Supercharge DynamoDB Reads with DAX (Hands-on Tutorial)

If you read my previous article on DAX, you already know what it is and when to use it. Now, let’s build it together, step-by-step.

We'll:

Create a DynamoDB table

Deploy a DAX cluster using Terraform

Write a Python script to query the table via DAX

Compare DynamoDB vs DAX read performance

🛠️ Prerequisites

  • AWS account
  • Python 3.8+
  • Boto3 + DAX SDK
  • Terraform CLI

Set Up DynamoDB + DAX with Terraform

Here's a minimal Terraform setup:

provider "aws" {
  region = "us-east-1"
}

resource "aws_dynamodb_table" "products" {
  name           = "Products"
  billing_mode   = "PAY_PER_REQUEST"
  hash_key       = "ProductID"

  attribute {
    name = "ProductID"
    type = "S"
  }
}

resource "aws_dax_cluster" "dax_cluster" {
  cluster_name       = "dax-demo"
  node_type          = "dax.r4.large"
  replication_factor = 1
  iam_role_arn       = aws_iam_role.dax_role.arn
  parameter_group_name = "default.dax1.0"
  subnet_group_name    = aws_dax_subnet_group.dax_subnets.name
  security_group_ids   = [aws_security_group.dax_sg.id]
}

resource "aws_dax_subnet_group" "dax_subnets" {
  name       = "dax-subnets"
  subnet_ids = [/* your private subnet IDs here */]
}

resource "aws_iam_role" "dax_role" {
  name = "dax-access-role"
  assume_role_policy = jsonencode({
    Version = "2012-10-17"
    Statement = [{
      Action = "sts:AssumeRole"
      Effect = "Allow"
      Principal = {
        Service = "dax.amazonaws.com"
      }
    }]
  })
}

⚠️ Don’t forget to replace placeholders with your actual VPC, subnet IDs, and IPs.

Install Python DAX Client 

pip install amazondax boto3

Insert Sample Data into DynamoDB 

import boto3

dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('Products')

for i in range(1, 101):
    table.put_item(Item={"ProductID": f"P{i}", "Name": f"Item {i}", "Price": 10.99 + i})

Query DynamoDB via DAX 

import amazondax.AmazonDaxClient
import boto3
import time

# Replace with your DAX endpoint
dax_endpoint = "dax-demo.xxxxxx.clustercfg.dax.us-east-1.amazonaws.com:8111"

dax = amazondax.AmazonDaxClient.DaxClient(
    endpoints=[dax_endpoint],
    region_name="us-east-1"
)

table = dax.Table("Products")

def read_from_dax(product_id):
    response = table.get_item(Key={"ProductID": product_id})
    return response.get("Item", {})

start = time.time()
print(read_from_dax("P10"))
print("DAX Latency:", time.time() - start)

Compare with Native DynamoDB Client 

dynamodb = boto3.resource("dynamodb")
table = dynamodb.Table("Products")

def read_from_dynamodb(product_id):
    response = table.get_item(Key={"ProductID": product_id})
    return response.get("Item", {})

start = time.time()
print(read_from_dynamodb("P10"))
print("DynamoDB Latency:", time.time() - start)

You should see DAX returning results faster after the first access (cache hit).

📊 Monitor DAX in CloudWatch

Go to CloudWatch > Metrics > DAX to monitor:

  • Cache hit/miss ratio
  • Latency
  • Item fetch count
  • Eviction rate

Set alarms if hit rate drops!

Final Thoughts

DAX is drop-in easy if you’re already using DynamoDB

It’s freakishly fast for read-heavy use cases

Requires almost zero extra code

But remember: it’s read-only optimization — writes go straight to DynamoDB

💬 Let’s Build Together

If you want to:

  • Deploy this with AWS CDK instead of Terraform
  • Use Node.js or Go for the DAX client
  • Automate it with GitHub Actions
  • Secure it with IAM + KMS best practices

Drop your stack below or DM me — let’s get your app blazing fast 🚀

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