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HyukjinKwon 6bdd710c4d [SPARK-32316][TESTS][INFRA] Test PySpark with Python 3.8 in Github Actions 
### What changes were proposed in this pull request?

This PR aims to test PySpark with Python 3.8 in Github Actions. In the script side, it is already ready:

4ad9bfd53b/python/run-tests.py (L161)

This PR includes small related fixes together:

1. Install Python 3.8
2. Only install one Python implementation instead of installing many for SQL and Yarn test cases because they need one Python executable in their test cases that is higher than Python 2.
3. Do not install Python 2 which is not needed anymore after we dropped Python 2 at SPARK-32138
4. Remove a comment about installing PyPy3 on Jenkins - SPARK-32278. It is already installed.

### Why are the changes needed?

Currently, only PyPy3 and Python 3.6 are being tested with PySpark in Github Actions. We should test the latest version of Python as well because some optimizations can be only enabled with Python 3.8+. See also https://github.com/apache/spark/pull/29114

### Does this PR introduce _any_ user-facing change?

No, dev-only.

### How was this patch tested?

Was not tested. Github Actions build in this PR will test it out.

Closes #29116 from HyukjinKwon/test-python3.8-togehter.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: Dongjoon Hyun <dongjoon@apache.org>
2020-07-14 20:44:09 -07:00
..
docs [SPARK-31748][PYTHON] Document resource module in PySpark doc and rename/move classes 2020-05-19 17:09:37 -07:00
lib [SPARK-30884][PYSPARK] Upgrade to Py4J 0.10.9 2020-02-20 09:09:30 -08:00
pyspark [SPARK-32301][PYTHON][TESTS] Add a test case for toPandas to work with empty partitioned Spark DataFrame 2020-07-15 08:44:48 +09:00
test_coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
test_support [SPARK-23094][SPARK-23723][SPARK-23724][SQL] Support custom encoding for json files 2018-04-29 11:25:31 +08:00
.coveragerc [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
.gitignore [SPARK-3946] gitignore in /python includes wrong directory 2014-10-14 14:09:39 -07:00
MANIFEST.in [SPARK-26803][PYTHON] Add sbin subdirectory to pyspark 2019-02-27 08:39:55 -06:00
pylintrc [SPARK-13596][BUILD] Move misc top-level build files into appropriate subdirs 2016-03-07 14:48:02 -08:00
README.md [SPARK-30884][PYSPARK] Upgrade to Py4J 0.10.9 2020-02-20 09:09:30 -08:00
run-tests [SPARK-29672][PYSPARK] update spark testing framework to use python3 2019-11-14 10:18:55 -08:00
run-tests-with-coverage [SPARK-26252][PYTHON] Add support to run specific unittests and/or doctests in python/run-tests script 2018-12-05 15:22:08 +08:00
run-tests.py [SPARK-32316][TESTS][INFRA] Test PySpark with Python 3.8 in Github Actions 2020-07-14 20:44:09 -07:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-32138] Drop Python 2.7, 3.4 and 3.5 2020-07-14 11:22:44 +09:00

README.md

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.

https://spark.apache.org/

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).