Build a multi-tenant Email Pipeline

Question

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

Codemia · Accepted Answer

### Functional Requirements
1. **Multi-Tenancy:** Support multiple organizations with isolated email processing.
2. **Email Sending:** Ability to send transactional and marketing emails.
3. **Email Queuing:** Queue emails for scheduled delivery and retry on failure.
4. **User Management:** Authentication and authorization for different tenants.
5. **Analytics Dashboard:** Provide insights on email delivery rates, open rates, and engagement metrics.

### Non-Functional Requirements
1. **Scalability:** Handle up to 10 million emails per day.
2. **Availability:** 99.9% uptime, ensuring that emails are sent reliably.
3. **Latency:** Emails should be sent within 2 seconds of the request.
4. **Security:** Ensure data privacy and compliance with regulations (e.g., GDPR).
5. **Fault Tolerance:** Ability to recover from failures without data loss.

To support up to 10 million emails per day:
- Assuming the workload is evenly distributed across 24 hours, that's approximately 115,740 emails/hour or about 1,929 emails/minute.
- If we consider peak traffic, let's say we experience a 10x surge during promotional events, we should be prepared for up to 19,290 emails/minute.
- **Infrastructure Needs:**
  - Each email takes approximately 100 ms to process.
  - To handle peak loads, we may need around 20 processing instances (assuming each handles 1,000 emails/minute).

The architecture will consist of the following components:
1. **API Gateway:** For handling incoming email requests, implementing rate limiting and authentication (e.g., AWS API Gateway).
2. **Load Balancer:** Distributes requests to email processing servers (e.g., AWS Elastic Load Balancer).
3. **Email Processing Service:** Microservices that handle email generation, queuing, and sending (e.g., built with Node.js).
4. **Message Queue:** Use Amazon SQS for queuing emails to ensure reliability and decouple the processing layer.
5. **Database:** Use PostgreSQL for storing user data, email templates, and logs, ensuring ACID compliance for transactional emails.
6. **Analytics Service:** A separate service for aggregating and presenting metrics (e.g., using Apache Kafka for real-time data processing).

The database schema will consist of the following tables:
1. **Users Table:**
   - `user_id` (PK)
   - `tenant_id`
   - `email`
   - `password_hash`
   - `created_at`

2. **Emails Table:**
   - `email_id` (PK)
   - `user_id` (FK)
   - `subject`
   - `body`
   - `status`
   - `created_at`

3. **Email Logs Table:**
   - `log_id` (PK)
   - `email_id` (FK)
   - `timestamp`
   - `event_type` (sent, opened, bounced)

This design optimizes for quick retrieval of user emails and logs, which will be accessed frequently for analytics.

1. **Consistency vs. Availability:** Choosing eventual consistency for email logs allows for higher availability and performance, especially under heavy load but might delay log visibility.
2. **Database Choice:** PostgreSQL is chosen for its relational capabilities, but a NoSQL solution like DynamoDB could offer better scalability. The trade-off is in complex querying capabilities.
3. **Caching Strategy:** Implementing a caching layer (e.g., Redis) to store frequently accessed email templates can reduce database load, but introduces complexity in cache invalidation.
4. **Message Queuing vs. Direct Processing:** Using SQS for queuing introduces latency but ensures robust failure handling. Direct processing could be faster but risks losing emails on failure.

Build a multi-tenant Email Pipeline

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