I’ve been thinking a lot about how modern applications handle complexity and scale. Recently, I worked on a project where traditional monolithic architecture kept causing bottlenecks. Every small change felt like rearranging furniture in a packed room. That experience pushed me toward event-driven microservices, and I want to share what I’ve learned about building resilient systems with Go, NATS, and MongoDB.
Why do most distributed systems struggle with consistency? The answer often lies in how services communicate. Traditional request-response patterns create tight coupling. Event-driven architecture changes this dynamic completely. Services publish events when something meaningful happens, and other services react without knowing who sent the message.
Let me show you how to structure such a system. We begin with a clear project layout that separates concerns.
mkdir event-driven-microservices
cd event-driven-microservices
mkdir -p {cmd/{order-service,inventory-service,payment-service,notification-service,saga-orchestrator},pkg/{events,database,messaging,monitoring,middleware},internal/{order,inventory,payment,notification,saga},deployments,configs}Our dependency management starts with a well-defined go.mod file. I carefully selected libraries that provide robustness without unnecessary complexity.
module github.com/yourorg/event-driven-microservices
go 1.21
require (
github.com/nats-io/nats.go v1.31.0
go.mongodb.org/mongo-driver v1.13.1
github.com/gin-gonic/gin v1.9.1
// ... additional dependencies
)Have you ever wondered how services maintain data consistency across distributed systems? Events become our single source of truth. Each event carries enough information for services to make independent decisions.
package events
type EventType string
const (
OrderCreated EventType = "order.created"
InventoryReserved EventType = "inventory.reserved"
PaymentProcessed EventType = "payment.processed"
)
type Event struct {
ID string
Type EventType
AggregateID string
Timestamp time.Time
Data json.RawMessage
}When an order gets created, multiple services need to react. The inventory service reserves items, the payment service processes payment, and notifications go out to customers. But what happens if one service fails? That’s where our messaging layer provides reliability.
NATS JetStream gives us persistent messaging with exactly-once delivery semantics. I configure it to handle reconnections and ensure no messages get lost during network issues.
func NewEventBus(config NATSConfig, logger *zap.Logger) (*EventBus, error) {
opts := []nats.Option{
nats.MaxReconnects(config.MaxReconnects),
nats.ReconnectWait(config.ReconnectWait),
}
nc, err := nats.Connect(config.URL, opts...)
if err != nil {
return nil, err
}
js, err := nc.JetStream()
return &EventBus{conn: nc, js: js, logger: logger}, nil
}MongoDB serves as our primary data store with connection pooling and transactions. Each service maintains its own database, ensuring data isolation. The order service might store order entities, while inventory service manages product availability.
How do we handle scenarios where multiple services must coordinate? Saga patterns manage distributed transactions without tight coupling. The saga orchestrator directs the flow by publishing and subscribing to events.
Observability becomes crucial in distributed systems. I implement structured logging, metrics, and tracing across all services. This helps quickly identify where issues occur and how events flow through the system.
Deployment uses Docker Compose to orchestrate all components. The setup includes the microservices, NATS server, MongoDB instances, and monitoring tools like Prometheus and Grafana.
What makes this architecture stand out? Services remain loosely coupled and highly cohesive. They can evolve independently while maintaining system integrity. The event-driven approach naturally handles scaling and fault tolerance.
I’ve found that proper error handling and circuit breakers prevent cascading failures. When a service becomes unavailable, others continue operating and retry later.
Building this system taught me valuable lessons about distributed systems design. The combination of Go’s performance, NATS’s reliability, and MongoDB’s flexibility creates a solid foundation for modern applications.
If you found this useful, please share it with others who might benefit. I’d love to hear about your experiences with microservices in the comments. What challenges have you faced when building distributed systems?
