History

Shixiong Zhu 376d782164 [SPARK-19986][TESTS] Make pyspark.streaming.tests.CheckpointTests more stable ## What changes were proposed in this pull request? Sometimes, CheckpointTests will hang on a busy machine because the streaming jobs are too slow and cannot catch up. I observed the scheduled delay was keeping increasing for dozens of seconds locally. This PR increases the batch interval from 0.5 seconds to 2 seconds to generate less Spark jobs. It should make `pyspark.streaming.tests.CheckpointTests` more stable. I also replaced `sleep` with `awaitTerminationOrTimeout` so that if the streaming job fails, it will also fail the test. ## How was this patch tested? Jenkins Author: Shixiong Zhu <shixiong@databricks.com> Closes #17323 from zsxwing/SPARK-19986.		2017-03-17 11:12:23 -07:00
..
docs	[SPARK-19002][BUILD][PYTHON] Check pep8 against all Python scripts	2017-01-02 15:23:19 +00:00
lib	[SPARK-17960][PYSPARK][UPGRADE TO PY4J 0.10.4]	2016-10-21 09:48:24 +01:00
pyspark	[SPARK-19986][TESTS] Make pyspark.streaming.tests.CheckpointTests more stable	2017-03-17 11:12:23 -07:00
test_support	[SPARK-19610][SQL] Support parsing multiline CSV files	2017-02-28 13:34:33 -08:00
.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	[SPARK-1267][SPARK-18129] Allow PySpark to be pip installed	2016-11-16 14:22:15 -08:00
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.py	[SPARK-19604][TESTS] Log the start of every Python test	2017-02-15 14:41:15 -08:00
setup.cfg	[SPARK-1267][SPARK-18129] Allow PySpark to be pip installed	2016-11-16 14:22:15 -08:00
setup.py	[SPARK-19064][PYSPARK] Fix pip installing of sub components	2017-01-25 14:43:39 -08:00

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 setup 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 0.10.4), but additional sub-packages have their own requirements (including numpy and pandas).