Design a large-scale Messaging Platform

Question

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

Codemia · Accepted Answer

### Functional Requirements:
1. **User Registration and Authentication**: Users must be able to create accounts and log in securely.
2. **Real-Time Messaging**: The system must support sending and receiving messages in real-time.
3. **Message Storage**: Messages should be stored for retrieval later, supporting both direct messages and group chats.
4. **Notification System**: Users should receive notifications for new messages, even when offline.
5. **Message Search**: Users should be able to search through their message history.

### Non-Functional Requirements:
1. **Scalability**: The system must handle millions of messages per day with low latency.
2. **High Availability**: The platform should ensure 99.99% uptime.
3. **Security**: Messages must be encrypted in transit and at rest, with user authentication enforced.
4. **Performance**: The system should have a response time of less than 200 ms for sending and receiving messages.

Assuming Plaid aims to support 10 million users, with an average of 5 messages sent per user per day:
- **Total Messages per Day**: 10,000,000 users * 5 messages/user = 50,000,000 messages/day.
- **Peak Load Estimation**: Assuming peak usage is 10% of daily traffic occurring within a 1-hour window:
  - **Messages per Second**: 50,000,000 messages/day ÷ 86400 seconds/day = ~578 messages/sec.
  - Assuming peak is 10x average: 578 * 10 = ~5780 messages/sec.
- **Storage Requirements**: If each message is approximately 1KB, we would need around 50 GB of storage per day, leading to:
  - **Total Storage in 30 Days**: 50 GB/day * 30 days = 1.5 TB/month.

### Component Diagram:
1. **Frontend**: Web and mobile clients using React and React Native.
2. **API Gateway**: AWS API Gateway to manage requests, providing a single entry point.
3. **Authentication Service**: AWS Cognito for user authentication and management.
4. **Message Queue**: Apache Kafka for handling real-time messaging and decoupling services.
5. **Message Service**: A microservice built with Node.js and Express to handle message processing and storage.
6. **Database**: Amazon DynamoDB for scalable storage of messages, with appropriate indexing for search.
7. **Notification Service**: AWS SNS for push notifications to users.
8. **Monitoring**: AWS CloudWatch for logging and alerting on system health.

### Schema Design:
1. **Users Table**:
   - `user_id` (PK, String)
   - `username` (String)
   - `email` (String)
   - `hashed_password` (String)
   - `created_at` (Timestamp)

2. **Messages Table**:
   - `message_id` (PK, String)
   - `sender_id` (FK, String referencing Users)
   - `receiver_id` (FK, String referencing Users or null for group messages)
   - `content` (String)
   - `timestamp` (Timestamp)
   - `status` (String: sent, delivered, read)

3. **Groups Table**:
   - `group_id` (PK, String)
   - `group_name` (String)
   - `members` (List of user_ids)

This model allows for efficient retrieval of messages by sender/receiver and supports group messaging.

### Architectural Decisions:
1. **Consistency vs. Availability**: Using Kafka ensures high availability and throughput but introduces eventual consistency for message delivery. This trade-off is acceptable for a messaging platform where immediate consistency is not critical.
2. **SQL vs. NoSQL**: Choosing DynamoDB over a relational database allows for horizontal scaling and better performance with large volumes of data, but sacrifices complex querying capabilities.
3. **Real-time vs. Batch Processing**: Real-time processing provides immediate feedback to users but requires robust infrastructure. Balancing between real-time notifications and delayed processing for analytics is essential.
4. **Caching**: Implementing Redis for caching frequently accessed messages can significantly reduce database load but adds complexity in cache invalidation.
5. **Monitoring and Alerting**: Prioritizing comprehensive monitoring using AWS CloudWatch over building custom monitoring solutions maximizes reliability and reduces development time.

Design a large-scale Messaging Platform

Plaid

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