Design a multi-tenant Recommendation System

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**:
  - Support multi-tenancy, allowing multiple clients to use the recommendation system independently.
  - Provide personalized recommendations for users based on historical interactions and preferences.
  - Allow clients to customize their recommendation algorithms and input features.
  - Enable A/B testing capabilities to evaluate different recommendation strategies.

- **Non-Functional Requirements**:
  - Latency: Response time for recommendations must be under 100ms for real-time requests.
  - Scalability: The system should handle up to 10 million requests per day.
  - Availability: The system must maintain 99.9% uptime.
  - Data Security: Ensure tenant data isolation and compliance with data protection regulations.

To estimate capacity:
- Assume an average of 100 requests per user per day.
- If we target 1 million users: 1,000,000 users x 100 requests = 100,000,000 requests/day.
- With peak traffic assumed to be 10% of daily traffic, our peak requests would be around 11,574 requests/second.
- To handle this, we would provision for redundancy and load balancing to ensure that the system could handle bursts above this average, targeting a throughput of 15,000 requests/second.

The architecture consists of several key components:
- **Data Ingestion Layer**: Apache Kafka for streaming user interactions and feedback.
- **Feature Store**: AWS S3 with Apache Parquet format for efficient retrieval of features used in model training.
- **Model Training**: Use TensorFlow or PyTorch for building models, orchestrated via Kubeflow for reproducibility.
- **Serving Layer**: A microservices architecture using Flask or FastAPI for real-time recommendation serving, backed by Redis for caching frequent requests.
- **Monitoring and Logging**: Prometheus and Grafana for monitoring system performance and user engagement metrics.

The schema design will focus on two primary entities: `User` and `Recommendation`:
- **User Table**:
  - `user_id`: String (PK)
  - `tenant_id`: String (FK)
  - `preferences`: JSON (user preferences)
  - `interaction_history`: Array of JSON (past interactions)

- **Recommendation Table**:
  - `recommendation_id`: String (PK)
  - `user_id`: String (FK)
  - `recommended_items`: Array of Strings (item IDs)
  - `score`: Float (relevance score)
  - `timestamp`: DateTime (when the recommendation was generated)

This design allows for efficient querying based on user preferences and interaction history.

Several trade-offs must be considered:
- **Consistency vs. Availability**: Opting for eventual consistency in a multi-tenant system can enhance availability but may lead to stale recommendations. We can mitigate this by using a background process that periodically updates recommendations based on the latest data.
- **Complexity vs. Interpretability**: We could use a complex deep learning model for recommendations, which may provide better performance but is harder to interpret. A hybrid approach could be beneficial, combining simpler models for some tenants with complex models for others.
- **Real-time vs. Batch Serving**: While real-time serving reduces latency, it can be resource-intensive. A hybrid model where frequent users have real-time recommendations and infrequent users receive batch updates can balance the load.

Design a multi-tenant Recommendation System

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