当前Spark最新版本为Spark 2.4.5。Spark 使用Hadoop’s client libraries 存取HDFS and YARN。下载是流行hadoop版的便宜预包装。用户也可以下载免hadoop的二进制包，通过参数Spark’s classpath 运行spark 用于任何Hadoop版。scala和java用户可以用maven把spark包含进项目工程，将来python用户也可以从pypi安装spark。

Spark可以运行与windows和类unix系统（例如Linux，Mac OS）。它很容易本地运行于一台机器上，你只需要在系统PATH路径有JAVA安装或者有JAVA_HOME环境变量指向Java安装目录。

Spark2.4.5 runs on Java 8, Python 2.7+/3.4+ and R 3.1+. For the Scala API, Spark 2.4.5 uses Scala 2.12. You will need to use a compatible Scala version (2.12.x).

注意：

Spark在spark 2.2.0 版去掉支持Java 7, Python 2.6 and old Hadoop versions before 2.6.5。

在spark 2.3.0 版去掉支持Scala 2.10， 在spark 2.3.0 版去掉支持Scala 2.11。

在spark 2.4.1版不建议使用Scala 2.11。

http://spark.apache.org/docs/latest/

This documentation is for Spark version 2.4.5. 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 version by 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.

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 8, Python 2.7+/3.4+ and R 3.1+. For the Scala API, Spark 2.4.5 uses Scala 2.12. You will need to use a compatible Scala version (2.12.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. Support for Scala 2.10 was removed as of 2.3.0. Support for Scala 2.11 is deprecated as of Spark 2.4.1 and will be removed in Spark 3.0.

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 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

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:

./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 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:

• Standalone Deploy Mode: simplest way to deploy Spark on a private cluster
• Apache Mesos
• Hadoop YARN
• Kubernetes

Where to Go from Here

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:

• Spark Scala API (Scaladoc)
• Spark Java API (Javadoc)
• Spark Python API (Sphinx)
• Spark R API (Roxygen2)
• Spark SQL, Built-in Functions (MkDocs)

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)
  • Kubernetes: 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:
  • Cloud Infrastructures
  • OpenStack Swift
• Building Spark: build Spark using the Maven system
• Contributing to Spark
• Third Party Projects: related third party Spark projects

External Resources:

• Spark Homepage
• 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 and exercises about Spark, 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, R)