Caching & Redis
Cache-aside, TTL, invalidation, hot keys, and in-memory lookup patterns at scale.
How to Build a Bloom Filter to Reduce Cache Misses in Python
Implement a probabilistic Bloom filter in Python that lets a cache quickly determine which keys are definitely not present, reducing expensive source lookups on cache misses.
import hashlib
import random
class BloomFilter:
def __init__(self, size=100, num_hashes=3):
self.size = size
self.num_hashes = num_hashes
self.bit_array = [0] * size
def _hashes(self, item):
result = []
for i in range(self.num_hashes):
hash_value = int(hash…How to Implement Probabilistic Early Expiration in Python
A Python mock of probabilistic early expiration for caches, using a heap-based expiry queue and random eviction to approximate cache stampede protection.
import heapq
import random
import time
class ProbabilisticEarlyExpirationMock:
def __init__(self, capacity=1024, expiration_probability=0.1):
self.capacity = capacity
self.expiration_probability = expiration_probability
self._items = {}
self._expiry_heap = []
self._next_id…Browse by section
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Caching & Redis — Python code examples
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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.
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