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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.
Spark是一个快速的、通用的大数据集群计算系统。 它提供了高层API接口(支持Scala，Java及Python)以及一个优化过的支持图计算和数据处理的引擎。 同时它也一些高级工具也提供了支持，包括对SQL及结构化数据处理的Spark SQL， 机器学习的MLLib， 图像处理的GraphX和实时流处理的Spark Streaming
在Spark的项目主页上，你可以找到最新的Spark文档，其中还包括一个编程指南。 该 README 文档仅仅包含了基础的安装信息
Spark is built using Apache Maven. To build Spark and its example programs, run:
mvn -DskipTests clean package
(You do not need to do this if you downloaded a pre-built package.) More detailed documentation is available from the project site, at "Building Spark".
The easiest way to start using Spark is through the Scala shell:
Try the following command, which should return 1000:
scala> sc.parallelize(1 to 1000).count()
使用Spark最简单的方法是Scala Shell： ./bin/spark-shell 试试下面的命令，其执行结果应该是1000： scala> sc.parallelize(1 to 1000).count()
Alternatively, if you prefer Python, you can use the Python shell:
And run the following command, which should also return 1000:
Spark also comes with several sample programs in the
To run one of them, use
./bin/run-example <class> [params]. For example:
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
package. For instance:
MASTER=spark://host:7077 ./bin/run-example SparkPi
Many of the example programs print usage help if no params are given.
Testing first requires building Spark. Once Spark is built, tests can be run using:
Please see the guidance on how to run all automated tests.
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.
Please refer to the build documentation at "Specifying the Hadoop Version" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. See also "Third Party Hadoop Distributions" for guidance on building a Spark application that works with a particular distribution.
Please refer to the Configuration guide in the online documentation for an overview on how to configure Spark.