Architect a multi-tenant Monitoring Engine

Question

Design a multi-tenant monitoring 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 must support multiple tenants (users) with isolated monitoring data.
2. **Real-Time Monitoring**: Users should be able to view monitoring metrics in real-time, with updates at least every few seconds.
3. **Alerting Mechanism**: The system should provide alerting based on user-defined thresholds for various metrics.
4. **API Access**: Tenants should have access to a RESTful API to query metrics, configure alerts, and manage settings.
5. **Historical Data**: Users should be able to access historical metrics for a configurable period (e.g., last 30 days).

### Non-Functional Requirements
1. **Scalability**: The system should handle millions of requests per second as the number of tenants grows.
2. **High Availability**: The system should be up 99.99% of the time, with minimal downtime during maintenance.
3. **Performance**: API response times should be under 100ms for 95% of requests.
4. **Data Consistency**: The system should provide eventual consistency for real-time data while ensuring strong consistency for configuration changes.

Assuming we are targeting 1 million tenants, each generating 10 metrics per second:

- **Total Requests per Second**: 1 million tenants * 10 requests/tenant = 10 million requests/second.
- **Data Retention**: If each metric data point is about 100 bytes and we retain data for 30 days:
  - Daily Data Generation = 10 million requests/second * 86400 seconds/day = 864 billion data points/day.
  - Total Data for 30 days = 864 billion * 30 = 25.92 trillion data points.
- **Storage Requirements**: 25.92 trillion * 100 bytes = 2.592 petabytes of data. We need to ensure storage can scale to this size while allowing efficient querying.

### Component Diagram
- **Load Balancer**: Distributes incoming API requests to multiple application servers.
- **API Gateway**: Handles authentication, rate limiting, and routing to appropriate services.
- **Application Services**: Microservices to handle metrics ingestion, alerting, and data retrieval. Each service can be scaled independently.
- **Database**: A distributed time-series database (e.g., InfluxDB) to store metrics data with high write and read performance.
- **Caching Layer**: Redis for caching frequently accessed metrics and configuration data to reduce load on the database.
- **Message Queue**: Apache Kafka for processing metrics ingestion asynchronously, ensuring decoupled architecture and better fault tolerance.
- **Monitoring Dashboard**: A web interface for users to visualize their metrics and manage alerts.

### Schema Design
- **Tenant Table**: `tenant_id (PK), name, created_at`
- **Metric Table**: `metric_id (PK), tenant_id (FK), name, type, created_at`
- **Metric Data Table**: `data_id (PK), metric_id (FK), timestamp, value`
- **Alert Configuration Table**: `alert_id (PK), tenant_id (FK), metric_id (FK), threshold, alert_type, created_at`

### Access Patterns
1. **Ingesting Metrics**: Write to the Metric Data Table as metrics are received.
2. **Querying Metrics**: Read from the Metric Data Table for visualizations and alert evaluations.
3. **Managing Alerts**: Read and write operations on the Alert Configuration Table to manage user-defined alerts.

1. **Eventual Consistency vs. Strong Consistency**: For real-time metrics, we favor eventual consistency, allowing metrics to be processed and displayed quickly, while configuration changes (like alert thresholds) will be strongly consistent to avoid false alerts.
2. **Caching Strategies**: Using Redis for caching may introduce stale data; however, it significantly enhances read performance and reduces database load. We can use TTL for cache invalidation to mitigate this.
3. **Asynchronous Processing**: By using Kafka for metrics ingestion, we can handle spikes in incoming metrics but introduce a slight delay in metrics being available for querying. This trade-off improves system resilience but could impact real-time monitoring during peak loads.

Architect a multi-tenant Monitoring Engine

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