DAX
DAX (DynamoDB Accelerator) is a fully managed, in-memory cache that sits in front of DynamoDB and returns cached reads in microseconds instead of DynamoDB's normal single-digit-millisecond latency. It's the only AWS caching layer that requires zero application code changes — the SDK call is identical, only the endpoint changes — because DAX speaks the native DynamoDB API.
A ticketing platform handling flash-sale traffic points its product-catalog reads at a DAX cluster instead of DynamoDB directly; the first request for a hot item is a cache miss that hits DynamoDB, but the next thousand requests for that same item return from DAX in microseconds, sparing DynamoDB the repeated load.
What Gets Cached
DAX caches both individual GetItem results and Query/Scan result sets. Writes (PutItem, UpdateItem, DeleteItem) always go straight to DynamoDB, with DAX invalidating the affected cached items automatically.
RememberDAX doesn't guarantee strongly consistent reads — if your access pattern requires reading your own writes immediately, DAX's eventual consistency may return stale data. Reach for DAX on read-heavy, cache-tolerant workloads first; it's simpler and safer than rolling your own ElastiCache logic.
Frequently Asked Questions
Why is DAX described as requiring zero application code changes when other caches don't?
DAX implements the same API surface as the DynamoDB SDK, so an application only needs to swap its client endpoint from DynamoDB directly to the DAX cluster endpoint — every existing GetItem, Query, and Scan call works unmodified. Compare that to a typical cache-aside pattern with Redis or Memcached, where you write explicit logic to check the cache, fall through to the database on a miss, and manually populate the cache — DAX handles that transparently inside the client.
What's a common mistake teams make expecting DAX to solve write-heavy workloads?
Assuming DAX speeds up writes the same way it speeds up reads — it doesn't meaningfully help write-heavy or read-after-write-consistency-critical workloads, since DAX's write-through caching still has to hit DynamoDB and its default query cache has its own TTL, meaning a strongly consistent read immediately after a write may not reflect the change from the item cache. DAX is specifically a fix for read-heavy, repeat-key access patterns, not a general-purpose DynamoDB accelerator.