(WHY? Doesn't Beam ship with a Spark runner?)

This project introduces a custom Apache Beam runner that leverages PySpark directly. This is not a 'portability' framework compliant runner! It is designed for environments where a SparkSession is available but a Spark master server is not. This is useful for e.g. serverless environments where jobs are triggered without a long-running cluster, sidestepping the expectations of Beam's default Spark runner.

The other benefit is that this strategy for building a runner helps to keep the stack as python-centric as possible. The compilation process, the optimizations, the execution planning - these all happen in python (for better or worse). Depending on your needs, this might be a significant advantage.

• Direct Integration with PySpark: Utilizes a PySpark assumed SparkSession directly.
• Serverless Compatibility: Ideal for environments without a dedicated Spark master, supporting execution in serverless frameworks.
• Simplified Setup: Potentially reduces the complexity of job submission by avoiding the need for port listening on a Spark master.

• Apache Spark
• Apache Beam
• Python 3.8 or later

To use this custom runner, just pip install as you would any library

pip install beam-pyspark-runner