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Cache expensive function with lru_cache in Python
Use functools.lru_cache to memoize an expensive recursive function and show the dramatic speedup on repeated calls.
from functools import lru_cache
import time
@lru_cache(maxsize=128)
def expensive_operation(n):
"""Simulate an expensive Fibonacci-like calculation."""
if n < 2:
return n
return expensive_operation(n - 1) + expensive_operation(n - 2)
if __name__ == "__main__":
# First call (uncached) - take…Cache LLM Completions by Hashing the Prompt in Python
A simple in-memory cache that stores LLM completions keyed by a SHA-256 hash of the prompt to avoid recomputing identical requests.
import hashlib
import json
class PromptCache:
def __init__(self):
self.cache = {}
def _hash_prompt(self, prompt: str) -> str:
return hashlib.sha256(prompt.encode("utf-8")).hexdigest()
def get(self, prompt: str) -> str | None:
key = self._hash_prompt(prompt)
return self.ca…How to Memoize Async Functions with lru_cache in Python
Cache async function results with functools.lru_cache to avoid repeated expensive awaits, cutting total execution from ~0.4s to ~0.2s in this example.
from functools import lru_cache
import asyncio
@lru_cache(maxsize=128)
async def fetch_data(user_id: int) -> str:
# Simulate expensive async operation
await asyncio.sleep(0.1)
return f"Data for user {user_id}"
async def main():
start = asyncio.get_event_loop().time()
# First calls (miss cach…How to Mock HTTP 304 Responses with If-None-Match in Python
Spin up a local HTTP server that returns a 304 Not Modified when a request carries a matching ETag, useful for testing cache behavior.
from http.server import BaseHTTPRequestHandler, HTTPServer
from threading import Thread
import urllib.request
ETAG = '"abc123"'
BODY = b'{"status": "ok"}'
class MockServer(BaseHTTPRequestHandler):
def do_GET(self):
if self.headers.get('If-None-Match') == ETAG:
self.send_response(304)
…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.
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…Cache Warming with Python: Preload Hot Keys
Demonstrates a simple LRU-like cache with a warm method that preloads hot keys with mock values using OrderedDict.
import time
from collections import OrderedDict
class CacheWarm:
def __init__(self, capacity=3):
self.capacity = capacity
self.cache = OrderedDict()
self.hot_keys = []
def warm(self, keys):
"""Preload hot keys into cache with mock values."""
for key in keys:
…How to Cache Function Results with Redis in Python
A RedisCache helper class caches function results using a decorator, with JSON serialization and TTL-based expiry.
import redis
import json
from functools import wraps
class RedisCache:
def __init__(self, host='localhost', port=6379, db=0, ttl=60):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
self.ttl = ttl
def cached(self, key_prefix):
def decorator(func):
…How to Iterate Redis Keys with SCAN in Python
Iterate all Redis keys matching a pattern using the SCAN command with a mock client to simulate pagination.
import redis
def scan_keys(client, pattern="*", count=10):
keys = []
cursor = 0
while True:
cursor, batch = client.scan(cursor=cursor, match=pattern, count=count)
keys.extend(batch)
if cursor == 0:
break
return keys
if __name__ == "__main__":
# Mock Redis clien…How to Mock a Cache Key Schema Version Bump in Python
Show how to test a cache key schema bump by mocking the class-level version attribute with unittest.mock.
from unittest import mock
class VersionCache:
SCHEMA_VERSION = 1
def __init__(self, key_prefix="cache"):
self.key_prefix = key_prefix
def build_key(self, resource_id):
return f"{self.key_prefix}:schema-v{self.SCHEMA_VERSION}:{resource_id}"
def bump_schema(self):
# Simulated …How to Use Redis HSET and HGET in Python
This code demonstrates how to store and retrieve hash data in Redis using Python's redis library with HSET, HGET, HGETALL, and HDEL commands.
import redis
# Connect to Redis (adjust host/port as needed)
r = redis.Redis(host='localhost', port=6379, db=0, decode_responses=True)
# Clear any existing data for demonstration
r.delete('user:1')
# HSET - Store a hash
r.hset('user:1', mapping={'name': 'Alice', 'age': 30, 'city': 'New York'})
# HGET - Retrieve a …How to Use Redis ZADD and ZRANGE in Python
Add members to a Redis sorted set with ZADD and retrieve them in score order with ZRANGE in Python.
import redis
client = redis.Redis(host='localhost', port=6379, db=0)
client.delete('scores')
members = {'alice': 30, 'bob': 20, 'carol': 50}
for name, score in members.items():
client.zadd('scores', {name: score})
result = client.zrange('scores', 0, -1)
print(result)How to Use lru_cache in Python for Cache-on-Miss Population
Demonstrates lru_cache to automatically populate cache on a miss and serve subsequent calls from cache, with cache info stats.
from functools import lru_cache
@lru_cache(maxsize=None)
def fetch_user(user_id):
"""Simulates a slow database fetch."""
print(f"Cache miss: fetching user {user_id} from database")
return {"id": user_id, "name": f"User {user_id}"}
if __name__ == "__main__":
user = fetch_user(1)
print(f"First call…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.
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…How to create a stable cache key from function arguments in Python
Generate a stable SHA-256 cache key from normalized function arguments, with keyword order normalized and tests using mocks.
import hashlib
import json
from unittest.mock import Mock
def make_cache_key(*args, **kwargs):
"""Normalize args/kwargs into a stable hash key for caching."""
normalized = {
"args": [repr(arg) for arg in args],
"kwargs": {key: repr(value) for key, value in sorted(kwargs.items())}
}
pa…How to implement a token bucket rate limiter in Python
A thread-safe in-memory token bucket rate limiter that tracks per-key tokens with refill logic, including a usage example after a timed refill.
import time
import threading
class TokenBucketRateLimiter:
def __init__(self, capacity, refill_rate):
self.capacity = capacity
self.refill_rate = refill_rate
self.tokens = capacity
self.last_refill_time = time.time()
self.lock = threading.Lock()
def allow_request(self,…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 Cache Helper Class in Python with TTL
Build a DataHelper class that caches function results in Redis with a default TTL, using get_or_set and clear methods.
import redis
import json
import time
class DataHelper:
def __init__(self, host="localhost", port=6379, db=0, default_ttl=60):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
self.default_ttl = default_ttl
def get_or_set(self, key, data_func, ttl=None):
c…Redis GET SET EX TTL mock in Python
A thread-safe Python class mimicking Redis GET, SET with EX, and TTL commands for in-memory testing.
import time
import threading
from typing import Optional, Callable
class RedisTTLMock:
def __init__(self):
self._store: dict[str, tuple[str, float]] = {}
self._lock = threading.Lock()
def set(self, key: str, value: str, ex: Optional[int] = None) -> bool:
expiry = time.time() + ex if …Redis SADD SMEMBERS Set Mock in Python
A lightweight mock of Redis SADD and SMEMBERS using Python sets for testing or local caching.
class RedisSetMock:
def __init__(self):
self.sets = {}
def sadd(self, key, *members):
if key not in self.sets:
self.sets[key] = set()
before = len(self.sets[key])
self.sets[key].update(members)
return len(self.sets[key]) - before
def smembers(self, key)…Simple Redis Cache Helper in Python
Build a minimal Redis-backed cache with TTL, JSON serialization, and automated fetching to speed up repeated expensive lookups.
import time
import redis
import json
class SimpleCache:
def __init__(self, host="localhost", port=6379, db=0, default_ttl=60):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
self.default_ttl = default_ttl
def get(self, key):
value = self.client.get(key)…Cache-Aside Pattern in Python: Per-Service Mock
A Python mock of the cache-aside pattern for a single microservice—lazy-load from a database into an in-memory cache and invalidate on updates.
class ServiceCache:
def __init__(self):
self.database = {"user:1": "Alice", "user:2": "Bob", "user:3": "Charlie"}
self.cache = {}
def get_user(self, user_id):
cache_key = f"user:{user_id}"
if cache_key in self.cache:
print(f"CACHE HIT: {cache_key}")
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