caching

Caching Patterns

Strategies and implementation patterns for application-level, distributed, and HTTP caching.

When to Activate

• Adding Redis or Memcached to reduce database load or API latency
• Designing TTL values and cache invalidation strategies
• Preventing cache stampede on high-traffic keys
• Configuring HTTP Cache-Control and CDN caching rules
• Choosing between cache-aside, write-through, or write-behind
• Debugging stale data, cache poisoning, or thundering herd problems
• Sizing a cache or deciding what to cache vs. not cache

Strategy Selection

Cache-Aside (Most Common)

# Python — cache-aside with Redis
import redis, json, hashlib
from typing import Callable, TypeVar

T = TypeVar("T")
r = redis.Redis(host="redis", port=6379, decode_responses=True)

def get_or_set(key: str, loader: Callable[[], T], ttl: int = 300) -> T:
    cached = r.get(key)
    if cached is not None:
        return json.loads(cached)

    value = loader()
    r.setex(key, ttl, json.dumps(value, default=str))
    return value

# Usage
user = get_or_set(f"user:{user_id}", lambda: db.query(User).get(user_id), ttl=600)

// TypeScript — cache-aside
import { createClient } from "redis";

const redis = createClient({ url: "redis://redis:6379" });

async function getOrSet<T>(
  key: string,
  loader: () => Promise<T>,
  ttlSeconds = 300,
): Promise<T> {
  const cached = await redis.get(key);
  if (cached) return JSON.parse(cached) as T;

  const value = await loader();
  await redis.setEx(key, ttlSeconds, JSON.stringify(value));
  return value;
}