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## What changes were proposed in this pull request? When using PySpark broadcast variables in a multi-threaded environment, `SparkContext._pickled_broadcast_vars` becomes a shared resource. A race condition can occur when broadcast variables that are pickled from one thread get added to the shared ` _pickled_broadcast_vars` and become part of the python command from another thread. This PR introduces a thread-safe pickled registry using thread local storage so that when python command is pickled (causing the broadcast variable to be pickled and added to the registry) each thread will have their own view of the pickle registry to retrieve and clear the broadcast variables used. ## How was this patch tested? Added a unit test that causes this race condition using another thread. Author: Bryan Cutler <cutlerb@gmail.com> Closes #18695 from BryanCutler/pyspark-bcast-threadsafe-SPARK-12717. |
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| README.md | ||
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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.
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 0.10.6), but additional sub-packages have their own requirements (including numpy and pandas).