b54103016a
### What changes were proposed in this pull request? This PR proposes to: - add a notebook with a Binder integration which allows users to try PySpark in a live notebook. Please [try this here](https://mybinder.org/v2/gh/HyukjinKwon/spark/SPARK-32204?filepath=python%2Fdocs%2Fsource%2Fgetting_started%2Fquickstart.ipynb). - reuse this notebook as a quickstart guide in PySpark documentation. Note that Binder turns a Git repo into a collection of interactive notebooks. It works based on Docker image. Once somebody builds, other people can reuse the image against a specific commit. Therefore, if we run Binder with the images based on released tags in Spark, virtually all users can instantly launch the Jupyter notebooks. <br/> I made a simple demo to make it easier to review. Please see: - [Main page](https://hyukjin-spark.readthedocs.io/en/stable/). Note that the link ("Live Notebook") in the main page wouldn't work since this PR is not merged yet. - [Quickstart page](https://hyukjin-spark.readthedocs.io/en/stable/getting_started/quickstart.html) <br/> When reviewing the notebook file itself, please give my direct feedback which I will appreciate and address. Another way might be: - open [here](https://mybinder.org/v2/gh/HyukjinKwon/spark/SPARK-32204?filepath=python%2Fdocs%2Fsource%2Fgetting_started%2Fquickstart.ipynb). - edit / change / update the notebook. Please feel free to change as whatever you want. I can apply as are or slightly update more when I apply to this PR. - download it as a `.ipynb` file:  - upload the `.ipynb` file here in a GitHub comment. Then, I will push a commit with that file with crediting correctly, of course. - alternatively, push a commit into this PR right away if that's easier for you (if you're a committer). References: - https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html - https://databricks.com/jp/blog/2020/03/31/10-minutes-from-pandas-to-koalas-on-apache-spark.html - my own blog post .. :-) and https://koalas.readthedocs.io/en/latest/getting_started/10min.html ### Why are the changes needed? To improve PySpark's usability. The current quickstart for Python users are very friendly. ### Does this PR introduce _any_ user-facing change? Yes, it will add a documentation page, and expose a live notebook to PySpark users. ### How was this patch tested? Manually tested, and GitHub Actions builds will test. Closes #29491 from HyukjinKwon/SPARK-32204. Lead-authored-by: HyukjinKwon <gurwls223@apache.org> Co-authored-by: Fokko Driesprong <fokko@apache.org> Signed-off-by: HyukjinKwon <gurwls223@apache.org> |
||
|---|---|---|
| .. | ||
| docs | ||
| lib | ||
| pyspark | ||
| test_coverage | ||
| test_support | ||
| .coveragerc | ||
| .gitignore | ||
| MANIFEST.in | ||
| 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).