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Shivaram Venkataraman cf4122e4d4 [SPARK-6806] [SPARKR] [DOCS] Add a new SparkR programming guide 
This PR adds a new SparkR programming guide at the top-level. This will be useful for R users as our APIs don't directly match the Scala/Python APIs and as we need to explain SparkR without using RDDs as examples etc.

cc rxin davies pwendell

cc cafreeman -- Would be great if you could also take a look at this !

Author: Shivaram Venkataraman <shivaram@cs.berkeley.edu>

Closes #6490 from shivaram/sparkr-guide and squashes the following commits:

d5ff360 [Shivaram Venkataraman] Add a section on HiveContext, HQL queries
408dce5 [Shivaram Venkataraman] Fix link
dbb86e3 [Shivaram Venkataraman] Fix minor typo
9aff5e0 [Shivaram Venkataraman] Address comments, use dplyr-like syntax in example
d09703c [Shivaram Venkataraman] Fix default argument in read.df
ea816a1 [Shivaram Venkataraman] Add a new SparkR programming guide Also update write.df, read.df to handle defaults better

(cherry picked from commit 5f48e5c33b)
Signed-off-by: Davies Liu <davies@databricks.com>
2015-05-29 14:12:18 -07:00

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layout displayTitle title description
global Spark Overview Overview Apache Spark SPARK_VERSION_SHORT documentation homepage

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala, Python and R, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Spark SQL for SQL and structured data processing, MLlib for machine learning, GraphX for graph processing, and Spark Streaming.

Downloading

Get Spark from the downloads page of the project website. This documentation is for Spark version {{site.SPARK_VERSION}}. The downloads page contains Spark packages for many popular HDFS versions. If you'd like to build Spark from scratch, visit Building Spark.

Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It's easy to run locally on one machine --- all you need is to have java installed on your system PATH, or the JAVA_HOME environment variable pointing to a Java installation.

Spark runs on Java 6+, Python 2.6+ and R 3.1+. For the Scala API, Spark {{site.SPARK_VERSION}} uses Scala {{site.SCALA_BINARY_VERSION}}. You will need to use a compatible Scala version ({{site.SCALA_BINARY_VERSION}}.x).

Running the Examples and Shell

Spark comes with several sample programs. Scala, Java, Python and R examples are in the examples/src/main directory. To run one of the Java or Scala sample programs, use bin/run-example <class> [params] in the top-level Spark directory. (Behind the scenes, this invokes the more general spark-submit script for launching applications). For example,

./bin/run-example SparkPi 10

You can also run Spark interactively through a modified version of the Scala shell. This is a great way to learn the framework.

./bin/spark-shell --master local[2]

The --master option specifies the master URL for a distributed cluster, or local to run locally with one thread, or local[N] to run locally with N threads. You should start by using local for testing. For a full list of options, run Spark shell with the --help option.

Spark also provides a Python API. To run Spark interactively in a Python interpreter, use bin/pyspark:

./bin/pyspark --master local[2]

Example applications are also provided in Python. For example,

./bin/spark-submit examples/src/main/python/pi.py 10

Spark also provides an experimental R API since 1.4 (only DataFrames APIs included). To run Spark interactively in a R interpreter, use bin/sparkR:

./bin/sparkR --master local[2]

Example applications are also provided in R. For example,

./bin/spark-submit examples/src/main/r/dataframe.R

Launching on a Cluster

The Spark cluster mode overview explains the key concepts in running on a cluster. Spark can run both by itself, or over several existing cluster managers. It currently provides several options for deployment:

Where to Go from Here

Programming Guides:

API Docs:

Deployment Guides:

Other Documents:

External Resources: