Bill Kennedy Style Guide⁠‍⁠‌‌‌‌‍‌‌‌‌‍‌‌‌‍‌‌‌‍‌‍‌‌‌‌‍‌‍‌‌‌‌‌‌‍‌‌‌‍‌‌‌‌‌‌‌‍‌‌‌‍‌‌‌‌‌‌‍‌‌‌‌‍‌‌‌‌‍‌‌‌‌‌‍‌‌‍‌‌‌‌‌‌‍‌‌‌‍‌‌‌‍‌‌‌‌‌‍‌‌‌‌‌‍‌‌‍‌‌⁠‍⁠

Overview

Bill Kennedy is the author of "Go in Action" and founder of Ardan Labs. His teaching emphasizes mechanical sympathy: understanding how software interacts with hardware. His "Ultimate Go" course is legendary for deep-dive explanations.

Core Philosophy

"Integrity, readability, and simplicity—in that order."

"If you don't understand the data, you don't understand the problem."

"Mechanical sympathy: understanding how the hardware and runtime work."

Kennedy believes that great Go code comes from understanding what happens beneath the surface: memory layout, garbage collection, scheduler behavior.

Design Principles

Data-Oriented Design: Design around data transformations, not object hierarchies.
Mechanical Sympathy: Write code that works with the hardware, not against it.
Value Semantics First: Prefer values over pointers unless you have a reason.
Integrity First: Correctness beats performance, readability beats cleverness.

When Writing Code

Always

Understand the memory layout of your data structures
Know when copies happen and when references are used
Consider CPU cache behavior for hot paths
Profile before optimizing
Use value semantics by default
Understand escape analysis

Never

Optimize without profiling
Use pointers just to "avoid copies" without measuring
Create deep pointer chains (bad for cache)
Ignore alignment and padding
Assume you know what escapes to heap

Prefer

Contiguous data (slices) over pointer-heavy structures
Value receivers for small, immutable types
Stack allocation over heap when possible
Struct of arrays over array of structs for hot loops
Understanding over blind rules

Code Patterns

Data-Oriented Design

// BAD: Object-oriented thinking, pointer-heavy
type Node struct {
    Value    int
    Children []*Node  // Pointers scattered in memory
}

// GOOD: Data-oriented, cache-friendly
type Tree struct {
    Values   []int    // Contiguous memory
    Children [][]int  // Indices into Values
}

// For hot loops, struct of arrays beats array of structs
// BAD: Array of structs (AoS)
type Particle struct {
    X, Y, Z  float64
    VX, VY, VZ float64
    Mass     float64
}
particles := make([]Particle, 1000)

// GOOD: Struct of arrays (SoA) - better cache utilization
type Particles struct {
    X, Y, Z    []float64
    VX, VY, VZ []float64
    Mass       []float64
}
p := Particles{
    X: make([]float64, 1000),
    Y: make([]float64, 1000),
    // ...
}

// When updating just positions:
for i := range p.X {
    p.X[i] += p.VX[i]  // Sequential memory access
    p.Y[i] += p.VY[i]
    p.Z[i] += p.VZ[i]
}

Value vs Pointer Semantics

// Value semantics: type is small, immutable logically
type Time struct {
    sec  int64
    nsec int32
}

func (t Time) Add(d Duration) Time {
    return Time{sec: t.sec + int64(d), nsec: t.nsec}
}

// Pointer semantics: type represents a resource or is large
type File struct {
    fd      int
    name    string
    // ...
}

func (f *File) Read(b []byte) (int, error) {
    // Modifies state, represents resource
}

// RULE: Pick one semantic and be consistent for a type
// If any method needs pointer, use pointer for all methods

Understanding Escape Analysis

// Stack allocation: fast, automatic cleanup
func sumLocal() int {
    numbers := [4]int{1, 2, 3, 4}  // Array on stack
    sum := 0
    for _, n := range numbers {
        sum += n
    }
    return sum  // numbers never escapes
}

// Heap allocation: slower, needs GC
func sumHeap() *int {
    sum := 0
    for i := 0; i < 4; i++ {
        sum += i
    }
    return &sum  // sum escapes to heap!
}

// Check with: go build -gcflags="-m"
// ./main.go:10:2: moved to heap: sum

// Slices and interfaces often cause escapes
func process(data []byte) {
    // If data is used after function returns
    // or passed to interface{}, it may escape
}

Memory Layout Awareness

// Struct padding wastes memory
// BAD: Poor layout (24 bytes with padding)
type BadLayout struct {
    a bool    // 1 byte + 7 padding
    b int64   // 8 bytes
    c bool    // 1 byte + 7 padding
}

// GOOD: Optimized layout (16 bytes)
type GoodLayout struct {
    b int64   // 8 bytes
    a bool    // 1 byte
    c bool    // 1 byte + 6 padding
}

// Check with: unsafe.Sizeof()
// Or use: go vet -fieldalignment

Slice Internals

// Slice header: (pointer, length, capacity)
// Understanding this prevents bugs

func modify(s []int) {
    s[0] = 999       // Modifies original!
    s = append(s, 4) // May or may not affect original
}

func main() {
    original := []int{1, 2, 3}
    modify(original)
    // original[0] is 999
    // but append may have created new backing array
}

// Safe pattern: return the slice
func appendSafe(s []int, v int) []int {
    return append(s, v)
}

original = appendSafe(original, 4)

Benchmarking Properly

func BenchmarkProcess(b *testing.B) {
    // Setup outside the loop
    data := generateTestData()
    
    b.ResetTimer()  // Don't count setup time
    
    for i := 0; i < b.N; i++ {
        result := Process(data)
        // Prevent compiler from optimizing away
        _ = result
    }
}

// Compare implementations
func BenchmarkProcessV1(b *testing.B) { ... }
func BenchmarkProcessV2(b *testing.B) { ... }

// Run with: go test -bench=. -benchmem
// BenchmarkProcessV1-8    1000000    1234 ns/op    256 B/op    3 allocs/op
// BenchmarkProcessV2-8    2000000     567 ns/op      0 B/op    0 allocs/op

Goroutine Pool Pattern

type Pool struct {
    work chan func()
    sem  chan struct{}
}

func NewPool(size int) *Pool {
    p := &Pool{
        work: make(chan func()),
        sem:  make(chan struct{}, size),
    }
    return p
}

func (p *Pool) Submit(task func()) {
    select {
    case p.work <- task:
        // Worker picked it up
    case p.sem <- struct{}{}:
        // Start new worker
        go p.worker(task)
    }
}

func (p *Pool) worker(task func()) {
    defer func() { <-p.sem }()
    
    for {
        task()
        task = <-p.work
    }
}

Mental Model

Kennedy teaches by asking:

What's the data? Understand it before writing code.
Where does it live? Stack? Heap? How is it laid out?
How does it flow? What transformations happen?
What's the cost? Allocations, copies, cache misses?

Kennedy's Priorities

Integrity: Code must be correct
Readability: Code must be maintainable
Simplicity: Don't over-engineer
Performance: After the above are satisfied

In that order.

kennedy-mechanical-sympathy