Locality and Scheduling Delay

The last piece of the scheduler is locality: when the TaskScheduler places a task, it prefers an executor that already has the task's data nearby. Moving compute to the data is cheaper than moving data to the compute, so Spark ranks placement options and tries the best one first. The delay knob that can quietly cost you When the ideal executor is busy, the TaskScheduler faces a choice: wait a moment for it to free up, or give up locality and run the task somewhere it has to fetch the data. The wait is governed by spark.locality.wait (3 seconds by default). On most jobs this is invisible. But if many tasks are all waiting out their locality timeout, you can see a stage that is mysteriously slow with idle-looking executors: everyone is politely waiting for a preferred slot. Lowering the wait

About This Interactive Section

This section is part of the How a Spark Job Runs: Scheduler Internals 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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