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

IntermediatePrefer not to say

May 2026

Summary

Amazon SWE coding round focused on a music player design problem that looked like a frequency-counting exercise but had a nasty scheduling constraint layered on top. Harder than I expected going in.

Questions Asked (1)

Design a music player where song play entries stream in continuously as {user, songs[]} pairs, updating per-user or global song frequencies. The player must always play the most frequent song, but a song cannot be replayed within the current cycle. A cycle resets once every distinct song has been played at least once. Define a deterministic tie-breaker for equal frequencies.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

I recognized the frequency-counting angle pretty fast and jumped to a max-heap.

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AI HintsAI Generated

Suggested Approach

Clarify requirements and constraints, then propose a data structure that maintains per-user and global frequency counts while enforcing the no-repeat rule within a cycle. Use a max-heap with lazy deletion or a balanced BST to efficiently retrieve the most frequent eligible song, and define a deterministic tie-breaker such as lexicographical order of song IDs. Discuss trade-offs between different approaches and how to handle streaming updates and cycle resets.

Pro tip: Explicitly state your tie-breaking rule (e.g., lexicographically smallest song ID) and justify it for determinism; also mention how you'd handle concurrent updates and scalability to show production awareness.

1. Clarify Requirements and Constraints

Ask about expected scale (number of users, songs, update rate), whether per-user and global frequencies are both needed, and if the player is single-threaded or distributed. Confirm that a cycle resets only after every distinct song has been played at least once.

2. Design Data Structures

Propose a hash map for per-user and global frequency counts, and a max-heap or balanced BST to track songs by frequency. For the no-repeat rule, maintain a set of played songs in the current cycle and filter candidates.

3. Define Tie-Breaker and Cycle Logic

Specify a deterministic tie-breaker (e.g., lexicographically smallest song ID) and explain how to reset the cycle: when all distinct songs have been played, clear the played set and start a new cycle.

4. Handle Streaming Updates

Describe how to process each incoming {user, songs[]} pair: increment frequencies, update the heap/BST, and if the played set is empty, select the most frequent eligible song. Discuss lazy deletion or re-heapification for efficiency.

5. Analyze Trade-offs and Scalability

Compare approaches (e.g., heap vs. sorted list) in terms of time complexity for updates and queries. Mention potential bottlenecks and how to scale (e.g., sharding by user, using Redis for counts).

Key Points to Mention

Use of hash maps for O(1) frequency updates per user and globally.
Max-heap with lazy deletion to efficiently get the most frequent song, or a balanced BST for ordered access.
Deterministic tie-breaker: lexicographical order of song IDs ensures consistent selection.
Cycle management: maintain a set of played songs; reset when size equals total distinct songs.
Handling streaming updates: process each entry, update counts, and select next song if needed.
Scalability considerations: sharding, caching, and concurrency control for high throughput.

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