Music Playlist (OOD)

Amazon 'Music Playlist (OOD)' Interview Question

The "Music Playlist (OOD)" is an Amazon object-oriented design (OOD) interview problem focused on implementing a playlist system for a user's most recently played songs on Amazon Music. It emphasizes backend data structures, system design principles, and efficient operations, with tags like Backend, OOD, web, machine_learning (potentially for future extensions like recommendations), and System Design.[3]

Full Problem Statement

Design and implement a playlist that maintains a user's most recently played songs on Amazon Music. The playlist should support efficient operations to add new songs and retrieve existing ones, ensuring no duplicates and preserving recent play order (e.g., like a queue or LRU cache). Key requirements include handling song searches and additions scalably for real-time web usage.[3]

Operations

• addSong(Song song): Adds the song to the playlist if it is not already present. If the song exists, move it to the most recent position (update recency).
• getSong(String songId): Searches for and returns the song by ID if it exists in the playlist; otherwise, return null or indicate not found. Retrieval should be efficient (ideally O(1) average case).

Songs are typically represented with attributes like ID, title, artist, and duration. The design must use appropriate OOP principles (classes, interfaces) and data structures like HashMap for lookups combined with DoublyLinkedList for order.[3]

Input/Output Examples

No formal input/output test cases are provided in sources, as this is an OOD problem emphasizing class design over algorithmic coding. Example usage (pseudocode):

` Playlist playlist = new Playlist(maxSize); // e.g., maxSize=100 for recent songs

// addSong examples playlist.addSong(new Song("123", "Song A", "Artist X")); // Adds to playlist playlist.addSong(new Song("123", "Song A", "Artist X")); // Updates recency, no duplicate

// getSong examples Song song1 = playlist.getSong("123"); // Returns Song A (most recent) Song song2 = playlist.getSong("456"); // Returns null (not found) `

Expected behavior mimics LRU (Least Recently Used) cache: oldest songs evict on capacity overflow.[3]

Constraints

• Maximum playlist size: Up to 100-1000 songs (capacity-limited to simulate "recently played").
• Song IDs: Unique strings (e.g., UUIDs).
• Operations: 10^5 adds/gets per session; requires O(1) average time for add/get.
• No duplicates: Songs identified by unique ID.
• Scalability: Design for web backend (multi-user, distributed potential via sharding).
• Edge cases: Empty playlist, full capacity, frequent repeats, case-sensitive IDs.

This matches Amazon SDE interviews for new grads (2021 reports), testing OOP, data structures, and trade-offs like HashMap + DLL vs. LinkedHashMap.[10][3]