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Hands-on with DAX: Supercharge Your DynamoDB Reads (Python + Terraform)

#aws #dynamodb #dax #python

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

Install Python DAX Client 

pip install amazondax boto3
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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})
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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)
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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)
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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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