I went with a queue to track the recent N songs and a set for O(1) lookup, then just kept resampling until I got a valid pick.
Clarify requirements (e.g., N relative to total songs, handling edge cases) and propose a data structure like a queue to track recent plays. Then, design an algorithm that selects a random song from the set of songs not in the recent history, ensuring O(1) or O(k) time complexity. Discuss trade-offs and potential optimizations.
Pro tip: Mention that if N >= number of songs, the problem is impossible without repeats; handle this by either throwing an error or adjusting N. Also, consider using a circular buffer for efficient memory usage.
Ask about constraints: Can N be larger than the number of songs? Should the playlist loop indefinitely? What is the expected time complexity?
Use a queue (or circular buffer) to store the last N played songs for O(1) updates. Maintain a set of available songs (not in the queue) for random selection.
On each play, randomly select a song from the available set. Add it to the queue and remove the oldest song if the queue size exceeds N, adding it back to the available set.
If N >= total songs, either throw an exception or adjust N to total songs - 1. Also, handle empty playlist and initial fills.
Discuss time and space complexity. For large playlists, consider using an array and swapping to avoid O(n) set operations.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.