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Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.
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…How to Implement Memoized Fibonacci in Python with functools.cache
Use functools.cache to memoize a recursive Fibonacci function, avoiding repeated computation and dramatically speeding up the calculation.
from functools import cache
@cache
def fibonacci(n: int) -> int:
"""Return the n-th Fibonacci number (0-indexed)."""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fibonacci({i}) = {fibonacci(i)}")
print(f"Cache…How to Invalidate Cache When Arguments Change in Python
A memoization decorator that caches function results keyed by arguments, automatically invalidating when inputs change.
from functools import wraps
def memoize(func):
cache = {}
@wraps(func)
def wrapper(*args, **kwargs):
key = (args, tuple(sorted(kwargs.items())))
if key not in cache:
cache[key] = func(*args, **kwargs)
return cache[key]
return wrapper
@memoize
def expensiv…How to Build a TTL Cache Dict in Python
Create a dictionary subclass that automatically expires keys after a fixed time-to-live using timestamps.
import time
class TTLDict(dict):
def __init__(self, ttl, *args, **kwargs):
self.ttl = ttl
self._expires = {}
super().__init__(*args, **kwargs)
def __setitem__(self, key, value):
super().__setitem__(key, value)
self._expires[key] = time.time() + self.ttl
def __geti…LRU Cache with OrderedDict in Python
Implement an LRU cache using collections.OrderedDict to track insertion order and evict the least-recently-used item when capacity is exceeded.
from collections import OrderedDict
class LRUCache:
def __init__(self, capacity):
self.capacity = capacity
self.cache = OrderedDict()
def get(self, key):
if key not in self.cache:
return -1
self.cache.move_to_end(key)
return self.cache[key]
def put(sel…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 Build a Simple Semantic Cache for Similar Prompts in Python
Mock a semantic cache that finds the closest matching prompt using word-overlap similarity and returns cached results above a threshold.
prompt_cache = [
"What is the capital of France?",
"How does recursion work?",
"Best practices for Python logging?",
"Explain binary search in one line.",
"How to reverse a string in Python?"
]
def normalize(text):
return " ".join(text.lower().split())
def similarity(a, b):
a_words = set(…How to cache embeddings with a Python dict to avoid recomputation
Caches embeddings computed from text in a dictionary keyed by SHA-256 hash, returning cached results for repeated calls.
import hashlib
import time
class EmbeddingCache:
def __init__(self):
self.cache = {}
def _hash_text(self, text):
return hashlib.sha256(text.encode()).hexdigest()
def get_embedding(self, text, compute_func):
key = self._hash_text(text)
if key not in self.cache:
…How to Archive a Repository as a ZIP in Python
Create a ZIP archive of a repository directory with a mock export, skipping hidden files and __pycache__ folders.
import zipfile
import io
import os
from pathlib import Path
def archive_repo_mock(repo_path, output_path="repo_archive.zip"):
"""Create a zip archive of a repository directory (mock export)."""
repo = Path(repo_path)
if not repo.exists():
raise FileNotFoundError(f"Repository not found: {repo}")
…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 Memoize Pure Functions with functools.lru_cache in Python
Use functools.lru_cache to memoize a pure Fibonacci function and avoid recomputing repeated values.
from functools import lru_cache
@lru_cache(maxsize=128)
def fibonacci(n: int) -> int:
"""Return the nth Fibonacci number (0-indexed) using memoization."""
if n < 2:
return n
return fibonacci(n - 1) + fibonacci(n - 2)
if __name__ == "__main__":
for i in range(10):
print(f"fibonacci({…How to Use a Weakref Cache to Avoid Memory Leaks in Python
This code demonstrates building a value cache with weakref.WeakValueDictionary so objects can be garbage collected when no longer referenced, preventing memory leaks.
import weakref
import gc
class ExpensiveObject:
def __init__(self, name):
self.name = name
def __repr__(self):
return f"ExpensiveObject('{self.name}')"
class ObjectCache:
def __init__(self):
self._cache = weakref.WeakValueDictionary()
def get_or_create(self, name):
…How to Use functools.cache for Unbounded Memoization in Python
Speed up repeated recursive calls by memoizing function results with Python's built-in functools.cache decorator.
```python
import functools
import time
@functools.cache
def fib(n):
if n < 2:
return n
return fib(n - 1) + fib(n - 2)
if __name__ == "__main__":
start = time.perf_counter()
result = fib(30)
elapsed = time.perf_counter() - start
print(f"fib(30) = {result}")
print(f"computed in {…How to implement stale-while-revalidate caching in Python
A Python cache wrapper that returns a stale cached value with a fallback flag when the upstream fetch fails, using TTL-based freshness checks.
import time
from functools import lru_cache
class CachedService:
def __init__(self, fetch_func, ttl=5):
self.fetch_func = fetch_func
self.ttl = ttl
self._cache = {}
self._timestamp = {}
def get(self, key):
now = time.time()
if key in self._cache and now - self…Lazy loading with a proxy in Python: defer expensive service creation
A lazy proxy defers creating an expensive service object until its method is first called, then caches it for reuse.
import time
import random
class ExpensiveService:
def __init__(self, name):
self.name = name
print(f"Creating expensive service: {self.name}")
def fetch_data(self):
time.sleep(1)
return f"Data from {self.name}: {random.randint(1, 100)}"
class LazyProxy:
def __init__(sel…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 Data in Redis with Python
A beginner-friendly Redis cache helper that stores JSON strings with a TTL and retrieves them with the redis-py client.
import redis
class DataCache:
def __init__(self, host="localhost", port=6379, db=0):
self.client = redis.Redis(host=host, port=port, db=db, decode_responses=True)
def cache_data(self, key, value, ttl=60):
self.client.setex(key, ttl, value)
def get_cached_data(self, key):
return …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…Cache Stampede Prevention with SingleFlight in Python
Implements a SingleFlight pattern in Python to deduplicate concurrent cache-miss computations and prevent cache stampede.
import threading
import time
from functools import wraps
class SingleFlight:
def __init__(self):
self._lock = threading.Lock()
self._inflight = None
def do(self, key, fn):
with self._lock:
if self._inflight is not None:
return self._inflight[1]
…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:
…Consistent Hashing Cache Shard in Python
A minimal consistent hashing ring with virtual nodes that distributes cache keys across shards and minimizes re-mapping when a node is removed.
import hashlib
import bisect
class ConsistentHashRing:
def __init__(self, nodes=None, replicas=3):
self.replicas = replicas
self.ring = {}
self.sorted_keys = []
if nodes:
for node in nodes:
self.add_node(node)
def _hash(self, key):
return i…How to Add TTL Jitter to Cache Expiration in Python
A Python decorator that adds random jitter to cache TTLs, staggering expiration times to prevent cache avalanche.
import random
import time
from functools import wraps
def add_jitter(ttl: float, jitter_range: float = 0.1) -> float:
"""Add random jitter (as % of TTL) to stagger cache expiration and prevent avalanche."""
jitter = random.uniform(-jitter_range, jitter_range)
return ttl * (1 + jitter)
def cache_with_jitt…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…Browse by section
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