stefansavev / mlstream-spark-udfs

A repository of Spark UDFs (extensions)

Handy Spark Tools Accessible From Spark SQL

Spark and PySpark packages that extend Apache Spark (UDFs) with additional SQL functions. Available for both Scala and Python.

For Data Scientists and Engineers using Spark

Objectives:

• "As a data scientist, I can get more done by using SQL functions"

• "As a data engineer, I can get real-time aggregations via SQL functions"

Available Functions

Generic Extensions

UDF Function	Description	Example	Status
last_k	Returns the lask k occurences	SELECT user_id, last_k(page_id, timestamp, 100, "unique") FROM dataset	In Development
approx_topk	Returns the most frequent items using a fast approximation algorithm with limited memory	SELECT approx_topk(ip_address, 1000, "10MB") FROM dataset	Available
approx_cond_topk	Returns the most frequent items conditioned on anyother item using a fast approximation algorithm with limited memory	NA	In Development

Getting Started

Start with a Prepared Docker Image with PySpark and Jupyter

1. Browse the Jupyter notebooks.

2. Clone the repository

git clone [e-mail]:MLStream/mlstream-spark-udfs.git

3. Run the demo (Linux and Mac only)

The following command starts a demo Jupyter server which is ready to use with local files.

./demo.sh

Install via Python pip or as a Spark Package

TODO

Disclaimer

The project mlstream-spark-udfs is distributed in the hope it will be useful and help you solve pressing problems. At the same time its still early days for mlstream-spark-udfs. mlstream-spark-udfs may contain many bugs - known or unknown, it may crash, force yor computer to run out of memory and produce erroneous results. Please carry out due diligence before using and deploying in your organization. The developers developers of mlstream-spark-udfs, be they organizations or people, should not be held liable for any damages which result from running the code. The code is distributed under Apache License which should be consulted for warranties and liabilities. This disclaimer to does not replace the license.

License

The code is distributed under Apache License. Please check the source files in the repositories for third-party libraries used. We further use Source code derived from GoLang sort. Please consult GoLang LICENSE

Looking for Help with Spark and Spark Extensions for Your Organization

Code of Conduct

TODO

Support

Please file an issue or contact [e-mail].