Lately, I’ve been thinking about how modern systems handle massive transaction volumes while staying reliable. That’s why I want to share practical insights about building event-driven microservices that scale. Picture this: an e-commerce platform processing thousands of orders while coordinating inventory, payments, and notifications in real-time. How do we make such systems resilient? Let’s explore together.

Our foundation starts with Go – its concurrency model shines for event processing. We’ll use NATS JetStream for durable messaging. Unlike traditional queues, JetStream retains messages even after delivery, preventing data loss during failures. This becomes crucial when handling payment workflows or inventory updates. Ever wondered what happens when a service crashes mid-transaction? JetStream’s replay capability saves the day.

For observability, we integrate OpenTelemetry directly into our events. Notice the TraceID and SpanID in our event metadata:

type EventMetadata struct {
    ID      string
    Type    string
    TraceID string // Distributed tracing
    SpanID  string // Transaction correlation
}

This allows tracing an order’s journey across services. When a payment fails, we see exactly where it broke – was it inventory check or card processing?

Now, let’s configure our JetStream infrastructure. We define streams as persistent event logs:

js.AddStream(&nats.StreamConfig{
    Name:     "ORDERS",
    Subjects: []string{"orders.*"},
    Replicas: 3, // HA across nodes
    Retention: nats.WorkQueuePolicy,
})

Replication ensures messages survive node failures. WorkQueue policy automatically balances load across consumer instances. Why does this matter? During Black Friday sales, your payment service can scale horizontally without reconfiguring.

For message processing, Go’s goroutines handle concurrency elegantly:

msgHandler := func(msg *nats.Msg) {
    ctx := otel.GetTextMapPropagator().Extract(context.Background(), msg.Header)
    _, span := tracer.Start(ctx, "ProcessOrder")
    defer span.End()

    var order events.OrderCreatedData
    if err := json.Unmarshal(msg.Data, &order); err != nil {
        msg.Nak() // Negative acknowledgment
        return
    }
    
    if err := process(order); err != nil {
        msg.Term() // Permanent failure
    }
    msg.Ack() // Success
}

js.QueueSubscribe("orders.created", "order-processors", msgHandler)

The queue group (order-processors) enables competing consumers. If one instance dies, others pick up its messages. Notice the three acknowledgment states: Ack (success), Nak (retry), Term (poison message). How many systems have you seen that properly handle poison pills?

Error handling requires more than retries. We implement circuit breakers:

breaker := gobreaker.NewCircuitBreaker(gobreaker.Settings{
    Name: "PaymentAPI",
    ReadyToTrip: func(counts gobreaker.Counts) bool {
        return counts.ConsecutiveFailures > 5
    },
})

result, err := breaker.Execute(func() (interface{}, error) {
    return paymentClient.Charge(order)
})

When payment services fail repeatedly, the breaker opens to avoid cascading failures. But what happens to pending orders? They stay in JetStream until services recover.

For stateful services like inventory, we use event sourcing:

type Inventory struct {
    mu      sync.Mutex
    Current map[string]int // productID -> stock
    Changes []events.Event // append-only log
}

func (i *Inventory) Apply(event events.Event) {
    i.mu.Lock()
    defer i.mu.Unlock()
    
    switch event.Type {
    case events.StockLevelChanged:
        data := event.Data.(events.StockLevelChangedData)
        i.Current[data.ProductID] = data.NewQty
    }
    i.Changes = append(i.Changes, event)
}

Rebuilding state becomes trivial by replaying events. No more midnight database restoration panics!

Deploying to Kubernetes? Our NATS configuration includes account isolation:

accounts:
  ecommerce:
    users:
      - user: order-service
        permissions:
          publish: ["orders.*"]
          subscribe: ["payments.*"]

Each service gets least-privilege access. Notice how order-service can publish orders but only subscribe to payment events. Security isn’t an afterthought.

Finally, our monitoring stack tracks key metrics:

• JetStream consumer lag
• Go routine counts
• Circuit breaker state
• 99th percentile event processing latency

When alerts fire, distributed traces show us the hot paths. Remember that payment timeout last week? We fixed it by adjusting the gRPC deadline after tracing revealed the bottleneck.

Building production-ready systems requires these safeguards. I’ve seen too many teams focus only on happy paths. What happens when your cloud zone goes down? Or when a downstream service returns 503s? Our combination of JetStream persistence, OpenTelemetry tracing, and Go’s concurrency handles these realities.

If you found these patterns useful, share this with your team. Have you implemented similar architectures? What challenges did you face? Comment below – let’s learn from each other’s experiences.