Caching & Redis
Cache-aside, TTL, invalidation, hot keys, and in-memory lookup patterns at scale.
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 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 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 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 =…Browse by section
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Caching & Redis — Python code examples
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