Design a large-scale Recommendation Platform

Question

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

Codemia · Accepted Answer

### Functional Requirements:
1. **Multi-tenant Support**: The system should support multiple tenants (clients) with data isolation.
2. **Real-time Recommendations**: Generate personalized recommendations based on user behavior in real-time.
3. **User Feedback Loop**: Allow users to provide feedback on recommendations to improve future suggestions.
4. **Analytics Dashboard**: Provide analytics for tenants to understand recommendation performance and user engagement.

### Non-Functional Requirements:
1. **Scalability**: The system should handle millions of requests per second (QPS) as the user base grows.
2. **Availability**: Achieve 99.9% uptime with a strong focus on low-latency responses.
3. **Security**: Ensure data is encrypted at rest and in transit, with strong authentication mechanisms.
4. **Consistency**: Provide eventual consistency for recommendations to balance performance and accuracy.

Assuming Workday has around 1 million active users, with an average of 10 recommendations requested per user per day:
- **Total Requests per Day**: 1,000,000 users * 10 requests/user = 10,000,000 requests/day.
- **Requests per Second (QPS)**: 10,000,000 requests/day / 86,400 seconds/day ≈ 115 QPS.
- Considering peak usage during certain times, we can estimate a peak load of around 500 QPS.

For storage, if each recommendation is around 1KB and we maintain a history of 30 recommendations per user:
- **Total Storage**: 1,000,000 users * 30 recommendations * 1KB = 30 GB of storage.

### Component Diagram:
- **API Gateway**: Handles incoming requests, routes them to appropriate services.
- **Recommendation Service**: Responsible for generating recommendations using machine learning models.
- **User Profile Service**: Manages user data and preferences, storing in a NoSQL database (e.g., MongoDB) for flexibility.
- **Feedback Service**: Collects user feedback on recommendations and updates the recommendation model.
- **Analytics Service**: Processes data for analytics dashboards, using a data warehouse solution (e.g., Amazon Redshift).
- **Caching Layer**: Uses Redis for caching frequent requests and recommendations to reduce latency.
- **Load Balancer**: Distributes incoming traffic across multiple instances of services for scalability.

### Schema Design:
1. **User Table**:
   - `user_id`: String (Primary Key)
   - `tenant_id`: String
   - `preferences`: JSON
   - `created_at`: Timestamp

2. **Recommendations Table**:
   - `recommendation_id`: String (Primary Key)
   - `user_id`: String (Foreign Key)
   - `item_id`: String
   - `score`: Float
   - `timestamp`: Timestamp

3. **Feedback Table**:
   - `feedback_id`: String (Primary Key)
   - `user_id`: String (Foreign Key)
   - `recommendation_id`: String (Foreign Key)
   - `rating`: Int (1-5)
   - `comments`: String
   - `timestamp`: Timestamp

This schema allows for efficient querying of recommendations and user feedback, optimizing read performance.

1. **Consistency vs. Availability**: Given the need for real-time recommendations, we lean towards eventual consistency. This allows us to serve requests faster while updates propagate in the background.
2. **Caching Strategies**: Caching recommendations can improve performance, but we must ensure that stale data doesn’t lead to poor user experiences. We can implement cache invalidation strategies based on user feedback and time-to-live (TTL) settings.
3. **Database Choices**: While a NoSQL database allows for flexible schemas and quick writes, it may complicate queries requiring joins. We might consider using a combination of NoSQL for user profiles and a relational database for transactional data.
4. **Partitioning Strategy**: We will partition user data by `tenant_id` to ensure data isolation and optimize query performance. However, this might complicate cross-tenant analytics.

Design a large-scale Recommendation Platform

Workday

What the Interviewer Expects

Key Topics to Cover

How to Approach This

Possible Follow-up Questions

Practice a Similar Problem on Codemia

Sample Answer

Requirements

Functional Requirements:

Capacity Estimation

Submit Your Answer

Workday Software Engineer Interview Guide

Related Questions

Design a high-throughput Inventory Management System

Design a low-latency Rate Limiting System

Design a fault-tolerant Payment System

Design a fault-tolerant Messaging System

Design Walmart Product Search