Reference library

Caching & Redis

Cache-aside, TTL, invalidation, hot keys, and in-memory lookup patterns at scale.

3 matches
Caching & Redis easy

Cache Asides in Python with a Read-Through Loader

Implements a cache-aside pattern with a read-through loader that fetches missing keys from a backing data store and caches them.

caching cache-aside read-through
Python
class DataStore:
    """Mock database with a few records."""
    def __init__(self):
        self.data = {1: "Alice", 2: "Bob", 3: "Charlie"}

    def get(self, key):
        print(f"Loading key {key} from database")
        return self.data.get(key)


class CacheAsideLoader:
    """Cache-aside pattern with a read-thr…
16 0 Open
Caching & Redis easy

How to cache filtered data in Redis with Python

This code caches filtered list results in Redis using an MD5 hash key, returning cached results when available.

redis caching filtering
Python
import redis
import json
import hashlib
import time

cache = redis.Redis(host='localhost', port=6379, db=0, decode_responses=True)

def filter_data(data, predicate_key, predicate_value):
    """Filter a list of dicts by key-value pair, with Redis caching."""
    cache_key = hashlib.md5(
        f"{predicate_key}:{pred…
13 0 Open
Caching & Redis easy

How to memoize a function in Python with lru_cache

Use functools.lru_cache to memoize a recursive Fibonacci function, caching results for a fixed number of calls to avoid repeated computation.

lru_cache memoization functools
Python
from functools import lru_cache

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

if __name__ == "__main__":
    for i in range(10):
        print(f"fib({i}) = {fibonacci(i)}")
    print(f"Cache info: {fibonacci.cache_info()}")
13 0 Open

Browse by section

Each section groups closely related Python snippets.

Caching & Redis — Python code examples

What you will find here

This page collects caching & redis snippets — short, copy-ready Python you can paste into our free online IDE and run without installing anything. Each sample includes a plain-English explanation and the full source code.

Samples vs tutorials and challenges

Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.