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Bryan Cutler 43d9c7e7e5 [SPARK-30640][PYTHON][SQL] Prevent unnecessary copies of data during Arrow to Pandas conversion 
### What changes were proposed in this pull request?

Prevent unnecessary copies of data during conversion from Arrow to Pandas.

### Why are the changes needed?

During conversion of pyarrow data to Pandas, columns are checked for timestamp types and then modified to correct for local timezone. If the data contains no timestamp types, then unnecessary copies of the data can be made. This is most prevalent when checking columns of a pandas DataFrame where each series is assigned back to the DataFrame, regardless if it had timestamps. See https://www.mail-archive.com/devarrow.apache.org/msg17008.html and ARROW-7596 for discussion.

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

No

### How was this patch tested?

Existing tests

Closes #27358 from BryanCutler/pyspark-pandas-timestamp-copy-fix-SPARK-30640.

Authored-by: Bryan Cutler <cutlerb@gmail.com>
Signed-off-by: Bryan Cutler <cutlerb@gmail.com>
2020-01-26 15:21:06 -08:00
..
docs [SPARK-30434][PYTHON][SQL] Move pandas related functionalities into 'pandas' sub-package 2020-01-09 10:22:50 +09:00
lib [SPARK-25891][PYTHON] Upgrade to Py4J 0.10.8.1 2018-10-31 09:55:03 -07:00
pyspark [SPARK-30640][PYTHON][SQL] Prevent unnecessary copies of data during Arrow to Pandas conversion 2020-01-26 15:21:06 -08:00
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 [MINOR][DOCS] Tighten up some key links to the project and download pages to use HTTPS 2019-05-21 10:56:42 -07:00
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-30480][PYTHON][TESTS] Increases the memory limit being tested in 'WorkerMemoryTest.test_memory_limit' 2020-01-13 18:47:15 +09:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-30434][PYTHON][SQL] Move pandas related functionalities into 'pandas' sub-package 2020-01-09 10:22:50 +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 (currently version 0.10.8.1), but some additional sub-packages have their own extra requirements for some features (including numpy, pandas, and pyarrow).