Quality Behind the Scenes
Anyone can write code that works on sunny days. I write systems that survive the storm.
Standard 'Quick' Fix
The typical approach. Works now, but crashes when the network lags or API is slow. No safety net, high risk of downtime.
func FetchData(url string) error {
// Basic request - no timeouts!
resp, err := http.Get(url)
if err != nil {
// Just fails immediately
return err
}
defer resp.Body.Close()
// No status check? 404 is valid here...
return nil
}Azlo Standard (Production-Ready)
True backend engineering. Self-healing (retries), timeouts preventing hangs, and structured logging for instant debugging.
func (s *Service) FetchDataSecure(ctx context.Context, url string) error {
const maxRetries = 3
// Resilience: Retry loop with backoff
for i := 0; i < maxRetries; i++ {
// Safety: Context ensures we don't hang
reqCtx, cancel := context.WithTimeout(ctx, 5*time.Second)
defer cancel()
// Observability: Structured logging
s.log.Info("attempting fetch", "attempt", i+1)
req, _ := http.NewRequestWithContext(reqCtx, "GET", url, nil)
resp, err := s.client.Do(req)
if err == nil && resp.StatusCode == 200 {
defer resp.Body.Close()
return nil // Success!
}
// Smart logic: Exponential Backoff
time.Sleep(time.Second * time.Duration(i+1))
}
return fmt.Errorf("failed after %d attempts", maxRetries)
}Architecture & Patterns
Good code isn't just syntax, it's structure. I use 'Hexagonal Architecture' to keep your business logic clean and independent of the database.
📂 Standard Go Project Layout
I adhere to the 'Standard Go Project Layout'. This ensures the code is easy to navigate for other developers and scales without clutter.
- cmd/: Entry point. Clean and minimal.
- internal/calculator/: Pure business logic (The "Core").
- internal/handlers/: HTTP layer. Handles requests, not logic.
- internal/telemetry/: Observability (Tracing & Metrics).
api-service/
├── cmd/
│ └── main.go
├── internal/
│ ├── config/ # Env Loading
│ ├── database/ # DB Connection
│ ├── calculator/ # Domain Logic (Service)
│ ├── handlers/ # HTTP Transport
│ ├── middleware/ # Auth & Zero Trust
│ ├── models/ # Data Structures
│ ├── telemetry/ # OpenTelemetry
│ └── validation/ # Input Checks
├── Dockerfile
└── go.mod🧩 Separation of Concerns (Ports & Adapters)
By using interfaces (Ports), I isolate your business logic. This means we can swap databases or API providers without rewriting the core system.
Notice how Handlers (HTTP) are completely separated from calculator (Logic).
// Service defines business logic
type Service interface {
AnalyzeText(ctx context.Context, input string) (*Result, error)
}
// Repository defines data access (The Port)
type Repository interface {
SaveAnalysis(ctx context.Context, r Result) error
}func (h *Handler) HandleAnalyze(w http.ResponseWriter, r *http.Request) {
// 1. Parse & Validate Input
var req models.Input
if err := json.NewDecoder(r.Body).Decode(&req); err != nil {
h.telemetry.LogWarn("bad_request", err)
http.Error(w, "Invalid JSON", 400)
return
}
// 2. Call Business Logic (Decoupled)
result, err := h.calculatorService.AnalyzeText(r.Context(), req.Text)
// 3. Send Response
json.NewEncoder(w).Encode(result)
}🔐 Zero Trust Security
I never trust input—even from internal systems. Middleware strictly validates every request before it touches your logic.
func ZeroTrustGuard(next http.Handler) http.Handler {
return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
// 1. Strict Scope Validation
claims, err := auth.VerifyToken(r)
if err != nil || !claims.HasScope("azlo:analyze") {
// 2. Audit Logging (Telemetry)
telemetry.LogSecurityEvent(r.Context(), "unauthorized_access", claims.Sub)
http.Error(w, "Forbidden", 403)
return
}
next.ServeHTTP(w, r)
})
}World-Class Technology Stack
I don't reinvent the wheel. I utilize the battle-tested tools built by tech giants to handle billions of users.
Golang (Go)
Developed by Google to power their cloud infrastructure. I use it because it offers unmatched speed and stability under pressure.
Netflix-Grade Logging
Netflix pioneered the concept of deep system observability. I implement 'Structured Logging' everywhere, so bugs are caught before users complain.
PostgreSQL
The world's most advanced open-source database. The same technology safeguarding data at companies like Apple, Instagram, and Spotify.
Redis
The world's fastest in-memory data store. I use Redis for caching and lightning-fast data access, ensuring your users never wait for the database.
Google Cloud
Cloud-native infrastructure that scales automatically. I leverage GCP to ensure your application can handle growth without manual intervention.
AWS
Amazon Web Services provides enterprise-grade stability. I use AWS for mission-critical systems where uptime is non-negotiable.
Docker
I containerize all applications. This guarantees that the code runs identically on my machine and your server – eliminating 'it works on my machine' issues.
Kubernetes
The next step on the journey. I am currently learning Kubernetes to offer orchestration of complex microservices at scale.
Why pay for robustness?
Bad code is like high-interest debt. It's fast initially, but the interest (bug fixes and downtime) kills your business over time.
Protect Revenue
When a payment gateway flickers, standard code drops the order. My code automatically retries with backoff logic. That saves sales.
Minimize Maintenance
Fragile systems need constant babysitting. Robust systems handle their own errors, saving you expensive support hours.
Scalability from Day 1
By utilizing patterns like Context Cancellation and Graceful Shutdown, your backend systems handle high loads without crashing.
Technical Deep Dive
For the technical leads: I specialize in Golang and cloud-native architecture. Here is how I eliminate technical debt before it starts.
I implement **Circuit Breakers** and **Exponential Backoff** strategies to prevent cascading failures in microservices. This ensures one failing dependency doesn't take down your entire platform.
Through structured logging (OpenTelemetry/Zap) and distributed tracing, I make the system transparent. We don't guess about bugs; we trace them instantly.