Logical vs Physical Plan: Reading explain()

The DAG in the UI is the visual form of the plan; explain is the textual form, and it shows you two related things. When you call explain on a DataFrame, Spark prints the plan it intends to run, and at the detailed level it shows both the logical plan, what you asked for, and the physical plan, how Spark will actually execute it. The gap between the two is the optimisation that laziness made possible. You read a plan tree 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 output of the one below it, until the top of the tree is your final result. So the bottom tells you what gets read and what filter got pushed down to the read. If your WHERE clause shows up as a PushedFilter at the scan, the optimi

About This Interactive Section

This section is part of the Reading the Plan: DAG, Stages, and explain() 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.

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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.