I’ve been thinking about how modern applications need to handle complex workflows while staying reliable. That’s why I want to share my experience with event-driven microservices. This approach helps systems handle high traffic while remaining resilient when things go wrong.

Traditional synchronous communication between services creates tight coupling. If one service fails, everything stops working. But what if services could communicate without waiting for each other? That’s where event-driven architecture shines.

Let me show you the difference in code.

// The old way - everything waits in line
async createOrder(orderData) {
  const inventory = await inventoryService.checkStock(orderData.items);
  const payment = await paymentService.process(orderData.payment);
  
  if (inventory.available && payment.success) {
    return orderService.create(orderData);
  }
}

See the problem? If inventory service is slow, payment processing waits. If payment fails, we already checked inventory for nothing. Now look at the event-driven approach:

// Services work independently
async createOrder(orderData) {
  const order = await this.createPendingOrder(orderData);
  
  this.eventBus.emit('order.created', {
    orderId: order.id,
    items: orderData.items
  });
  
  return order;
}

The order service just creates a pending order and announces it happened. Other services listen and do their jobs independently. This makes your system much more robust.

But how do we handle transactions across multiple services? That’s where the Saga pattern comes in. Instead of a single database transaction, we use a series of events to manage the entire process.

Here’s a simple setup for our e-commerce system:

// docker-compose.yml for our infrastructure
services:
  rabbitmq:
    image: rabbitmq:3-management
    ports: ["5672:5672"]
  
  mongodb:
    image: mongo:6
    ports: ["27017:27017"]

We need RabbitMQ for message passing and MongoDB for data storage. Now let’s define what our services will communicate about.

// Shared events between services
export class OrderCreatedEvent {
  constructor(
    public readonly orderId: string,
    public readonly items: OrderItem[],
    public readonly totalAmount: number
  ) {}
}

export class PaymentProcessedEvent {
  constructor(
    public readonly orderId: string,
    public readonly status: 'success' | 'failed'
  ) {}
}

Each service focuses on its specific job. The order service manages orders, payment service handles payments, and inventory service tracks stock. They communicate through events rather than direct calls.

Have you considered what happens when a payment fails after inventory is reserved? We need to handle such scenarios gracefully.

Here’s how the inventory service might reserve items:

async handleOrderCreated(event: OrderCreatedEvent) {
  try {
    for (const item of event.items) {
      const product = await this.productModel.findById(item.productId);
      if (product.quantity < item.quantity) {
        throw new Error('Insufficient stock');
      }
      product.quantity -= item.quantity;
      await product.save();
    }
    
    this.eventBus.emit('inventory.reserved', {
      orderId: event.orderId
    });
  } catch (error) {
    this.eventBus.emit('inventory.failed', {
      orderId: event.orderId,
      reason: error.message
    });
  }
}

The beauty of this approach is that each service can work at its own pace. If the payment service is slow, it doesn’t block the inventory service. If one service fails, others can continue working.

But what about data consistency? In traditional systems, we use database transactions. In distributed systems, we use compensating actions.

For example, if payment fails after inventory is reserved, we need to put the items back in stock:

async handlePaymentFailed(event: PaymentFailedEvent) {
  const order = await this.orderModel.findById(event.orderId);
  
  // Return items to inventory
  for (const item of order.items) {
    await this.productModel.updateOne(
      { _id: item.productId },
      { $inc: { quantity: item.quantity } }
    );
  }
  
  order.status = 'cancelled';
  await order.save();
}

This is called a compensating transaction - we undo what was done previously. It’s not perfect, but it’s practical for distributed systems.

Monitoring becomes crucial in such architectures. How do you know if messages are being processed? Are there bottlenecks? We need proper logging and metrics.

// Adding observability to our event handlers
async handleOrderCreated(event: OrderCreatedEvent) {
  const startTime = Date.now();
  
  try {
    await this.processOrder(event);
    this.logger.log(`Order processed in ${Date.now() - startTime}ms`);
  } catch (error) {
    this.logger.error(`Order failed: ${error.message}`);
    throw error;
  }
}

Testing event-driven systems requires a different approach too. You need to verify that events are emitted and handled correctly.

// Testing our order creation
it('should emit order.created event', async () => {
  const orderData = { items: [], totalAmount: 100 };
  
  await orderController.createOrder(orderData);
  
  expect(eventBus.emit).toHaveBeenCalledWith(
    'order.created',
    expect.any(Object)
  );
});

The transition to event-driven architecture might seem daunting, but the benefits are substantial. Your system becomes more resilient, scalable, and maintainable. Services can be developed and deployed independently.

Have you thought about how this approach could solve scaling challenges in your current projects?

I’d love to hear about your experiences with microservices architecture. What challenges have you faced? Share your thoughts in the comments below, and if you found this useful, please like and share with others who might benefit from this approach.