Seeing the Optimization Happen with explain()

You do not have to take the optimizer on faith; you can watch it work. The explain method prints the plan Spark intends to run, and comparing it to the query you wrote shows what Catalyst changed. The clearest thing to look for is a filter you wrote showing up in the plan pushed down to the scan, proof that the optimizer moved your work to where it costs least. You read a plan from the bottom up, because that is the order data flows: the leaves are the scans that read your tables, and each operator above consumes the one below it. So the bottom of the plan tells you what gets read, and what got pushed into the read. When your filter shows up as a PushedFilters entry at the scan, Catalyst pushed it down and you are reading fewer rows. When it shows up as a separate Filter operator higher up

About This Interactive Section

This section is part of the The Optimizer Works For You lesson on DataDriven, a free data engineering interview prep platform. Each section includes explanations, worked examples, and hands-on code challenges that execute in real time. SQL queries run against a live PostgreSQL database. Python runs in a sandboxed Docker container. Data modeling problems validate against interactive schema canvases. All content is framed around what data engineering interviewers actually test at companies like Meta, Google, Amazon, Netflix, Stripe, and Databricks.

How DataDriven Lessons Work

DataDriven combines four interview rounds (SQL, Python, Data Modeling, Pipeline Architecture) with adaptive difficulty and spaced repetition. Easy problems get harder as you improve. Weak concepts resurface until you master them. Your readiness score tracks progress across every topic interviewers test. Every lesson section ends with problems you solve by writing and running real code, not by picking multiple-choice answers.