Spotify System Design Interview
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Spotify System Design Interview Topics
Our AI coach helps you master these critical system design concepts for Spotify backend engineering interviews
Music Streaming Architecture
Design scalable music streaming systems, handle millions of concurrent streams, and optimize for low-latency audio delivery worldwide.
Search & Discovery Systems
Build real-time music search, recommendation engines, personalization algorithms, and content discovery at massive scale.
Analytics & Data Pipeline
Process billions of listening events, real-time analytics, user behavior tracking, and machine learning feature engineering.
Global CDN & Caching
Distribute music content globally, implement multi-tier caching strategies, and optimize for regional performance requirements.
Microservices Architecture
Design loosely coupled services, handle service discovery, implement circuit breakers, and manage distributed system complexity.
Security & Rights Management
Implement digital rights management, secure API authentication, prevent piracy, and handle licensing restrictions.
Spotify System Design Interview in Action
Interviewer: "Design a system like Spotify that can handle 500M users, 100M songs, and support real-time streaming, search, recommendations, and social features. Consider global scale, high availability, and low latency requirements."
System Requirements & Scale Estimation
Scale Requirements:
- Users: 500M total, 200M daily active users
- Songs: 100M tracks, 10M artists, 1M podcasts
- Streaming: 50M concurrent streams at peak
- Storage: 100M songs × 5MB = 500TB audio data
- Bandwidth: 50M streams × 256kbps = 12.8 Tbps
- QPS: Search: 50K QPS, Recommendations: 100K QPS
High-Level Architecture
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────────┐
│ Mobile Apps │ │ Web Client │ │ Desktop Client │
│ iOS/Android │ │ React/Vue │ │ Electron │
└─────────┬───────┘ └────────┬─────────┘ └─────────┬───────────┘
│ │ │
└─────────────────────┼────────────────────────┘
│
┌─────────────┴─────────────┐
│ Load Balancer │
│ (AWS ALB/Cloudflare) │
└─────────────┬─────────────┘
│
┌────────────────────────────────────────────┐
│ API Gateway │
│ (Kong/AWS API Gateway) │
│ - Authentication │
│ - Rate Limiting │
│ - Request Routing │
└─────────────────┬──────────────────────────┘
│
┌────────────────────┼────────────────────┐
│ │ │
┌─────▼─────┐ ┌─────────▼────────┐ ┌─────▼─────┐
│ User │ │ Music Streaming │ │ Search │
│ Service │ │ Service │ │ Service │
└─────┬─────┘ └─────────┬────────┘ └─────┬─────┘
│ │ │
Authentication Stream Management Elasticsearch
User Profiles Quality Selection Auto-complete
Preferences Playback Control Fuzzy Search
│ │ │
┌─────▼─────┐ ┌─────────▼────────┐ ┌─────▼─────┐
│Recommendation│ │ Playlist │ │ Analytics │
│ Service │ │ Service │ │ Service │
└─────┬─────┘ └─────────┬────────┘ └─────┬─────┘
│ │ │
ML Pipeline Collaborative Event Stream
Content-Based CRUD Operations Real-time Analytics
Collaborative Social Features A/B Testing
│ │ │
└──────────┬─────────────────┬───────────┘
│ │
┌─────────────────────────────────────┐
│ Data Layer │
└─────────────────────────────────────┘
Architecture Design Principles:
This Spotify-scale system demonstrates key design patterns:
1. Service Decomposition:
- Single Responsibility: Each service handles one domain (user, music, search)
- Loose Coupling: Services communicate via APIs and message queues
- Independent Scaling: Scale services based on individual load patterns
2. Data Flow Optimization:
- Read-Heavy Workload: Optimize for 95% read operations vs 5% writes
- Event-Driven: Use Kafka for real-time data streaming
- CQRS Pattern: Separate read/write models for optimal performance
3. Global Scale Considerations:
- Multi-Region: Deploy across AWS regions globally
- Edge Caching: CDN for audio content and metadata
- Data Locality: Store user data in regional databases
Music Streaming Service Deep Dive
Music Streaming Service Architecture
Client Request Stream Controller Redis Cache
↓ ↓
Metadata Service Cassandra
↓ ↓
Quality Selector Song Metadata
↓
CDN Router
↓
Audio CDN Client Playback
Audio Quality Selection Logic:
┌─────────────────────────────────────────────────────┐
│ Network Speed │ Quality │ Bitrate │ Buffer Size │
├─────────────────────────────────────────────────────┤
│ < 1 Mbps │ Low │ 96kbps │ 30s │
│ 1-5 Mbps │ Med │ 160kbps │ 45s │
│ 5-10 Mbps │ High │ 320kbps │ 60s │
│ > 10 Mbps │ Lossless│ 1411kbps│ 90s │
└─────────────────────────────────────────────────────┘
CDN Strategy:
US-East: Primary US-West: Mirror Europe: Regional
↓ ↓ ↓
S3 Origin S3 Cross-Region S3 EU
🎵 Spotify Streaming Performance Targets:
- Stream Start Time: < 200ms (cached), < 500ms (uncached)
- Buffer Health: Maintain 30-90s buffer based on quality
- CDN Hit Ratio: > 95% for popular content, > 85% overall
- Global Availability: 99.9% uptime with < 1s failover
Data Storage & Management
Data Storage Architecture
┌──────────────────────────────────────────────────────────┐
│ Application Layer │
└─────────────────────┬────────────────────────────────────┘
│
┌───────────────────────────────────┐
│ Caching Layer │
├───────────────────────────────────┤
│ Redis Cluster │
│ - User sessions (TTL: 24h) │
│ - Recently played (TTL: 7d) │
│ - Search autocomplete (TTL: 1h) │
│ - Song metadata (TTL: 24h) │
└─────────────┬─────────────────────┘
│
┌─────────────────┼─────────────────┐
│ │ │
┌─────▼─────┐ ┌──────▼──────┐ ┌─────▼─────┐
│PostgreSQL│ │Cassandra │ │Elasticsearch│
│ Cluster │ │ Cluster │ │ Cluster │
└───────────┘ └─────────────┘ └───────────┘
PostgreSQL (OLTP): Cassandra (Time-Series): Elasticsearch (Search):
- User accounts - Listening history - Song metadata
- Artist/Label data - Playlist interactions - Artist information
- Subscription billing - Real-time analytics - Search indices
- Digital rights - User behavior events - Autocomplete data
Storage Estimates:
┌──────────────────────────────────────────────────────┐
│ Data Type │ Size │ Growth │ Storage │
├──────────────────────────────────────────────────────┤
│ Audio Files │ 500TB │ 50TB/year │ S3/CDN │
│ User Data │ 2TB │ 500GB/year│ PostgreSQL│
│ Listening Events │ 100TB │ 2TB/month │ Cassandra │
│ Search Indices │ 50GB │ 5GB/month │ Elasticsearch│
└──────────────────────────────────────────────────────┘
Advanced System Design Patterns:
1. Data Consistency Strategy:
- Eventual Consistency: Listening history can be eventually consistent
- Strong Consistency: Billing and licensing require ACID properties
- Read Replicas: Offload read traffic from primary databases
2. Real-time Features:
- WebSocket Connections: Real-time playlist collaboration
- Server-Sent Events: Live radio and social updates
- Message Queues: Kafka for event streaming and analytics
3. Monitoring & Observability:
- Distributed Tracing: Track requests across microservices
- Metrics Collection: Prometheus/Grafana for system health
- Log Aggregation: ELK stack for centralized logging
- Alerting: PagerDuty integration for incident response
Interview Deep-Dive Topics:
- "How would you handle a viral song that gets 10M plays in one hour?"
- "Design the recommendation algorithm architecture for personalization."
- "How would you implement offline music downloads and sync?"
- "What's your strategy for handling copyright violations at scale?"
- "How would you optimize the system for emerging markets with poor connectivity?"
🎵 Streaming Architecture
Design high-throughput music streaming systems, handle millions of concurrent streams, and optimize for global low-latency audio delivery.
🔍 Search & Discovery
Build real-time search engines, recommendation systems, personalization algorithms, and content discovery at massive scale.
📊 Big Data Analytics
Process billions of events, implement real-time analytics pipelines, and build machine learning feature engineering systems.
🌍 Global Distribution
Design CDN strategies, implement multi-region deployments, and optimize for global performance and availability.
⚖️ Microservices Design
Architect loosely coupled services, implement service mesh, handle distributed system complexity, and ensure fault tolerance.
🔒 Security & Compliance
Implement digital rights management, secure authentication systems, prevent piracy, and handle regulatory compliance.
Spotify System Design Interview Topics
🎵 Streaming Systems
- High-throughput audio streaming architecture
- Adaptive bitrate and quality selection
- Buffer management and preloading strategies
- CDN optimization for global delivery
🔍 Search & Recommendations
- Real-time search with auto-complete
- Machine learning recommendation engines
- Collaborative and content-based filtering
- Personalization at scale
📊 Analytics & Data
- Real-time event processing with Kafka
- Batch processing for ML model training
- User behavior analytics and insights
- A/B testing infrastructure
🌍 Global Architecture
- Multi-region deployment strategies
- Data replication and consistency
- Edge computing and regional optimization
- Cross-region failover mechanisms
⚖️ Microservices
- Service decomposition and boundaries
- API gateway and service mesh
- Circuit breakers and fault tolerance
- Inter-service communication patterns
🔒 Security & Rights
- Digital rights management (DRM)
- Content protection and anti-piracy
- OAuth and API authentication
- Licensing and royalty systems
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