spark-instrumented-optimizer/python
HyukjinKwon 2526fdea48 [SPARK-34657][PYTHON][DOCS] Replace the tag of release to the hash to hide RC tags in Binder
### What changes were proposed in this pull request?

Currently Binder link at Spark 3.1.1 (https://mybinder.org/v2/gh/apache/spark/v3.1.1-rc3?filepath=python%2Fdocs%2Fsource%2Fgetting_started%2Fquickstart.ipynb) shows  `v3.1.1-rc3` like:
![Screen Shot 2021-03-08 at 10 10 55 AM](https://user-images.githubusercontent.com/6477701/110262729-ecb70880-7ff7-11eb-92ba-f151d74985a6.png)

After the fix, it will shows the explicit hash:

![Screen Shot 2021-03-08 at 10 17 25 AM](https://user-images.githubusercontent.com/6477701/110262740-f476ad00-7ff7-11eb-8632-5b418ff87024.png)

In addition, this also fixes the examples URL while I am fixing it. For example: https://github.com/apache/spark/tree/v3.1.1-rc3/examples/src/main/python -> https://github.com/apache/spark/tree/1d550c4e902/examples/src/main/python

Note that it is hash in order to make both dev and release easier.

### Why are the changes needed?

To hide RC tags.

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

It will just change the URL shown when Binder is being loaded.

### How was this patch tested?

Manually tested:

```bash
make clean html
```

![Screen Shot 2021-03-08 at 10 17 06 AM](https://user-images.githubusercontent.com/6477701/110262813-2ee04a00-7ff8-11eb-9983-c4484f7832c4.png)

```bash
git_hash=`git rev-parse --short HEAD`
export GIT_HASH=$git_hash
make clean html
```

![Screen Shot 2021-03-08 at 10 17 25 AM](https://user-images.githubusercontent.com/6477701/110262805-2982ff80-7ff8-11eb-8560-e1e2aa7b263a.png)

Closes #31773 from HyukjinKwon/SPARK-34657.

Authored-by: HyukjinKwon <gurwls223@apache.org>
Signed-off-by: HyukjinKwon <gurwls223@apache.org>
2021-03-08 10:48:17 +09:00
..
docs [SPARK-34657][PYTHON][DOCS] Replace the tag of release to the hash to hide RC tags in Binder 2021-03-08 10:48:17 +09:00
lib [SPARK-33984][PYTHON] Upgrade to Py4J 0.10.9.1 2021-01-04 10:23:38 -08:00
pyspark [SPARK-34545][SQL] Fix issues with valueCompare feature of pyrolite 2021-03-07 19:12:42 -06:00
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-32320][PYSPARK] Remove mutable default arguments 2020-12-08 09:35:36 +08:00
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-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-33869][PYTHON][SQL][TESTS] Have a separate metastore directory for each PySpark test job 2020-12-21 11:11:25 -08:00
setup.cfg [SPARK-1267][SPARK-18129] Allow PySpark to be pip installed 2016-11-16 14:22:15 -08:00
setup.py [SPARK-32017][PYTHON][FOLLOW-UP] Rename HADOOP_VERSION to PYSPARK_HADOOP_VERSION in pip installation option 2021-01-05 17:21:32 +09:00

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).