# Performance Optimization Guide
## Overview
This document describes performance optimization strategies for Cocoanut, including profiling hot paths, memory optimization, and compilation improvements.
---
## Part 1: Hot Path Profiling
### Identifying Hot Paths
Hot paths are code sections that execute frequently and consume significant CPU time.
#### Common Hot Paths in Cocoanut
1. **Event System** - Fires on every user interaction
2. **Layout System** - Recalculates on window resize
3. **Rendering** - Redraws on every frame
4. **String Conversion** - NSString ↔ Rust string conversions
5. **Memory Management** - Retain/release operations
### Profiling Tools
#### Using Instruments (macOS)
```bash
# Profile with Instruments
cargo build --release
instruments -t "System Trace" ./target/release/cocoanut_app
# Profile with Time Profiler
instruments -t "Time Profiler" ./target/release/cocoanut_app
```
#### Using flamegraph
```bash
# Install flamegraph
cargo install flamegraph
# Generate flamegraph
cargo flamegraph --release
# View result
open flamegraph.svg
```
#### Using perf (Linux)
```bash
# Profile with perf
perf record -g ./target/release/cocoanut_app
perf report
```
### Optimization Techniques
#### 1. Caching
```rust
// Before: Recalculates every time
fn get_class_name(obj: *mut Object) -> Result<String> {
unsafe { cstring_to_string(get_objc_class_name(obj)) }
}
// After: Cache the result
pub struct CachedObject {
obj: *mut Object,
class_name: Option<String>,
}
impl CachedObject {
pub fn class_name(&mut self) -> Result<&str> {
if self.class_name.is_none() {
self.class_name = Some(unsafe {
cstring_to_string(get_objc_class_name(self.obj))?
});
}
Ok(self.class_name.as_ref().unwrap())
}
}
```
#### 2. Lazy Initialization
```rust
// Before: Initialize everything upfront
pub struct Component {
event_system: EventSystem,
layout_system: LayoutSystem,
animation_system: AnimationSystem,
}
// After: Initialize on demand
pub struct Component {
event_system: Option<EventSystem>,
layout_system: Option<LayoutSystem>,
animation_system: Option<AnimationSystem>,
}
impl Component {
pub fn event_system(&mut self) -> &mut EventSystem {
if self.event_system.is_none() {
self.event_system = Some(EventSystem::new());
}
self.event_system.as_mut().unwrap()
}
}
```
#### 3. Batch Operations
```rust
// Before: Multiple individual operations
for item in items {
component.add_item(item)?;
}
// After: Batch operation
component.add_items(items)?;
```
#### 4. Early Exit
```rust
// Before: Process everything
fn process(items: Vec<Item>) -> Result<()> {
for item in items {
validate_item(&item)?;
process_item(&item)?;
}
Ok(())
}
// After: Exit early on error
fn process(items: Vec<Item>) -> Result<()> {
// Validate all first
for item in &items {
validate_item(item)?;
}
// Then process
for item in items {
process_item(&item)?;
}
Ok(())
}
```
---
## Part 2: Memory Optimization
### Memory Profiling
#### Using Valgrind (Linux)
```bash
valgrind --leak-check=full ./target/debug/cocoanut_app
```
#### Using Heaptrack (Linux)
```bash
heaptrack ./target/debug/cocoanut_app
heaptrack_gui heaptrack.cocoanut_app.*.gz
```
#### Using Xcode Instruments (macOS)
```bash
# Memory profiling
instruments -t "Allocations" ./target/release/cocoanut_app
```
### Memory Optimization Techniques
#### 1. String Interning
```rust
// Before: Duplicate strings
pub struct Button {
id: String, // "button_1"
name: String, // "button_1"
}
// After: Shared strings
use std::sync::Arc;
pub struct Button {
id: Arc<str>,
name: Arc<str>,
}
```
#### 2. Object Pooling
```rust
// Before: Create/destroy on each use
pub struct EventPool {
events: Vec<UIEvent>,
}
impl EventPool {
pub fn acquire(&mut self) -> UIEvent {
self.events.pop().unwrap_or_default()
}
pub fn release(&mut self, event: UIEvent) {
self.events.push(event);
}
}
```
#### 3. Reduce Allocations
```rust
// Before: Multiple allocations
fn process(items: &[Item]) -> Result<Vec<String>> {
let mut results = Vec::new();
for item in items {
let name = item.name.clone(); // Allocation
results.push(name);
}
Ok(results)
}
// After: Reuse references
fn process(items: &[Item]) -> Result<Vec<&str>> {
items.iter().map(|item| Ok(item.name.as_str())).collect()
}
```
#### 4. Smart Pointers
```rust
// Before: Manual memory management
pub struct Component {
ns_view: *mut Object,
}
// After: Automatic with Arc
use std::sync::Arc;
pub struct Component {
ns_view: Arc<NSView>,
}
```
#### 5. Stack Allocation
```rust
// Before: Heap allocation
let large_buffer = Box::new([0u8; 1024]);
// After: Stack allocation (if size is known)
let large_buffer = [0u8; 1024];
```
---
## Part 3: Compilation Optimization
### Build Time Optimization
#### 1. Incremental Compilation
```toml
# Cargo.toml
[profile.dev]
incremental = true
[profile.release]
incremental = true
```
#### 2. Parallel Compilation
```bash
# Use all available cores
CARGO_BUILD_JOBS=8 cargo build
# Or set in .cargo/config.toml
[build]
jobs = 8
```
#### 3. Link-Time Optimization (LTO)
```toml
# Cargo.toml
[profile.release]
lto = true
codegen-units = 1
```
#### 4. Reduce Dependencies
```bash
# Find unused dependencies
cargo tree --duplicates
# Remove unused crates
cargo remove unused_crate
```
#### 5. Feature Flags
```toml
# Cargo.toml
[features]
default = ["core"]
core = []
advanced = ["core"]
full = ["core", "advanced"]
```
### Runtime Optimization
#### 1. Release Profile
```toml
# Cargo.toml
[profile.release]
opt-level = 3
lto = true
codegen-units = 1
strip = true
```
#### 2. Inline Hints
```rust
// Hot path - inline
#[inline]
pub fn is_null(ptr: *mut Object) -> bool {
ptr.is_null()
}
// Cold path - don't inline
#[inline(never)]
pub fn handle_error(error: CocoanutError) {
eprintln!("Error: {}", error);
}
```
#### 3. Avoid Allocations in Loops
```rust
// Before: Allocation in loop
for _ in 0..1000 {
let vec = Vec::new(); // Allocates each iteration
process(&vec)?;
}
// After: Allocate once
let mut vec = Vec::new();
for _ in 0..1000 {
vec.clear();
process(&vec)?;
}
```
---
## Part 4: Benchmarking
### Creating Benchmarks
```rust
#[cfg(test)]
mod benches {
use super::*;
use std::time::Instant;
#[test]
fn bench_string_conversion() {
let iterations = 10_000;
let start = Instant::now();
for i in 0..iterations {
let s = format!("string_{}", i);
let _ = string_to_cstring(&s);
}
let elapsed = start.elapsed();
println!("Time: {:?}, Per iteration: {:?}",
elapsed,
elapsed / iterations as u32);
}
}
```
### Using Criterion
```toml
# Cargo.toml
[dev-dependencies]
criterion = "0.5"
```
```rust
use criterion::{black_box, criterion_group, criterion_main, Criterion};
fn string_conversion_benchmark(c: &mut Criterion) {
c.bench_function("string_to_cstring", |b| {
b.iter(|| string_to_cstring(black_box("test")))
});
}
criterion_group!(benches, string_conversion_benchmark);
criterion_main!(benches);
```
Run benchmarks:
```bash
cargo bench
```
---
## Part 5: Performance Checklist
### Before Optimization
- [ ] Identify bottlenecks with profiling
- [ ] Measure baseline performance
- [ ] Document current metrics
### During Optimization
- [ ] Change one thing at a time
- [ ] Measure impact of each change
- [ ] Keep changes that improve performance
- [ ] Revert changes that don't help
### After Optimization
- [ ] Document optimizations made
- [ ] Update benchmarks
- [ ] Compare before/after metrics
- [ ] Share results with team
---
## Part 6: Performance Targets
### Cocoanut Performance Goals
| Component Creation | < 1ms | TBD | ⏳ |
| Event Emission | < 0.1ms | TBD | ⏳ |
| Layout Calculation | < 5ms | TBD | ⏳ |
| String Conversion | < 0.01ms | TBD | ⏳ |
| Memory per Component | < 1KB | TBD | ⏳ |
| Build Time (Release) | < 30s | TBD | ⏳ |
---
## Part 7: Common Pitfalls
### ❌ Don't
- Optimize prematurely without profiling
- Sacrifice readability for micro-optimizations
- Ignore memory leaks
- Create unnecessary copies
- Use inefficient algorithms
### ✅ Do
- Profile first, optimize second
- Maintain code clarity
- Test performance improvements
- Use appropriate data structures
- Consider algorithmic improvements
---
## Part 8: Tools and Resources
### Profiling Tools
- **Instruments** (macOS) - Built-in profiler
- **flamegraph** - Visualize CPU usage
- **perf** (Linux) - Performance analysis
- **Valgrind** (Linux) - Memory profiling
### Benchmarking
- **Criterion** - Statistical benchmarking
- **Bencher** - Continuous benchmarking
- **Flamegraph** - Flame graphs
### Documentation
- [Rust Performance Book](https://nnethercote.github.io/perf-book/)
- [Criterion.rs](https://bheisler.github.io/criterion.rs/book/)
- [Flamegraph](https://www.brendangregg.com/flamegraphs.html)
---
## Summary
Performance optimization is an iterative process:
1. **Profile** - Identify bottlenecks
2. **Optimize** - Apply targeted improvements
3. **Measure** - Verify improvements
4. **Repeat** - Continue optimizing
Focus on:
- ✅ Hot paths (high frequency, high cost)
- ✅ Memory efficiency (reduce allocations)
- ✅ Compilation speed (faster iteration)
---
**Last Updated:** October 25, 2025
**Version:** 1.0
**Status:** Active
