Heaps power scheduling, streaming analytics, and greedy optimization. Interviewers love them for k-selection and merging tasks.
- Heap mechanics: binary heap properties, push/pop operations, and zero-based indexing math.
- Priority queue tricks: merging k-sorted arrays, streaming medians, and task scheduling.
- Adapters: using Go's
container/heapinterface vs building bespoke array-backed heaps. - Complexity awareness: when heapifying beats incremental insertion, and how to tune space usage.
- Job schedulers: CPU task ordering, throttled background work, and rate limiting.
- Recommendation feeds: merging sorted ranking lists or selecting top-k items in a stream.
- Graph algorithms: Dijkstra, Prim, and A* rely on priority queues for frontier management.
max_heap_using_array.go: manual array-backed max heap with explanatory operations.priority_queue.go: Gocontainer/heapwrapper tailored for interview-style usage.priority_queue_test.go: unit tests showing push, pop, update, and custom comparator patterns.
- Implement a min-heap variant and mirror the tests to ensure confidence both ways.
- Extend the priority queue with decrease-key logic for Dijkstra or Prim implementations.
- Combine heaps with hashing from
hashing/to implement an LRU or LFU cache. - Practice k-way merge and running median problems to reinforce core patterns.
- Explore indexed priority queues and Fibonacci heaps for theoretical depth.
- Benchmark heap operations vs sorting for various data sizes to internalize trade-offs.
- Implement a scheduler simulator: push tasks with deadlines and prioritize based on slack.