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Cache expensive function with lru_cache in Python
Use functools.lru_cache to memoize an expensive recursive function and show the dramatic speedup on repeated calls.
from functools import lru_cache
import time
@lru_cache(maxsize=128)
def expensive_operation(n):
"""Simulate an expensive Fibonacci-like calculation."""
if n < 2:
return n
return expensive_operation(n - 1) + expensive_operation(n - 2)
if __name__ == "__main__":
# First call (uncached) - take…How to Implement Memoized Fibonacci in Python with functools.cache
Use functools.cache to memoize a recursive Fibonacci function, avoiding repeated computation and dramatically speeding up the calculation.
from functools import cache
@cache
def fibonacci(n: int) -> int:
"""Return the n-th Fibonacci number (0-indexed)."""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fibonacci({i}) = {fibonacci(i)}")
print(f"Cache…How to Invalidate Cache When Arguments Change in Python
A memoization decorator that caches function results keyed by arguments, automatically invalidating when inputs change.
from functools import wraps
def memoize(func):
cache = {}
@wraps(func)
def wrapper(*args, **kwargs):
key = (args, tuple(sorted(kwargs.items())))
if key not in cache:
cache[key] = func(*args, **kwargs)
return cache[key]
return wrapper
@memoize
def expensiv…How to Memoize Async Functions with lru_cache in Python
Cache async function results with functools.lru_cache to avoid repeated expensive awaits, cutting total execution from ~0.4s to ~0.2s in this example.
from functools import lru_cache
import asyncio
@lru_cache(maxsize=128)
async def fetch_data(user_id: int) -> str:
# Simulate expensive async operation
await asyncio.sleep(0.1)
return f"Data for user {user_id}"
async def main():
start = asyncio.get_event_loop().time()
# First calls (miss cach…How to Memoize Pure Functions with functools.lru_cache in Python
Use functools.lru_cache to memoize a pure Fibonacci function and avoid recomputing repeated values.
from functools import lru_cache
@lru_cache(maxsize=128)
def fibonacci(n: int) -> int:
"""Return the nth Fibonacci number (0-indexed) using memoization."""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fibonacci({…How to Use functools.cache for Unbounded Memoization in Python
Speed up repeated recursive calls by memoizing function results with Python's built-in functools.cache decorator.
```python
import functools
import time
@functools.cache
def fib(n):
if n < 2:
return n
return fib(n - 1) + fib(n - 2)
if __name__ == "__main__":
start = time.perf_counter()
result = fib(30)
elapsed = time.perf_counter() - start
print(f"fib(30) = {result}")
print(f"computed in {…How to Use lru_cache in Python for Cache-on-Miss Population
Demonstrates lru_cache to automatically populate cache on a miss and serve subsequent calls from cache, with cache info stats.
from functools import lru_cache
@lru_cache(maxsize=None)
def fetch_user(user_id):
"""Simulates a slow database fetch."""
print(f"Cache miss: fetching user {user_id} from database")
return {"id": user_id, "name": f"User {user_id}"}
if __name__ == "__main__":
user = fetch_user(1)
print(f"First call…How to memoize a function in Python with lru_cache
Use functools.lru_cache to memoize a recursive Fibonacci function, caching results for a fixed number of calls to avoid repeated computation.
from functools import lru_cache
@lru_cache(maxsize=128)
def fibonacci(n):
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fib({i}) = {fibonacci(i)}")
print(f"Cache info: {fibonacci.cache_info()}")Browse by section
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