Design a Messaging for CrowdStrike

Question

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

Codemia · Accepted Answer

### Functional Requirements
1. **Multi-tenant Architecture**: The system must support multiple tenants (organizations) with isolation of data.
2. **Real-time Messaging**: Support for real-time messaging between users and the CrowdStrike platform for alerts and notifications.
3. **Feedback Loop**: Capture user interactions with messages for model retraining and improvement.
4. **A/B Testing**: Ability to run A/B tests on messaging strategies to optimize engagement.
5. **Analytics Dashboard**: Provide an analytics interface for clients to view message performance metrics.

### Non-Functional Requirements
1. **Scalability**: Must handle millions of requests with the ability to scale horizontally.
2. **Availability**: 99.99% uptime to ensure critical alerts are delivered timely.
3. **Latency**: Messages should be delivered with a latency of under 200ms.
4. **Data Security**: Ensure encryption of messages both in transit and at rest, adhering to industry standards.

Assuming CrowdStrike has 10 million users across all tenants:
- **Messages per User**: On average, each user sends/receives 10 messages per day.
- **Total Messages**: 10 million users * 10 messages/user = 100 million messages per day.
- **Messages per Second**: 100 million messages / 86400 seconds/day = ~1157 messages/second.
- **Peak Load**: Assuming peak usage is 5x the average, the system should be designed to handle approximately 5785 messages/second. 
This necessitates a scalable backend that can accommodate sudden spikes in messaging volume.

### Component Diagram
- **Frontend**: Web and mobile clients for user interaction.
- **API Gateway**: Manages requests and routes them to appropriate services. (e.g., Kong or AWS API Gateway)
- **Microservices**: 
  - **Message Service** (Node.js/Express): Handles sending, receiving, and storing messages.
  - **User Service** (Spring Boot): Manages user profiles and tenant isolation.
  - **Analytics Service** (Python/Flask): Collects and processes engagement data.
  - **Feedback Loop Service** (Kafka for event streaming): Captures user interactions for model retraining.
- **Database**: PostgreSQL for structured data, Redis for caching messages.
- **ML Model Serving**: TensorFlow Serving for real-time predictions and model serving.
- **Monitoring**: Prometheus and Grafana for system health and performance metrics.

### Schema Design
1. **Users Table**: 
   - `user_id (PK)`: Unique identifier for users.
   - `tenant_id`: Identifier for the tenant.
   - `created_at`: Timestamp of user creation.

2. **Messages Table**:
   - `message_id (PK)`: Unique identifier for messages.
   - `user_id (FK)`: Identifier for the sender.
   - `recipient_id (FK)`: Identifier for the recipient.
   - `content`: Message content.
   - `timestamp`: When the message was sent.

3. **Engagement Table**:
   - `engagement_id (PK)`: Unique identifier for engagement logs.
   - `message_id (FK)`: Identifier for the related message.
   - `user_id (FK)`: Identifier for the user interacting with the message.
   - `action_type`: Type of interaction (e.g., read, replied).
   - `timestamp`: When the interaction occurred.

1. **Consistency vs. Availability**: Opting for eventual consistency to ensure high availability during peak loads might lead to temporary message delivery delays, which could impact critical alerts.
2. **Complexity vs. Interpretability**: Choosing complex ML models (like deep learning) could improve prediction accuracy but reduce interpretability. Simpler models may be favored for critical alerts where understanding model decisions is essential.
3. **Real-time vs. Batch Processing**: While real-time processing is crucial for user messages, implementing batch processing for analytics might reduce load on the system but could introduce latency in feedback loops.
4. **Data Quality vs. Speed**: Prioritizing thorough data validation might slow down the ingestion process, but is necessary for maintaining high model performance and reliability.

Design a Messaging for CrowdStrike

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