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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.

Get Spark from the downloads page of the project website. This documentation is for Spark version 2.2.1. 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.Scala and Java users can include Spark in their projects using its Maven coordinates and in the future Python users can also install Spark from PyPI.

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 8+, Python 2.7+/3.4+ and R 3.1+. For the Scala API, Spark 2.2.1uses Scala 2.11. You will need to use a compatible Scala version(2.11.x).

Note that support for Java 7, Python 2.6 and old Hadoop versions before 2.6.5 were removed as of Spark 2.2.0.

Note that support for Scala 2.10 is deprecated as of Spark 2.1.0, and may be removed in Spark 2.3.0.

Spark 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 <class> [params] in the top-level Spark directory. (Behind the scenes, thisinvokes the more generalspark-submit script forlaunching applications). For example,

You can also run Spark interactively through a modified version of the Scala shell. This is agreat way to learn the framework.

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:

Example applications are also provided in Python. For example,

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:

Example applications are also provided in R. For example,

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 severaloptions for deployment:

• Standalone Deploy Mode: simplest way to deploy Spark on a private cluster

Programming Guides:

• Quick Start: a quick introduction to the Spark API; start here!
• RDD Programming Guide: overview of Spark basics - RDDs (core but old API), accumulators, and broadcast variables
• Spark SQL, Datasets, and DataFrames: processing structured data with relational queries (newer API than RDDs)
• Structured Streaming: processing structured data streams with relation queries (using Datasets and DataFrames, newer API than DStreams)
• Spark Streaming: processing data streams using DStreams (old API)
• MLlib: applying machine learning algorithms
• GraphX: processing graphs

API Docs:

Deployment Guides:

Sparkle 2.8

• 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)
  • Kubernetes (experimental): deploy Spark on top of Kubernetes

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:
• Building Spark: build Spark using the Maven system
• Third Party Projects: related third party Spark projects

Sparkle 1.2.2 Model

External Resources:

• Spark Community resources, including local meetups
• 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)

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