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Spark Vagrant VM definition and runnable examples.
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Spark Vagrant VM

Vagrant VM box for Spark.

Preliminaries: VirtualBox and Vagrant

Vagrant is a tool to "create and configure lightweight, reproducible, and portable development environments." Vagrant itself is a virtual instance creation and startup tool on top of Oracle VirtualBox which takes care of the virtualisation.

Download and install the Open Source Edition of VirtualBox from virtualbox.

Then download and install Vagrant from vagrant. The Linux packages install the vagrant executable at /opt/vagrant/bin and you will need to add this to your path.

Building the VM

There is a Rakefile with useful targets for creating and generating the Spark Vagrant VM. To create a new VM run the default Rake target:


This will create the Spark box in Vagrant and run the necessary Puppet provisioning. This step will take some time to install Java, Hadoop, download and compile Spark, etc.

When the box is complete, you will find it in target.

You will likely only need to do this once unless you want to adapt the VM and make it available to others. If you are the trusting type, there is a prebuilt VM at:

Copy the download to the target directory if you are cheating and continue.

You can test the VM by using the Vagrant definition in example.

cd example
vagrant up
vagrant ssh

The Spark Web UI will be port forwarded to port 8080 on your host so you can open http://localhost:8080 on your host computer to see some Spark details.

The HDFS Web UI is also port forwarded to port 50070 and 50075 so you can browse the HDFS on the VM by opening http://localhost:50070 on your host.

When finished, you destroy the VM using:

vagrant destroy

Note for the Paranoid

If you are inclined to paranoia, see modules\spark\manifests\ssh.pp for notes on changing the passwordless root SSH needed on the VM instance to start a Spark slave.

Note on the Versions

The VM uses Spark 0.7.2 and Hadoop 1.0.3. The reason for the slightly peculiar Hadoop is to match the version in Elastic MapReduce which this work originally targetted.

A more recent Hadoop 1 can be selected by changing the download in /modules/spark/manifests/hdfs.pp,updating the sed substitution in /modules/spark/templates/root/spark.setup.erb and rebuilding.

To use the examples, you may also need to update the dependencies in /example/project/Spark.scala.


To run some sample applications, cd to examples and compile a fat jar from the SBT project there:

cd examples
./sbt012 assembly

The jar can be run on your host machine directly using e.g.:

java -cp target/scala-2.9.3/spark-assembly-1-SNAPSHOT.jar \
  org.boringtechiestuff.spark.TweetWordCount \
  --local \
  dev/sample.json output

To run it on the VM, first SSH to it and put the necessary in HDFS:

vagrant ssh

hadoop fs -mkdir /lib
hadoop fs -put /vagrant/target/scala-2.9.3/spark-assembly-1-SNAPSHOT.jar /lib

hadoop fs -mkdir /input
hadoop fs -put /vagrant/dev/sample.json /input

The /vagrant directory is a convenience mount of the examples directory onto the VM.

Run the same application as earlier but in cluster mode this time:

java -cp /vagrant/target/scala-2.9.3/spark-assembly-1-SNAPSHOT.jar \
    org.boringtechiestuff.spark.TweetWordCount \
    hdfs://localhost:9000/input \

Check the Web UI on localhost:8080 to prove it is doing something. When done, the output can be checked using:

hadoop fs -ls /output
hadoop fs -text /output/part-*

Streaming Examples

Spark also provides a streaming mode.

A streaming version of the previous can be run on your host machine directly using:

java -cp target/scala-2.9.3/spark-assembly-1-SNAPSHOT.jar \
  org.boringtechiestuff.spark.StreamingTweetWordCount \
  --local \
  input output

In this case new files added in input will be picked up and processed and result left in output by timestamp. For instance, copy the input file:

mkdir input
cp dev/sample.json input

After a few seconds, a new directory will be added in output with the results:

cd output ls -alR

And look for the directory with a nonzero part-00000.

The application runs until explicitly killed.

As before, this works on the VM also:

vagrant ssh

hadoop fs -rmr /input
hadoop fs -mkdir /input
hadoop fs -rmr /output

java -cp /vagrant/target/scala-2.9.3/spark-assembly-1-SNAPSHOT.jar \
        org.boringtechiestuff.spark.StreamingTweetWordCount \
        hdfs://localhost:9000/input \

In another console:

vagrant ssh

hadoop fs -put /vagrant/dev/sample.json /input/sample2.json

hadoop fs -lsr /output

And look for the nonempty part files again.

Vagrant Commmands

Some useful Vagrant commands.

  • vagrant suspend: Disable the virtual instance. The allocated disc space for the instance is retained but the instance will not be available. The running state at suspend time is saved for resumption.
  • vagrant resume: Wake up a previously suspended virtual instance.
  • vagrant halt: Turn off the virtual instance. Calling vagrant up after this is the equivalent of a reboot.
  • vagrant destroy: Hose your virtual instance, reclaiming the allocated disc space.
  • vagrant provision: Rerun puppet or chef provisioning on the virtual instance.
  • vagrant box list: List the VM definitions that Vagrant has imported.
  • vagrant box remove <name>: Remove the named VM definition from Vagrant, possibly to allow for an updated version to be imported.

Vagrant SSH X Forwarding

X applications on VMs can be displayed on the host machine by specifying a Vagrant SSH connection with X11 forwarding in the Vagrantfile:

config.ssh.forward_x11 = true

On the host machine, add an xhost for the Vagrant VM:

xhost +

Then X applications started from the VM should display on the host machine.

Vagrant Troubleshooting

To see more verbose output on any vagrant command, add a VAGRANT_LOG environment variable setting, e.g.:

VAGRANT_LOG=INFO /opt/vagrant/bin/vagrant up

Further help troubleshooting can be obtained by editing your Vagrantfile and enabling the config.vm.boot_mode = :gui setting. This will pop up a VirtualBox GUI window on boot.

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