduce TaskTracker

Continuing to build on other examples of services run as jobs, such as HDFS NameNode and HDFS DataNode, here is an example for the Hadoop MapReduce framework’s TaskTracker.

#!/bin/sh -x

# condor_chirp in /usr/libexec/condor
export PATH=$PATH:/usr/libexec/condor


# Note: bin/hadoop uses JAVA_HOME to find the runtime and tools.jar,
#       except tools.jar does not seem necessary therefore /usr works
#       (there's no /usr/lib/tools.jar, but there is /usr/bin/java)
export JAVA_HOME=/usr

# When we get SIGTERM, which Condor will send when
# we are kicked, kill off the datanode and gather logs
function term {
   ./bin/ stop tasktracker
# Useful if we can transfer data back
#   tar czf logs.tgz logs
#   cp logs.tgz $_CONDOR_SCRATCH_DIR

# Unpack

# Move into tarball, inefficiently
cd $(tar tzf $HADOOP_TARBALL | head -n1)

# Configure,
#  . http.address must be set to port 0 (ephemeral)
cat > conf/mapred-site.xml <<EOF
<?xml version="1.0"?>
<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

# Try to shutdown cleanly
trap term SIGTERM

export HADOOP_CONF_DIR=$PWD/conf
./bin/ start tasktracker

# Wait for pid file
PID_FILE=$(echo hadoop-*
while [ ! -s $PID_FILE ]; do sleep 1; done
PID=$(cat $PID_FILE)

# Report back some static data about the tasktracker
# e.g. condor_chirp set_job_attr SomeAttr SomeData
# None at the moment.

# While the tasktracker is running, collect and report back stats
while kill -0 $PID; do
   # Collect stats and chirp them back into the job ad
   # None at the moment.
   sleep 30

The job description below uses the same templating technique as the DataNode example. The description uses a variable JobTrackerAddress, provided on the command-line as an argument to condor_submit.


# Submit w/ condor_submit -a JobTrackerAddress=<address>
# e.g. <address> = $HOSTNAME:9001

cmd =
args = hadoop-1.0.1-bin.tar.gz $(JobTrackerAddress)

transfer_input_files = hadoop-1.0.1-bin.tar.gz

# RFE: Ability to get output files even when job is removed
#transfer_output_files = logs.tgz
#transfer_output_remaps = "logs.tgz logs.$(cluster).tgz"
output = tasktracker.$(cluster).out
error = tasktracker.$(cluster).err

log = tasktracker.$(cluster).log

kill_sig = SIGTERM

# Want chirp functionality
+WantIOProxy = TRUE

should_transfer_files = yes
when_to_transfer_output = on_exit

requirements = HasJava =?= TRUE


From here you can run condor_submit -a JobTrackerAddress=$HOSTNAME:9001 mapred_tasktracker.job a few times and build up a MapReduce cluster. Assuming you are already running a JobTrackers. You can also hit the JobTracker’s web interface to see TaskTrackers check in.

Assuming you are also running an HDFS instance, you can run some jobs against your new cluster.

For fun, I ran time hadoop jar hadoop-test-1.0.1.jar mrbench -verbose -maps 100 -inputLines 100 three times against 4 TaskTrackers, 8 TaskTrackers and 12 TaskTrackers. The resulting run times were,

# nodes runtime
4 01:26
8 00:50
12 00:38

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