Memory Management

Concepts covered: paMemoryManagement

What They Want to Hear 'Each executor gets a fixed amount of memory, split between storage (caching data) and execution (shuffles, joins, sorts). When execution memory runs out, Spark spills data to disk, which is much slower. When the disk fills up too, the job fails with an out-of-memory error. The fix depends on the cause: too few partitions means each one is too large, so repartition to create smaller chunks. Too much data cached means storage is crowding out execution, so unpersist unused caches.' That is the answer. Memory splits into storage and execution. Spill to disk is the warning sign.

About This Interactive Section

This section is part of the Distributed Compute: Beginner 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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