Performance Patterns

Profiling, optimization, caching, and memory management.

Overview

Master performance optimization.

Profiling

import cProfile
import time

def slow_function():
    return sum(range(1000000))

# Profile execution
cProfile.run('slow_function()')

# Manual timing
start = time.perf_counter()
slow_function()
elapsed = time.perf_counter() - start
print(f"Time: {elapsed:.4f}s")

Caching

from functools import lru_cache
import time

@lru_cache(maxsize=128)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)

start = time.time()
fibonacci(100)
print(f"Time: {time.time() - start:.6f}s")

Memory Optimization

import sys

# Use __slots__ for memory efficiency
class Point:
    __slots__ = ['x', 'y']
    
    def __init__(self, x, y):
        self.x = x
        self.y = y

p = Point(1, 2)
print(sys.getsizeof(p))  # Much smaller than regular class

# Use generators for large datasets
def large_dataset():
    for i in range(1000000):
        yield i

# Memory efficient processing
total = sum(large_dataset())

Practice

Optimize a slow data processing pipeline.