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Update README to include DataFrames and zinc.
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README.md

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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 structured
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data processing, MLlib for machine learning, GraphX for graph processing,
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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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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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mvn -DskipTests clean package
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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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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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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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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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./dev/run-tests
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Please see the guidance on how to
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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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