If you’ve ever run a Spark job and wondered how it can process millions or billions of rows so efficiently, the secret lies in the Catalyst Optimizer. Think of it as Spark’s internal brain — taking your high-level transformations and figuring out the most efficient way to execute them across a cluster. Understanding Catalyst isn’t... Continue Reading →
Logical vs Physical Plan in Spark: Understanding How Your Code Really Runs
If you’ve worked with Apache Spark, you’ve likely written transformations like filter(), map(), or select() and wondered, “How does Spark actually execute this under the hood?” The answer lies in logical and physical plans — two key steps Spark uses to turn your code into distributed computation efficiently. Understanding this will help you optimize performance... Continue Reading →
Lazy Evaluation vs Eager Evaluation: Compute Now or Compute When Needed
Have you ever noticed that some Python operations don’t execute immediately? Or why creating huge lists can crash your program? That’s where lazy evaluation vs eager evaluation comes into play — two contrasting approaches for handling computation. Understanding them is critical if you work with Python, Spark, or any data-intensive pipeline. 1. Eager Evaluation: Compute... Continue Reading →
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