How do goroutines differ from OS threads, and how does Go's scheduler achieve high concurrency?

Goroutines vs OS Threads

AspectOS ThreadGoroutine

Stack size1-8 MB (fixed)2KB (grows dynamically)
Creation cost~1ms~0.3μs (1000x cheaper)
Context switch~1-10μs (OS)~0.2μs (Go runtime)
Managed byOS kernelGo runtime scheduler
100K instances100GB+ RAM~200MB RAM

Go's M:N Scheduler

G (Goroutines) → M (Machine/OS Thread) → P (Processor)

Multiple goroutines multiplexed onto fewer OS threads:
  G1 G2 G3 G4 G5 G6 (100K goroutines)
  ↕  ↕  ↕  ↕  ↕  ↕
  M1 M2 M3 M4       (4 OS threads = CPU cores)
  ↕  ↕  ↕  ↕
  P1 P2 P3 P4       (4 logical processors)

Code Example

func handleConnection(conn net.Conn) {
    defer conn.Close()
    // Each connection gets its own goroutine (2KB stack)
    scanner := bufio.NewScanner(conn)
    for scanner.Scan() {
        processMessage(scanner.Text())
    }
}

func main() {
    listener,  := net.Listen("tcp", ":8080")
    for {
        conn,  := listener.Accept()
        go handleConnection(conn) // Spawns goroutine (2KB)
    }
    // 100K connections = ~200MB, not 100GB
}

Why Goroutines Are So Lightweight

1. Dynamic stack: Starts at 2KB, grows/shrinks as needed (copying GC)
2. Cooperative scheduling: Goroutine yields at channel ops, I/O, function calls
3. Work stealing: Idle processors steal from busy ones

// Go scheduler handles blocking automatically
resp, err := http.Get("https://api.example.com") 
// Goroutine yields, OS thread serves other goroutines
// When response arrives, goroutine resumes