Amazon ElastiCache와 DAX를 활용한 캐싱

Reading from a database on every invocation is slow and expensive. A cache keeps hot data in memory so repeated reads are near-instant.

Two managed caches pair well with Lambda:

• ElastiCache (Redis or Memcached) for general key-value caching.
• DAX, a cache built specifically for DynamoDB.

The most common pattern: check the cache first, fall back to the database on a miss, then store the result for next time.

A time to live expires stale entries automatically. Short TTLs keep data fresh; long TTLs maximize hit rate. Choose based on how often data changes.

DAX is a write-through cache for DynamoDB. You point the DynamoDB client at the DAX endpoint and reads transparently use the cache.

ElastiCache and DAX run inside a VPC. Your Lambda must be VPC-attached with the right security group to reach the cache.

Create the cache client outside the handler so it persists across warm invocations, avoiding a new connection on every call.

When data changes, update or delete the cache entry so readers do not see stale values. Write-through and write-around are two strategies.

Memcached is simple and multi-threaded; Redis adds data structures, persistence, and replication. Pick Redis when you need richer features.

Caching adds complexity. Skip it for rarely read data or data that must always be perfectly current, where staleness is unacceptable.

Track the cache hit ratio in CloudWatch. A low ratio means the cache is not helping and your TTL or keys may need tuning.

You learned to cut latency and DB load with ElastiCache and DAX using the cache-aside pattern, TTLs, connection reuse, invalidation, and hit-rate monitoring.