Partitions: The Unit of Parallelism

Your data does not arrive at an executor as one big table. Spark splits it into chunks called partitions. A partition is a contiguous slice of the rows, typically targeted around 128 MB, that lives in memory on one executor. A billion-row table might be 8,000 partitions. This split is the single most important idea in Spark, because it is the unit of parallelism: one task processes exactly one partition. 1 : 1 Why this is the number that matters Because tasks map one-to-one onto partitions, the partition count is the parallelism ceiling. If your data is in 4 partitions, at most 4 things can happen at once, no matter how many machines you rented. If it is in 8,000 partitions and you have 200 CPU cores, Spark works through them 200 at a time in waves. A SQL engine hides this from you; Spark

About This Interactive Section

This section is part of the How a Spark Job Runs 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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