» Getting Started
To get started, you can use Nomad's example Terraform configuration to automatically provision an environment in AWS, or you can manually provision a cluster.
» Provision a Cluster in AWS
Nomad's Terraform configuration can be used to quickly provision a Spark-enabled Nomad environment in AWS. The embedded Spark example provides for a quickstart experience that can be used in conjunction with this guide. When you have a cluster up and running, you can proceed to Submitting applications.
» Manually Provision a Cluster
To manually configure provision a cluster, see the Nomad Getting Started guide. There are two basic prerequisites to using the Spark integration once you have a cluster up and running:
Access to a Spark distribution built with Nomad support. This is required for the machine that will submit applications as well as the Nomad tasks that will run the Spark executors.
A Java runtime environment (JRE) for the submitting machine and the executors.
The subsections below explain further.
» Configure the Submitting Machine
To run Spark applications on Nomad, the submitting machine must have access to the cluster and have the Nomad-enabled Spark distribution installed. The code snippets below walk through installing Java and Spark on Ubuntu:
$ sudo add-apt-repository -y ppa:openjdk-r/ppa $ sudo apt-get update $ sudo apt-get install -y openjdk-8-jdk $ JAVA_HOME=$(readlink -f /usr/bin/java | sed "s:bin/java::")
$ wget -O - https://nomad-spark.s3.amazonaws.com/spark-2.1.0-bin-nomad.tgz \ | sudo tar xz -C /usr/local $ export PATH=$PATH:/usr/local/spark-2.1.0-bin-nomad/bin
Export NOMAD_ADDR to point Spark to your Nomad cluster:
$ export NOMAD_ADDR=http://NOMAD_SERVER_IP:4646
» Executor Access to the Spark Distribution
When running on Nomad, Spark creates Nomad tasks to run executors for use by the application's driver program. The executor tasks need access to a JRE, a Spark distribution built with Nomad support, and (in cluster mode) the Spark application itself. By default, Nomad will only place Spark executors on client nodes that have the Java runtime installed (version 7 or higher).
In this example, the Spark distribution and the Spark application JAR file are being pulled from Amazon S3:
$ spark-submit \ --class org.apache.spark.examples.JavaSparkPi \ --master nomad \ --deploy-mode cluster \ --conf spark.executor.instances=4 \ --conf spark.nomad.sparkDistribution=https://nomad-spark.s3.amazonaws.com/spark-2.1.0-bin-nomad.tgz \ https://nomad-spark.s3.amazonaws.com/spark-examples_2.11-2.1.0-SNAPSHOT.jar 100
» Using a Docker Image
An alternative to installing the JRE on every client node is to set the
configuration property to the URL of a Docker image that has the Java runtime
installed. If set, Nomad will use the
docker driver to run Spark executors in
a container created from the image. The
configuration property can be set to a JSON object to provide Docker repository
When using a Docker image, both the Spark distribution and the application
itself can be included (in which case local URLs can be used for
$ spark-submit \ --class org.apache.spark.examples.JavaSparkPi \ --master nomad \ --deploy-mode cluster \ --conf spark.nomad.dockerImage=rcgenova/spark \ --conf spark.executor.instances=4 \ --conf spark.nomad.sparkDistribution=/spark-2.1.0-bin-nomad.tgz \ /spark-examples_2.11-2.1.0-SNAPSHOT.jar 100
» Next Steps
Learn how to submit applications.