I’ve been thinking a lot lately about how we build systems that not only work well today but remain understandable and maintainable years from now. Traditional approaches often leave us with applications where we know the current state but have no clear record of how we got there. This led me down the path of exploring distributed event-driven architectures using Node.js, EventStore, and TypeScript.
Have you ever tried to debug a production issue where the data doesn’t match expectations, but you can’t trace back to what caused the discrepancy? This frustration is exactly what drove me to adopt event sourcing.
Let me show you how I approach building these systems. The core idea is simple: instead of storing just the current state, we store every change as an immutable event. Think of it like a detailed transaction history rather than just a current balance.
// Traditional approach loses history
interface UserAccount {
id: string;
balance: number;
status: string;
}
// Event sourcing preserves complete story
interface AccountEvent {
type: 'AccountCreated' | 'FundsDeposited' | 'FundsWithdrawn';
data: any;
timestamp: Date;
version: number;
}Setting up our environment starts with EventStore DB. I prefer using Docker for consistency across development and production:
docker run -d --name eventstore \
-p 2113:2113 -p 1113:1113 \
eventstore/eventstore:latest \
--insecure --run-projections=AllThe beauty of TypeScript comes into play when we model our domain. Strong typing ensures our events maintain their structure over time. Here’s how I define a basic event structure:
abstract class DomainEvent {
public readonly eventId: string;
public readonly occurredAt: Date;
constructor(
public readonly aggregateId: string,
public readonly eventType: string
) {
this.eventId = uuid.v4();
this.occurredAt = new Date();
}
}But what happens when business requirements change and we need to modify our event structure? This is where event versioning becomes crucial. I’ve found that adding a version field and writing migration scripts ensures backward compatibility.
Handling distributed transactions across services requires careful coordination. I implement saga patterns to manage workflows that span multiple boundaries. Here’s a simplified example:
class TransferSaga {
async execute(transfer: TransferCommand) {
await this.debitSourceAccount(transfer);
await this.creditDestinationAccount(transfer);
await this.recordTransaction(transfer);
}
async compensate(failedStep: string) {
// Rollback logic for failed operations
}
}Monitoring event streams is essential for maintaining system health. I instrument key metrics around event processing times, error rates, and throughput. This visibility helps quickly identify bottlenecks or issues in the system.
Testing event-sourced systems requires a different approach. I focus on testing the behavior through events rather than state assertions:
describe('Account aggregate', () => {
it('should allow valid withdrawals', () => {
const account = Account.open('acc-1', 1000);
account.withdraw(200);
expect(account.getUncommittedEvents()).toContainEqual(
expect.objectContaining({
type: 'FundsWithdrawn',
amount: 200
})
);
});
});Performance considerations often come up when discussing event sourcing. I use snapshots to optimize read performance for aggregates with long event histories:
class AccountSnapshot {
constructor(
public readonly aggregateId: string,
public readonly balance: number,
public readonly version: number
) {}
}The journey to building robust distributed systems continues to fascinate me. Each project brings new insights and challenges that shape my approach. What aspects of event-driven architecture have you found most valuable in your projects?
I’d love to hear your thoughts and experiences with these patterns. If this resonated with you, please share it with others who might benefit, and feel free to leave comments about your own journey with distributed systems.
