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 pyspark # pick your version pip install sparkly (compatible with spark >= 2.4)
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.
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
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 |