Search⌘ K
AI Features
Home
Courses
The Hacker's Guide to Scaling Python

Local Caching

Explore local caching techniques in Python, including dictionary-based caches and algorithms like LRU, LFU, and TTL. Learn how to implement and manage cache expiration policies to optimize performance and avoid memory overflow in your applications.

We'll cover the following...

Local caching has the advantage of being (very) fast, as it does not require access to a remote cache over the network. Usually, caching works by storing cached data under a key that identifies it. This technique makes the Python dictionary the most obvious data structure for implementing a caching mechanism.

A simple cache using Python dictionary

Python 3.8
cache = {}
cache['key'] = 'value'
cache = {}
def compute_length_or_read_in_cache(s):
  try:
    return cache[s]
  except KeyError:
    cache[s] = len(s)
    return cache[s]
print("Foobar: ", compute_length_or_read_in_cache("foobar"))
print('Cache: ', cache)
print("Babaz: ", compute_length_or_read_in_cache("babaz"))
print('Cache: ', cache)

Obviously, such a simple cache has a few drawbacks. First, its size is unbound, which means it can grow to a substantial size that can fill up the entire system memory. That would result in the death of either the process, or even the whole ...