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The Hacker's Guide to Scaling Python

Memoization

Explore memoization as a caching strategy to improve performance in Python applications by storing function results. Understand how to use Python decorators like functools.lru_cache and external packages such as cachetools to implement efficient, limited-size caches. Learn to measure cache effectiveness and apply memoization to pure functions for faster, repeated computations.

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Memoization is a technique used to speed up function calls by caching their results. The results can be cached only if the function is pure, meaning that it has no side effects or outputs and that it does not depend on any global state.

Memoizing sin function

A trivial function that can be memoized is the sine function sin.

Python 3.8
import math
_SIN_MEMOIZED_VALUES = {}
def memoized_sin(x):
  if x not in _SIN_MEMOIZED_VALUES:
    _SIN_MEMOIZED_VALUES[x] = math.sin(x)
  return _SIN_MEMOIZED_VALUES[x]
print('memoized_sin(1): ', memoized_sin(1))
print('_SIN_MEMOIZED_VALUES:', _SIN_MEMOIZED_VALUES)
print('memoized_sin(2):', memoized_sin(2))
print('memoized_sin(2):', memoized_sin(2))
print('_SIN_MEMOIZED_VALUES:', _SIN_MEMOIZED_VALUES)
print('memoized_sin(1):', memoized_sin(1))
print('_SIN_MEMOIZED_VALUES:', _SIN_MEMOIZED_VALUES)

The first time that memoized_sin is called with an argument that is not stored in ...