Achieving Microsecond Latency with Amazon DynamoDB Accelerator (DAX) - In-Memory Cache Design
Learn about read acceleration for DynamoDB with DAX, cache strategies, and cluster design.
DAX Overview
DAX is an in-memory cache for DynamoDB that reduces read latency from milliseconds to microseconds. Unlike ElastiCache (Redis/Memcached), DAX provides a DynamoDB-compatible API, so migration is completed simply by changing the SDK endpoint to the DAX cluster. A DAX cluster is deployed within a VPC, with each node in the cluster holding a replica of the same cached data. The primary node handles writes while replica nodes distribute reads, and replicas are automatically promoted in case of primary failure.
Cache Strategy and Cluster Design
The item cache caches results from GetItem and BatchGetItem, with automatic expiration via TTL (default 5 minutes). The query cache caches result sets from Query and Scan operations. Write-through ensures that the cache is also updated when PutItem or UpdateItem is executed, reducing the risk of stale data on reads. A minimum of 3 nodes (Multi-AZ) is recommended for the cluster, and node type should be selected based on the volume of data to cache. dax.r5.large (13.5 GB memory) suits medium workloads, while dax.r5.xlarge (27 GB memory) is appropriate for large hot datasets.
Cache Strategy and Consistency
DAX manages caching in two layers: item cache and query cache. The item cache holds GetItem and BatchGetItem results with a TTL (default 5 minutes). The query cache holds Query and Scan results. Write operations (PutItem, UpdateItem, DeleteItem) are reflected in both DAX and DynamoDB through write-through. Eventually consistent reads are served from the cache, while strongly consistent reads access DynamoDB directly. There is a trade-off where shorter TTL settings reduce cache hit rates, while longer settings decrease data freshness. For learning about DynamoDB cache performance optimization, related books (Amazon) can be helpful.
DAX vs. ElastiCache Comparison
DAX is a cache dedicated to DynamoDB, and its greatest advantage is that it can be adopted simply by replacing the DynamoDB SDK endpoint. There is no need to implement cache invalidation logic or key design on the application side, as write-through automatically maintains consistency. ElastiCache (Redis), on the other hand, can cache data from sources other than DynamoDB (RDS, external API responses) and leverages data structures such as Pub/Sub and sorted sets. DAX's query cache determines hits by exact parameter match, so hit rates decrease for workloads where Query filter conditions change frequently. In such cases, designing application-side cache keys with ElastiCache is more efficient. DAX does not cache transaction APIs (TransactGetItems / TransactWriteItems), so its effectiveness is limited for transaction-heavy workloads.
Design Pitfalls and Operational Considerations
DAX clusters must be deployed within a VPC. When connecting from Lambda, either place Lambda in the same VPC or access via a VPC endpoint. Placing Lambda in a VPC increases cold start time, so consider using Provisioned Concurrency. During DAX node failures, the client SDK automatically retries, but read latency returns to milliseconds during failover (several seconds). Applications should be designed to tolerate this temporary latency increase. Item cache and query cache TTLs can be configured independently. For tables with frequent writes, shorten item cache TTL to 1 minute or less and make query cache TTL even shorter to maintain data freshness. Cluster scale-out (adding nodes) can be performed online, but scale-in (removing nodes) involves cache data redistribution and should be performed during low-traffic periods.
DAX Pricing
DAX pricing is based on hourly charges per node. A dax.r5.large costs approximately $0.269 per hour (about $194 per month). A minimum 3-node (Multi-AZ) configuration is recommended, costing approximately $582 per month. Compare the cost savings from reducing DynamoDB Read Capacity Units (RCU) against DAX node costs to determine whether DAX adoption is advantageous for read-heavy workloads. With cache hit rates of 90% or higher, significant RCU reductions can be expected, exceeding the cost of DAX.
Summary
DAX is an in-memory cache for DynamoDB that achieves microsecond-level read latency. It maintains write consistency through write-through caching while significantly reducing read load with two-layer caching of items and queries. The DynamoDB RCU cost reduction effect becomes pronounced at cache hit rates of 90% or higher, making it ideal for read-intensive workloads.