Design a large-scale Recommendation Platform

Question

Design a distributed recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Codemia · Accepted Answer

### Functional Requirements
1. **User Profiles**: Ability to create and manage user profiles with preferences, search history, and saved properties.
2. **Recommendation Algorithms**: Provide personalized property recommendations based on user behavior, preferences, and collaborative filtering.
3. **Search Integration**: Integrate with Redfin's existing search feature to enhance property search results with recommended listings.
4. **Real-time Updates**: Update recommendations in real-time as users interact with the platform, such as viewing or saving properties.
5. **Feedback Loop**: Allow users to provide feedback on recommendations to improve future results.

### Non-Functional Requirements
1. **Scalability**: Must handle millions of requests per second as Redfin grows.
2. **Availability**: 99.99% uptime to ensure users can always access recommendations.
3. **Latency**: Recommendations should be delivered in under 200 milliseconds to maintain a good user experience.
4. **Consistency**: Eventual consistency is acceptable since user interactions may not need immediate updates.
5. **Security**: Ensure user data privacy and secure handling of personal information.

### Back-of-Envelope Calculations
1. **User Base**: Assume 10 million active users.
2. **Requests per User**: Each user makes approximately 10 requests per day for recommendations.
3. **Total Requests**: 
   - Total daily requests = 10 million users * 10 requests = 100 million requests/day.
   - Total requests per second (QPS) = 100 million requests / 86400 seconds ≈ 1157 QPS.
4. **Data Storage**: 
   - Each user profile may require 1 KB (preferences, history, etc.). 
   - Total storage = 10 million users * 1 KB/user = 10 GB of user data.
   - Assuming 100 properties per user for recommendations = 1 billion properties * 2 KB (metadata) = 2 TB.
   - Total estimated storage = 2 TB + 10 GB ≈ 2.01 TB.

### Component Diagram
The architecture consists of the following components:
1. **User Service**: Handles user profiles and preferences. Built with Spring Boot and deployed on Kubernetes.
2. **Recommendation Engine**: Implements collaborative filtering and content-based filtering algorithms using Apache Spark for batch processing and TensorFlow for machine learning models.
3. **API Gateway**: Manages incoming traffic, rate limits requests, and routes to appropriate services (AWS API Gateway).
4. **Caching Layer**: Redis for in-memory caching of frequently accessed user profiles and recommendations.
5. **Database**: PostgreSQL for user data and property listings, with read replicas for load balancing.
6. **Monitoring & Analytics**: ELK stack (Elasticsearch, Logstash, Kibana) for logging and real-time monitoring of system performance.

### Schema Design
1. **User Table**: 
   - `user_id` (PK, UUID)  
   - `preferences` (JSONB)  
   - `search_history` (TEXT[])  
   - `saved_properties` (UUID[])

2. **Property Table**: 
   - `property_id` (PK, UUID)  
   - `location` (TEXT)  
   - `price` (NUMERIC)  
   - `features` (JSONB)  
   - `recommendation_scores` (FLOAT[])

3. **Recommendations Table**: 
   - `user_id` (FK, UUID)  
   - `recommended_property_id` (FK, UUID)  
   - `score` (FLOAT)  
   - `timestamp` (TIMESTAMP)

### Access Patterns
- Frequent reads for user profiles and recommendations.
- Batch processing of property data for updating recommendation scores.

### Architectural Decisions
1. **Consistency vs. Availability**: Opted for eventual consistency to allow for faster reads and updates, which is crucial for user experience in recommendations.
2. **Caching Strategy**: Chose Redis for caching to improve read performance, but must handle cache invalidation carefully to ensure fresh data.
3. **Database Choice**: PostgreSQL provides strong relational integrity and supports complex queries for recommendations, but may introduce latency for writes compared to NoSQL options.
4. **Scalability Considerations**: Using microservices allows independent scaling of components. However, it introduces complexity in service communication and deployment.
5. **Monitoring and Alerting**: Implementing the ELK stack ensures real-time insights into system health but could increase operational overhead.

Design a large-scale Recommendation Platform

Redfin

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