Running Apache Spark on a cluster

Apache Spark is a general-purpose data processing and analysis engine.

On the surface, it helps developers working with large data sets by providing easy to use libraries and modules. Spark integrates with various data sources (CSV, HDFS, remote databases, etc…), actions can then be performed against the data.

In the background, Spark achieves performance by spreading tasks across your cluster - This will sound familiar if you’ve ever worked with Hadoop.

With the above in mind, I’ll try to accomplish a few things with this guide:

  • Download Spark
  • Start a master server
  • Start slave workers
  • Launch an example application on the cluster

Downloading Spark

The latest version of Spark can be downloaded here. Even if you’re not planning on using Hadoop, a pre-built version is more than fine.

tar -xvzf spark-1.6.1-bin-hadoop2.6.tgz
rm -rf spark-1.6.1-bin-hadoop2.6.tgz
mv spark-1.6.1-bin-hadoop2.6/ spark 

You should download Spark into all the nodes you want your applications to run on.

If you want to see Spark in action, run one of the example scripts. Spark comes with plenty examples for both Scala and Python (these can be found under examples/src/main/).

cd spark/
./bin/run-example SparkPi 10

Spark will do its thing and eventually return you a result.

Starting the master server

One of your nodes needs to run as master, which will delegate tasks across the workers.


A master web ui should now be available at http://localhost:8080, it will list your hardware and slave resources, as well as details about the applications you run against the cluster. You should also see a Master URL, this will be needed to start the slave workers.

Starting the slave workers

Slaves should be started on all nodes, including the one where the master is setup.

./sbin/ <Spark Master URL>

If you visit the Spark Master web ui, your workers should now be visible.

Launching an example application on the cluster

We use the spark-submit script to launch applications.

./bin/spark-submit --master <Spark Master URL> examples/src/main/python/ 100

The –master option is used to specify the master URL.

You should now see your tasks being spread across your workers, Spark does quite a bit of logging by default:

16/05/03 19:50:46 INFO TaskSetManager: Finished task 85.0 in stage 0.0 (TID 85) in 180 ms...
16/05/03 19:50:46 INFO TaskSetManager: Starting task 94.0 in stage 0.0 (TID 94...

And that’s it! Spark is now ready to run as a Standalone Cluster.

If you’d like to read more on Standalone Mode click here, at the time of writing the latest Spark version is 1.6.1.

comments powered by Disqus