I’ve been building systems for years, and I keep coming back to event sourcing when I need absolute certainty about what happened in an application. Last month, I worked on a financial application where every transaction mattered. Traditional databases felt limiting. That’s when I decided to document my approach to implementing event sourcing with EventStore and Node.js.

Event sourcing changes how we think about data storage. Instead of saving the current state, we store every change as an event. This gives us a complete history of everything that occurred in the system. Imagine having a perfect memory of every decision and action.

Have you ever tried to debug why a user’s balance changed three months ago? With event sourcing, you can replay the events and see exactly what happened. This pattern works exceptionally well for financial systems, audit trails, and complex business workflows.

Let’s start by setting up our development environment. I prefer using Docker for EventStore because it simplifies deployment and testing. Here’s a basic docker-compose file to get EventStore running locally:

version: '3.8'
services:
  eventstore:
    image: eventstore/eventstore:21.10.0-buster-slim
    environment:
      - EVENTSTORE_CLUSTER_SIZE=1
      - EVENTSTORE_RUN_PROJECTIONS=All
      - EVENTSTORE_START_STANDARD_PROJECTIONS=true
      - EVENTSTORE_INSECURE=true
    ports:
      - "1113:1113"
      - "2113:2113"

Run docker-compose up -d to start the server. Now, let’s initialize our Node.js project with TypeScript. I find TypeScript invaluable for maintaining type safety across events and aggregates.

npm init -y
npm install @eventstore/db-client uuid date-fns
npm install -D typescript @types/node @types/uuid

What if your business requirements change and you need to understand past behavior? Event sourcing makes this possible. The core of event sourcing lies in domain events. These represent something that happened in your system.

Here’s how I define a base domain event:

export abstract class DomainEvent {
  public readonly eventId: string;
  public readonly aggregateId: string;
  public readonly occurredOn: Date;

  constructor(aggregateId: string) {
    this.eventId = uuidv4();
    this.aggregateId = aggregateId;
    this.occurredOn = new Date();
  }
}

For a banking system, I might create events like AccountOpened or MoneyDeposited. Each event carries the data needed to reconstruct state. Aggregates are the heart of your domain. They process commands and produce events.

Consider this account aggregate example:

class Account extends AggregateRoot {
  private balance: number = 0;
  private isClosed: boolean = false;

  openAccount(holderName: string, initialBalance: number) {
    if (this.isClosed) throw new Error("Account closed");
    this.addEvent(new AccountOpenedEvent(this.id, holderName, initialBalance));
  }

  private applyAccountOpened(event: AccountOpenedEvent) {
    this.balance = event.initialBalance;
  }
}

How do you handle reading data when events are stored sequentially? Projections transform events into read-optimized views. This separation allows your system to scale reads independently from writes.

Here’s a simple projection for account balances:

class AccountBalanceProjection {
  private balances: Map<string, number> = new Map();

  processEvent(event: DomainEvent) {
    if (event instanceof MoneyDepositedEvent) {
      const current = this.balances.get(event.aggregateId) || 0;
      this.balances.set(event.aggregateId, current + event.amount);
    }
  }
}

Event versioning can be challenging. When business rules change, you might need to modify event structures. I handle this by including version numbers in events and writing migration scripts.

What happens when you have thousands of events for a single aggregate? Snapshots help optimize performance by periodically saving the current state. This way, you don’t need to replay all events every time.

Testing event-sourced systems requires a different approach. I focus on testing the behavior through events. Here’s how I might test an account deposit:

test('deposit increases balance', () => {
  const account = new Account('acc-123');
  account.deposit(100);
  const events = account.getUncommittedEvents();
  expect(events[0]).toBeInstanceOf(MoneyDepositedEvent);
});

Performance considerations include event store configuration and projection design. I monitor event stream lengths and implement snapshot strategies when needed. Common mistakes include not planning for event schema changes and overcomposing read models.

Event sourcing isn’t for every situation. It shines when you need auditability, temporal queries, or complex business logic. For simple CRUD applications, it might add unnecessary complexity.

I’ve found that the initial learning curve pays off in maintainability and system reliability. The ability to reconstruct state at any point in time has saved me countless hours during incident investigations.

What challenges have you faced with traditional data storage? Could event sourcing solve them? I’d love to hear your thoughts in the comments. If this guide helped you understand event sourcing better, please like and share it with others who might benefit. Your engagement helps me create more content like this.