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## What changes were proposed in this pull request? After upgrading cloudpickle to 0.6.1 at https://github.com/apache/spark/pull/20691, one regression was found. Cloudpickle had a critical https://github.com/cloudpipe/cloudpickle/pull/240 for that. Basically, it currently looks existing globals would override globals shipped in a function's, meaning: **Before:** ```python >>> def hey(): ... return "Hi" ... >>> spark.range(1).rdd.map(lambda _: hey()).collect() ['Hi'] >>> def hey(): ... return "Yeah" ... >>> spark.range(1).rdd.map(lambda _: hey()).collect() ['Hi'] ``` **After:** ```python >>> def hey(): ... return "Hi" ... >>> spark.range(1).rdd.map(lambda _: hey()).collect() ['Hi'] >>> >>> def hey(): ... return "Yeah" ... >>> spark.range(1).rdd.map(lambda _: hey()).collect() ['Yeah'] ``` Therefore, this PR upgrades cloudpickle to 0.8.0. Note that cloudpickle's release cycle is quite short. Between 0.6.1 and 0.7.0, it contains minor bug fixes. I don't see notable changes to double check and/or avoid. There is virtually only this fix between 0.7.0 and 0.8.1 - other fixes are about testing. ## How was this patch tested? Manually tested, tests were added. Verified unit tests were added in cloudpickle. Closes #23904 from HyukjinKwon/SPARK-27000. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <gurwls223@apache.org> |
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| pyspark | ||
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| .coveragerc | ||
| .gitignore | ||
| MANIFEST.in | ||
| pylintrc | ||
| README.md | ||
| run-tests | ||
| run-tests-with-coverage | ||
| run-tests.py | ||
| setup.cfg | ||
| setup.py | ||
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 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 (currently version 0.10.8.1), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).