name: performance description: Optimize code for speed and efficiency. Use this skill when profiling, identifying bottlenecks, or improving performance. Covers profiling tools, common patterns, and optimization strategies.

Performance Optimization

Guidance for identifying and fixing performance bottlenecks.

Core Principle

Measure first, optimize second. Never optimize without profiling data.

Profiling Tools

JavaScript/Node.js

# Node.js built-in profiler
node --prof app.js
node --prof-process isolate-*.log > profile.txt

# Chrome DevTools
node --inspect app.js
# Open chrome://inspect

# Clinic.js (recommended)
npx clinic doctor -- node app.js
npx clinic flame -- node app.js

Python

# cProfile
python -m cProfile -s cumtime app.py

# py-spy (sampling profiler, low overhead)
py-spy record -o profile.svg -- python app.py

# line_profiler for line-by-line
kernprof -l -v script.py

Go

# CPU profile
go test -cpuprofile=cpu.prof -bench=.
go tool pprof cpu.prof

# Memory profile
go test -memprofile=mem.prof -bench=.

# Built-in pprof server
import _ "net/http/pprof"
# Then: go tool pprof http://localhost:6060/debug/pprof/profile

Rust

# Flamegraph
cargo install flamegraph
cargo flamegraph

# Criterion for benchmarks
# Add criterion to dev-dependencies
cargo bench

Common Bottlenecks

1. N+1 Queries

# Bad: N+1 queries
for user in users:
    posts = db.query(f"SELECT * FROM posts WHERE user_id = {user.id}")

# Good: Single query with join or batch
posts = db.query("SELECT * FROM posts WHERE user_id IN (...)")

2. Unnecessary Allocations

// Bad: Creates new array each iteration
items
  .map((x) => x)
  .filter((x) => x.active)
  .map((x) => x.name);

// Good: Single pass
items.reduce((acc, x) => {
  if (x.active) acc.push(x.name);
  return acc;
}, []);

3. Blocking I/O

# Bad: Sequential requests
for url in urls:
    response = requests.get(url)

# Good: Concurrent requests
import asyncio
import aiohttp

async def fetch_all(urls):
    async with aiohttp.ClientSession() as session:
        return await asyncio.gather(*[session.get(url) for url in urls])

4. Missing Indexes

-- Check slow queries
EXPLAIN ANALYZE SELECT * FROM users WHERE email = 'x@y.com';

-- Add index
CREATE INDEX idx_users_email ON users(email);

5. Memory Leaks

// Bad: Event listener never removed
element.addEventListener("click", handler);

// Good: Clean up
element.addEventListener("click", handler);
// Later:
element.removeEventListener("click", handler);

Optimization Strategies

Caching

from functools import lru_cache

@lru_cache(maxsize=128)
def expensive_computation(n):
    # ...

Lazy Loading

// Load on demand
const HeavyComponent = lazy(() => import("./HeavyComponent"));

Pagination

-- Don't load everything
SELECT * FROM items LIMIT 20 OFFSET 40;

-- Or cursor-based for large datasets
SELECT * FROM items WHERE id > :last_id LIMIT 20;

Connection Pooling

# Reuse database connections
from sqlalchemy import create_engine
engine = create_engine(url, pool_size=10, max_overflow=20)

Batching

// Bad: Individual inserts
for (const item of items) {
  await db.insert(item);
}

// Good: Batch insert
await db.insertMany(items);

Performance Checklist

• Profiled before optimizing
• Identified actual bottleneck (not guessing)
• Database queries have appropriate indexes
• No N+1 query problems
• Heavy computations are cached where appropriate
• Large lists are paginated
• I/O operations are concurrent when possible
• Memory usage is bounded
• Benchmarks exist for critical paths