Designing a notification system might sound simple at first, but as the scale and complexity grow, so do the architectural challenges. A well-designed notification system needs to handle various message types, user preferences, delivery guarantees, and performance expectations while remaining flexible enough to adapt to changing requirements. In this post, we'll explore several architectural patterns for notification systems, focusing on both high-level design (HLD) and low-level design (LLD) aspects.

Understanding Notification Types

Before jumping into architectural patterns, let's categorize different types of notifications:

1. Push Notifications: Messages sent to users' devices, often mobile phones. Examples include alerts from apps or messages from social media.
2. Email Notifications: Emails sent to users for various purposes like updates, promotions, or important alerts.
3. In-App Notifications: Messages displayed within an application while users are actively using it.
4. SMS Notifications: Text messages sent to users’ mobile numbers.
5. Webhooks and API Notifications: Real-time notification deliveries to external services or systems.

The way we structure our notification system will heavily depend on the types of notifications we need to support.

Architectural Patterns

1. Broker Pattern

The Broker Pattern acts as a mediator in the design of the notification system. It manages communication between different producers (e.g., microservices generating events) and consumers (e.g., users).

HLD:

• Components: Producers, Notification Broker, Consumers (both real-time users and various channels).
• Flow: Producers send messages to the broker. The broker holds queues for different notification types and channels. Consumers subscribe to these queues.

LLD:

• Implement a message queue (like RabbitMQ or Kafka) for the broker.
• Create consumer services for processing different types of notifications. Each consumer pulls messages from the appropriate queue.
• Use a database to track user preferences for notification channels (e.g., email vs. SMS).

Example: An e-commerce platform that uses the broker pattern might send order updates to various channels (SMS, push notifications, and emails). The order service publishes messages to the broker, which routes them to different subscribers based on user preferences.

2. Publish-Subscribe Pattern

The Publish-Subscribe (Pub/Sub) Pattern allows components to communicate asynchronously, enabling high scalability and decoupling of services.

HLD:

• Components: Publishers, Subscribers, Topics, Notification Service.
• Flow: Publishers send messages to specific topics. Subscribers listen to topics of interest and receive notifications.

LLD:

• Use a Pub/Sub service like Google Cloud Pub/Sub or AWS SNS.
• Create topic-based subscriptions allowing users to subscribe to specific event types they are interested in.
• Leverage message filtering based on user preferences.

Example: A social media app could use this pattern to notify users about new likes or comments. When a user likes a post, the system publishes a message to the "likes" topic, and all users who follow that post receive notifications.

3. Event Sourcing Pattern

In the Event Sourcing Pattern, all changes to application state are stored as a sequence of events. Instead of storing just the current state, we focus on the events that led to that state—helpful in auditing and reconstructing user actions.

HLD:

• Components: Event Store, Notification Service, User State Service.
• Flow: Events are stored in an event store. The notification service listens for events and sends notifications as necessary.

LLD:

• Utilize a relational or NoSQL database to implement an event store.
• The notification service should monitor for specific events (like user posts, interactions) and send notifications accordingly.
• In the case of user inferences (like unread notifications), events can be replayed to reconstruct the user's interaction history.

Example: In an online booking system, when a user cancels a reservation, that event is stored. The notification system uses the event to alert the user about the cancellation and offers potential alternatives.

4. Command Query Responsibility Segregation (CQRS)

The CQRS pattern separates the read and write operations of a system, which can greatly enhance performance and scalability, especially in notification systems that require quick reads for users.

HLD:

• Components: Command Handler, Query Handler, Notification Store, User Preferences Service.
• Flow: Commands (notifications to be sent) are processed by command handlers, while queries about sent notifications, user preferences, and stats are handled by separate query handlers.

LLD:

• Implement distinction between write database (for storing sent notifications) and read database (for fetching user-specific notifications).
• Use caching for retrieving preference data quickly to enhance performance.

Example: In a real-time sports application, a user can command the system to start receiving notifications for specific teams. The system processes these commands while allowing fast and scalable queries for the notifications that users have already received or missed.

5. Lambda Architecture

The Lambda Architecture is designed to handle massive quantities of data while providing a comprehensive solution to data processing. It combines batch and real-time processing in a robust manner.

HLD:

• Components: Batch Layer, Speed Layer, Serving Layer.
• Flow: Both batch and real-time data is processed, with real-time notifications immediately outputted to users.

LLD:

• Use streaming platforms like Apache Flink for real-time processing.
• For batch processing, leverage Hadoop or Spark to analyze historical notification data.
• Store results in a NoSQL database for quick read access.

Example: A news aggregation site could use lambda architecture to deliver breaking news notifications in real-time while also processing user engagement metrics in batch to refine targeting for future notifications.

Conclusion

Having explored various architectural patterns for designing effective notification systems, it’s clear that selecting the right pattern can significantly influence the scalability, maintainability, and user experience of your system. By understanding your specific requirements and leveraging these patterns, you can build a robust notification service that meets both user needs and system demands. Happy designing!