I’ve been thinking a lot about how modern applications handle complexity and scale. In my work, I’ve seen systems struggle with tight coupling, difficult debugging, and scaling challenges. This led me to explore event-driven architecture—a pattern that fundamentally changes how components communicate.
Imagine building a system where every state change becomes an immutable event. This approach creates a complete history of everything that happens, making debugging simpler and scaling more natural. But how do we actually implement this in practice?
Let me show you how to build a robust event-driven system using Node.js, TypeScript, and EventStoreDB.
First, we set up our foundation with core event interfaces:
export interface DomainEvent {
readonly metadata: EventMetadata;
readonly payload: Record<string, any>;
}
export abstract class BaseEvent implements DomainEvent {
public readonly metadata: EventMetadata;
constructor(aggregateId: string, aggregateType: string) {
this.metadata = {
eventId: this.generateEventId(),
eventType: this.constructor.name,
aggregateId,
aggregateType,
eventVersion: 1,
timestamp: new Date()
};
}
}
Have you considered what happens when events need to change over time? We handle this through versioning strategies that maintain backward compatibility.
The event bus acts as our communication backbone:
export class EventBus extends EventEmitter {
private handlers: Map<string, EventHandler[]> = new Map();
public subscribe(eventType: string, handler: EventHandler): void {
if (!this.handlers.has(eventType)) {
this.handlers.set(eventType, []);
}
this.handlers.get(eventType)!.push(handler);
}
}
Integrating EventStoreDB gives us persistent, reliable event storage:
const client = EventStoreDBClient.connectionString(
'esdb://localhost:2113?tls=false'
);
async function appendEvent(streamName: string, event: DomainEvent) {
const eventData = {
type: event.metadata.eventType,
data: event.payload,
metadata: event.metadata
};
await client.appendToStream(streamName, eventData);
}
What about reading these events back when we need to reconstruct state? Projections help us build read models optimized for specific queries:
async function buildUserProjection(userId: string) {
const events = await client.readStream(`user-${userId}`);
let userState = {};
for await (const resolvedEvent of events) {
const event = resolvedEvent.event?.data;
userState = applyEvent(userState, event);
}
return userState;
}
Error handling becomes crucial in distributed systems. We implement retry mechanisms and dead letter queues:
async function withRetry(operation: () => Promise<any>, maxRetries = 3) {
for (let attempt = 1; attempt <= maxRetries; attempt++) {
try {
return await operation();
} catch (error) {
if (attempt === maxRetries) throw error;
await new Promise(resolve => setTimeout(resolve, 1000 * attempt));
}
}
}
Testing event-driven systems requires a different approach. We focus on behavior rather than state:
describe('UserRegistration', () => {
it('should emit UserRegistered event', async () => {
const user = User.create('[email protected]');
const events = user.getUncommittedEvents();
expect(events).toHaveLength(1);
expect(events[0].metadata.eventType).toBe('UserRegistered');
});
});
The beauty of this architecture lies in its flexibility. Components can evolve independently, and new features can be added by simply subscribing to relevant events. Systems become more resilient because failures in one component don’t necessarily break others.
Have you thought about how this approach might simplify your current systems? The initial investment in setting up event-driven architecture pays dividends in maintainability and scalability.
I’d love to hear about your experiences with event-driven systems. What challenges have you faced? What successes have you celebrated? Share your thoughts in the comments below, and if you found this helpful, please consider sharing it with others who might benefit from these concepts.