perf-profiler

# Performance Profiler

Measure, profile, and optimize application performance. Covers CPU profiling, memory analysis, flame graphs, benchmarking, load testing, and language-specific optimization patterns.

## When to Use

- Diagnosing why an application or function is slow
- Measuring CPU and memory usage
- Generating flame graphs to visualize hot paths
- Benchmarking functions or endpoints
- Load testing APIs before deployment
- Finding and fixing memory leaks
- Optimizing database query performance
- Comparing performance before and after changes

## Quick Timing

### Command-line timing

```bash
# Time any command
time my-command --flag

# More precise: multiple runs with stats
for i in $(seq 1 10); do
  /usr/bin/time -f "%e" my-command 2>&1
done | awk '{sum+=$1; sumsq+=$1*$1; count++} END {
  avg=sum/count;
  stddev=sqrt(sumsq/count - avg*avg);
  printf "runs=%d avg=%.3fs stddev=%.3fs\n", count, avg, stddev
}'

# Hyperfine (better benchmarking tool)
# Install: https://github.com/sharkdp/hyperfine
hyperfine 'command-a' 'command-b'
hyperfine --warmup 3 --runs 20 'my-command'
hyperfine --export-json results.json 'old-version' 'new-version'
```

### Inline timing (any language)

```javascript
// Node.js
console.time('operation');
await doExpensiveThing();
console.timeEnd('operation'); // "operation: 142.3ms"

// High-resolution
const start = performance.now();
await doExpensiveThing();
const elapsed = performance.now() - start;
console.log(`Elapsed: ${elapsed.toFixed(2)}ms`);
```

```python
# Python
import time

start = time.perf_counter()
do_expensive_thing()
elapsed = time.perf_counter() - start
print(f"Elapsed: {elapsed:.4f}s")

# Context manager
from contextlib import contextmanager

@contextmanager
def timer(label=""):
    start = time.perf_counter()
    yield
    elapsed = time.perf_counter() - start
    print(f"{label}: {elapsed:.4f}s")

with timer("data processing"):
    process_data()
```

```go
// Go
start := time.Now()
doExpensiveThing()
fmt.Printf("Elap...