Tips and Tricks #103: Use functools.cache for Automatic Memoization

Cache expensive function results automatically with the built-in cache decorator.

Code Snippet

from functools import cache
import time

@cache
def expensive_computation(n: int) -> int:
    """Simulate expensive calculation."""
    time.sleep(0.1)  # Pretend this is slow
    return sum(i * i for i in range(n))

# First call: takes 0.1 seconds
result1 = expensive_computation(1000)

# Second call: instant (cached)
result2 = expensive_computation(1000)

# Check cache stats
print(expensive_computation.cache_info())
# CacheInfo(hits=1, misses=1, maxsize=None, currsize=1)

Why This Helps

• Zero-effort memoization for pure functions
• Built-in cache statistics for monitoring
• Thread-safe implementation

How to Test

• Compare execution times for repeated calls
• Monitor cache_info() hit rates

When to Use

Recursive algorithms, API response caching, any pure function called repeatedly with same args.

Performance/Security Notes

Use @lru_cache(maxsize=N) to limit memory. Arguments must be hashable.

References

• https://docs.python.org/3/library/functools.html#functools.cache

Try this tip in your next project and share your results in the comments!