spark开发环境配置

以后spark，mapreduce，mpi可能三者集于同一平台，各自的侧重点有所不用，相当于云计算与高性能计算的集合，互补，把spark的基础看了看，现在把开发环境看看，主要是看源码，最近Apache Spark源码走读系列挺好的，看了些。具体环境配置不是太复杂，具体可以看https://github.com/apache/spark

1、代码下载

git clone  https://github.com/apache/spark.git

2、直接构建spark

我是基于hadoop2.2.0的，因此执行如下：

SPARK_HADOOP_VERSION=2.2.0 SPARK_YARN=true sbt/sbt assembly

3、具体使用参考https://github.com/apache/spark

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 <class> [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. Once Spark is built, tests can be run using:

./sbt/sbt test

使用IDE，安装 Intellj Idea，并安装scala插件

去idea官网下载idea的tar.gz包，解压就行。运行idea，安装scala插件。

在源码根目录，使用如下命令

./sbt/sbt gen-idea

就生成了idea项目文件。使用 idea，点击File->Open project，浏览到 incubator-spark文件夹，打开项目，就可以修改Spark代码了。

具体参考：https://github.com/apache/spark

http://cn.soulmachine.me/blog/20140130/

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