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### What changes were proposed in this pull request? This PR is a sub-task of [SPARK-33138](https://issues.apache.org/jira/browse/SPARK-33138). In order to make SQLConf.get reliable and stable, we need to make sure user can't pollute the SQLConf and SparkSession Context via calling setActiveSession and clearActiveSession. Change of the PR: * add legacy config spark.sql.legacy.allowModifyActiveSession to fallback to old behavior if user do need to call these two API. * by default, if user call these two API, it will throw exception * add extra two internal and private API setActiveSessionInternal and clearActiveSessionInternal for current internal usage * change all internal reference to new internal API except for SQLContext.setActive and SQLContext.clearActive ### Why are the changes needed? Make SQLConf.get reliable and stable. ### Does this PR introduce any user-facing change? No. ### How was this patch tested? * Add UT in SparkSessionBuilderSuite to test the legacy config * Existing test Closes #30042 from leanken/leanken-SPARK-33139. Authored-by: xuewei.linxuewei <xuewei.linxuewei@alibaba-inc.com> Signed-off-by: Wenchen Fan <wenchen@databricks.com> |
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| pyspark | ||
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| .coveragerc | ||
| .gitignore | ||
| MANIFEST.in | ||
| mypy.ini | ||
| pylintrc | ||
| README.md | ||
| run-tests | ||
| run-tests-with-coverage | ||
| run-tests.py | ||
| setup.cfg | ||
| setup.py | ||
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).