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### What changes were proposed in this pull request? This PR is a followup of https://github.com/apache/spark/pull/32429 and https://github.com/apache/spark/pull/32644. I was thinking about creating separate PRs but decided to include all in this PR because it shares the same context, and should be easier to review together. This PR includes: - Use `InheritableThread` and `inheritable_thread_target` in the current code base to prevent potential resource leak (since we enabled pinned thread mode by default now at https://github.com/apache/spark/pull/32429) - Copy local properties when `start` at `InheritableThread` is called to mimic JVM behaviour. Previously it was copied when `InheritableThread` instance was created (related to #32644). - https://github.com/apache/spark/pull/32429 missed one place at `inheritable_thread_target` (https://github.com/apache/spark/blob/master/python/pyspark/util.py#L308). More specifically, I missed one place that should enable pinned thread mode by default. ### Why are the changes needed? To mimic the JVM behaviour about thread lifecycle ### Does this PR introduce _any_ user-facing change? Ideally no. One possible case is that users use `InheritableThread` with pinned thread mode enabled. In this case, the local properties will be copied when starting the thread instead of defining the `InheritableThread` object. This is a small difference that wouldn't likely affect end users. ### How was this patch tested? Existing tests should cover this. Closes #32962 from HyukjinKwon/SPARK-35498-SPARK-35303. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org> |
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| README.md | ||
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Apache Spark
Spark is a unified analytics engine for large-scale data processing. 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 Structured 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 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, but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).