I have spent countless hours debugging slow Spark jobs, and it almost always comes down to a handful of common pitfalls. Apache Spark is a powerful distributed processing engine, but getting top performance requires more than just running your code on a cluster. Even with Spark’s built-in Catalyst optimizer and Tungsten execution engine, a poorly written or configured Spark job can run slowly or inefficiently.
In my years as a software engineer, I have learned that getting top performance from Spark requires moving beyond the defaults and treating performance tuning as a core part of the development process. In this article, I will share the practical lessons I use to optimize Spark programs for speed and resource efficiency.