← Walmart Labs Interview Insights
My first instinct was a heap because of the priority angle, but then the example they gave had all tasks running at once, so a sort was really all that was needed.
Start by clarifying requirements: task properties, priority semantics, and execution order. Then propose a data structure (e.g., heap) for efficient priority-based retrieval, and discuss trade-offs. Finally, outline the API design and consider concurrency and scalability for a mobile environment.
Pro tip: Mention that on mobile, you'd likely use a priority queue with a comparator, but also consider battery and network constraints—e.g., batching tasks or deferring non-urgent work. This shows you think beyond pure algorithms.
Ask about task definition, priority range, execution semantics (e.g., highest priority first, FIFO within same priority), and whether tasks can be cancelled or updated.
Propose a priority queue (binary heap) for O(log n) insertion and O(log n) extraction. Discuss alternatives like sorted list or bucket queue if priorities are bounded.
Define methods: addTask(task, priority) and executeAll(). Consider return types, error handling, and whether executeAll should block or be async.
Discuss thread safety if tasks can be added from multiple threads. Mention mobile-specific concerns: battery, background execution limits, and using WorkManager/JobScheduler on Android or BGTaskScheduler on iOS.
State time and space complexity for each operation. Discuss trade-offs between different data structures and execution strategies (e.g., eager vs. lazy execution).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.