How to Use DAX for Caching in DynamoDB

Use DynamoDB Accelerator (DAX) as an in-memory cache to speed up read operations in DynamoDB. Configure a DAX cluster, connect your application to it using the DAX client SDK, and your reads will be served from the cache when possible, reducing latency and costs.

Syntax

To use DAX for caching in DynamoDB, you create a DAX client in your application that connects to the DAX cluster endpoint instead of directly to DynamoDB. The DAX client has the same API as the DynamoDB client, so you use it to perform read and write operations.

• DAXClient(config): Initializes the DAX client with cluster endpoint and credentials.
• getItem(params): Reads an item, served from cache if available.
• putItem(params): Writes an item, updates cache and DynamoDB.

javascript

const AmazonDaxClient = require('amazon-dax-client');

const dax = new AmazonDaxClient({
  endpoints: ['mydaxcluster.abc123.dax-clusters.amazonaws.com:8111'],
  region: 'us-west-2'
});

// Use dax.get(params) or dax.put(params) like DynamoDB client

Example

This example shows how to read an item from DynamoDB using DAX caching. The first read fetches from DynamoDB and caches it. Subsequent reads get the item from the DAX cache, reducing latency.

javascript

const AmazonDaxClient = require('amazon-dax-client');

const dax = new AmazonDaxClient({
  endpoints: ['mydaxcluster.abc123.dax-clusters.amazonaws.com:8111'],
  region: 'us-west-2'
});

const params = {
  TableName: 'Movies',
  Key: { 'year': 2015, 'title': 'The Big New Movie' }
};

async function getMovie() {
  try {
    const data = await dax.get(params).promise();
    console.log('Movie:', data.Item);
  } catch (err) {
    console.error('Error fetching item:', err);
  }
}

getMovie();

Output

Movie: { year: 2015, title: 'The Big New Movie', info: { plot: '...', rating: 8.5 } }

Common Pitfalls

• Not using the DAX client: Using the regular DynamoDB client bypasses the cache.
• Incorrect cluster endpoint: Using wrong or outdated DAX cluster endpoints causes connection failures.
• Write consistency: Writes go directly to DynamoDB and update the cache; failing to handle eventual consistency can cause stale reads.
• Cache misses: First reads always hit DynamoDB; expect some latency initially.

javascript

/* Wrong: Using DynamoDB client directly (no caching) */
const AWS = require('aws-sdk');
const dynamodb = new AWS.DynamoDB.DocumentClient();
dynamodb.get(params).promise();

/* Right: Using DAX client for caching */
const AmazonDaxClient = require('amazon-dax-client');
const dax = new AmazonDaxClient({ endpoints: ['mydaxcluster.abc123.dax-clusters.amazonaws.com:8111'], region: 'us-west-2' });
dax.get(params).promise();

Quick Reference

Concept	Description
DAX Cluster	In-memory cache cluster for DynamoDB reads
DAX Client	SDK client that connects to DAX cluster instead of DynamoDB
getItem	Reads item, served from cache if available
putItem	Writes item, updates DynamoDB and cache
Cache Miss	First read fetches from DynamoDB and caches result
Eventual Consistency	Writes update cache asynchronously, may cause stale reads briefly

Key Takeaways

Use the DAX client SDK to connect your app to the DAX cluster for caching.

Reads through DAX are faster because they come from the in-memory cache when possible.

Writes go directly to DynamoDB and update the cache to keep data fresh.

Ensure you use the correct DAX cluster endpoint to avoid connection errors.

Expect some latency on first reads due to cache misses before data is cached.