Architect a multi-tenant Feature Flag Engine

Question

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

Codemia · Accepted Answer

### Functional Requirements
1. **Multi-tenancy**: The system must support multiple tenants, ensuring isolation of feature flags and configurations.
2. **Feature Flag Management**: Users can create, update, delete, and retrieve feature flags.
3. **Real-time Evaluation**: The system should evaluate feature flags in real-time for user requests.
4. **A/B Testing Support**: Users must be able to define experiments and gather metrics for performance evaluation.
5. **Analytics Dashboard**: Provide insights into feature flag performance and A/B testing results.

### Non-Functional Requirements
1. **Scalability**: The system should handle millions of requests per day across tenants.
2. **High Availability**: The system should ensure 99.9% uptime.
3. **Low Latency**: Feature flag evaluations must be completed in under 100ms.
4. **Data Durability**: All configurations and logs must be stored durably without loss.
5. **Security**: Ensure data security and compliance, especially for multi-tenant architectures.

Assuming Neon serves 10 million users, and each user triggers 100 feature flag evaluations per day:
- **Total Requests per Day**: 10 million users * 100 requests = 1 billion requests per day.
- **Requests per Second**: 1 billion / 86400 seconds = ~11,574 requests per second.

To accommodate spikes, we would plan for a 2x buffer:
- **Peak Load**: ~23,148 requests per second.

Given a 24-hour operational model, we need to ensure our infrastructure can handle this load efficiently.

### Component Diagram
1. **API Gateway**: Manages incoming requests, routes them to the appropriate services.
2. **Feature Flag Service**: Core service that handles CRUD operations for feature flags.
3. **Evaluation Service**: Responsible for real-time evaluation of feature flags using cached values.
4. **Data Store**: **PostgreSQL** for structured data and **Redis** for caching feature flags for low-latency reads.
5. **Analytics Service**: Collects and processes metrics for A/B tests and feature usage.
6. **Monitoring & Alerting**: Integrate **Prometheus** and **Grafana** for performance and health monitoring.
7. **ML Model Service**: Hosts the A/B testing model and metric evaluation logic.

### Feature Flags Schema
- **FeatureFlag**: `id, tenant_id, name, description, status, rules (JSON), created_at, updated_at`
- **Experiment**: `id, feature_flag_id, variant_a_id, variant_b_id, start_date, end_date, metrics (JSON)`
- **Metrics**: `id, experiment_id, conversion_rate, sample_size, created_at`

### Access Patterns
1. **CRUD on Feature Flags**: Frequent updates should be efficient, so indexing on `tenant_id` and `name` is critical.
2. **Real-time Evaluations**: Use caching to reduce load on the PostgreSQL instance, utilizing Redis for quick lookups.

### Architectural Decisions
1. **Consistency vs. Availability**: Opting for eventual consistency with Redis caching allows for higher availability and performance at the cost of immediate consistency, which is acceptable for feature flags.
2. **Online vs. Offline Evaluation**: Online evaluations provide real-time feedback, while offline A/B testing can be processed in batch, allowing for deeper insights but with a delay. A hybrid approach could be beneficial.
3. **Data Quality vs. Speed**: Prioritizing real-time processing may lead to reduced data quality assurance checks. Implementing a feedback mechanism to ensure data integrity post-evaluation can help mitigate this.
4. **Feature Flag Complexity**: Supporting complex rules for feature flags increases the evaluation time. It may be beneficial to limit rules complexity for performance reasons, especially under high load.

Architect a multi-tenant Feature Flag Engine

Neon

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