140 lines
7.2 KiB
Markdown
140 lines
7.2 KiB
Markdown
---
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layout: global
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displayTitle: Spark Overview
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title: Overview
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description: Apache Spark SPARK_VERSION_SHORT documentation homepage
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---
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Apache Spark is a fast and general-purpose cluster computing system.
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It provides high-level APIs in Java, Scala, Python and R,
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and an optimized engine that supports general execution graphs.
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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).
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# Downloading
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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}}. Spark uses Hadoop's client libraries for HDFS and YARN. Downloads are pre-packaged for a handful of popular Hadoop versions.
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Users can also download a "Hadoop free" binary and run Spark with any Hadoop version
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[by augmenting Spark's classpath](hadoop-provided.html).
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If you'd like to build Spark from
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source, visit [Building Spark](building-spark.html).
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Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It's easy to run
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locally on one machine --- all you need is to have `java` installed on your system `PATH`,
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or the `JAVA_HOME` environment variable pointing to a Java installation.
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Spark runs on Java 7+, Python 2.6+ and R 3.1+. For the Scala API, Spark {{site.SPARK_VERSION}} uses
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Scala {{site.SCALA_BINARY_VERSION}}. You will need to use a compatible Scala version
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({{site.SCALA_BINARY_VERSION}}.x).
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# Running the Examples and Shell
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Spark comes with several sample programs. Scala, Java, Python and R examples are in the
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`examples/src/main` directory. To run one of the Java or Scala sample programs, use
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`bin/run-example <class> [params]` in the top-level Spark directory. (Behind the scenes, this
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invokes the more general
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[`spark-submit` script](submitting-applications.html) for
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launching applications). For example,
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./bin/run-example SparkPi 10
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You can also run Spark interactively through a modified version of the Scala shell. This is a
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great way to learn the framework.
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./bin/spark-shell --master local[2]
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The `--master` option specifies the
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[master URL for a distributed cluster](submitting-applications.html#master-urls), or `local` to run
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locally with one thread, or `local[N]` to run locally with N threads. You should start by using
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`local` for testing. For a full list of options, run Spark shell with the `--help` option.
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Spark also provides a Python API. To run Spark interactively in a Python interpreter, use
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`bin/pyspark`:
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./bin/pyspark --master local[2]
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Example applications are also provided in Python. For example,
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./bin/spark-submit examples/src/main/python/pi.py 10
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Spark also provides an experimental [R API](sparkr.html) since 1.4 (only DataFrames APIs included).
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To run Spark interactively in a R interpreter, use `bin/sparkR`:
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./bin/sparkR --master local[2]
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Example applications are also provided in R. For example,
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./bin/spark-submit examples/src/main/r/dataframe.R
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# Launching on a Cluster
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The Spark [cluster mode overview](cluster-overview.html) explains the key concepts in running on a cluster.
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Spark can run both by itself, or over several existing cluster managers. It currently provides several
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options for deployment:
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* [Standalone Deploy Mode](spark-standalone.html): simplest way to deploy Spark on a private cluster
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* [Apache Mesos](running-on-mesos.html)
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* [Hadoop YARN](running-on-yarn.html)
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# Where to Go from Here
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**Programming Guides:**
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* [Quick Start](quick-start.html): a quick introduction to the Spark API; start here!
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* [Spark Programming Guide](programming-guide.html): detailed overview of Spark
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in all supported languages (Scala, Java, Python, R)
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* Modules built on Spark:
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* [Spark Streaming](streaming-programming-guide.html): processing real-time data streams
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* [Spark SQL, Datasets, and DataFrames](sql-programming-guide.html): support for structured data and relational queries
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* [MLlib](mllib-guide.html): built-in machine learning library
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* [GraphX](graphx-programming-guide.html): Spark's new API for graph processing
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**API Docs:**
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* [Spark Scala API (Scaladoc)](api/scala/index.html#org.apache.spark.package)
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* [Spark Java API (Javadoc)](api/java/index.html)
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* [Spark Python API (Sphinx)](api/python/index.html)
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* [Spark R API (Roxygen2)](api/R/index.html)
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**Deployment Guides:**
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* [Cluster Overview](cluster-overview.html): overview of concepts and components when running on a cluster
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* [Submitting Applications](submitting-applications.html): packaging and deploying applications
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* Deployment modes:
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* [Amazon EC2](https://github.com/amplab/spark-ec2): scripts that let you launch a cluster on EC2 in about 5 minutes
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* [Standalone Deploy Mode](spark-standalone.html): launch a standalone cluster quickly without a third-party cluster manager
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* [Mesos](running-on-mesos.html): deploy a private cluster using
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[Apache Mesos](http://mesos.apache.org)
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* [YARN](running-on-yarn.html): deploy Spark on top of Hadoop NextGen (YARN)
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**Other Documents:**
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* [Configuration](configuration.html): customize Spark via its configuration system
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* [Monitoring](monitoring.html): track the behavior of your applications
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* [Tuning Guide](tuning.html): best practices to optimize performance and memory use
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* [Job Scheduling](job-scheduling.html): scheduling resources across and within Spark applications
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* [Security](security.html): Spark security support
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* [Hardware Provisioning](hardware-provisioning.html): recommendations for cluster hardware
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* Integration with other storage systems:
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* [OpenStack Swift](storage-openstack-swift.html)
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* [Building Spark](building-spark.html): build Spark using the Maven system
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* [Contributing to Spark](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark)
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* [Supplemental Projects](https://cwiki.apache.org/confluence/display/SPARK/Supplemental+Spark+Projects): related third party Spark projects
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**External Resources:**
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* [Spark Homepage](http://spark.apache.org)
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* [Spark Wiki](https://cwiki.apache.org/confluence/display/SPARK)
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* [Spark Community](http://spark.apache.org/community.html) resources, including local meetups
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* [StackOverflow tag `apache-spark`](http://stackoverflow.com/questions/tagged/apache-spark)
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* [Mailing Lists](http://spark.apache.org/mailing-lists.html): ask questions about Spark here
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* [AMP Camps](http://ampcamp.berkeley.edu/): a series of training camps at UC Berkeley that featured talks and
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exercises about Spark, Spark Streaming, Mesos, and more. [Videos](http://ampcamp.berkeley.edu/3/),
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[slides](http://ampcamp.berkeley.edu/3/) and [exercises](http://ampcamp.berkeley.edu/3/exercises/) are
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available online for free.
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* [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),
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[Java]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/java/org/apache/spark/examples),
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[Python]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/python),
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[R]({{site.SPARK_GITHUB_URL}}/tree/master/examples/src/main/r))
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