spark-submit and the Config Surface

Everything we have described is shaped by a handful of numbers you set when you launch the job. These are the levers. You do not need to memorize the whole config surface, but you must connect each of these to a concept you already learned, because that connection is exactly what an interviewer probes. Why these numbers interact These are not four independent dials. num-executors times executor-cores is your total slots, which only helps if you have enough partitions to fill them. executor-memory has to cover the partitions a core is holding plus shuffle buffers, so cranking executor-cores without raising memory can cause spills or out-of-memory errors. The right way to answer a sizing question is to derive the numbers from the data: data size sets partition count, partition count and targ

About This Interactive Section

This section is part of the How a Spark Job Runs: Stages and Plans 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.