Caching Strategies: Redis Patterns for Production Backends

Caching Strategies with Redis

Caching is the most impactful optimization in web applications. The right pattern depends on read/write ratio, consistency requirements, and freshness needs.

import redis, json
r = redis.Redis(decode_responses=True)

def get_post(post_id: int) -> dict:
    key    = f"post:{post_id}"
    cached = r.get(key)
    if cached: return json.loads(cached)

    post = db.query('SELECT * FROM posts WHERE id=?', [post_id])
    if post:
        r.setex(key, 3600, json.dumps(post))
    return post

def update_post(post_id: int, data: dict):
    db.update('posts', data, {'id': post_id})
    r.delete(f"post:{post_id}")   # invalidate cache

Advanced Redis Patterns

# Rate limiting
def is_limited(user_id, limit=100, window=60):
    key   = f"rl:{user_id}"
    count = r.incr(key)
    if count == 1: r.expire(key, window)
    return count > limit

# Leaderboard with sorted set
r.zadd('scores', {'Rahul': 950, 'Priya': 1200, 'Amit': 880})
top = r.zrevrange('scores', 0, 9, withscores=True)

# Distributed lock
import uuid
def acquire_lock(resource, ttl=30):
    lock_id = str(uuid.uuid4())
    acquired = r.set(f"lock:{resource}", lock_id, nx=True, ex=ttl)
    return lock_id if acquired else None