I’ve been thinking a lot about building resilient systems lately. After working on several projects where data consistency and audit trails became critical, I realized traditional database approaches often fall short. That’s what led me to explore event sourcing with EventStore and Node.js. This approach has transformed how I handle data, and I want to share what I’ve learned about creating production-ready systems.

Have you ever considered what it would mean to have a complete history of every change in your application?

Setting up the environment begins with a solid foundation. I start by creating a new Node.js project with TypeScript for type safety. Here’s my initial setup:

npm init -y
npm install @eventstore/db-client uuid express
npm install -D typescript @types/node nodemon

The TypeScript configuration ensures we catch errors early. My tsconfig.json looks like this:

{
  "compilerOptions": {
    "target": "ES2020",
    "module": "commonjs",
    "strict": true,
    "outDir": "./dist"
  }
}

Running EventStore locally via Docker makes development straightforward. I use this docker-compose.yml:

services:
  eventstore:
    image: eventstore/eventstore:latest
    ports:
      - "1113:1113"
      - "2113:2113"
    environment:
      - EVENTSTORE_INSECURE=true

What if you could reconstruct your application’s state from any point in time?

Event sourcing stores state changes as immutable events. This differs from traditional CRUD where you overwrite data. Here’s how I define a base domain event:

abstract class DomainEvent {
  constructor(
    public readonly id: string,
    public readonly aggregateId: string,
    public readonly occurredOn: Date
  ) {}
}

Aggregates manage consistency boundaries. I build them to handle commands and produce events. Consider a bank account aggregate:

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

  open(initialBalance: number) {
    this.apply(new AccountOpenedEvent(this.id, initialBalance));
  }

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

How do you ensure that events are processed correctly?

Event handlers respond to published events. They update read models or trigger other processes. Here’s a simple handler:

class AccountOpenedHandler {
  async handle(event: AccountOpenedEvent) {
    await database.insert('read_accounts', {
      id: event.aggregateId,
      balance: event.initialBalance
    });
  }
}

Projections transform event streams into readable views. I often create multiple projections for different query needs. This separation allows scaling reads independently from writes.

Concurrency control prevents data conflicts. I use optimistic locking by checking expected versions:

async function saveEvents(aggregateId, events, expectedVersion) {
  const stream = `account-${aggregateId}`;
  try {
    await eventStore.appendToStream(stream, events, {
      expectedRevision: expectedVersion
    });
  } catch (error) {
    throw new ConcurrencyError();
  }
}

Have you thought about how to handle high-volume event streams?

Snapshots improve performance for aggregates with long event histories. I periodically save the current state:

class AccountSnapshot {
  constructor(
    public aggregateId: string,
    public balance: number,
    public version: number
  ) {}
}

Testing requires a different approach. I verify behavior by checking emitted events:

test('account opening emits correct event', () => {
  const account = new Account('123');
  account.open(100);
  expect(account.uncommittedEvents).toContainEqual(
    expect.objectContaining({ type: 'AccountOpened' })
  );
});

Deployment involves monitoring event streams and projection lag. I use health checks and metrics to ensure system reliability. Proper logging helps trace issues through the event chain.

Common mistakes include over-complicating event schemas and neglecting versioning. I keep events simple and always include version numbers for evolution.

Some teams prefer alternative approaches like change data capture, but event sourcing provides explicit business intent capture.

I’ve found this architecture invaluable for systems requiring strong audit capabilities and temporal queries. The initial learning curve pays off in maintainability and insight.

What challenges have you faced with data consistency in your projects?

If this guide helped you understand event sourcing better, I’d love to hear your thoughts. Please like, share, or comment with your experiences and questions. Let’s continue the conversation about building robust systems together.