NOTE: This Wiki is obsolete as of November 2016 and is retained for reference only.

Overview

PySpark is built on top of Spark's Java API. Data is processed in Python and cached / shuffled in the JVM:

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RDD transformations in Python are mapped to transformations on PythonRDD objects in Java. On remote worker machines, PythonRDD objects launch Python subprocesses and communicate with them using pipes, sending the user's code and the data to be processed.

Tips for contributing to PySpark

Unit Testing

PySpark's tests are a mixture of doctests and unittests.  The doctests serve as simple usage examples and are a lightweight way to test new RDD transformations and actions.  The unittests are used for more involved testing, such as testing job cancellation.

To run the entire PySpark test suite, run ./python/run-tests.  When adding a new file that contains doctests or unittests, make sure to update run-tests so that the new tests are automatically executed by Jenkins.

To run individual test suites:

• For unittest suites run SPARK_TESTING=1 ./bin/pyspark python/pyspark/my_file.py.  Some of our doctest suites (such as the tests in rdd.py) have a custom __main__ method that sets up a global SparkContext that's shared among the tests.  These suites should be also be run with this command.
• For pure-doctest suites (without special __main__ setup code), run SPARK_TESTING=1 ./bin/pyspark python/pyspark/my_file.py.

Shipping code across the cluster

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