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Helpers & syntax sugar for PySpark.

Project description

Sparkly PyPi Version Sparkly Build Status 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.


Sparkly itself is easy to install:

pip install sparkly

The tricky part is pyspark. There is no official distribution on PyPI. As a workaround we can suggest:

  1. Use env variable PYTHONPATH to point to your Spark installation, something like:

    export PYTHONPATH="/usr/local/spark/python/lib/"
  2. Use our file for pyspark. Just add this to your requirements.txt:

    -e git+

Here in Tubular, we published pyspark to our internal PyPi repository.

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 = [

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.


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

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