Apache Spark

Apache Spark is an open-source, distributed computing system that has gained immense popularity for its speed, ease of use, and versatility in handling large-scale data processing tasks. Developed to overcome the limitations of the MapReduce paradigm, Spark offers a unified platform for various data processing workloads, including batch processing, real-time data streaming, machine learning, and graph processing.

Spark provides high-level APIs in languages like Scala, Java, Python, and R, making it accessible to a wide range of developers with different programming backgrounds.

In this chapter, we will:

• Set up a mini Spark cluster in M3.
• Take a closer look at the internal data structure, specifically Resilient Distributed Datasets (RDDs).
• Explore data processing in Spark and JupyterLab.
• Submit batch jobs utilizing both Slurm and Spark.
• Engage in some challenges.

Notes:

• The material covered in this chapter draws heavily from the official documentation of Spark 3.5.0.
• Contents of Setting up a Spark Cluster within M3 via Slurm and Submit Spark Job inside Slurm Job are both derived from a trial-and-error approach, and doesn't adhere to any official documentation. Consequently, there is a likelihood that it may not be the best practice. Thus, if you've discovered alternative methods or more effective approaches or may be even security vulnerabilities, please don't hesitate to submit a pull request.