DynamoDB Accelerator

In this lesson, we’ll explore Amazon DynamoDB Accelerator (DAX), a fully managed, in-memory caching service built specifically for Amazon DynamoDB. DAX can improve read performance by up to 10x — reducing response times from single-digit milliseconds to microseconds — even at millions of requests per second.

What DAX does for you

Provides in-memory acceleration for supported DynamoDB read operations, handling cache population, invalidation (for operations issued through the DAX client), replication, and cluster management.
Returns cached items in microseconds when available, and transparently fetches and caches items from DynamoDB on cache misses.
Is API-compatible with DynamoDB for the supported operations, allowing minimal code changes when integrating.

DAX architecture overview

A DAX cluster consists of one primary node and up to nine read replicas (maximum 10 nodes).
The DAX client sends your application’s DynamoDB API calls to the DAX cluster. The cluster manages routing and load balancing across nodes.
The service handles replication and automatically fails over if a node becomes unhealthy, enabling high availability and scaling for read-heavy workloads.

How a read request works (high level)

Cache hit: DAX serves the item directly from the cache in microseconds.
Cache miss: DAX forwards the request to DynamoDB, returns the item to the application, and caches it for subsequent requests.

A simplified AWS DAX architecture diagram showing an EC2 instance (Application and DAX client) inside a VPC connecting to a DAX cluster of cache nodes. The DAX cluster is shown communicating with Amazon DynamoDB to accelerate reads.

Key patterns and integration

Read-through caching: DAX returns cached items when available. On a miss, DAX loads items from DynamoDB and caches them.
Write-through caching: When write operations go through the DAX client and write-through mode is used, writes update both DynamoDB and the DAX cache to keep the cache consistent.
Integration is typically straightforward: replace your DynamoDB client with the DAX client and use the same API calls (for supported operations). DAX works with applications on EC2, Lambda, containers, and other compute platforms.

Quick conceptual example (pseudo-code)

Replace the DynamoDB client with a DAX client pointing at your cluster endpoint, then call the same operations:

# Pseudocode - replace your DynamoDB client with a DAX client
daxClient = new DaxClient(clusterEndpoint = "dax-cluster-identifier:8111")

# Use the same DynamoDB-style API via daxClient
result = daxClient.getItem({ TableName: "Users", Key: { userId: "123" } })

Important limitations (read before migrating)

DAX accelerates eventually-consistent reads only. It does not support strongly consistent reads or DynamoDB transactions (TransactGetItems / TransactWriteItems). Some DynamoDB APIs are not supported by DAX — check the official AWS DAX documentation for the current supported API list before migrating: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/DAX.html

When to use DAX vs when not to

Use case / requirement	Recommendation
Read-heavy workloads with strict low-latency requirements	Use DAX to reduce read latency to microseconds and offload read traffic from DynamoDB.
Need for strongly consistent reads or transactions	Do not use DAX — it only accelerates eventually-consistent reads and does not support transactions.
Want minimal application changes	DAX is API-compatible for supported operations — often only a client swap is required.
Write-heavy workloads where caching would cause staleness	DAX can be used with write-through patterns, but evaluate write consistency and invalidation needs carefully.

Benefits summary

Reduced read latency: Microsecond-level responses for cached items.
High throughput: Scale clusters up to 10 nodes (1 primary + 9 replicas) to support millions of reads per second.
Fully managed: AWS handles replication, failover, and cluster management.
Minimal code changes: API compatibility for supported operations eases migration.

Best practices

Start with a multi-node cluster (e.g., three nodes) for high availability and then scale as needed.
Ensure your application can tolerate eventual consistency for reads that are accelerated by DAX.
Execute supported write operations through the DAX client if you plan to use write-through caching so the cache is updated consistently.
Validate that the DynamoDB APIs your application uses are supported by DAX by consulting the official documentation.

DAX reduces application-level caching complexity by providing read-through and write-through patterns and handling cache invalidation for supported operations executed through the DAX client. For the latest list of supported APIs and integration details, refer to the AWS documentation: https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/DAX.html

Summary

Amazon DAX is a fully managed in-memory cache for DynamoDB that significantly reduces read latency for supported, eventually-consistent read operations.
A DAX cluster has up to 10 nodes and provides replication and automatic failover for high availability.
Integration typically requires swapping your DynamoDB client for a DAX client — supported API calls continue to work with minimal changes.
DAX is ideal for read-heavy workloads where lower read latency and higher throughput are required, but it is not suitable for workloads requiring strongly consistent reads or DynamoDB transactions.

Introduction

Services - Networking

Services - Storage

Services - Compute

Services - Database

Services - Application Integration

Services - Data and ML

Services - Migration and Transfer

Services - Management and Governance

Services - Security

Bringing it all together

Designing for Security

Designing for Reliability

Designing for Performance

Designing for Cost-Optimization

Applying your Design Skills

DynamoDB Accelerator

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