ODIn/spark-instrumented-optimizer
1
0
Fork 0
Code Issues Pull requests Projects Releases Wiki Activity
spark-instrumented-optimizer/python
History
Hyukjin Kwon afff42178c [SPARK-35647][PYTHON][DOCS] Restructure User Guide in PySpark documentation 
### What changes were proposed in this pull request?

This PR proposes to restructure User Guide in PySpark documentation for pandas APIs on Spark.

**Before**

![Screen Shot 2021-06-08 at 8 47 41 PM](https://user-images.githubusercontent.com/6477701/121179493-cb85e280-c89a-11eb-8b93-552ebe7cd0a8.png)

**After**

![Screen Shot 2021-06-08 at 8 46 58 PM](https://user-images.githubusercontent.com/6477701/121179419-b3ae5e80-c89a-11eb-82a0-6dabbf1de12d.png)

Note that I mostly just moved the contents around except minor changes:
- Removing some questions in FAQ that don't make sense in Apache Spark
- Rename a subtitle "Working with pandas and PySpark" to "From/to pandas and PySpark DataFrames"

For renaming Koalas to either pandas-on-Spark or pandas APIs on Spark, it will be done at SPARK-35591

### Why are the changes needed?

For better readability.

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

Yes, it restructures the documentation as shown above.

### How was this patch tested?

I manually built the docs and tested.

Closes #32820 from HyukjinKwon/SPARK-35647.

Authored-by: Hyukjin Kwon <gurwls223@apache.org>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>
2021-06-09 12:13:25 +09:00
..
docs [SPARK-35647][PYTHON][DOCS] Restructure User Guide in PySpark documentation 2021-06-09 12:13:25 +09:00
lib [SPARK-34688][PYTHON] Upgrade to Py4J 0.10.9.2 2021-03-11 09:51:41 -06:00
pyspark [SPARK-35512][PYTHON] Fix OverflowError(cannot convert float infinity to integer) in partitionBy function 2021-06-09 10:57:27 +09:00
test_coverage [SPARK-7721][PYTHON][TESTS] Adds PySpark coverage generation script 2018-01-22 22:12:50 +09:00
test_support Spelling r common dev mlib external project streaming resource managers python 2020-11-27 10:22:45 -06:00
.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-32714][PYTHON] Initial pyspark-stubs port 2020-09-24 14:15:36 +09:00
mypy.ini [SPARK-35467][SPARK-35468][SPARK-35477][PYTHON] Fix disallow_untyped_defs mypy checks 2021-05-24 09:31:00 +09:00
pylintrc [SPARK-32435][PYTHON] Remove heapq3 port from Python 3 2020-07-27 20:10:13 +09:00
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-34968][TEST][PYTHON] Add the -fr argument to xargs rm 2021-04-06 15:20:55 -07:00
run-tests.py [SPARK-32194][PYTHON] Use proper exception classes instead of plain Exception 2021-05-26 11:54:40 +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-35338][PYTHON] Separate arithmetic operations into data type based structures 2021-05-19 19:47:00 -07:00

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).