Go Concurrency Mastery: Preventing Goroutine Leaks with Context, Timeout & Cancellation Best Practices

Goroutines are Go's superpower — lightweight, highly concurrent, and capable of handling thousands of simultaneous operations with minimal overhead. They're the foundation of Go's promise for building scalable, high-performance systems.

But with great power comes great responsibility. Goroutine leaks are a silent killer in production systems.

Unlike memory leaks in garbage-collected languages, leaked goroutines don't just consume memory — they hold onto file descriptors, network connections, and CPU cycles. In high-throughput production environments, even a small leak can compound into service degradation or complete outages.

In this comprehensive guide, we'll master:

• Deep understanding of goroutine leak mechanics and detection
• Production-grade context patterns for bulletproof cancellation
• Advanced techniques using Go 1.20+ features like context.WithCancelCause
• Real-world scenarios from microservices, batch processing, and stream handling
• Performance monitoring strategies for leak-free production systems

---

🔍 Understanding Goroutine Leaks: The Silent Production Killer

A goroutine leak occurs when a goroutine remains alive indefinitely, consuming system resources without performing useful work. Unlike process leaks in other languages, goroutine leaks are particularly insidious because:

1. Memory consumption — Each goroutine uses ~2KB of stack space (minimum)
2. Scheduler overhead — The Go scheduler must track and manage leaked goroutines
3. Resource holding — Leaked goroutines can hold file handles, network connections, or locks
4. GC pressure — Associated heap objects can't be garbage collected

Common Leak Patterns in Production

Pattern 1: Unbuffered Channel Deadlock

// ❌ Classic leak: sender blocks forever
func processRequests() {
    requests := make(chan Request) // unbuffered

    for i := 0; i < 10; i++ {
        go func() {
            requests <- generateRequest() // blocks forever if no receiver
        }()
    }
    // No receiver goroutines started - all senders leak
}

Pattern 2: Missing Context Propagation

// ❌ HTTP client without timeout - can hang indefinitely  
func fetchUserData(userID int) (*User, error) {
    resp, err := http.Get(fmt.Sprintf("https://api.example.com/users/%d", userID))
    if err != nil {
        return nil, err
    }
    defer resp.Body.Close()
    // If server is slow/unresponsive, this goroutine leaks
}

Pattern 3: Forgotten Background Workers

// ❌ Background worker without shutdown mechanism
func startMetricsCollector() {
    go func() {
        ticker := time.NewTicker(30 * time.Second)
        for range ticker.C {
            collectMetrics() // runs forever, no way to stop
        }
    }()
}

Real-World Leak Example: Event Processing Pipeline

// ❌ This production-like example has multiple leak vectors
func processEvents(eventSource <-chan Event) {
    // Leak 1: Worker pool with no shutdown
    workers := make(chan Event, 100)
    for i := 0; i < 10; i++ {
        go func() {
            for event := range workers {
                // Leak 2: HTTP call without context/timeout
                resp, err := http.Post("https://webhook.example.com", 
                    "application/json", bytes.NewReader(event.Data))
                if err == nil {
                    resp.Body.Close()
                }
            }
        }()
    }

    // Leak 3: Main loop with no exit condition
    for event := range eventSource {
        select {
        case workers <- event:
        default:
            // Leak 4: Dropped events spawn recovery goroutines
            go func(e Event) {
                time.Sleep(time.Second) // retry delay
                processEvent(e) // might recursively leak more goroutines
            }(event)
        }
    }
}

In production, this pattern can easily spawn thousands of leaked goroutines under high load, eventually exhausting system resources.

---

� Production-Grade Leak Detection and Monitoring

Detecting goroutine leaks in production requires a multi-layered monitoring strategy. Here's how engineering teams at scale companies implement leak detection:

1. Real-Time Monitoring with runtime/metrics (Go 1.16+)

Go's built-in metrics provide zero-overhead runtime monitoring:

package main

import (
    "context"
    "fmt"
    "log/slog"
    "runtime/metrics"
    "time"
)

type GoroutineMonitor struct {
    logger          *slog.Logger
    alertThreshold  uint64
    baselineCount   uint64
    serviceName     string
}

func NewGoroutineMonitor(threshold uint64, serviceName string) *GoroutineMonitor {
    return &GoroutineMonitor{
        logger:         slog.Default(),
        alertThreshold: threshold,
        serviceName:    serviceName,
    }
}

func (gm *GoroutineMonitor) StartMonitoring(ctx context.Context) {
    ticker := time.NewTicker(30 * time.Second)
    defer ticker.Stop()

    // Establish baseline
    gm.baselineCount = gm.getCurrentGoroutineCount()

    for {
        select {
        case <-ctx.Done():
            gm.logger.InfoContext(ctx, "Goroutine monitor shutting down")
            return
        case <-ticker.C:
            current := gm.getCurrentGoroutineCount()
            growth := current - gm.baselineCount

            gm.logger.InfoContext(ctx, "Goroutine stats",
                "current", current,
                "baseline", gm.baselineCount,
                "growth", growth)

            if growth > gm.alertThreshold {
                gm.logger.ErrorContext(ctx, "GOROUTINE LEAK DETECTED",
                    "current_count", current,
                    "growth_since_baseline", growth,
                    "threshold", gm.alertThreshold)

                // Trigger leak investigation
                gm.captureGoroutineProfile(ctx)
            }
        }
    }
}

func (gm *GoroutineMonitor) getCurrentGoroutineCount() uint64 {
    samples := make([]metrics.Sample, 1)
    samples[0].Name = "/sched/goroutines:goroutines"
    metrics.Read(samples)
    return samples[0].Value.Uint64()
}

func (gm *GoroutineMonitor) captureGoroutineProfile(ctx context.Context) {
    // Automated profile capture for leak analysis
    timestamp := time.Now().Unix()
    filename := fmt.Sprintf("goroutine_leak_%d.pprof", timestamp)

    gm.logger.ErrorContext(ctx, "Capturing goroutine profile", "filename", filename)
    // Implementation would write to file or send to observability platform
}

2. Advanced pprof Integration for Deep Analysis

Professional leak detection requires automated pprof integration:

import (
    _ "net/http/pprof" // Enables /debug/pprof endpoints
    "bytes"
    "encoding/json"
    "net/http"
    "runtime/pprof"
)

func setupProfilerEndpoint(port string) {
    mux := http.NewServeMux()

    // Custom endpoint with enhanced goroutine analysis  
    mux.HandleFunc("/debug/goroutines/analysis", func(w http.ResponseWriter, r *http.Request) {
        analysis := analyzeGoroutineStacks()
        w.Header().Set("Content-Type", "application/json")
        json.NewEncoder(w).Encode(analysis)
    })

    // Standard pprof endpoints
    mux.Handle("/debug/pprof/", http.DefaultServeMux)

    go func() {
        log.Fatal(http.ListenAndServe(port, mux))
    }()
}

3. Continuous Integration Leak Testing

Prevent leaks from reaching production with automated testing:

func TestNoGoroutineLeaks(t *testing.T) {
    // Capture baseline goroutine count
    baseline := runtime.NumGoroutine()

    // Run your application code
    ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
    defer cancel()

    runApplicationLogic(ctx)

    // Allow time for cleanup
    time.Sleep(100 * time.Millisecond)
    runtime.GC()
    runtime.GC() // Double GC to ensure cleanup

    // Verify no leaks
    final := runtime.NumGoroutine()
    if final > baseline {
        t.Fatalf("Goroutine leak detected: baseline=%d, final=%d, leaked=%d", 
            baseline, final, final-baseline)
    }
}

Key Production Monitoring Metrics:

• Goroutine count trends and growth rates
• Goroutine states distribution (running, waiting, blocked)
• Stack trace pattern analysis for leak identification
• Resource correlation (memory, file descriptors, connections)
• Service performance correlation with goroutine growth

---

🚀 Mastering Context: The Foundation of Leak-Free Go

The context package is Go's most powerful tool for controlling goroutine lifecycles. Understanding its advanced patterns is crucial for building production-grade concurrent systems.

Why Context is Essential for Production Systems

context.Context provides four critical capabilities:

1. Cancellation propagation — Cascade shutdown signals across goroutine hierarchies
2. Deadline management — Enforce time boundaries on operations
3. Request-scoped values — Safely pass metadata without global variables
4. Observability integration — Enable tracing and monitoring across service boundaries

Go 1.20+ Advanced Context Features

Go 1.20 introduced context.WithCancelCause and context.Cause, enabling rich cancellation semantics:

package main

import (
    "context"
    "fmt" 
    "errors"
    "time"
)

// Production-grade context management with detailed error causes
func advancedContextPattern() {
    // Create a context with cause tracking (Go 1.20+)
    ctx, cancel := context.WithCancelCause(context.Background())

    // Start a background worker
    done := make(chan error, 1)
    go func() {
        defer close(done)

        select {
        case <-ctx.Done():
            // Access the specific cancellation cause
            if cause := context.Cause(ctx); cause != nil {
                fmt.Printf("Worker cancelled due to: %v\n", cause)
            }
            done <- ctx.Err()
        case <-time.After(5 * time.Second):
            done <- errors.New("work completed normally")
        }
    }()

    // Simulate cancellation with a specific cause  
    time.AfterFunc(2*time.Second, func() {
        cancel(errors.New("user initiated shutdown"))
    })

    // Wait for completion and examine the cause
    err := <-done
    fmt.Printf("Final result: %v\n", err)
}

Context Propagation Best Practices

Pattern 1: HTTP Request Context Chain

// ✅ Proper context propagation through HTTP middleware
func httpMiddleware(next http.Handler) http.Handler {
    return http.HandlerFunc(func(w http.ResponseWriter, r *http.Request) {
        // Extract or create request context with timeout
        ctx := r.Context()
        ctx, cancel := context.WithTimeout(ctx, 30*time.Second)
        defer cancel()

        // Add request metadata
        ctx = context.WithValue(ctx, "request-id", generateRequestID())
        ctx = context.WithValue(ctx, "user-id", extractUserID(r))

        // Propagate enhanced context
        next.ServeHTTP(w, r.WithContext(ctx))
    })
}

func businessLogicHandler(w http.ResponseWriter, r *http.Request) {
    ctx := r.Context()

    // All downstream operations inherit the context
    result, err := processBusinessLogic(ctx)
    if err != nil {
        if errors.Is(err, context.DeadlineExceeded) {
            http.Error(w, "Request timeout", http.StatusRequestTimeout)
            return
        }
        http.Error(w, "Internal error", http.StatusInternalServerError)
        return
    }

    json.NewEncoder(w).Encode(result)
}

func processBusinessLogic(ctx context.Context) (*Result, error) {
    // Check cancellation before expensive operations
    if err := ctx.Err(); err != nil {
        return nil, err
    }

    // Propagate context to all child operations
    user, err := fetchUser(ctx, getUserID(ctx))
    if err != nil {
        return nil, fmt.Errorf("user fetch failed: %w", err) 
    }

    permissions, err := checkPermissions(ctx, user.ID)
    if err != nil {
        return nil, fmt.Errorf("permission check failed: %w", err)
    }

    data, err := generateReport(ctx, user.ID, permissions)
    if err != nil {
        return nil, fmt.Errorf("report generation failed: %w", err)
    }

    return &Result{Data: data}, nil
}

Pattern 2: Worker Pool with Graceful Shutdown

// ✅ Production-grade worker pool with proper lifecycle management
type WorkerPool struct {
    workers    int
    jobQueue   chan Job
    wg         sync.WaitGroup
    logger     *slog.Logger
}

type Job struct {
    ID      string
    Payload interface{}
}

func NewWorkerPool(workers int, queueSize int) *WorkerPool {
    return &WorkerPool{
        workers:  workers,
        jobQueue: make(chan Job, queueSize),
        logger:   slog.Default(),
    }
}

func (wp *WorkerPool) Start(ctx context.Context) {
    wp.logger.InfoContext(ctx, "Starting worker pool", "workers", wp.workers)

    // Start worker goroutines
    for i := 0; i < wp.workers; i++ {
        wp.wg.Add(1)
        go wp.worker(ctx, i)
    }

    // Wait for shutdown signal
    go func() {
        <-ctx.Done()
        wp.logger.InfoContext(ctx, "Shutting down worker pool", "reason", ctx.Err())
        close(wp.jobQueue) // Signal workers to stop
    }()
}

func (wp *WorkerPool) worker(ctx context.Context, id int) {
    defer wp.wg.Done()

    wp.logger.InfoContext(ctx, "Worker starting", "worker_id", id)

    for {
        select {
        case <-ctx.Done():
            wp.logger.InfoContext(ctx, "Worker cancelled", "worker_id", id, "reason", ctx.Err())
            return

        case job, ok := <-wp.jobQueue:
            if !ok {
                wp.logger.InfoContext(ctx, "Worker stopping - job queue closed", "worker_id", id)
                return
            }

            // Process job with context awareness
            if err := wp.processJob(ctx, job); err != nil {
                wp.logger.ErrorContext(ctx, "Job processing failed", 
                    "worker_id", id, 
                    "job_id", job.ID, 
                    "error", err)
            }
        }
    }
}

func (wp *WorkerPool) processJob(ctx context.Context, job Job) error {
    // Always check context before expensive operations
    if err := ctx.Err(); err != nil {
        return fmt.Errorf("context cancelled before job processing: %w", err)
    }

    // Simulate work with context-aware operations
    select {
    case <-time.After(time.Second): // Simulate work
        wp.logger.InfoContext(ctx, "Job completed", "job_id", job.ID)
        return nil
    case <-ctx.Done():
        return fmt.Errorf("job cancelled during processing: %w", ctx.Err())
    }
}

func (wp *WorkerPool) Shutdown(ctx context.Context) error {
    wp.logger.InfoContext(ctx, "Initiating worker pool shutdown")

    // Wait for all workers to finish with timeout
    done := make(chan struct{})
    go func() {
        wp.wg.Wait()
        close(done)
    }()

    select {
    case <-done:
        wp.logger.InfoContext(ctx, "All workers shutdown cleanly")
        return nil
    case <-ctx.Done():
        return fmt.Errorf("shutdown timeout exceeded: %w", ctx.Err())
    }
}

Pattern 3: Context Chaining and Inheritance

// ✅ Advanced context chaining for complex operation pipelines
func processOrderPipeline(ctx context.Context, orderID string) error {
    // Create pipeline-specific context with extended timeout
    pipelineCtx, cancel := context.WithTimeout(ctx, 2*time.Minute)
    defer cancel()

    // Add pipeline metadata
    pipelineCtx = context.WithValue(pipelineCtx, "pipeline", "order-processing")
    pipelineCtx = context.WithValue(pipelineCtx, "order_id", orderID)

    // Stage 1: Validation (inherits parent timeout)
    if err := validateOrder(pipelineCtx, orderID); err != nil {
        return fmt.Errorf("validation failed: %w", err)
    }

    // Stage 2: Payment with shorter timeout
    paymentCtx, paymentCancel := context.WithTimeout(pipelineCtx, 30*time.Second)
    defer paymentCancel()

    if err := processPayment(paymentCtx, orderID); err != nil {
        return fmt.Errorf("payment failed: %w", err)
    }

    // Stage 3: Fulfillment can use remaining pipeline time
    if err := fulfillOrder(pipelineCtx, orderID); err != nil {
        return fmt.Errorf("fulfillment failed: %w", err)
    }

    return nil
}

func validateOrder(ctx context.Context, orderID string) error {
    select {
    case <-ctx.Done():
        return ctx.Err()
    case <-time.After(5 * time.Second): // Simulated validation
        return nil
    }
}

func processPayment(ctx context.Context, orderID string) error {
    // Payment operations must respect context deadlines
    client := &http.Client{Timeout: 15 * time.Second}

    req, err := http.NewRequestWithContext(ctx, "POST", 
        "https://payment-api.example.com/charge", nil)
    if err != nil {
        return err
    }

    resp, err := client.Do(req)
    if err != nil {
        if errors.Is(err, context.DeadlineExceeded) {
            return fmt.Errorf("payment timeout: %w", err)
        }
        return err
    }
    defer resp.Body.Close()

    return nil
}

Key Benefits of Advanced Context Patterns:

• Hierarchical cancellation — Child contexts automatically cancelled when parent cancels
• Granular timeout control — Different timeouts for different operation stages
• Rich error semantics — Detailed cancellation causes with context.WithCancelCause
• Request tracing — Context values enable distributed tracing across services
• Resource cleanup — Guaranteed cleanup via defer and context cancellation

---

⏰ Advanced Timeout & Deadline Strategies

Effective timeout management is crucial for preventing cascading failures in distributed systems. Go 1.21+ introduced enhanced timeout capabilities that provide better control and observability.

Multi-Level Timeout Architecture

Production systems require sophisticated timeout strategies that handle both fast failures and retry scenarios:

// ✅ Production-grade timeout management with fallbacks and retries
type ServiceClient struct {
    httpClient *http.Client
    baseURL    string
    logger     *slog.Logger
}

func NewServiceClient(baseURL string) *ServiceClient {
    return &ServiceClient{
        httpClient: &http.Client{
            Timeout: 30 * time.Second, // Global client timeout
        },
        baseURL: baseURL,
        logger:  slog.Default(),
    }
}

func (sc *ServiceClient) FetchUserData(ctx context.Context, userID string) (*UserData, error) {
    // Create operation-specific timeout with cause tracking (Go 1.21+)
    opCtx, cancel := context.WithTimeoutCause(ctx, 10*time.Second, 
        fmt.Errorf("user data fetch timeout for user %s", userID))
    defer cancel()

    // Add retry logic with exponential backoff
    return sc.fetchWithRetry(opCtx, userID, 3)
}

func (sc *ServiceClient) fetchWithRetry(ctx context.Context, userID string, maxRetries int) (*UserData, error) {
    var lastErr error

    for attempt := 0; attempt < maxRetries; attempt++ {
        // Per-attempt timeout (shorter than operation timeout)
        attemptCtx, cancel := context.WithTimeout(ctx, 3*time.Second)

        data, err := sc.singleFetch(attemptCtx, userID)
        cancel() // Always cancel to free resources

        if err == nil {
            sc.logger.InfoContext(ctx, "Fetch successful", 
                "user_id", userID, "attempt", attempt+1)
            return data, nil
        }

        lastErr = err

        // Check if we should retry
        if errors.Is(err, context.DeadlineExceeded) && attempt < maxRetries-1 {
            // Exponential backoff: 100ms, 200ms, 400ms...
            delay := time.Duration(100*(1<<attempt)) * time.Millisecond

            sc.logger.WarnContext(ctx, "Retrying after timeout", 
                "user_id", userID, 
                "attempt", attempt+1, 
                "delay", delay)

            select {
            case <-time.After(delay):
                continue
            case <-ctx.Done():
                return nil, fmt.Errorf("retry cancelled: %w", ctx.Err())
            }
        } else {
            break // Don't retry non-timeout errors or final attempt
        }
    }

    return nil, fmt.Errorf("all attempts failed: %w", lastErr)
}

Key Benefits: Multi-level timeouts prevent cascading failures, exponential backoff handles transient issues, and context cause tracking provides detailed error diagnostics.

---

🏭 Enterprise-Grade Real-World Scenarios

High-Throughput Message Processing Pipeline

This production example demonstrates a complete message processing system with proper resource management, error handling, and graceful shutdown:

func StartWorker(ctx context.Context, messages <-chan string) {
    for {
        select {
        case <-ctx.Done():
            log.Println("Worker shutting down:", ctx.Err())
            return
        case msg := <-messages:
            // process message
            process(msg)
        }
    }
}

func main() {
    ctx, cancel := context.WithCancel(context.Background())
    messages := make(chan string)

    go StartWorker(ctx, messages)

    // simulate messages
    go func() {
        for i := 0; i < 10; i++ {
            messages <- fmt.Sprintf("msg-%d", i)
            time.Sleep(100 * time.Millisecond)
        }
    }()

    // simulate shutdown
    time.Sleep(time.Second)
    cancel()
    log.Println("Graceful shutdown complete")
}

Here, StartWorker exits instantly when the context is canceled — no stuck goroutines, no leaks, even under heavy load.

---

🎯 Production-Grade Goroutine Management Checklist

Context & Lifecycle Management

• ✅ Always propagate context.Context through your entire call stack
• ✅ Use context.WithTimeout for external API calls and database operations
• ✅ Implement context.WithCancelCause (Go 1.20+) for detailed error tracking
• ✅ Check ctx.Err() before expensive operations in long-running goroutines
• ✅ Use signal.NotifyContext (Go 1.16+) for graceful OS signal handling

Channel & Communication Patterns

• ✅ Close channels from sender side and check ok values when receiving
• ✅ Use buffered channels for decoupling producers from consumers
• ✅ Implement proper select statements with context cancellation in all cases
• ✅ Avoid infinite blocking on channel operations without timeout/cancellation

Monitoring & Observability

• ✅ Monitor goroutine count with runtime/metrics (Go 1.16+)
• ✅ Set up automated pprof collection at /debug/pprof/goroutine
• ✅ Implement health checks that include goroutine health metrics
• ✅ Create alerts for goroutine count growth beyond baseline thresholds
• ✅ Use structured logging with slog.InfoContext/ErrorContext (Go 1.21+)

Testing & CI/CD

• ✅ Write goroutine leak tests that verify baseline counts before/after
• ✅ Use goleak package for automated leak detection in unit tests
• ✅ Load test with goroutine monitoring to catch leaks under realistic conditions
• ✅ Implement circuit breakers to prevent cascade failures that cause leaks

Architecture & Design

• ✅ Limit concurrency with worker pools and semaphores
• ✅ Implement graceful shutdown with proper resource cleanup sequencing
• ✅ Use timeouts at multiple levels (connection, request, operation)
• ✅ Design for failure — assume external services will be slow/unavailable

---

� Advanced Leak Detection Tool

For comprehensive leak detection in CI/CD pipelines:

// ✅ Enterprise-grade leak detection for automated testing
func TestGoroutineLeakDetection(t *testing.T) {
    // Capture baseline with multiple measurements for accuracy
    baseline := captureGoroutineBaseline()

    // Run application code with realistic load
    ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
    defer cancel()

    err := runApplicationUnderTest(ctx)
    require.NoError(t, err)

    // Allow sufficient cleanup time
    waitForCleanup(2 * time.Second)

    // Verify no leaks with detailed analysis
    verifyNoGoroutineLeaks(t, baseline)
}

func captureGoroutineBaseline() GoroutineSnapshot {
    // Take multiple measurements to account for runtime variation
    samples := make([]int, 5)
    for i := 0; i < 5; i++ {
        runtime.GC() // Force GC to cleanup any existing goroutines
        time.Sleep(10 * time.Millisecond)
        samples[i] = runtime.NumGoroutine()
    }

    return GoroutineSnapshot{
        Count:     samples[len(samples)-1], // Use final measurement
        Timestamp: time.Now(),
        Stacks:    captureStackTraces(),
    }
}

func verifyNoGoroutineLeaks(t *testing.T, baseline GoroutineSnapshot) {
    final := captureGoroutineBaseline()

    if final.Count > baseline.Count {
        leakCount := final.Count - baseline.Count

        // Capture detailed leak information
        leakReport := generateLeakReport(baseline, final)

        t.Fatalf("GOROUTINE LEAK DETECTED:\n"+
            "Baseline: %d goroutines\n"+
            "Final: %d goroutines\n"+
            "Leaked: %d goroutines\n"+
            "Leak Analysis:\n%s",
            baseline.Count, final.Count, leakCount, leakReport)
    }
}

---

🎯 Key Takeaways for Production Excellence

Goroutine leaks are preventable with disciplined engineering practices. The patterns in this article aren't just theoretical — they're battle-tested in production systems handling millions of requests.

Context is your lifeline. Master context.Context patterns and you'll eliminate 90% of potential goroutine leaks. The remaining 10% come down to careful channel management and proper resource cleanup.

Observability is non-negotiable. You can't fix what you can't measure. Implement comprehensive monitoring from day one, not as an afterthought when leaks bring down production.

Test early, test often. Goroutine leak tests should be as common as unit tests. Use tools like goleak and build custom detection into your CI/CD pipeline.

Design for resilience. Assume everything will fail — networks will be slow, databases will timeout, external APIs will return errors. Build your goroutine management with these assumptions and your systems will be unbreakable.

The investment in proper goroutine lifecycle management pays dividends in production stability, performance predictability, and engineering team confidence. Your future on-call rotations will thank you.

---

� Advanced Resources & Further Reading

Official Go Documentation:

• Go Blog: Concurrency Patterns — Essential reading for production patterns
• Context Package Documentation — Complete API reference with examples
• Runtime Metrics — Built-in monitoring capabilities

Production Tools & Libraries:

• uber-go/goleak — Automated goroutine leak detection for tests
• Prometheus Go Client — Metrics collection and monitoring
• OpenTelemetry Go — Distributed tracing for context propagation

Advanced Topics:

• Go Memory Model — Understanding concurrent memory operations
• Effective Go: Concurrency — Official best practices
• Go Runtime Scheduler — Deep dive into goroutine scheduling

✍️ Written by Şerif Çolakel.

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