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sqlCtx -> sqlContext You can check the docs by: ``` $ cd docs $ SKIP_SCALADOC=1 jekyll serve ``` cc shivaram Author: Davies Liu <davies@databricks.com> Closes #5442 from davies/r_docs and squashes the following commits: 7a12ec6 [Davies Liu] remove rdd in R docs 8496b26 [Davies Liu] remove the docs related to RDD e23b9d6 [Davies Liu] delete R docs for RDD API 222e4ff [Davies Liu] Merge branch 'master' into r_docs 89684ce [Davies Liu] Merge branch 'r_docs' of github.com:davies/spark into r_docs f0a10e1 [Davies Liu] address comments from @shivaram f61de71 [Davies Liu] Update pairRDD.R 3ef7cf3 [Davies Liu] use + instead of function(a,b) a+b 2f10a77 [Davies Liu] address comments from @cafreeman 9c2a062 [Davies Liu] mention R api together with Python API 23f751a [Davies Liu] Fill in SparkR examples in programming guide
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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:
- Amazon EC2: our EC2 scripts let you launch a cluster in about 5 minutes
- Standalone Deploy Mode: simplest way to deploy Spark on a private cluster
- Apache Mesos
- Hadoop YARN
Where to Go from Here
Programming Guides:
- Quick Start: a quick introduction to the Spark API; start here!
- Spark Programming Guide: detailed overview of Spark in all supported languages (Scala, Java, Python, R)
- Modules built on Spark:
- Spark Streaming: processing real-time data streams
- Spark SQL and DataFrames: support for structured data and relational queries
- MLlib: built-in machine learning library
- GraphX: Spark's new API for graph processing
- Bagel (Pregel on Spark): older, simple graph processing model
API Docs:
- Spark Scala API (Scaladoc)
- Spark Java API (Javadoc)
- Spark Python API (Sphinx)
- Spark R API (Roxygen2)
Deployment Guides:
- Cluster Overview: overview of concepts and components when running on a cluster
- Submitting Applications: packaging and deploying applications
- Deployment modes:
- Amazon EC2: scripts that let you launch a cluster on EC2 in about 5 minutes
- Standalone Deploy Mode: launch a standalone cluster quickly without a third-party cluster manager
- Mesos: deploy a private cluster using Apache Mesos
- YARN: deploy Spark on top of Hadoop NextGen (YARN)
Other Documents:
- Configuration: customize Spark via its configuration system
- Monitoring: track the behavior of your applications
- Tuning Guide: best practices to optimize performance and memory use
- Job Scheduling: scheduling resources across and within Spark applications
- Security: Spark security support
- Hardware Provisioning: recommendations for cluster hardware
- 3rd Party Hadoop Distributions: using common Hadoop distributions
- Integration with other storage systems:
- Building Spark: build Spark using the Maven system
- Contributing to Spark
- Supplemental Projects: related third party Spark projects
External Resources:
- Spark Homepage
- Spark Wiki
- Spark Community resources, including local meetups
- StackOverflow tag
apache-spark - Mailing Lists: ask questions about Spark here
- AMP Camps: a series of training camps at UC Berkeley that featured talks and exercises about Spark, Spark Streaming, Mesos, and more. Videos, slides and exercises are available online for free.
- Code Examples: more are also available in the
examplessubfolder of Spark (Scala, Java, Python, R)