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.
DownloadingGet Spark from the downloads page of the project website. This documentation is for Spark version 1.6.0. Spark uses Hadoop’s client libraries for HDFS and YARN. Downloads are pre-packaged for a handful of popular Hadoop versions.Users can also download a “Hadoop free” binary and run Spark with any Hadoop versionby augmenting Spark’s classpath.
If you’d like to build Spark from source, visit Building Spark.
Spark runs on both Windows and UNIX-like systems (e.g. Linux, Mac OS). It’s easy to runlocally 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 7+, Python 2.6+ and R 3.1+. For the Scala API, Spark 1.6.0 usesScala 2.10. You will need to use a compatible Scala version (2.10.x).
Running the Examples and ShellSpark comes with several sample programs. Scala, Java, Python and R examples are in theexamples/src/main directory. To run one of the Java or Scala sample programs, usebin/run-example [params] in the top-level Spark directory. (Behind the scenes, thisinvokes the more generalspark-submit script forlaunching applications). For example,
./bin/run-example SparkPi 10You can also run Spark interactively through a modified version of the Scala shell. This is agreat way to learn the framework.
./bin/spark-shell --master local[2]The --master option specifies themaster URL for a distributed cluster, or local to runlocally with one thread, or local[N] to run locally with N threads. You should start by usinglocal 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, usebin/pyspark:
./bin/pyspark --master local[2]Example applications are also provided in Python. For example,
./bin/spark-submit examples/src/main/python/pi.py 10Spark 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.RLaunching on a ClusterThe 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 severaloptions 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 YARNWhere to Go from HereProgramming Guides:
Quick Start: a quick introduction to the Spark API; start here! Spark Programming Guide: detailed overview of Sparkin all supported languages (Scala, Java, Python, R) Modules built on Spark: Spark Streaming: processing real-time data streams Spark SQL, Datasets, and DataFrames: support for structured data and relational queries MLlib: built-in machine learning library GraphX: Spark’s new API for graph processingAPI 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 Integration with other storage systems: OpenStack Swift Building Spark: build Spark using the Maven system Contributing to Spark Supplemental Projects: related third party Spark projectsExternal 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 andexercises about Spark, Spark Streaming, Mesos, and more. Videos,slides and exercises areavailable online for free. Code Examples: more are also available in the examples subfolder of Spark (Scala, Java, Python, R)