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https://github.com/litaotao/learn-spark


https://github.com/litaotao/learn-spark

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README 

        
# Apache Spark



Spark is a fast and general cluster computing system for Big Data. It provides

high-level APIs in Scala, Java, and Python, and an optimized engine that

supports general computation graphs for data analysis. 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 for stream processing.



## Online Documentation



You can find the latest Spark documentation, including a programming

guide, on the project webpage at .

This README file only contains basic setup instructions.



## Building Spark



Spark is built on Scala 2.10. To build Spark and its example programs, run:



    ./sbt/sbt assembly



(You do not need to do this if you downloaded a pre-built package.)



## Interactive Scala Shell



The easiest way to start using Spark is through the Scala shell:



    ./bin/spark-shell



Try the following command, which should return 1000:



    scala> sc.parallelize(1 to 1000).count()



## Interactive Python Shell



Alternatively, if you prefer Python, you can use the Python shell:



    ./bin/pyspark

    

And run the following command, which should also return 1000:



    >>> sc.parallelize(range(1000)).count()



## Example Programs



Spark also comes with several sample programs in the `examples` directory.

To run one of them, use `./bin/run-example  [params]`. For example:



    ./bin/run-example SparkPi



will run the Pi example locally.



You can set the MASTER environment variable when running examples to submit

examples to a cluster. This can be a mesos:// or spark:// URL, 

"yarn-cluster" or "yarn-client" to run on YARN, and "local" to run 

locally with one thread, or "local[N]" to run locally with N threads. You 

can also use an abbreviated class name if the class is in the `examples`

package. For instance:



    MASTER=spark://host:7077 ./bin/run-example SparkPi



Many of the example programs print usage help if no params are given.



## Running Tests



Testing first requires [building Spark](#building-spark). Once Spark is built, tests

can be run using:



    ./dev/run-tests



## A Note About Hadoop Versions



Spark uses the Hadoop core library to talk to HDFS and other Hadoop-supported

storage systems. Because the protocols have changed in different versions of

Hadoop, you must build Spark against the same version that your cluster runs.

You can change the version by setting `-Dhadoop.version` when building Spark.



For Apache Hadoop versions 1.x, Cloudera CDH MRv1, and other Hadoop

versions without YARN, use:



    # Apache Hadoop 1.2.1

    $ sbt/sbt -Dhadoop.version=1.2.1 assembly



    # Cloudera CDH 4.2.0 with MapReduce v1

    $ sbt/sbt -Dhadoop.version=2.0.0-mr1-cdh4.2.0 assembly



For Apache Hadoop 2.2.X, 2.1.X, 2.0.X, 0.23.x, Cloudera CDH MRv2, and other Hadoop versions

with YARN, also set `-Pyarn`:



    # Apache Hadoop 2.0.5-alpha

    $ sbt/sbt -Dhadoop.version=2.0.5-alpha -Pyarn assembly



    # Cloudera CDH 4.2.0 with MapReduce v2

    $ sbt/sbt -Dhadoop.version=2.0.0-cdh4.2.0 -Pyarn assembly



    # Apache Hadoop 2.2.X and newer

    $ sbt/sbt -Dhadoop.version=2.2.0 -Pyarn assembly



When developing a Spark application, specify the Hadoop version by adding the

"hadoop-client" artifact to your project's dependencies. For example, if you're

using Hadoop 1.2.1 and build your application using SBT, add this entry to

`libraryDependencies`:



    "org.apache.hadoop" % "hadoop-client" % "1.2.1"



If your project is built with Maven, add this to your POM file's `` section:



    

      org.apache.hadoop

      hadoop-client

      1.2.1

    



## A Note About Thrift JDBC server and CLI for Spark SQL



Spark SQL supports Thrift JDBC server and CLI.

See sql-programming-guide.md for more information about using the JDBC server and CLI.

You can use those features by setting `-Phive` when building Spark as follows.



    $ sbt/sbt -Phive  assembly



## Configuration



Please refer to the [Configuration guide](http://spark.apache.org/docs/latest/configuration.html)

in the online documentation for an overview on how to configure Spark.



## Contributing to Spark



Contributions via GitHub pull requests are gladly accepted from their original

author. Along with any pull requests, please state that the contribution is

your original work and that you license the work to the project under the

project's open source license. Whether or not you state this explicitly, by

submitting any copyrighted material via pull request, email, or other means

you agree to license the material under the project's open source license and

warrant that you have the legal authority to do so.