cf4122e4d4
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>
139 lines
7.3 KiB
Markdown
139 lines
7.3 KiB
Markdown
---
|
|
layout: global
|
|
displayTitle: Spark Overview
|
|
title: Overview
|
|
description: 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](sql-programming-guide.html) for SQL and structured data processing, [MLlib](mllib-guide.html) for machine learning, [GraphX](graphx-programming-guide.html) for graph processing, and [Spark Streaming](streaming-programming-guide.html).
|
|
|
|
# Downloading
|
|
|
|
Get Spark from the [downloads page](http://spark.apache.org/downloads.html) 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](building-spark.html).
|
|
|
|
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](submitting-applications.html) 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](submitting-applications.html#master-urls), 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](sparkr.html) 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](cluster-overview.html) 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](ec2-scripts.html): our EC2 scripts let you launch a cluster in about 5 minutes
|
|
* [Standalone Deploy Mode](spark-standalone.html): simplest way to deploy Spark on a private cluster
|
|
* [Apache Mesos](running-on-mesos.html)
|
|
* [Hadoop YARN](running-on-yarn.html)
|
|
|
|
# Where to Go from Here
|
|
|
|
**Programming Guides:**
|
|
|
|
* [Quick Start](quick-start.html): a quick introduction to the Spark API; start here!
|
|
* [Spark Programming Guide](programming-guide.html): detailed overview of Spark
|
|
in all supported languages (Scala, Java, Python, R)
|
|
* Modules built on Spark:
|
|
* [Spark Streaming](streaming-programming-guide.html): processing real-time data streams
|
|
* [Spark SQL and DataFrames](sql-programming-guide.html): support for structured data and relational queries
|
|
* [MLlib](mllib-guide.html): built-in machine learning library
|
|
* [GraphX](graphx-programming-guide.html): Spark's new API for graph processing
|
|
* [Bagel (Pregel on Spark)](bagel-programming-guide.html): older, simple graph processing model
|
|
|
|
**API Docs:**
|
|
|
|
* [Spark Scala API (Scaladoc)](api/scala/index.html#org.apache.spark.package)
|
|
* [Spark Java API (Javadoc)](api/java/index.html)
|
|
* [Spark Python API (Sphinx)](api/python/index.html)
|
|
* [Spark R API (Roxygen2)](api/R/index.html)
|
|
|
|
**Deployment Guides:**
|
|
|
|
* [Cluster Overview](cluster-overview.html): overview of concepts and components when running on a cluster
|
|
* [Submitting Applications](submitting-applications.html): packaging and deploying applications
|
|
* Deployment modes:
|
|
* [Amazon EC2](ec2-scripts.html): scripts that let you launch a cluster on EC2 in about 5 minutes
|
|
* [Standalone Deploy Mode](spark-standalone.html): launch a standalone cluster quickly without a third-party cluster manager
|
|
* [Mesos](running-on-mesos.html): deploy a private cluster using
|
|
[Apache Mesos](http://mesos.apache.org)
|
|
* [YARN](running-on-yarn.html): deploy Spark on top of Hadoop NextGen (YARN)
|
|
|
|
**Other Documents:**
|
|
|
|
* [Configuration](configuration.html): customize Spark via its configuration system
|
|
* [Monitoring](monitoring.html): track the behavior of your applications
|
|
* [Tuning Guide](tuning.html): best practices to optimize performance and memory use
|
|
* [Job Scheduling](job-scheduling.html): scheduling resources across and within Spark applications
|
|
* [Security](security.html): Spark security support
|
|
* [Hardware Provisioning](hardware-provisioning.html): recommendations for cluster hardware
|
|
* [3<sup>rd</sup> Party Hadoop Distributions](hadoop-third-party-distributions.html): using common Hadoop distributions
|
|
* Integration with other storage systems:
|
|
* [OpenStack Swift](storage-openstack-swift.html)
|
|
* [Building Spark](building-spark.html): build Spark using the Maven system
|
|
* [Contributing to Spark](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark)
|
|
* [Supplemental Projects](https://cwiki.apache.org/confluence/display/SPARK/Supplemental+Spark+Projects): related third party Spark projects
|
|
|
|
**External Resources:**
|
|
|
|
* [Spark Homepage](http://spark.apache.org)
|
|
* [Spark Wiki](https://cwiki.apache.org/confluence/display/SPARK)
|
|
* [Spark Community](http://spark.apache.org/community.html) resources, including local meetups
|
|
* [StackOverflow tag `apache-spark`](http://stackoverflow.com/questions/tagged/apache-spark)
|
|
* [Mailing Lists](http://spark.apache.org/mailing-lists.html): ask questions about Spark here
|
|
* [AMP Camps](http://ampcamp.berkeley.edu/): a series of training camps at UC Berkeley that featured talks and
|
|
exercises about Spark, Spark Streaming, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/3/),
|
|
[slides](http://ampcamp.berkeley.edu/3/) and [exercises](http://ampcamp.berkeley.edu/3/exercises/) are
|
|
available online for free.
|
|
* [Code Examples](http://spark.apache.org/examples.html): more are also available in the `examples` subfolder of Spark ([Scala]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/scala/org/apache/spark/examples),
|
|
[Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
|
|
[Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python),
|
|
[R]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/r))
|