0001 # Apache Spark
0002
0003 Spark is a unified analytics engine for large-scale data processing. It provides
0004 high-level APIs in Scala, Java, Python, and R, and an optimized engine that
0005 supports general computation graphs for data analysis. It also supports a
0006 rich set of higher-level tools including Spark SQL for SQL and DataFrames,
0007 MLlib for machine learning, GraphX for graph processing,
0008 and Structured Streaming for stream processing.
0009
0010 <https://spark.apache.org/>
0011
0012 ## Online Documentation
0013
0014 You can find the latest Spark documentation, including a programming
0015 guide, on the [project web page](https://spark.apache.org/documentation.html)
0016
0017
0018 ## Python Packaging
0019
0020 This README file only contains basic information related to pip installed PySpark.
0021 This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility).
0022 Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at
0023 ["Building Spark"](https://spark.apache.org/docs/latest/building-spark.html).
0024
0025 The Python packaging for Spark is not intended to replace all of the other use cases. This Python packaged version of Spark is suitable for interacting with an existing cluster (be it Spark standalone, YARN, or Mesos) - but does not contain the tools required to set up your own standalone Spark cluster. You can download the full version of Spark from the [Apache Spark downloads page](https://spark.apache.org/downloads.html).
0026
0027
0028 **NOTE:** If you are using this with a Spark standalone cluster you must ensure that the version (including minor version) matches or you may experience odd errors.
0029
0030 ## Python Requirements
0031
0032 At its core PySpark depends on Py4J, but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).
