How does functools.lru_cache work?

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Accepted Answer

**`@functools.lru_cache(maxsize=N)`** wraps a function with a **memoization** cache: it stores return values keyed by the call's arguments, so a repeat call with the same arguments returns the cached result instead of recomputing. **LRU** = Least Recently Used: when the cache is full (`maxsize` entries), the least-recently-used entry is evicted to make room. `maxsize=None` (or `@functools.cache`, 3.9+) means an **unbounded** cache that never evicts.

Mechanics and constraints:

- **Arguments must be hashable** (they form the dict key), so it works for `(int, str, tuple)` args but not `list`/`dict` args.
- It introspects via **`.cache_info()`** (hits, misses, size) and resets with **`.cache_clear()`**.
- It only makes sense for **pure** functions (same input → same output, no side effects); caching an impure or time-dependent function returns stale results.

The headline use is collapsing expensive or exponential recomputation — memoized recursion (Fibonacci, edit distance) goes from ex

How does functools.lru_cache work?

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