R on Spark

SparkR is an R package that provides a light-weight frontend to use Spark from R.

Installing SparkR

Requirements

SparkR requires Scala 2.10 and Spark version >= 0.9.0. Current build by default uses Apache Spark 1.1.0. You can also build SparkR against a different Spark version (>= 0.9.0) by modifying pkg/src/build.sbt.

SparkR also requires the R package rJava to be installed. To install rJava, you can run the following command in R:

install.packages("rJava")

Package installation

To develop SparkR, you can build the scala package and the R package using

./install-dev.sh

If you wish to try out the package directly from github, you can use install_github from devtools. Note that you can specify which branch, tag etc to install from.

library(devtools)
install_github("amplab-extras/SparkR-pkg", subdir="pkg")

SparkR by default uses Apache Spark 1.1.0. You can switch to a different Spark version by setting the environment variable SPARK_VERSION. For example, to use Apache Spark 1.2.0, you can run

SPARK_VERSION=1.2.0 ./install-dev.sh

SparkR by default links to Hadoop 1.0.4. To use SparkR with other Hadoop versions, you will need to rebuild SparkR with the same version that Spark is linked to. For example to use SparkR with a CDH 4.2.0 MR1 cluster, you can run

SPARK_HADOOP_VERSION=2.0.0-mr1-cdh4.2.0 ./install-dev.sh

By default, SparkR uses sbt to build an assembly jar. If you wish to use maven instead, you can set the environment variable USE_MAVEN=1. For example

USE_MAVEN=1 ./install-dev.sh

If you are building SparkR from behind a proxy, you can setup maven to use the right proxy server.

Running sparkR

If you have cloned and built SparkR, you can start using it by launching the SparkR shell with

./sparkR

The sparkR script automatically creates a SparkContext with Spark by default in local mode. To specify the Spark master of a cluster for the automatically created SparkContext, you can run

MASTER=<Spark master URL> ./sparkR

If you have installed it directly from github, you can include the SparkR package and then initialize a SparkContext. For example to run with a local Spark master you can launch R and then run

library(SparkR)
sc <- sparkR.init(master="local")

To increase the memory used by the driver you can export the SPARK_MEM environment variable. For example to use 1g, you can run

SPARK_MEM=1g ./sparkR

In a cluster setting to set the amount of memory used by the executors you can pass the variable spark.executor.memory to the SparkContext constructor.

library(SparkR)
sc <- sparkR.init(master="spark://<master>:7077",
                  sparkEnvir=list(spark.executor.memory="1g"))

Examples, Unit tests

SparkR comes with several sample programs in the examples directory. To run one of them, use ./sparkR <filename> <args>. For example:

./sparkR examples/pi.R local[2]

You can also run the unit-tests for SparkR by running

./run-tests.sh

Running on EC2

Instructions for running SparkR on EC2 can be found in the SparkR wiki.

Running on YARN

Currently, SparkR supports running on YARN with the yarn-client mode. These steps show how to build SparkR with YARN support and run SparkR programs on a YARN cluster:

# assumes Java, R, rJava, yarn, spark etc. are installed on the whole cluster.
cd SparkR-pkg/
USE_YARN=1 SPARK_YARN_VERSION=2.4.0 SPARK_HADOOP_VERSION=2.4.0 ./install-dev.sh

Before launching an application, make sure each worker node has a local copy of lib/SparkR/sparkr-assembly-0.1.jar. With a cluster launched with the spark-ec2 script, do:

~/spark-ec2/copy-dir ~/SparkR-pkg

Finally, when launching an application, the environment variable YARN_CONF_DIR needs to be set to the directory which contains the client-side configuration files for the Hadoop cluster (with a cluster launched with spark-ec2, this defaults to /root/ephemeral-hdfs/conf/):

YARN_CONF_DIR=/root/ephemeral-hdfs/conf/ MASTER=yarn-client ./sparkR
YARN_CONF_DIR=/root/ephemeral-hdfs/conf/ ./sparkR examples/pi.R yarn-client

Report Issues/Feedback

For better tracking and collaboration, issues and TODO items are reported to a dedicated SparkR JIRA.

In your pull request, please cross reference the ticket item created. Likewise, if you already have a pull request ready, please reference it in your ticket item.