Amazon·Software Engineer·Onsite - Coding / Algorithms·Intermediate

Intermediate

May 2026

Summary

Amazon SWE coding round with a music player scheduling problem that looks like a clean OOP design question but turns into a priority queue + round-reset tracking exercise pretty fast. Solid problem, not a typical LeetCode grind.

Questions Asked (1)

Design a music player scheduling system that always plays the globally most frequent unplayed song, resets all songs to playable once every song has been played at least once in the current round, and supports adding user song lists that update global frequencies. Implement addUserSongs and nextSong operations with time/space complexity analysis.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

My first instinct was a max-heap sorted by frequency and I went with that, but the round-reset logic tripped me up for a bit.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the requirements and constraints, then propose a data structure that efficiently tracks global frequencies and unplayed songs, such as a max-heap combined with a hash map. Walk through the implementation of addUserSongs and nextSong, analyzing time and space complexity for each operation.

Pro tip: Discuss trade-offs between different data structures (e.g., heap vs. balanced BST) and mention how you would handle concurrency and persistence in a real-world system, showing system design awareness.

1. Clarify Requirements

Ask questions to confirm assumptions: Are song frequencies global across all users? Should ties be broken randomly or by some rule? What are the expected scales (number of songs, users, operations)?

2. Design Data Structures

Propose using a hash map to store global play counts and a max-heap (priority queue) to retrieve the most frequent unplayed song. Maintain a set or boolean array to track played songs in the current round.

3. Implement Operations

For addUserSongs, update the global frequency map and heap for each song. For nextSong, pop from the heap until an unplayed song is found, mark it played, and if all songs are played, reset the played set and rebuild the heap.

4. Analyze Complexity

Provide time and space complexity for each operation. For example, addUserSongs takes O(k log n) for k songs, nextSong takes O(log n) amortized, and space is O(n) where n is the number of unique songs.

5. Discuss Trade-offs and Extensions

Mention alternative approaches (e.g., using a balanced BST or bucket sort) and their trade-offs. Discuss how to handle concurrency, persistence, and scaling to large datasets.

Key Points to Mention

Use of a max-heap (priority queue) to efficiently retrieve the most frequent unplayed song.
Hash map to maintain global frequency counts for each song.
Set or boolean array to track which songs have been played in the current round.
Reset mechanism: when all songs are played, clear the played set and rebuild the heap.
Time complexity: O(k log n) for addUserSongs (k songs), O(log n) amortized for nextSong.
Space complexity: O(n) for storing frequencies and heap, where n is the number of unique songs.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.