Why Skew Wastes the Cluster

Concepts covered: paDataSkew

It is tempting to file one slow task under annoyance rather than emergency: the job still finishes, after all. The reason skew deserves the emergency file is arithmetic. While the straggler grinds, every other core in the cluster is idle, and you are paying for all of them. Take a modest cluster of 50 executors with 4 cores each, 200 cores total. The stage's 199 normal tasks finish in 30 seconds; the straggler runs another 44 minutes. For those 44 minutes, 199 of your 200 cores do nothing. That is close to 150 core-hours of paid, allocated, idle compute burned on a single stage, every time it runs. The bill is one half of the waste; the schedule is the other. Because a shuffle boundary is a barrier, the stage after the straggler cannot start until it lands, so the delay propagates through

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This section is part of the Data Skew: 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 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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