https://github.com/Tubular/sparkly

Helpers & syntactic sugar for PySpark.
https://github.com/Tubular/sparkly

pyspark python spark

Last synced: about 2 months ago
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Helpers & syntactic sugar for PySpark.

• Host: GitHub
• URL: https://github.com/Tubular/sparkly
• Owner: tubular
• License: apache-2.0
• Created: 2016-10-07T15:50:02.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2023-06-26T21:09:23.000Z (about 1 year ago)
• Last Synced: 2024-07-26T07:42:02.988Z (2 months ago)
• Topics: pyspark, python, spark
• Language: Python
• Homepage:
• Size: 1.54 MB
• Stars: 60
• Watchers: 37
• Forks: 8
• Open Issues: 5
• Metadata Files:
  • Readme: README.rst
  • Changelog: CHANGELOG.md
  • License: LICENSE

Awesome Lists containing this project

README

        
Sparkly

=======

|Sparkly PyPi Version| |Documentation Status|

Helpers & syntax sugar for PySpark. There are several features to make your life easier:

- Definition of spark packages, external jars, UDFs and spark options within your code;

- Simplified reader/writer api for Cassandra, Elastic, MySQL, Kafka;

- Testing framework for spark applications.

More details could be found in `the official

documentation `__.

Installation

------------

Sparkly itself is easy to install::

    pip install pyspark  # pick your version

    pip install sparkly (compatible with spark >= 2.4)

Getting Started

---------------

Here is a small code snippet to show how to easily read Cassandra table

and write its content to ElasticSearch index::

    from sparkly import SparklySession

    class MySession(SparklySession):

        packages = [

            'datastax:spark-cassandra-connector:2.0.0-M2-s_2.11',

            'org.elasticsearch:elasticsearch-spark-20_2.11:6.5.4',

        ]

    if __name__ == '__main__':

        spark = MySession()

        df = spark.read_ext.cassandra('localhost', 'my_keyspace', 'my_table')

        df.write_ext.elastic('localhost', 'my_index', 'my_type')

See `the online documentation `__ for

more details.

Testing

-------

To run tests you have to have `docker `__ and

`docker-compose `__ installed on your

system. If you are working on MacOS we highly recommend you to use

`docker-machine `__. As soon as the

tools mentioned above have been installed, all you need is to run::

    make test

Supported Spark Versions

------------------------

At the moment we support:

+---------------------------------------------------------------------------+

| sparkly >= 2.7 | Spark 2.4.x                                              |

+---------------------------------------------------------------------------+

| sparkly 2.x    | Spark 2.0.x and Spark 2.1.x and Spark 2.2.x              |

+---------------------------------------------------------------------------+

| sparkly 1.x    | Spark 1.6.x                                              |

+---------------------------------------------------------------------------+

.. |Sparkly PyPi Version| image:: http://img.shields.io/pypi/v/sparkly.svg

   :target: https://pypi.python.org/pypi/sparkly

.. |Sparkly Build Status| image:: https://app.travis-ci.com/tubular/sparkly.svg?branch=master

   :target: https://app.travis-ci.com/github/tubular/sparkly

.. |Documentation Status| image:: https://readthedocs.org/projects/sparkly/badge/?version=latest

   :target: http://sparkly.readthedocs.io/en/latest/?badge=latest