functools Memoization: lru_cache & singledispatch

1Concept

`functools.lru_cache` provides high-performance Least-Recently-Used caching for pure functions. `functools.singledispatch` transforms a function into a generic function with polymorphic dispatch based on the type of the first argument.

2Architecture Diagram

lru_cache(maxsize=128):
Input: fib(35) ---> Checks internal hash table ---> Instant O(1) Cache Hit!

3Code Example

from functools import lru_cache, singledispatch

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

@singledispatch
def format_data(arg):
    return f"Generic: {arg}"

@format_data.register
def _(arg: int):
    return f"Integer Hex: 0x{arg:X}"

@format_data.register
def _(arg: list):
    return f"List Length: {len(arg)}"

print(f"Fibonacci 30: {fib(30)}")
print(f"Cache Info:   {fib.cache_info()}")
print(format_data(255))
print(format_data([1, 2, 3, 4]))

4Expected Output

Fibonacci 30: 832040
Cache Info:   CacheInfo(hits=28, misses=31, maxsize=128, currsize=31)
Integer Hex: 0xFF
List Length: 4

5Key Takeaways

• ✓Arguments to `@lru_cache` functions must be hashable.
• ✓`fib.cache_clear()` flushes the cache memory.
• ✓`@singledispatch` enables clean function polymorphism without giant `if isinstance(...)` trees.