Amazon ElastiCache

Caching changes the request path

Amazon ElastiCache provides managed in-memory data stores compatible with Valkey, Redis OSS, and Memcached. The most common architecture places ElastiCache in front of a durable database so repeated reads can be served from memory instead of repeatedly reaching the database. This can reduce database load and application latency when the working set has reuse. It does not automatically improve a workload whose reads rarely repeat or whose cache hit rate is poor.

In a cache-aside pattern, the application first asks the cache for a key. On a miss it reads the authoritative store, returns the result, and typically writes a cached representation with an appropriate lifetime. This means cache correctness belongs partly to application design: developers must choose keys, expiration behavior, invalidation strategy, and what happens when the cache is unavailable.

Choose the engine from the data structures and topology

Valkey and Redis OSS support rich data structures and features that make them useful beyond a simple string cache, including patterns for counters, leaderboards, session state, and pub/sub-style interactions. ElastiCache can organize these engines into replication groups with primaries and read replicas, and cluster-mode designs distribute key space across shards. The topology should follow availability, write/read throughput, and data-size requirements.

Memcached emphasizes a simpler distributed memory cache model and scales through cache nodes without the same Valkey/Redis replication feature set. Because the engines offer different persistence, replication, data-structure, and failover capabilities, the correct choice should follow workload requirements rather than treating all three as interchangeable cache protocols.

• Valkey/Redis OSS: richer data structures and replication-group/sharding options for stateful cache patterns.
• Memcached: simpler distributed cache behavior when advanced Redis-family structures or replication are unnecessary.
• Serverless ElastiCache reduces node-management decisions; node-based deployment gives more explicit node/topology control.
• A cache hit avoids the slower backing-store path; a miss still requires the application to fetch or compute the value.

TTL, eviction, and failure are application-visible behaviors

Caching requires an expiration policy. A time to live (TTL) limits how long a cached item should remain eligible before expiration, while an eviction policy determines how the engine handles memory pressure according to its configured behavior. These are different mechanisms: TTL expresses item lifetime; eviction responds to memory constraints. An application that assumes cached data is permanent has confused the performance tier with durable storage.

For resilient Valkey or Redis OSS designs, replicas and Multi-AZ-related failover capabilities can reduce the impact of node failure when configured appropriately. Monitoring cache hit rate, memory usage, evictions, CPU, connections, and replication behavior helps determine whether the cache is actually improving the system. A very low hit rate or constant eviction can turn the cache into extra complexity without reducing load on the database.