redis-patterns

Redis Patterns

Quick reference for Redis best practices across common backend use cases.

How It Works

Redis is an in-memory data structure store that supports strings, hashes, lists, sets, sorted sets, streams, and more. Individual Redis commands are atomic on a single instance; multi-step workflows require Lua scripts, MULTI/EXEC transactions, or explicit synchronization to stay atomic. Data is optionally persisted via RDB snapshots or AOF logs. Clients communicate over TCP using the RESP protocol; connection pools are essential to avoid per-request handshake overhead.

When to Activate

• Adding caching to an application
• Implementing rate limiting or throttling
• Building distributed locks or coordination
• Setting up session or token storage
• Using Pub/Sub or Redis Streams for messaging
• Configuring Redis in production (pooling, eviction, clustering)

Data Structure Cheat Sheet

Use Case	Structure	Example Key
Simple cache	String	product:123
User session	Hash	session:abc
Leaderboard	Sorted Set	scores:weekly
Unique visitors	Set	visitors:2024-01-01
Activity feed	List	feed:user:456
Event stream	Stream	events:orders
Counters / rate limits	String (INCR)	ratelimit:user:123
Bloom filter / HLL	HyperLogLog	hll:pageviews

Core Patterns

Cache-Aside (Lazy Loading)

import redis
import json

r = redis.Redis(host='localhost', port=6379, decode_responses=True)

def get_product(product_id: int):
    cache_key = f"product:{product_id}"
    cached = r.get(cache_key)

    if cached:
        return json.loads(cached)

    product = db.query("SELECT * FROM products WHERE id = %s", product_id)
    r.setex(cache_key, 3600, json.dumps(product))  # TTL: 1 hour
    return product

Write-Through Cache

def update_product(product_id: int, data: dict):
    # Write to DB first
    db.execute("UPDATE products SET ... WHERE id = %s", product_id)

    # Immediately update cache
    cache_key = f"product:{product_id}"
    r.setex(cache_key, 3600, json.dumps(data))