3c0156899d
Also cut trailing whitespaces. Author: Reynold Xin <rxin@databricks.com> Closes #6548 from rxin/readme and squashes the following commits: 630efc3 [Reynold Xin] Update README to include DataFrames and zinc.
99 lines
3.5 KiB
Markdown
99 lines
3.5 KiB
Markdown
# Apache Spark
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Spark is a fast and general cluster computing system for Big Data. It provides
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high-level APIs in Scala, Java, and Python, and an optimized engine that
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supports general computation graphs for data analysis. It also supports a
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rich set of higher-level tools including Spark SQL for SQL and DataFrames,
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MLlib for machine learning, GraphX for graph processing,
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and Spark Streaming for stream processing.
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<http://spark.apache.org/>
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## Online Documentation
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You can find the latest Spark documentation, including a programming
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guide, on the [project web page](http://spark.apache.org/documentation.html)
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and [project wiki](https://cwiki.apache.org/confluence/display/SPARK).
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This README file only contains basic setup instructions.
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## Building Spark
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Spark is built using [Apache Maven](http://maven.apache.org/).
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To build Spark and its example programs, run:
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build/mvn -DskipTests clean package
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(You do not need to do this if you downloaded a pre-built package.)
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More detailed documentation is available from the project site, at
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["Building Spark"](http://spark.apache.org/docs/latest/building-spark.html).
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## Interactive Scala Shell
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The easiest way to start using Spark is through the Scala shell:
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./bin/spark-shell
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Try the following command, which should return 1000:
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scala> sc.parallelize(1 to 1000).count()
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## Interactive Python Shell
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Alternatively, if you prefer Python, you can use the Python shell:
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./bin/pyspark
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And run the following command, which should also return 1000:
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>>> sc.parallelize(range(1000)).count()
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## Example Programs
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Spark also comes with several sample programs in the `examples` directory.
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To run one of them, use `./bin/run-example <class> [params]`. For example:
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./bin/run-example SparkPi
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will run the Pi example locally.
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You can set the MASTER environment variable when running examples to submit
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examples to a cluster. This can be a mesos:// or spark:// URL,
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"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run
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locally with one thread, or "local[N]" to run locally with N threads. You
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can also use an abbreviated class name if the class is in the `examples`
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package. For instance:
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MASTER=spark://host:7077 ./bin/run-example SparkPi
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Many of the example programs print usage help if no params are given.
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## Running Tests
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Testing first requires [building Spark](#building-spark). Once Spark is built, tests
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can be run using:
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./dev/run-tests
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Please see the guidance on how to
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[run tests for a module, or individual tests](https://cwiki.apache.org/confluence/display/SPARK/Useful+Developer+Tools).
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## A Note About Hadoop Versions
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Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported
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storage systems. Because the protocols have changed in different versions of
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Hadoop, you must build Spark against the same version that your cluster runs.
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Please refer to the build documentation at
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["Specifying the Hadoop Version"](http://spark.apache.org/docs/latest/building-spark.html#specifying-the-hadoop-version)
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for detailed guidance on building for a particular distribution of Hadoop, including
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building for particular Hive and Hive Thriftserver distributions. See also
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["Third Party Hadoop Distributions"](http://spark.apache.org/docs/latest/hadoop-third-party-distributions.html)
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for guidance on building a Spark application that works with a particular
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distribution.
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## Configuration
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Please refer to the [Configuration guide](http://spark.apache.org/docs/latest/configuration.html)
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in the online documentation for an overview on how to configure Spark.
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