I’ve been thinking a lot about building systems that can handle millions of events daily while remaining reliable and scalable. Recently, while working on a payment processing system, I realized how crucial it is to get event streaming right from the start. Let me share what I’ve learned about creating production-ready systems with Kafka and Go.

Getting started with Kafka in Go requires setting up a solid foundation. I prefer using Docker Compose for local development because it mirrors production environments closely. Have you ever wondered how to ensure your local setup behaves like production?

// Setting up a basic producer configuration
config := ProducerConfig{
    Brokers:      []string{"localhost:9092"},
    BatchSize:    100,
    BatchTimeout: 1 * time.Second,
    RequiredAcks: kafka.RequireAll,
    Compression:  kafka.Snappy,
    MaxAttempts:  3,
}

When building producers, I focus on three key aspects: reliability, performance, and observability. The segmentio/kafka-go library provides excellent defaults, but production systems need careful tuning. What happens when your producer faces network partitions or broker failures?

Here’s how I implement retry logic with exponential backoff:

func (p *Producer) publishWithRetry(ctx context.Context, message kafka.Message) error {
    backoff := time.Second
    for attempt := 0; attempt < p.config.MaxAttempts; attempt++ {
        err := p.writer.WriteMessages(ctx, message)
        if err == nil {
            return nil
        }
        
        if errors.Is(err, context.Canceled) {
            return err
        }
        
        time.Sleep(backoff)
        backoff *= 2
    }
    return fmt.Errorf("failed after %d attempts", p.config.MaxAttempts)
}

Consumer implementation requires even more attention. I’ve found that consumer groups are both powerful and tricky to get right. How do you ensure your consumers handle rebalances gracefully without losing messages?

// Consumer group implementation
reader := kafka.NewReader(kafka.ReaderConfig{
    Brokers:        brokers,
    GroupID:        groupID,
    Topic:          topic,
    MinBytes:       10e3, // 10KB
    MaxBytes:       10e6, // 10MB
    CommitInterval: time.Second,
})

for {
    msg, err := reader.ReadMessage(ctx)
    if err != nil {
        if errors.Is(err, context.Canceled) {
            break
        }
        continue
    }
    
    if err := p.processMessage(msg); err != nil {
        p.sendToDLQ(msg) // Dead letter queue
    }
}

Error handling deserves special attention. I implement multiple strategies: retries for transient errors, dead letter queues for poison pills, and circuit breakers to prevent cascading failures. But how do you decide when to retry versus when to send to DLQ?

Monitoring is non-negotiable in production systems. I expose metrics for message rates, error counts, and processing latency. This helps identify bottlenecks before they become critical issues. Have you considered what metrics would alert you to impending problems?

// Monitoring setup with Prometheus
var (
    processingDuration = prometheus.NewHistogramVec(prometheus.HistogramOpts{
        Name: "message_processing_duration_seconds",
        Help: "Time spent processing messages",
    }, []string{"topic", "status"})
    
    messagesConsumed = prometheus.NewCounterVec(prometheus.CounterOpts{
        Name: "messages_consumed_total",
        Help: "Total messages consumed",
    }, []string{"topic", "status"})
)

Testing event-driven systems presents unique challenges. I use testcontainers to spin up real Kafka instances for integration tests. This approach catches issues that unit tests might miss. What testing strategies have you found most effective for async systems?

// Integration test example
func TestOrderProcessing(t *testing.T) {
    kafkaContainer, err := setupKafkaContainer(ctx)
    defer kafkaContainer.Terminate(ctx)
    
    producer := NewProducer(Config{Brokers: kafkaContainer.Brokers})
    consumer := NewConsumer(Config{Brokers: kafkaContainer.Brokers})
    
    // Test the entire flow
    err = producer.PublishOrderEvent(testOrder)
    assert.NoError(t, err)
    
    // Verify consumer processed the event
    eventually(t, func() bool {
        return consumer.ProcessedCount() > 0
    })
}

Deployment considerations often get overlooked. I package applications as Docker images and use health checks to ensure they’re ready before receiving traffic. Resource limits prevent memory leaks from taking down entire nodes.

Scaling requires understanding your data patterns. I monitor partition lag and scale consumers horizontally when needed. But what happens when you need to reprocess large volumes of data?

Building production systems with Kafka and Go involves many moving parts, but the payoff is immense. Reliable event streaming enables robust microservices architectures and real-time data processing. The key is starting simple and iterating based on actual requirements rather than hypothetical scale.

What challenges have you faced with event streaming systems? I’d love to hear about your experiences and solutions. If you found this helpful, please share it with others who might benefit, and leave a comment with your thoughts or questions!