Spark Overview

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, 

Downloading

Get 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 version 

### in conf/spark-env.sh ### 三種方式來制定hadoop的配置# If 'hadoop' binary is on your PATH 如果已經在PATH環境變數中制定了hadoop的路徑export SPARK_DIST_CLASSPATH=$(hadoop classpath)# With explicit path to 'hadoop' binary  明確的指定hadoop的路徑export SPARK_DIST_CLASSPATH=$(/path/to/hadoop/bin/hadoop classpath)# Passing a Hadoop configuration directory  傳遞一個hadoop的配置檔案的路徑export SPARK_DIST_CLASSPATH=$(hadoop --config /path/to/configs classpath)

可以下載包含Hadoop庫的版本，這樣的版本可以使用HDFS和YARN，但是對Hadoop的版本有要求。也可以下載不包含預編譯Hadoop的版本，這樣可以使用任何版本的Hadoop，只需要在執行Spark時候在引數中指定任意版本的Hadoop的classpath。

If you’d like to build Spark from source, visit Building Spark.也可以利用原始碼自行編譯Spark。

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 7+, Python 2.6+ and R 3.1+. For the Scala API, Spark 1.6.0 uses Scala 2.10. You will need to use a compatible Scala version (2.10.x).

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,

Spark自帶一些scala，java，python和R的示例程式，位置在examples/src/main目錄。使用Spark頂層目錄的bin/run-example <class>[params]命令來執行java和scala程式。（其實這是呼叫spark-submit指令碼來啟動程式）。其中<class>就是準備執行的程式的類檔案，[params]是所需的引數列表。

./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.

也可以使用spark-shell來進行互動式的執行。

./bin/spark-shell --master local[2] 其中，--master用於指定master的url，這裡使用local[2]表示使用本地模式，2表示使用兩個執行緒。

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:使用python

./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, usebin/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:

• 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

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, R)
• Modules built on Spark:
  • Spark Streaming: processing real-time data streams
  • MLlib: built-in machine learning library
  • GraphX: Spark’s new API for graph processing

API Docs:

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

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 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)