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Maxim Gekk f4fed05121 [SPARK-24171] Adding a note for non-deterministic functions 
## What changes were proposed in this pull request?

I propose to add a clear statement for functions like `collect_list()` about non-deterministic behavior of such functions. The behavior must be taken into account by user while creating and running queries.

Author: Maxim Gekk <maxim.gekk@databricks.com>

Closes #21228 from MaxGekk/deterministic-comments.
2018-05-10 09:44:49 -07:00
..
docs [PYSPARK] Update py4j to version 0.10.7. 2018-05-09 10:47:35 -07:00
lib [PYSPARK] Update py4j to version 0.10.7. 2018-05-09 10:47:35 -07:00
pyspark [SPARK-24171] Adding a note for non-deterministic functions 2018-05-10 09:44:49 -07:00
test_coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
test_support [SPARK-23094][SPARK-23723][SPARK-23724][SQL] Support custom encoding for json files 2018-04-29 11:25:31 +08:00
.coveragerc [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
MANIFEST.in [SPARK-18652][PYTHON] Include the example data and third-party licenses in pyspark package. 2016-12-07 06:09:27 +08:00
pylintrc [SPARK-13596][BUILD] Move misc top-level build files into appropriate subdirs 2016-03-07 14:48:02 -08:00
README.md [PYSPARK] Update py4j to version 0.10.7. 2018-05-09 10:47:35 -07:00
run-tests [SPARK-8583] [SPARK-5482] [BUILD] Refactor python/run-tests to integrate with dev/run-tests module system 2015-06-27 20:24:34 -07:00
run-tests-with-coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
run-tests.py [SPARK-24126][PYSPARK] Use build-specific temp directory for pyspark tests. 2018-05-07 13:00:18 +08:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [PYSPARK] Update py4j to version 0.10.7. 2018-05-09 10:47:35 -07:00

README.md

Apache Spark

Spark is a fast and general cluster computing system for Big Data. It provides high-level APIs in Scala, Java, Python, and R, 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, MLlib for machine learning, GraphX for graph processing, and Spark Streaming for stream processing.

http://spark.apache.org/

Online Documentation

You can find the latest Spark documentation, including a programming guide, on the project web page

Python Packaging

This README file only contains basic information related to pip installed PySpark. This packaging is currently experimental and may change in future versions (although we will do our best to keep compatibility). Using PySpark requires the Spark JARs, and if you are building this from source please see the builder instructions at "Building Spark".

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.

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.

Python Requirements

At its core PySpark depends on Py4J (currently version 0.10.7), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).