丁久

Posted on May 17 • Originally published at dingjiu1989-hue.github.io

Redis Caching Patterns

#database #backend #data

This article was originally published on AI Study Room. For the full version with working code examples and related articles, visit the original post.

Redis Caching Patterns

Redis as Cache

Redis is an in-memory data store that excels as a cache due to sub-millisecond latency and rich data types.

Cache-Aside Pattern

Application checks cache first, falls back to database:

def get_user(user_id):

cache_key = f"user:{user_id}"

cached = redis.get(cache_key)

if cached:

return json.loads(cached)

user = db.query("SELECT * FROM users WHERE id = %s", [user_id])

if user:

redis.setex(cache_key, 3600, json.dumps(user))

return user

Read-Through

Cache sits between app and database, auto-loading on miss. Logic is in the cache layer, not the application.

Write-Through

Data written to cache first, then database:

def update_user(user_id, data):

cache_key = f"user:{user_id}"

redis.setex(cache_key, 3600, json.dumps(data))

db.execute("UPDATE users SET name = %s WHERE id = %s", [data['name'], user_id])

Write-Behind

Write to cache immediately, batch database writes asynchronously. Fastest writes but risk of data loss if cache fails.

Invalidation Strategies

| Strategy | Approach | Best For | |----------|----------|----------| | TTL | Auto-expire | Most cases | | Key deletion | Delete on update | Write-through | | Versioned | Include version in key | Schema changes | | Pub/sub | Notify all instances | Distributed caches |

Rate Limiting with Sorted Sets

def is_rate_limited(user_id, max_requests=100, window=60):

key = f"ratelimit:{user_id}"

now = time.time()

redis.zremrangebyscore(key, 0, now - window)

if redis.zcard(key) >= max_requests:

return True

redis.zadd(key, {now: now})

redis.expire(key, window)

return False

Conclusion

Use cache-aside as the default pattern. Always set TTLs to prevent memory exhaustion. Monitor cache hit rates. Implement mutex locking for stampede prevention. Pipeline batch operations for performance.

Read the full article on AI Study Room for complete code examples, comparison tables, and related resources.

Found this useful? Check out more developer guides and tool comparisons on AI Study Room.

DEV Community

Redis Caching Patterns

Redis Caching Patterns