What About Data Skew?

Concepts covered: paDataSkew

"Your join is fast for most keys but one key takes 10x longer. What is happening and how do you fix it?" Data skew is the single most common root cause of slow Spark jobs in production. Every interviewer expects you to handle it. Detecting Skew In the Spark UI, skew shows up as one task running dramatically longer than others in the same stage. The Tasks tab shows the min, median, and max task duration. If max is 50x the median, one partition holds massively more data than the others. Check the shuffle read size per task - the long task will show orders of magnitude more bytes. Salting: The Standard Fix Salting splits a hot key into N artificial keys, distributing its rows across N partitions. Add a random salt (0 to N-1) to the skewed side, replicate the other side N times with each sal

About This Interactive Section

This section is part of the The Spark Deep Dive: Intermediate 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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