Heaps power priority queues. They show up in schedulers, event loops, ranking pipelines, and shortest-path algorithms where the current best candidate must be extracted repeatedly.
Why it shows up
Choose it when you repeatedly ask for the next best item, not when you need arbitrary ordered iteration.
Common domains
Run the most urgent or earliest-deadline work next
Schedulers maintain a priority queue of jobs ordered by deadline, cost, or urgency.
Why this structure fits
The system repeatedly needs the next highest-priority task, not a full sort.
Leaderboards, recommendation candidates, and analytics
Pipelines keep a bounded heap of the best items seen so far instead of sorting the entire stream.
Why this structure fits
It efficiently tracks only the winners that matter.
Dijkstra, A*, and best-first search
Pathfinding algorithms repeatedly expand the next most promising node from a frontier.
Why this structure fits
A heap provides fast access to the frontier node with the lowest current cost.
Simulators, runtimes, and delayed work queues
Event-driven systems often need the earliest scheduled event next.
Why this structure fits
A min-heap naturally exposes the next event to fire.
