DAGScheduler vs TaskScheduler

Inside the driver there are two schedulers, and they have a clean division of labor. Knowing the split is what lets you answer 'how does Spark turn my code into running tasks' without hand-waving. The handoff The flow is: your transformations become a logical plan, the DAGScheduler slices that plan into stages and orders them by their shuffle dependencies, and then for each ready stage it hands a TaskSet to the TaskScheduler, which dispatches the individual tasks to slots. The DAGScheduler decides what runs and in what order at the stage level; the TaskScheduler decides where each task runs and what to do when one fails. Every advanced behavior we cover next lives in one of these two: stage ordering and retry-of-a-whole-stage in the DAGScheduler, task placement and per-task retry in the Ta

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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