Apache Spark on Nomad
This repository is a fork of Apache Spark that natively supports using HashiCorp's Nomad as Spark's cluster manager (as an alternative to Hadoop YARN and Mesos). When running on Nomad, the Spark executors that run tasks for your Spark application, and optionally the application driver itself, run as Nomad tasks in a Nomad job.
spark-submit command when using Nomad:
spark-submit \ --class org.apache.spark.examples.JavaSparkPi \ --master nomad \ --deploy-mode cluster \ --conf spark.executor.instances=4 \ --conf spark.nomad.sparkDistribution=https://s3.amazonaws.com/nomad-spark/spark-2.1.0-bin-nomad.tgz \ https://s3.amazonaws.com/nomad-spark/spark-examples_2.11-2.1.0-SNAPSHOT.jar 100
The ultimate goal is to integrate Nomad into Spark directly, either natively or via a backend/scheduler plugin interface.
Benefits of Spark on Nomad
Nomad's design is heavily inspired by Google's work on both Borg and Omega. This has enabled a set of features that make Nomad well-suited to run analytical applications. Particularly relevant are its native support for batch workloads and parallelized, high throughput scheduling (more on scheduler internals here).
Nomad is easy to set up and use. It consists of a single binary/process, has a simple and intuitive data model, utilizes a declarative job specification and supports high availability and multi-datacenter federation out-of-the-box. Nomad also integrates seamlessly with HashiCorp's other runtime tools: Consul and Vault.
To get started, see Nomad's official Apache Spark Integration Guide. You can also use Nomad's example Terraform configuration and embedded Spark quickstart to give the integration a test drive on AWS or Azure.
Builds for Nomad 0.7 and later are available on the releases page. Builds for Nomad 0.6 are available below:
Creating Your Own Build
You can create a Nomad-enabled Spark distribution using Spark's standard
and enabling the
nomad profile. E.g.:
./dev/make-distribution.sh --name nomad --tgz -Pnomad -Psparkr -Phive -Phadoop-2.7 -Phive-thriftserver -DskipTests
The standard Spark README follows below.
Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.
You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions.
Spark is built using Apache Maven. To build Spark and its example programs, run:
build/mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.)
You can build Spark using more than one thread by using the -T option with Maven, see "Parallel builds in Maven 3". More detailed documentation is available from the project site, at "Building Spark".
For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools".
Interactive Scala Shell
The easiest way to start using Spark is through the Scala shell:
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
Interactive Python Shell
Alternatively, if you prefer Python, you can use the Python shell:
And run the following command, which should also return 1000:
Spark also comes with several sample programs in the
To run one of them, use
./bin/run-example <class> [params]. For example:
will run the Pi example locally.
You can set the MASTER environment variable when running examples to submit
examples to a cluster. This can be a mesos:// or spark:// URL,
"yarn" to run on YARN, and "local" to run
locally with one thread, or "local[N]" to run locally with N threads. You
can also use an abbreviated class name if the class is in the
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Testing first requires building Spark. Once Spark is built, tests can be run using:
Please see the guidance on how to run tests for a module, or individual tests.
A Note About Hadoop Versions
Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported storage systems. Because the protocols have changed in different versions of Hadoop, you must build Spark against the same version that your cluster runs.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions.
Please refer to the Configuration Guide in the online documentation for an overview on how to configure Spark.
Please review the Contribution to Spark guide for information on how to get started contributing to the project.