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‎apigw-lambda-dsql/README.md‎

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# AWS Service 1 to AWS Service 2
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This pattern << explain usage >>
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Learn more about this pattern at Serverless Land Patterns: << Add the live URL here >>
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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 _patterns-model
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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 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
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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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Explain how the service interaction works.
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## Testing
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Provide steps to trigger the integration and show what should be observed if successful.
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## Cleanup
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1. Delete the stack
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```bash
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aws cloudformation delete-stack --stack-name STACK_NAME
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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 2024 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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SPDX-License-Identifier: MIT-0

‎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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