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This article explains DynamoDB DAX — a managed, in-memory caching service that reduces read latency and offloads read traffic from Amazon DynamoDB for read-heavy applications. Imagine an application that performs many reads against an Amazon DynamoDB table. Introducing a caching layer in front of DynamoDB can reduce latency and lower the read load on your table. DynamoDB Accelerator (DAX) is an in-memory cache built specifically for DynamoDB to serve that purpose. DAX stores frequently requested items in memory inside a DAX cluster so your application retrieves them with very low latency. On a cache miss, DAX fetches the item from the DynamoDB table and populates the cache for subsequent requests.
A simple architecture diagram of AWS DynamoDB DAX showing an EC2 instance (with Application and DAX Client) connecting to a DAX Cluster, which in turn communicates with a DynamoDB table. Arrows indicate the communication flow between the three components.

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How DAX works (high level)

  • DAX runs as a clustered, in-memory cache that sits between your application and DynamoDB.
  • Your application uses a DAX-enabled client (or AWS SDK wrapper) instead of the standard DynamoDB client.
  • On a cache hit, DAX returns the cached item with very low latency (microseconds to milliseconds).
  • On a cache miss, DAX reads from DynamoDB, returns the result to the application, and caches the item for future requests (read-through behavior).

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Key features and when to use them

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Supported operations and limitations

  • Supported: GetItem, BatchGetItem, Query, Scan (eventually-consistent reads).
  • Not supported: DynamoDB transactional APIs (TransactGetItems, TransactWriteItems) and strongly-consistent reads.
  • DAX is not a replacement for DynamoDB’s durability or transactional guarantees — it is a performance layer for read optimization.
An infographic slide titled "DynamoDB DAX" showing five numbered feature cards with colorful icons. The cards list: fully managed cache for DynamoDB; compatibility with the DynamoDB API; ideal for high read workloads; customizable TTL settings; and scalability/high availability.
Exam tip: If a question describes a read-heavy DynamoDB table that needs lower-latency reads, the correct service to consider is DynamoDB DAX.

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Best practices

  • Use DAX for read-heavy, latency-sensitive workloads where eventual consistency is acceptable.
  • Prefer DAX for workloads with a high cache hit ratio (frequent repeated reads of the same items).
  • Configure TTLs appropriately to balance freshness vs. cache performance.
  • Run DAX clusters in multiple Availability Zones for high availability.
  • Monitor DAX and DynamoDB with Amazon CloudWatch (cache hit rate, latency, node health).
  • Start with a small DAX cluster and scale up; DAX supports clusters up to 10 nodes.
  • Test your application with realistic traffic to measure cache hit ratio and end-to-end latency improvements.

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Quick integration notes

  • Switch your DynamoDB client to the DAX client or use the AWS-provided DAX client wrapper in your language SDK to keep application changes minimal.
  • Ensure your application logic does not rely on strongly consistent reads when using DAX.

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References and further reading

In summary, DAX is a managed, in-memory cache built specifically for DynamoDB that reduces read latency and offloads read traffic, making it an excellent choice for read-heavy, latency-sensitive applications.

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