Design a large-scale Feed Generation Platform

Question

Design a scalable feed generation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Codemia · Accepted Answer

### Functional Requirements
1. **User Feed Generation**: Generate personalized feeds for users based on their preferences, past interactions, and trending listings.
2. **Real-time Updates**: Update feeds in real-time as new listings, reviews, or changes occur.
3. **Search and Filter**: Allow users to search and filter the feed based on various criteria (e.g., location, price range).
4. **Engagement Metrics**: Track user interactions with the feed (clicks, saves, shares).

### Non-Functional Requirements
1. **Scalability**: Must handle millions of requests per second, especially during peak times (e.g., holiday seasons).
2. **High Availability**: Ensure 99.99% uptime with minimal latency.
3. **Consistency**: Prefer eventual consistency for feed updates to enhance performance.
4. **Fault Tolerance**: System should recover gracefully from failures without data loss.

### Back-of-Envelope Calculations
1. **User Base**: Assuming 100 million active users.
2. **Feed Requests**: Each user may request their feed 10 times per day on average.
   - Total daily feed requests = 100M users * 10 requests = 1 billion requests/day.
   - QPS (Queries Per Second) = 1 billion requests/day / 86400 seconds/day ≈ 11,574 QPS.
3. **Data Storage**: Each personalized feed may contain about 50 listings, with each listing averaging 1KB.
   - Total storage needed = 100M users * 50 listings * 1KB = 5TB.
4. **Bandwidth**: Assuming an average response size of 50KB per feed request.
   - Total daily bandwidth = 1 billion requests * 50KB = 50TB.

### Component Diagram
1. **API Gateway**: Routes requests to appropriate services, implements rate limiting.
2. **Feed Service**: Core service that generates feeds by aggregating data from various sources (listings, user preferences).
3. **Cache Layer**: Utilizes Redis as an in-memory cache for frequently accessed feeds to reduce load on databases.
4. **Database**: PostgreSQL for structured data storage with partitioning by geographical location and user IDs.
5. **Message Queue**: Kafka for handling asynchronous updates to feeds when new listings are added or user interactions occur.
6. **Monitoring**: Prometheus and Grafana for monitoring system health and performance metrics.

### Schema Design
1. **User Table**: 
   - `user_id` (PK)
   - `preferences` (JSON)
   - `last_active` (TIMESTAMP)

2. **Listing Table**: 
   - `listing_id` (PK)
   - `location` (STRING)
   - `price` (FLOAT)
   - `created_at` (TIMESTAMP)

3. **Feed Table** (Materialized View for quick access): 
   - `user_id` (PK)
   - `feed_data` (JSONB)
   - `last_updated` (TIMESTAMP)

### Access Patterns
- Frequent reads for user feeds, less frequent writes (when new listings or updates occur).

### Architectural Decisions
1. **Consistency vs. Availability**: Opting for eventual consistency in feed updates to enhance performance, as users can tolerate slightly outdated data in their feeds.
2. **Database Choice**: Choosing PostgreSQL for its relational capabilities and support for complex queries over a NoSQL database, which might offer higher write speeds but less complex querying.
3. **Caching Strategy**: Using Redis for caching feeds to minimize database load, but this introduces a risk of stale data, which we mitigate by setting TTLs and using cache invalidation strategies.
4. **Partitioning Strategy**: Geographical partitioning allows for better performance and scalability, but requires careful planning to avoid hotspots where certain partitions receive disproportionate traffic.
5. **Failure Handling**: Implementing retries and dead-letter queues in Kafka to ensure messages aren’t lost during processing.

Design a large-scale Feed Generation Platform

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