My Go Journey - From Skeptic to Believer in Just a Few Weeks
My Go Journey: From Skeptic to Believer in Just a Few Weeks
A candid story about discovering Go’s elegance and power in real-world scenarios
The Setup: Why I Was Skeptical
Let me start with a confession: I was one of those developers who looked at Go and thought, “Really? Another language?”
Coming from a background heavily rooted in JavaScript, Python, and C#, I had my comfort zones well-established. TypeScript gave me the type safety I craved, Python offered the flexibility I loved, and C# provided the enterprise-grade robustness I needed. So when the opportunity arose to work extensively with Go, I’ll admit I approached it with… let’s call it “cautious optimism.”
The syntax looked almost too simple. Where were the classes? The inheritance hierarchies? The familiar patterns I’d grown accustomed to? It felt like stepping back in time, not forward.
But sometimes, the best learning experiences come from having your preconceptions completely shattered.
Week 1: The Uncomfortable Truth
The first week was rough. I won’t sugarcoat it.
// This felt wrong to me initially
func (s *Service) ProcessData(data []byte) error {
// No try-catch? Just explicit error handling?
if err := s.validate(data); err != nil {
return fmt.Errorf("validation failed: %w", err)
}
// Goroutines everywhere... is this safe?
go s.backgroundProcess(data)
return nil
}Everything felt foreign. The explicit error handling seemed verbose compared to exception-based languages. The lack of classes made me question how to structure larger applications. And don’t get me started on the initial confusion around interfaces—they felt backwards compared to traditional OOP.
But here’s what happened: I started to get things done. Fast.
Week 2: The First “Aha!” Moment
It was during my second week when I had to build a concurrent data processing pipeline. In Python, I would have reached for threading or asyncio. In Node.js, I’d be juggling promises and async/await. But in Go?
func (p *Pipeline) ProcessConcurrently(items []Item) error {
const maxWorkers = 10
jobs := make(chan Item, len(items))
results := make(chan error, len(items))
// Start workers
for i := 0; i < maxWorkers; i++ {
go p.worker(jobs, results)
}
// Send jobs
for _, item := range items {
jobs <- item
}
close(jobs)
// Collect results
for i := 0; i < len(items); i++ {
if err := <-results; err != nil {
return err
}
}
return nil
}The elegance hit me like a truck. Channels and goroutines weren’t just language features—they were a completely different way of thinking about concurrent programming. No locks, no mutexes, no complex synchronization primitives. Just “don’t communicate by sharing memory; share memory by communicating.”
That’s when I realized Go wasn’t trying to be Python or Java. It was trying to be something entirely different.
Week 3: The Performance Revelation
By the third week, I was working on optimizing some data processing workflows. The application needed to handle thousands of requests per second while maintaining sub-millisecond response times.
In my previous experiences, this would have meant:
- Careful memory management
- Complex caching strategies
- Extensive profiling and optimization
- Probably some native code integration
But with Go, the story was different:
// Simple HTTP handler that just works
func (h *Handler) ServeHTTP(w http.ResponseWriter, r *http.Request) {
start := time.Now()
defer func() {
duration := time.Since(start)
log.Printf("Request processed in %v", duration)
}()
// Business logic here
result, err := h.service.Process(r.Context(), request)
if err != nil {
http.Error(w, err.Error(), http.StatusInternalServerError)
return
}
json.NewEncoder(w).Encode(result)
}The performance was… shocking. Good shocking. Without any optimization, the application was already handling the load with room to spare. The garbage collector didn’t cause noticeable pauses. Memory usage was predictable and low. The binary was small and started instantly.
This wasn’t just about raw performance—it was about operational simplicity.
Week 4: The Ecosystem Discovery
As I dove deeper, I discovered that Go’s ecosystem had matured beautifully. The standard library was comprehensive enough that I rarely needed external dependencies. When I did, the module system made dependency management straightforward.
// Building a complete HTTP server with middleware
func main() {
r := chi.NewRouter()
// Middleware
r.Use(middleware.Logger)
r.Use(middleware.Recoverer)
r.Use(middleware.Timeout(60 * time.Second))
// Routes
r.Route("/api/v1", func(r chi.Router) {
r.Post("/data", handler.CreateData)
r.Get("/data/{id}", handler.GetData)
r.Put("/data/{id}", handler.UpdateData)
})
log.Fatal(http.ListenAndServe(":8080", r))
}The tooling was exceptional. go fmt eliminated code style debates. go vet caught potential issues. go test made testing straightforward. go build produced a single, deployable binary. The whole development experience felt… clean.
The Mindset Shift
What I realized after these weeks is that Go forced me to think differently about software architecture. Instead of building complex class hierarchies, I started thinking in terms of:
- Composition over inheritance (interfaces are implicit)
- Clear error handling (no hidden exceptions)
- Concurrent design (goroutines and channels as first-class citizens)
- Simplicity over cleverness (readable code wins)
// Go's approach to "inheritance"
type Writer interface {
Write([]byte) (int, error)
}
type Logger struct {
writer Writer
}
func (l *Logger) Log(message string) error {
_, err := l.writer.Write([]byte(message))
return err
}
// Any type that implements Write can be used
logger := &Logger{writer: os.Stdout}
logger.Log("Hello, Go!")The Production Reality
By the end of my Go journey, I had built and deployed several production services. The deployment story was beautiful—single binaries that just worked. No runtime dependencies, no complex startup procedures, no environment-specific configurations.
Monitoring and debugging were straightforward. The pprof integration made performance analysis simple. The explicit error handling, while verbose, made debugging production issues much easier.
What I Learned About Learning
This experience taught me something valuable about being a developer: sometimes the best way to grow is to step completely outside your comfort zone.
Go challenged many of my assumptions about how software should be built:
- Maybe verbosity isn’t always bad if it improves clarity
- Maybe simple solutions are often better than clever ones
- Maybe explicit is better than implicit, even when it requires more typing
The Verdict
Would I choose Go for my next project? For certain types of applications—absolutely. Particularly for:
- Backend services and APIs
- Systems programming
- Command-line tools
- Microservices
- Anything requiring high concurrency
Go isn’t perfect. It’s not great for everything. But for the problems it’s designed to solve, it’s remarkably elegant.
Final Thoughts
The last few weeks have reminded me why I love this industry. There’s always something new to learn, always a different perspective to consider. Go didn’t just teach me a new language—it taught me a new way of thinking about software development.
Sometimes the best learning happens when you’re forced to forget what you think you know and start fresh. Go gave me that opportunity, and I’m grateful for it.
If you’re curious about Go but hesitant like I was, I’d encourage you to give it a real shot. Not just a weekend tutorial, but a few weeks of actual development. You might be surprised by what you discover.
What’s your experience with Go? Have you had similar moments of revelation with other technologies? I’d love to hear your stories in the comments below.
Related Technologies
- Concurrency: Goroutines, Channels, Context
- Web Development: HTTP server, Chi router, Middleware
- Testing: Built-in testing, Benchmarking
- Tooling: go fmt, go vet, go build, pprof
- Deployment: Single binary, Docker, Kubernetes
This post is part of my ongoing series about exploring new technologies and the lessons they teach us about software development.
~devjaime