Running Spark on Mesos

Running Spark on Mesos

To run on a cluster, Spark uses the Apache Mesos resource manager. Follow the steps below to install Mesos and Spark:

For Spark 0.5:

1. Download and build Spark using the instructions here.
2. Download Mesos 0.9.0 from a mirror.
3. Configure Mesos using the configure script, passing the location of your JAVA_HOME using --with-java-home. Mesos comes with "template" configure scripts for different platforms, such as configure.macosx, so you can just run the one on your platform if it exists. See the README file in the Mesos directory for other configuration options.
   • Note: If you want to run Mesos without installing it into the default paths on your system (e.g. if you don't have administrative privileges to install it), you should also pass the --prefix option to configure to tell it where to install. For example, pass --prefix=/home/user/mesos. By default the prefix is /usr/local.
4. Build Mesos using make and install it using make install.
5. Create a file called spark-env.sh in Spark's conf directory, by copying conf/spark-env.sh.template, and add the following lines in it:
   • export MESOS_NATIVE_LIBRARY=<path to libmesos.so>. This path is usually /usr/local/lib/libmesos.so, or <prefix>/lib/libmesos.so if you passed a prefix to configure. Also, on Mac OS X, the library is called libmesos.dylib instead of libmesos.so.
   • export SCALA_HOME=<path to Scala installation>. You can skip this if Scala is on your system path.
6. Copy Spark and Mesos to the same paths on all the nodes in the cluster (for Mesos the easiest way is to make install on every node).
7. Configure Mesos for deployment:
   • On your master node, edit <prefix>/var/mesos/deploy/masters to list your master and <prefix>/var/mesos/deploy/slaves to list the slaves, where <prefix> is the prefix where you installed Mesos (/usr/local by default).
   • On all nodes, edit <prefix>/var/mesos/deploy/mesos.conf and add the line master=HOST:5050, where HOST is your master node's hostname.
   • Run <prefix>/sbin/mesos-start-cluster.sh to start Mesos. If all goes well, you should see Mesos's web UI on port 8080 of the master machine.
   • See Mesos's README file for more information on deploying it.
8. To run a Spark job against the cluster, when you create your SparkContext, pass the string HOST:5050 as the first parameter, where HOST is the machine running your Mesos master. In addition, pass the location of Spark on your nodes as the third parameter, and a list of JAR files containing your JAR's code as the fourth (these will automatically get copied to the workers). For example:

new SparkContext("HOST:5050", "My Job Name", "/home/user/spark", List("my-job.jar"))

For Spark versions before 0.5:

1. Download and build Spark using the instructions here.
2. Download either revision 1205738 of Mesos if you're using the master branch of Spark, or the pre-protobuf branch of Mesos if you're using Spark 0.3 or earlier (note that for new users, we recommend the master branch instead of 0.3). For revision 1205738 of Mesos, use:

svn checkout -r 1205738 http://svn.apache.org/repos/asf/incubator/mesos/trunk mesos

For the pre-protobuf branch (for Spark 0.3 and earlier), use:

git clone git://github.com/mesos/mesos
cd mesos
git checkout --track origin/pre-protobuf

3. Configure Mesos using the configure script, passing the location of your JAVA_HOME using --with-java-home. Mesos comes with "template" configure scripts for different platforms, such as configure.template.macosx, so you can just run the one on your platform if it exists. See the Mesos wiki for other configuration options.
4. Build Mesos using make.
5. In Spark's conf/spark-env.sh file, add export MESOS_HOME=<path to Mesos directory>. If you don't have a spark-env.sh, copy conf/spark-env.sh.template. You should also set SCALA_HOME there if it's not on your system's default path.
6. Copy Spark and Mesos to the same path on all the nodes in the cluster.
7. Configure Mesos for deployment:
   • On your master node, edit MESOS_HOME/conf/masters to list your master and MESOS_HOME/conf/slaves to list the slaves. Also, edit MESOS_HOME/conf/mesos.conf and add the line failover_timeout=1 to change a timeout parameter that is too high by default.
   • Run MESOS_HOME/deploy/start-mesos to start it up. If all goes well, you should see Mesos's web UI on port 8080 of the master machine.
   • See Mesos's deploy instructions for more information on deploying it.
8. To run a Spark job against the cluster, when you create your SparkContext, pass the string master@HOST:5050 as the first parameter, where HOST is the machine running your Mesos master. In addition, pass the location of Spark on your nodes as the third parameter, and a list of JAR files containing your JAR's code as the fourth (these will automatically get copied to the workers). For example:

new SparkContext("master@HOST:5050", "My Job Name", "/home/user/spark", List("my-job.jar"))

Running on Amazon EC2

If you want to run Spark on Amazon EC2, there's an easy way to launch a cluster with Mesos, Spark, and HDFS pre-configured: the EC2 launch scripts. This will get you a cluster in about five minutes without any configuration on your part.

Running Alongside Hadoop

You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a separate service on the machines. To access Hadoop data from Spark, just use a hdfs:// URL (typically hdfs://<namenode>:9000/path, but you can find the right URL on your Hadoop Namenode's web UI).

In addition, it is possible to also run Hadoop MapReduce on Mesos, to get better resource isolation and sharing between the two. In this case, Mesos will act as a unified scheduler that assigns cores to either Hadoop or Spark, as opposed to having them share resources via the Linux scheduler on each node. Please refer to the Mesos wiki page on Running Hadoop on Mesos.

In either case, HDFS runs separately from Hadoop MapReduce, without going through Mesos.