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Merge pull request #2749 from jojo786/feature-apigw-lambda-dsql
New serverless pattern: apigw-lambda-dsql
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‎apigw-lambda-dsql/README.md‎

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# Amazon API Gateway, AWS Lambda and Amazon Aurora DSQL
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Amazon Aurora DSQL is the fastest serverless, distributed SQL database with active-active high availability and multi-Region strong consistency. Aurora DSQL enables you to build always available applications with virtually unlimited scalability, the highest availability, and zero infrastructure management. It is designed to make scaling and resilience effortless for your applications and offers the fastest distributed SQL reads and writes.
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This pattern deploys a API Gateway REST API, Lambda function and an Aurora DSQL PostgreSQL cluster.
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Learn more about this pattern at [Serverless Land Patterns](https://serverlessland.com/patterns/apigw-lambda-dsql)
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Important: this application uses various AWS services and there are costs associated with these services after the Free Tier usage - please see the [AWS Pricing page](https://aws.amazon.com/pricing/) for details. You are responsible for any AWS costs incurred. No warranty is implied in this example.
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## Requirements
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* [Create an AWS account](https://portal.aws.amazon.com/gp/aws/developer/registration/index.html) if you do not already have one and log in. The IAM user that you use must have sufficient permissions to make necessary AWS service calls and manage AWS resources.
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* [AWS CLI](https://docs.aws.amazon.com/cli/latest/userguide/install-cliv2.html) installed and configured
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* [Git Installed](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
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* [AWS Serverless Application Model](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-sam-cli-install.html) (AWS SAM) installed
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## Deployment Instructions
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1. Create a new directory, navigate to that directory in a terminal and clone the GitHub repository:
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```
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git clone https://github.com/aws-samples/serverless-patterns
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```
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1. Change directory to the pattern directory:
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```
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cd apigw-lambda-dsql
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```
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1. From the command line, use AWS SAM to deploy the AWS resources for the pattern as specified in the template.yml file:
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```
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sam build
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sam deploy --guided
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```
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1. During the prompts:
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* Enter a stack name
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* Enter the desired AWS Region (verify which [regions Aurora DSQL is available in](https://aws.amazon.com/rds/aurora/dsql/faqs/))
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* Allow SAM CLI to create IAM roles with the required permissions.
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Once you have run `sam deploy --guided` mode once and saved arguments to a configuration file (samconfig.toml), you can use `sam deploy` in future to use these defaults.
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1. Note the outputs from the SAM deployment process. These contain the resource names and/or ARNs which are used for testing.
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## How it works
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This sample project demonstrates how to use a Lambda function (invoked by API Gateway), that stores and retrieves data from an Amazon Aurora DSQL PostgreSQL cluster.
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## Testing
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Use the `Value` from `UsersApi` URL output from the above `sam deploy` command to invoke the API Gateway API, e.g:
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`curl https://abc123.execute-api.eu-west-1.amazonaws.com/Prod/users/`
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which should return with
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`{"id": "e3ce23b5-c6a1-4289-bb02-2f34a2f7b956", "name": "John", "city": "LA", "telephone": "555-555-0150"}`
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## Cleanup
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1. Delete the stack
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```bash
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sam delete
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```
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1. Confirm the stack has been deleted
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```bash
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aws cloudformation list-stacks --query "StackSummaries[?contains(StackName,'STACK_NAME')].StackStatus"
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```
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----
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Copyright 2025 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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SPDX-License-Identifier: MIT-0

‎apigw-lambda-dsql/apigw-lambda-dsql‎

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{
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"title": "Amazon API Gateway, AWS Lambda and Amazon Aurora DSQL",
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"description": "Creates an API Gateway REST API integrated with an AWS Lambda function that connects to an Amazon Aurora DSQL PostgreSQL cluster",
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"language": "Python",
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"level": "200",
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"framework": "AWS SAM",
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"introBox": {
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"headline": "How it works",
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"text": [
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"This sample project demonstrates how to use a Lambda function (invoked by API Gateway), that stores and retrieves data from an Amazon Aurora DSQL PostgreSQL cluster.",
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"Aurora DSQL enables you to build always available applications with virtually unlimited scalability, the highest availability, and zero infrastructure management.",
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"This pattern deploys a API Gateway REST API, Lambda function and an Aurora DSQL PostgreSQL cluster."
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]
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},
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"gitHub": {
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"template": {
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"repoURL": "https://github.com/aws-samples/serverless-patterns/tree/main/apigw-lambda-dsql",
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"templateURL": "serverless-patterns/apigw-lambda-dsql",
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"projectFolder": "apigw-lambda-dsql",
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"templateFile": "template.yaml"
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}
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},
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"resources": {
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"bullets": [
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{
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"text": "Amazon Aurora DSQL, the fastest serverless distributed SQL database is now generally available",
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"link": "https://aws.amazon.com/blogs/aws/amazon-aurora-dsql-is-now-generally-available/"
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},
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{
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"text": "DSQL Vignette: Aurora DSQL, and A Personal Story",
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"link": "https://brooker.co.za/blog/2024/12/03/aurora-dsql.html/"
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},
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{
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"text": "Amazon Aurora DSQL samples",
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"link": "https://github.com/aws-samples/aurora-dsql-samples/tree/main/python"
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}
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]
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},
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"deploy": {
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"text": [
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"sam deploy"
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]
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},
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"testing": {
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"text": [
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"See the GitHub repo for detailed testing instructions."
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]
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},
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"cleanup": {
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"text": [
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"sam delete"
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]
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},
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"authors": [
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{
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"name": "Yusuf Mayet",
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"image": "https://d2908q01vomqb2.cloudfront.net/9e6a55b6b4563e652a23be9d623ca5055c356940/2021/11/24/Yusuf-mayet-aws.jpg",
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"bio": "I am a Solutions Architect at AWS, where I help customers realise that true transformation lies at the intersection of Cloud, DevOps cultural practices, Agile principles, modular and scalable architectures, and efficient team structures.",
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"linkedin": "yusufmayet"
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}
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],
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"patternArch": {
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"icon1": {
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"x": 20,
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"y": 50,
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"service": "apigw",
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"label": "API Gateway REST API"
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},
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"icon2": {
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"x": 50,
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"y": 50,
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"service": "lambda",
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"label": "AWS Lambda"
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},
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"icon3": {
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"x": 80,
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"y": 50,
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"service": "aurora",
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"label": "Amazon Aurora DSQL"
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},
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"line1": {
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"from": "icon1",
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"to": "icon2"
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},
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"line2": {
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"from": "icon2",
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"to": "icon3"
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}
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}
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}

‎apigw-lambda-dsql/example-pattern.json‎

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{
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"title": "Amazon API Gateway, AWS Lambda and Amazon Aurora DSQL",
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"description": "Creates an API Gateway REST API integrated with an AWS Lambda function that connects to an Amazon Aurora DSQL PostgreSQL cluster",
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"language": "Python",
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"level": "200",
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"framework": "AWS SAM",
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"introBox": {
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"headline": "How it works",
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"text": [
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"This sample project demonstrates how to use a Lambda function (invoked by API Gateway), that stores and retrieves data from an Amazon Aurora DSQL PostgreSQL cluster.",
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"Aurora DSQL enables you to build always available applications with virtually unlimited scalability, the highest availability, and zero infrastructure management.",
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"This pattern deploys a API Gateway REST API, Lambda function and an Aurora DSQL PostgreSQL cluster."
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]
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},
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"gitHub": {
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"template": {
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"repoURL": "https://github.com/aws-samples/serverless-patterns/tree/main/apigw-lambda-dsql",
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"templateURL": "serverless-patterns/apigw-lambda-dsql",
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"projectFolder": "apigw-lambda-dsql",
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"templateFile": "template.yaml"
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}
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},
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"resources": {
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"bullets": [
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{
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"text": "Amazon Aurora DSQL, the fastest serverless distributed SQL database is now generally available",
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"link": "https://aws.amazon.com/blogs/aws/amazon-aurora-dsql-is-now-generally-available/"
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},
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{
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"text": "DSQL Vignette: Aurora DSQL, and A Personal Story",
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"link": "https://brooker.co.za/blog/2024/12/03/aurora-dsql.html/"
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},
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{
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"text": "Amazon Aurora DSQL samples",
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"link": "https://github.com/aws-samples/aurora-dsql-samples/tree/main/python"
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}
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]
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},
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"deploy": {
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"text": [
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"sam deploy"
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]
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},
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"testing": {
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"text": [
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"See the GitHub repo for detailed testing instructions."
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]
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},
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"cleanup": {
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"text": [
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"sam delete"
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]
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},
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"authors": [
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{
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"name": "Yusuf Mayet",
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"image": "https://d2908q01vomqb2.cloudfront.net/9e6a55b6b4563e652a23be9d623ca5055c356940/2021/11/24/Yusuf-mayet-aws.jpg",
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"bio": "I am a Solutions Architect at AWS, where I help customers realise that true transformation lies at the intersection of Cloud, DevOps cultural practices, Agile principles, modular and scalable architectures, and efficient team structures.",
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"linkedin": "yusufmayet"
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}
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]
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}

‎apigw-lambda-dsql/src/app.py‎

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#Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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#SPDX-License-Identifier: MIT-0
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import json
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import boto3
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import psycopg2
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import psycopg2.extensions
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import os
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cluster_endpoint = os.environ['cluster_endpoint']
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region = os.environ['AWS_REGION']
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client = boto3.client("dsql", region_name=region)
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def lambda_handler(event, context):
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# Generate a fresh password token for each connection, to ensure the token is not expired when the connection is established
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password_token = client.generate_db_connect_admin_auth_token(cluster_endpoint, region)
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conn_params = {
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"dbname": "postgres",
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"user": "admin",
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"host": cluster_endpoint,
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"port": "5432",
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"sslmode": "require",
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"password": password_token
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}
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# Use the more efficient connection method if it's supported.
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if psycopg2.extensions.libpq_version() >= 170000:
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conn_params["sslnegotiation"] = "direct"
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# Make a connection to the cluster
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conn = psycopg2.connect(**conn_params)
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try:
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with conn.cursor() as cur:
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conn.commit()
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except Exception as e:
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conn.close()
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raise e
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conn.set_session(autocommit=True)
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cur = conn.cursor()
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cur.execute("DROP TABLE IF EXISTS users")
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cur.execute(b"""
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CREATE TABLE IF NOT EXISTS users(
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id uuid NOT NULL DEFAULT gen_random_uuid(),
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name varchar(30) NOT NULL,
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city varchar(80) NOT NULL,
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telephone varchar(20) DEFAULT NULL,
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PRIMARY KEY (id))"""
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)
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# Insert some rows
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cur.execute("INSERT INTO users(name, city, telephone) VALUES('John', 'LA', '555-555-0150')")
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# Read back what we have inserted
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cur.execute("SELECT * FROM users")
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row = cur.fetchone()
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print(row)
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# return JSON back to API Gateway
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return {
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'statusCode': 200,
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'body': json.dumps({
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'id': str(row[0]),
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'name': row[1],
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'city': row[2],
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'telephone': row[3]
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})
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}

‎apigw-lambda-dsql/src/requirements.txt‎

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psycopg2-binary>=2.9
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botocore>=1.35.74
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boto3>=1.35.74

‎apigw-lambda-dsql/template.yaml‎

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AWSTemplateFormatVersion: '2010-09-09'
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Transform: AWS::Serverless-2016-10-31
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Description: Serverless patterns - Amazon API Gateway, AWS Lambda and Amazon Aurora DSQL
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# More info about Globals: https://github.com/awslabs/serverless-application-model/blob/master/docs/globals.rst
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# Global values that are applied to all applicable resources in this template
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Globals:
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Function:
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CodeUri: ./src
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Runtime: python3.13
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MemorySize: 128
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Timeout: 3
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LoggingConfig:
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LogGroup: !Sub /aws/lambda/${AWS::StackName}
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LogFormat: JSON
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Architectures:
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- arm64
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Tags:
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project: "apigw-lambda-dsql"
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Resources:
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UsersFunction:
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Type: AWS::Serverless::Function # More info about Function Resource: https://github.com/awslabs/serverless-application-model/blob/master/versions/2016-10-31.md#awsserverlessfunction
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Properties:
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Handler: app.lambda_handler
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Policies: # Creates an IAM Role that defines the services the function can access and which actions the function can perform
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- Statement:
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- Effect: Allow
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Action:
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- dsql:DbConnectAdmin
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Resource:
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- !Sub arn:${AWS::Partition}:dsql:${AWS::Region}:${AWS::AccountId}:cluster/${DSQL}
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Environment: # Function environment variables
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Variables:
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REGION: !Sub ${AWS::Region}
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cluster_endpoint: !Sub ${DSQL}.dsql.${AWS::Region}.on.aws
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Events:
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Users:
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Type: Api # More info about API Event Source: https://github.com/awslabs/serverless-application-model/blob/master/versions/2016-10-31.md#api
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Properties:
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Path: /users
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Method: get
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DSQL:
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Type: AWS::DSQL::Cluster
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Properties:
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DeletionProtectionEnabled: false
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Tags:
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- Key: project
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Value: "apigw-lambda-dsql"
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Outputs:
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# ServerlessRestApi is an implicit API created out of Events key under Serverless::Function
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# Find out more about other implicit resources you can reference within SAM
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# https://github.com/awslabs/serverless-application-model/blob/master/docs/internals/generated_resources.rst#api
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UsersApi:
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Description: API Gateway endpoint URL for Prod stage for Users function
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Value: !Sub "https://${ServerlessRestApi}.execute-api.${AWS::Region}.amazonaws.com/Prod/users/"

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