I’ve been thinking about how we build systems that not only work today but remain understandable and maintainable years from now. That’s what led me to explore Event Sourcing with EventStore and Node.js—a powerful combination for creating applications that truly remember their history.

Have you ever wondered what it would be like if your application could tell you exactly what happened, not just what currently exists? Event Sourcing makes this possible by storing every change as an event rather than just the current state. When paired with CQRS (Command Query Responsibility Segregation), we separate read and write operations, giving us flexibility and performance benefits.

Setting up our environment begins with a solid foundation. We’ll use TypeScript for type safety and structure our project to clearly separate concerns. Here’s how we initialize our project and connect to EventStore:

import { EventStoreDBClient } from '@eventstore/db-client';

const client = EventStoreDBClient.connectionString(
  process.env.EVENTSTORE_CONNECTION_STRING!
);

But why store events instead of just the current state? The answer lies in the audit trail and temporal querying capabilities. Imagine being able to reconstruct your system’s state at any point in time—that’s the power we gain.

Our domain events become the building blocks of our system. Each event represents something meaningful that happened in our business domain. Here’s how we might define a user creation event:

interface UserCreatedEvent {
  type: 'UserCreated';
  data: {
    userId: string;
    email: string;
    createdAt: Date;
  };
}

The command side handles our write operations. When a command comes in, we validate it, create the appropriate events, and persist them to EventStore. This separation ensures our write model remains focused on business rules and validation.

What happens after we store these events? That’s where projections come into play. They listen for new events and update our read models accordingly. This allows us to optimize our read side for query performance without affecting write operations.

// Projection updating MongoDB read model
async function onUserCreated(event: UserCreatedEvent) {
  await usersCollection.insertOne({
    _id: event.data.userId,
    email: event.data.email,
    status: 'active'
  });
}

Testing becomes more straightforward when we can replay events to reconstruct state. We can test our business logic by feeding in events and verifying the resulting state changes. This approach gives us confidence that our system behaves correctly under various scenarios.

Error handling and resilience are crucial in distributed systems. We need to consider what happens when projections fail or when we need to migrate event schemas. These considerations separate production-ready implementations from simple examples.

Performance optimization becomes interesting with this architecture. We can scale our read and write sides independently, cache read models aggressively, and even precompute complex queries. The separation of concerns gives us numerous optimization opportunities.

Have you considered how this approach might change how you think about data consistency? Instead of immediate consistency everywhere, we can embrace eventual consistency where appropriate, giving us better performance and scalability.

The journey to implementing Event Sourcing and CQRS requires careful thought about your domain and business requirements. It’s not a silver bullet, but when applied to the right problems, it provides maintainability and insight that traditional approaches struggle to match.

I’d love to hear your thoughts on this approach. Have you implemented Event Sourcing in your projects? What challenges did you face, and what benefits did you gain? Share your experiences in the comments below, and if you found this helpful, please like and share with others who might benefit from this architecture.