NOTE: This Wiki is obsolete as of November 2016 and is retained for reference only.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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In a few cases, PySpark's internal code needs take care to avoid including unserializable objects in function closures. For example, consider this excerpt from rdd.py that wraps a user-defined function to batch its output:

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Even with only one serializer, there are still some subtleties here due to how PySpark handles text files. PySpark implements SparkContext.textFile() by directly calling its Java equivalent. This produces a JavaRDD[String] instead of a JavaRDD[byte[]]. JavaRDD transfers these strings to Python workers using UTF-8 encoding (For 0.9.0 only, this used MUTF-8 encoding, but that was fixed for 0.9.1; see https://spark-project.atlassian.net/browse/SPARK-1043).

Prior to the custom serializers pull request, JavaRDD would send strings to Python as pickled UTF-8 strings by prepending the appropriate pickle opcodes. From the worker's point of view, all of its incoming data was in the same pickled format. The pull request removed all Python-pickle-specific code from JavaRDD.

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PySpark pipelines transformations by composing their functions. When using PySpark, there's a one-to-one correspondence between PySpark stages and Spark scheduler stages. Each PySpark stage corresponds to a PipelinedRDD instance.

TODO: describe PythonRDD

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Many JVMs use fork/exec to launch child processes in ProcessBuilder or Runtime.exec. These child processes initially have the same memory footprint as their parent. When the Spark worker JVM has a large heap and spawns many child processes, this can cause memory exhaustion, leading to "Cannot allocate memory" errors. In Spark, this affects both pipe() and PySpark.

Other developers have run into the same problem and discovered a variety of workarounds, including adding extra swap space or telling the kernel to overcommit memory. We can't use the java_posix_spawn library to solve this problem because it's too difficult to package due to its use of platform-specific native binaries.

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