spark-instrumented-optimizer/docs/index.md

layout	title
global	Spark Overview

Apache Spark is a fast and general-purpose cluster computing system. It provides high-level APIs in Java, Scala and Python, and an optimized engine that supports general execution graphs. It also supports a rich set of higher-level tools including Shark (Hive on Spark), Spark SQL for structured data, 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 with Maven.

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+ and Python 2.6+. 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 and Python 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

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)
• Modules built on Spark:
  • Spark Streaming: processing real-time data streams
  • Spark SQL: 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 (Epydoc)

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
• 3^rd Party Hadoop Distributions: using common Hadoop distributions
• Building Spark with Maven: build Spark using the Maven system
• Contributing to Spark

External Resources:

• Spark Homepage
• Shark: Apache Hive over 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, Shark, Spark Streaming, Mesos, and more. Videos, slides and exercises are available online for free.
• Code Examples: more are also available in the examples subfolder of Spark (Scala, Java, Python)

Community

To get help using Spark or keep up with Spark development, sign up for the user mailing list.

If you're in the San Francisco Bay Area, there's a regular Spark meetup every few weeks. Come by to meet the developers and other users.

Finally, if you'd like to contribute code to Spark, read how to contribute.