Caching & Redis
Cache-aside, TTL, invalidation, hot keys, and in-memory lookup patterns at scale.
Cache Penetration Null Object Mock in Python
Implement a cache that stores a null marker on misses to prevent repeated database hits, reducing cache penetration.
import time
from collections import defaultdict
from typing import Any, Optional
class Cache:
def __init__(self):
self.store: dict[str, Any] = {}
self.ttl: dict[str, float] = {}
self.null_marker = object()
def get(self, key: str, ttl: int = 60, fallback:
Any = None) -> An…Coalescing duplicate in-flight requests: one shared result for concurrent callers
Runs identical concurrent requests through a single shared call, caching the result while it's in flight and returning the same value to all callers.
import time
import threading
from collections import defaultdict
class CoalescingExecutor:
def __init__(self):
self._locks = defaultdict(threading.Lock)
self._in_flight = {}
def execute(self, key, func):
with self._locks[key]:
if key in self._in_flight:
re…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 an LFU Cache in Python
Implement a Least Frequently Used (LFU) cache with frequency tracking dictionaries to evict the least accessed items when capacity is reached.
class LFUCache:
def __init__(self, capacity: int):
self.capacity = capacity
self.data = {}
self.freq = {}
self.min_freq = 0
def get(self, key: int) -> int:
if key not in self.data:
return -1
self._increment_freq(key)
return self.data[key]
…How to implement a write-behind cache with async queue in Python
Build an async write-behind cache that queues writes in memory and flushes them in batches to persistent storage.
import asyncio
from collections import deque
from dataclasses import dataclass
@dataclass
class CacheEntry:
key: str
value: str
class WriteBehindCache:
def __init__(self, flush_interval=1.0):
self.cache = {}
self.queue = deque()
self.flush_interval = flush_interval
self._f…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.
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()}")How to use Redis MGET MSET pipeline in Python
Store multiple keys atomically and read them efficiently with Redis MSET/MGET, then batch commands with a pipeline to cut round trips.
import redis # v4.x+ required
r = redis.Redis(host="localhost", port=6379, db=0, decode_responses=True)
# Sample data to store
r.flushdb()
data = {"name": "Alice", "age": "30", "city": "Berlin"}
# MSET: store multiple key-value pairs in one command
r.mset(data)
# MGET: fetch multiple keys in one round trip
keys =…Redis Leaky Bucket Rate Limiting Mock in Python
Simulates a Redis-backed leaky bucket rate limiter using a local class with continuous leaking and token capacity checks.
import time
from collections import deque
class LeakyBucket:
def __init__(self, capacity, leak_rate):
self.capacity = capacity
self.leak_rate = leak_rate
self.water = 0.0
self.timestamp = time.time()
self.history = deque()
def allow(self):
current = time.time(…Browse by section
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Caching & Redis — Python code examples
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