Scaling Web Applications: Redis Caching & In-Memory Storage Strategies

Caching is the single most effective way to scale web applications without drastically increasing database infrastructure costs.

1. Cache-Aside (Lazy Loading) Pattern

In the cache-aside pattern, the application attempts to read data from Redis first. If a cache miss occurs, data is fetched from the primary database, written to Redis, and returned to the client.

def get_user_profile(user_id):
    cached = redis.get(f"user:{user_id}")
    if cached:
        return json.loads(cached)
    user = db.query("SELECT * FROM users WHERE id = %s", user_id)
    redis.setex(f"user:{user_id}", 3600, json.dumps(user))
    return user

2. Session Storage & Rate Limiting

Redis excels at transient data handling such as user authentication sessions and API rate limits due to sub-millisecond read/write latency.

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