Performance Expert

Performance Expert

Expert guidance for performance optimization, profiling, benchmarking, and system tuning.

Core Concepts

Performance Fundamentals

• Response time vs throughput
• Latency vs bandwidth
• CPU, memory, I/O bottlenecks
• Concurrency vs parallelism
• Caching strategies
• Load balancing

Optimization Areas

• Algorithm optimization
• Database optimization
• Network optimization
• Frontend performance
• Backend performance
• Infrastructure tuning

Profiling Tools

• CPU profilers
• Memory profilers
• Network profilers
• Application Performance Monitoring (APM)
• Load testing tools

Python Performance

import cProfile
import pstats
import timeit
import memory_profiler
from functools import lru_cache
from typing import List
import numpy as np

# Performance Profiling
def profile_function(func):
    """Decorator for profiling function execution"""
    def wrapper(*args, **kwargs):
        profiler = cProfile.Profile()
        profiler.enable()

        result = func(*args, **kwargs)

        profiler.disable()
        stats = pstats.Stats(profiler)
        stats.sort_stats('cumulative')
        stats.print_stats(10)  # Top 10 functions

        return result
    return wrapper

@profile_function
def slow_function():
    total = 0
    for i in range(1000000):
        total += i
    return total

# Memoization for expensive computations
@lru_cache(maxsize=128)
def fibonacci(n: int) -> int:
    """Cached Fibonacci calculation"""
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

# Vectorization with NumPy
def slow_loop(data: List[float]) -> List[float]:
    """Slow: Using Python loops"""
    return [x ** 2 + 2 * x + 1 for x in data]

def fast_vectorized(data: np.ndarray) -> np.ndarray:
    """Fast: Using NumPy vectorization"""
    return data ** 2 + 2 * data + 1

# Benchmarking
def benchmark_function(func, *args, iterations=1000):
    """Benchmark function execution time"""
    total_time = timeit.timeit(
        lambda: func(*args),
        number=iterations
    )
    avg_time = total_time / iterations

    return {
        'total_time': total_time,
        'avg_time': avg_time,
        'iterations': iterations
    }

# Memory profiling
@memory_profiler.profile
def memory_intensive_function():
    """Function that uses significant memory"""
    data = [i for i in range(1000000)]
    return sum(data)

# Efficient string concatenation
def slow_string_concat(items: List[str]) -> str:
    """Slow: String concatenation in loop"""
    result = ""
    for item in items:
        result += item  # Creates new string each time
    return result

def fast_string_concat(items: List[str]) -> str:
    """Fast: Using join"""
    return "".join(items)

# Generator for memory efficiency
def slow_list_comprehension(n: int) -> List[int]:
    """Returns all squares at once"""
    return [i ** 2 for i in range(n)]

def fast_generator(n: int):
    """Yields squares one at a time"""
    for i in range(n):
        yield i ** 2

Database Optimization

from sqlalchemy import create_engine, Index, text
from sqlalchemy.orm import sessionmaker
import redis

# Connection pooling
engine = create_engine(
    'postgresql://user:pass@localhost/db',
    pool_size=20,
    max_overflow=0,
    pool_pre_ping=True,
    pool_recycle=3600
)

# Query optimization
class DatabaseOptimizer:
    def __init__(self, session):
        self.session = session

    def bad_n_plus_one(self):
        """N+1 query problem"""
        users = self.session.query(User).all()
        for user in users:
            posts = user.posts  # Triggers additional query per user
            print(f"{user.name}: {len(posts)} posts")

    def good_eager_loading(self):
        """Eager loading to avoid N+1"""
        from sqlalchemy.orm import joinedload

        users = self.session.query(User)\
            .options(joinedload(User.posts))\
            .all()

        for user in users:
            posts = user.posts  # No additional query
            print(f"{user.name}: {len(posts)} posts")

    def use_indexes(self):
        """Create indexes for frequent queries"""
        # Index on frequently queried columns
        Index('idx_user_email', User.email)
        Index('idx_post_created_at', Post.created_at)

        # Composite index
        Index('idx_post_user_status', Post.user_id, Post.status)

    def batch_operations(self, items: List[dict]):
        """Batch insert instead of individual inserts"""
        # Bad: Individual inserts
        # for item in items:
        #     self.session.add(User(**item))
        #     self.session.commit()

        # Good: Batch insert
        self.session.bulk_insert_mappings(User, items)
        self.session.commit()

    def use_pagination(self, page: int = 1, page_size: int = 20):
        """Paginate large result sets"""
        offset = (page - 1) * page_size

        return self.session.query(User)\
            .order_by(User.created_at.desc())\
            .limit(page_size)\
            .offset(offset)\
            .all()

# Redis caching
class CacheOptimizer:
    def __init__(self, redis_client: redis.Redis):
        self.redis = redis_client

    def cache_query_result(self, key: str, query_func, ttl: int = 3600):
        """Cache database query results"""
        # Check cache first
        cached = self.redis.get(key)
        if cached:
            return json.loads(cached)

        # Execute query
        result = query_func()

        # Cache result
        self.redis.setex(key, ttl, json.dumps(result))

        return result

    def cache_aside_pattern(self, key: str, fetch_func, ttl: int = 3600):
        """Implement cache-aside pattern"""
        data = self.redis.get(key)

        if data is None:
            data = fetch_func()
            self.redis.setex(key, ttl, json.dumps(data))
        else:
            data = json.loads(data)

        return data

Frontend Performance

// Debouncing for expensive operations
function debounce(func, wait) {
  let timeout;
  return function executedFunction(...args) {
    const later = () => {
      clearTimeout(timeout);
      func(...args);
    };
    clearTimeout(timeout);
    timeout = setTimeout(later, wait);
  };
}

// Example: Debounce search input
const searchInput = document.getElementById('search');
const debouncedSearch = debounce((query) => {
  // Expensive search operation
  fetchSearchResults(query);
}, 300);

searchInput.addEventListener('input', (e) => {
  debouncedSearch(e.target.value);
});

// Lazy loading images
const lazyLoadImages = () => {
  const images = document.querySelectorAll('img[data-src]');

  const imageObserver = new IntersectionObserver((entries) => {
    entries.forEach((entry) => {
      if (entry.isIntersecting) {
        const img = entry.target;
        img.src = img.dataset.src;
        img.removeAttribute('data-src');
        imageObserver.unobserve(img);
      }
    });
  });

  images.forEach((img) => imageObserver.observe(img));
};

// Virtual scrolling for large lists
class VirtualScroller {
  constructor(container, items, itemHeight) {
    this.container = container;
    this.items = items;
    this.itemHeight = itemHeight;
    this.visibleItems = Math.ceil(container.clientHeight / itemHeight);
    this.render();
  }

  render() {
    const scrollTop = this.container.scrollTop;
    const startIndex = Math.floor(scrollTop / this.itemHeight);
    const endIndex = startIndex + this.visibleItems;

    // Only render visible items
    const visibleData = this.items.slice(startIndex, endIndex);

    this.container.innerHTML = visibleData
      .map((item) => `<div style="height: ${this.itemHeight}px">${item}</div>`)
      .join('');
  }
}

// Code splitting with dynamic imports
async function loadModule() {
  const module = await import('./heavy-module.js');
  module.init();
}

API Performance

from fastapi import FastAPI, BackgroundTasks
from fastapi.responses import StreamingResponse
import asyncio
from typing import AsyncGenerator

app = FastAPI()

# Async endpoints for I/O bound operations
@app.get("/users/{user_id}")
async def get_user(user_id: str):
    """Async endpoint for database queries"""
    user = await db.fetch_user(user_id)
    return user

# Streaming responses for large data
async def generate_large_file() -> AsyncGenerator[bytes, None]:
    """Stream large file in chunks"""
    chunk_size = 8192
    with open("large_file.csv", "rb") as f:
        while chunk := f.read(chunk_size):
            yield chunk
            await asyncio.sleep(0)  # Allow other tasks to run

@app.get("/download")
async def download_large_file():
    return StreamingResponse(
        generate_large_file(),
        media_type="text/csv"
    )

# Background tasks for long-running operations
@app.post("/process")
async def process_data(background_tasks: BackgroundTasks):
    """Offload heavy processing to background"""
    background_tasks.add_task(heavy_processing_task)
    return {"status": "processing started"}

# Connection pooling
import aiohttp

class APIClient:
    def __init__(self):
        self.session = None

    async def __aenter__(self):
        self.session = aiohttp.ClientSession(
            connector=aiohttp.TCPConnector(limit=100)
        )
        return self

    async def __aexit__(self, *args):
        await self.session.close()

    async def fetch(self, url: str):
        async with self.session.get(url) as response:
            return await response.json()

Load Testing

from locust import HttpUser, task, between

class PerformanceTest(HttpUser):
    wait_time = between(1, 3)

    @task(3)
    def get_users(self):
        """High frequency endpoint"""
        self.client.get("/api/users")

    @task(1)
    def create_user(self):
        """Lower frequency endpoint"""
        self.client.post("/api/users", json={
            "email": "test@example.com",
            "name": "Test User"
        })

    def on_start(self):
        """Login once per user"""
        response = self.client.post("/api/login", json={
            "username": "test",
            "password": "password"
        })
        self.token = response.json()["token"]

Best Practices

General

• Measure before optimizing
• Focus on bottlenecks
• Use appropriate data structures
• Cache expensive computations
• Minimize I/O operations
• Use connection pooling
• Implement pagination

Database

• Create proper indexes
• Avoid N+1 queries
• Use eager loading
• Batch operations
• Optimize queries
• Use read replicas
• Implement caching

Frontend

• Minimize bundle size
• Code splitting
• Lazy loading
• Compress assets
• Use CDN
• Optimize images
• Debounce/throttle events

Backend

• Use async for I/O
• Implement caching
• Connection pooling
• Background processing
• Horizontal scaling
• Load balancing

Anti-Patterns

❌ Premature optimization ❌ No profiling/measurement ❌ Optimizing wrong bottlenecks ❌ Ignoring caching ❌ Synchronous I/O operations ❌ No database indexes ❌ Loading all data at once

Resources

• Python Performance Tips: https://wiki.python.org/moin/PythonSpeed/PerformanceTips
• Web Performance: https://web.dev/performance/
• Locust Load Testing: https://locust.io/
• py-spy Profiler: https://github.com/benfred/py-spy
• Chrome DevTools: https://developer.chrome.com/docs/devtools/