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Caching Architecture in Cloud Computing

Learn about Caching Architecture in this comprehensive Cloud Computing tutorial. Design patterns.

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1Redis over Memcached

In modern AWS architectures, Redis is almost universally preferred over Memcached due to its High Availability (Multi-AZ), backup capabilities, and advanced data structures.

2Step-by-Step Breakdown

What is Caching?. Caching stores frequently accessed data in RAM, avoiding slow disk-based database queries.

Amazon ElastiCache. A managed service that deploys, operates, and scales open-source in-memory data stores.

Memcached. A simple, pure key-value store. Multi-threaded. No persistence or high availability. Great for basic HTML caching.

Redis. Advanced data structures (lists, sets, hashes). Supports persistence, replication, and Multi-AZ failover.

Knowledge Check. Which ElastiCache engine supports Multi-AZ failover and data persistence?

→Memcached
→Redis

Lazy Loading. A caching strategy where the app queries the cache; if data is missing, it queries the DB and writes to the cache.

Write-Through. A strategy where data is written to the cache and the database at the same time. Ensures data is never stale.

Cache Eviction. Caches have limited RAM. Time To Live (TTL) dictates how long data stays before being automatically deleted.

Redis Pub/Sub. Redis isn't just a cache; it supports Publish/Subscribe messaging patterns for real-time applications like chat rooms.

Summary. ElastiCache offloads database pressure and delivers sub-millisecond response times.

Pascual Vila

Frontend Instructor // Code Syllabus

Common Pitfalls & Errors

The Error //

Choosing Memcached when the app actually needs Redis's data structures or persistence

// Needs persistence, replication, or complex data structures -> Redis
// Simple, high-throughput flat cache only -> Memcached

The Solution //

Memcached is a simple, multi-threaded key-value cache with no persistence and no replication — fine for pure caching, but it can't do what Redis does with sorted sets, pub/sub, or snapshotting to disk. Pick Redis whenever the app needs anything beyond a flat cache.

The Error //

Treating the cache as a source of truth with no eviction/invalidation strategy

// On write to the database, also update or delete the cache key
await redisClient.set(`user:${id}`, JSON.stringify(user), { EX: 3600 });

The Solution //

Without a clear TTL and invalidation strategy, a cache can silently serve stale data indefinitely after the underlying record changes. Set appropriate TTLs and explicitly invalidate or update cache keys on writes to the source of truth.