Apache Spark

Spark is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R (Deprecated), 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 DataFrames, pandas API on Spark for pandas workloads, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. Official version: Development version: You can find the latest Spark documentation, including a programming guide, on the project web page. This README file only contains basic setup instructions. Spark is built using Apache Maven. To build Spark and its example programs, run: More detailed documentation is available from the project site, at "Building Spark". For general development tips, including info on developing Spark using an IDE, see "Useful Developer Tools". Spark also comes with several sample programs in the examples directory. To run one of them, use ./bin/run-example [params] . For example: You can set the MASTER environment variable when running examples to submit examples to a cluster. This can be spark:// URL, "yarn" 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: 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: 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 and Enabling YARN" for detailed guidance on building for a particular distribution of Hadoop, including building for particular Hive and Hive Thriftserver distributions. Please review the Contribution to Spark guide for information on how to get started contributing to the project.