Amazon DynamoDB Accelerator
A fully managed in-memory cache service that sits in front of DynamoDB, reducing read latency from milliseconds to microseconds
Overview
Amazon DynamoDB Accelerator (DAX) is an in-memory cache cluster purpose-built for DynamoDB. Since it's fully compatible with the DynamoDB API, simply changing your application's endpoint to the DAX cluster reduces read latency from milliseconds to microseconds. Writes use write-through to transparently propagate to DynamoDB, automatically maintaining cache consistency.
Selection Criteria vs. ElastiCache and DAX-Specific Constraints
DAX and ElastiCache for Redis are both in-memory caches, but their use cases are distinctly different. DAX is DynamoDB-exclusive and uses the DynamoDB API directly, minimizing application changes. It automatically caches results from GetItem, BatchGetItem, Query, and Scan in two layers - an item cache and a query cache - returning results without hitting DynamoDB when the same request comes in. ElastiCache is a general-purpose cache that can cache data from sources beyond DynamoDB (RDS, external API results, session data, etc.). It also enables complex caching logic using Redis data structures (Sorted Sets, Hashes, Lists). Choose DAX when DynamoDB read load is high and the same keys or query patterns are repeated. DAX constraints include: access only from within a VPC (Lambda requires VPC connectivity), strongly consistent reads bypass the cache and go directly to DynamoDB, and separate clusters are needed per table. For write-heavy, read-light workloads, DAX won't deliver cost-effective results.
Cluster Design and Caching Strategy
A minimum 3-node (multi-AZ) DAX cluster configuration is recommended for production. Node types are in the dax.r5 family, with memory size selected based on the volume of data to cache. Item cache TTL (default 5 minutes) and query cache TTL (default 5 minutes) are independently configurable and should be tuned based on data update frequency. Set longer TTLs (1 hour or more) for infrequently updated master data, and shorter TTLs (tens of seconds) for frequently updated data. Cache hit rate can be calculated from CloudWatch as ItemCacheHits / (ItemCacheHits + ItemCacheMisses), targeting 80% or higher. Low hit rates may indicate access patterns unsuited to caching (accessing different keys each time). DAX costs are billed per node-hour - a dax.r5.large (3-node) cluster runs approximately 600 USD per month. It's important to estimate beforehand whether the reduction in DynamoDB read capacity units (RCUs) justifies the DAX cost.
Use Cases in Game Leaderboards and Real-Time Applications
DAX delivers the greatest impact for workloads where reads concentrate on a small number of hot keys. In game leaderboards, top-ranking queries are repeatedly executed by all players. Without DAX, hot spots form on DynamoDB partitions with throttling risk, but DAX caching query results dramatically reduces DynamoDB load. E-commerce product catalogs work similarly - popular product detail pages generate massive identical GetItem calls, making DAX's item cache highly effective. In real-time bidding systems, the current highest bid is read frequently while updates occur only at bid time. Setting DAX TTL to a few seconds returns near-real-time data while reducing DynamoDB read load by over 90%. On the other hand, for analytical queries that run Scans with different conditions each time, query cache hit rates will be low and DAX's effectiveness is limited. In such cases, exporting DynamoDB data to S3 and analyzing with Athena is more appropriate.