Accelerate your Terraform development with Amazon Q Developer

This post demonstrates how Amazon Q Developer, a generative AI-powered assistant for software development, helps create Terraform templates. Terraform is an infrastructure as code (IaC) tool that provisions and manages infrastructure on AWS safely and predictably. When used in an integrated development environment (IDE), Amazon Q Developer assists with software development, including code generation, explanation, and improvements. This blog highlights the 5 most used cases to show how Amazon Q Developer can generate Terraform code snippets:

1. Secure Networking Deployment: Generate Terraform code snippet to create an Amazon Virtual Private Cloud (VPC) with subnets, route tables, and security groups.
2. Multi-Account CI/CD Pipelines with AWS CodePipeline: Generate Terraform code snippet for setting up continuous integration and continuous delivery (CI/CD) pipelines using AWS CodePipeline across multiple AWS accounts.
3. Event-Driven Architecture: Generate Terraform code snippet to set up an event-driven architecture using Amazon EventBridge, AWS Lambda, Amazon API Gateway, and other serverless services.
4. Container Orchestration (Amazon ECS Fargate): Generate Terraform code snippet for deploying and managing container orchestration using Amazon Elastic Container Service (ECS) with Fargate.
5. Machine Learning Workflows: Generate Terraform module for deploying machine learning workflows using AWS SageMaker.

Terraform Project Structure

First, you want to understand the best practices and requirements for setting up a multi-environment Terraform Infrastructure as Code (IaC) project. Following industry practices from the start is crucial to manage your multi-environment infrastructure and this is where Amazon Q will recommend a standard folder and file structure for organising Terraform templates and managing infrastructure deployments.

Amazon Q Developer recommended organising Terraform templates in separate folders for each environment, with sub-folders to group resources into modules. It provided best practices like version control, remote backend, and tagging for reusability and scalability. We can ask Amazon Q Developer to generate a sample example for better understanding.

You can see that the recommended folder structure includes a root project folder and sub-folders for environments and modules. The sub-folders manage multiple environments (like development, testing, production), reusable modules (like Virtual Private Cloud (VPC), Elastic Compute Cloud (EC2)) for various components and it demonstrates references to manage Terraform templates for individual environments and components. Let’s explore the top 5 most common use cases one by one.

1. Secure Networking Deployment

Once we setup Terraform project structure, we will ask Amazon Q Developer to give recommendations for networking services and requirements. Amazon Q Developer suggests to use Amazon Virtual Private Cloud (VPC), Subnets, Internet Gateways, Network Access Control Lists (ACLs) and Security Groups.

Now, we can ask Amazon Q Developer to generate a Terraform template for building components like Virtual Private Cloud (VPC) and subnets. The Terraform code generated by Amazon Q Developer for a VPC, private subnet, and public subnet. It also added tags to each resource, following best practices. To leverage the suggested code snippet in your template, open the vpc.tf file and either insert it at the cursor or copy it into the file. Additionally, Amazon Q Developer created an Internet Gateway, Route Tables, and associated them with the VPC.

2. Multi-Account CI/CD Pipelines with AWS CodePipeline

Let’s discuss the second use case: a CI/CD (Continuous Integration/Continuous Deployment) pipeline using AWS CodePipeline to deploy Terraform code across multiple AWS accounts. Assume this is your first time working on this use case. Use Amazon Q Developer to understand the pipeline’s design stages and requirements for creating your pipeline across AWS accounts. I asked Amazon Q Developer about the pipeline stages and requirements to deploy across AWS accounts.

Amazon Q Developer provided all the main stages for the CI/CD (Continuous Integration/Continuous Deployment) pipeline:

1. Source stage pulls the Terraform code from the source code repository.
2. Plan stage includes linting, validation, or running terraform plan to view the Terraform plan before deploying the code.
3. Build stage performs additional testing for the infrastructure to ensure all components will be created successfully.
4. Deploy stage runs terraform apply.

Amazon Q Developer also provided all the requirements needed before creating the CI/CD pipeline. These requirements include terraform is installed in the pipeline environment, IAM roles that will be assumed by the pipeline stages to deploy the terraform code across different accounts, an Amazon S3 bucket to store the state of the terraform code, source code repository to store our terraform files, use parameters to store sensitive data like AWS accounts IDs and AWS CodePipeline to create our CI/CD pipeline.

You will use Amazon Q Developer now to generate the terraform code for the CI/CD pipeline based on stages design proposed by the services in the previous question.

We would like to highlight three things here:

1. Amazon Q Developer suggested all the stages for our Continuous Integration/Continuous Deployment (CI/CD) design:

• Source Stage: this stage pulls the code from the source code repository.
• Build Stage: this stage will validates the infrastructure code, run a Terraform plan stage, and can automate any review.
• Deploy Stage: this stage deploys the terraform code to the target AWS account.

2. Amazon Q Developer generated the build stage before the plan and this is expected in any CI/CD pipeline. First, we start with building the artifacts, running any tests or validation steps. If this stage is passed successfully, it will then proceed to terraform plan.
3. Amazon’s Q Developer suggests deploying to separate stages for each target account, aligning with the best practice of using different AWS accounts for development, testing, and production environments. This reduces the blast radius and allows configuring a separate stage for each environment/account.

3. Event-Driven Architecture

For the next use case, we will start by asking Amazon Q Developer to explain Event-driven architecture. As shown below, Amazon Q Developer highlights the important aspects that we need to consider when designing event-driven architecture like:

• An event that represents an actual change in the application state like S3 object upload.
• The event source that produces the actual event like Amazon S3.
• An event route that routes the event between source and target.
• An event target that consumes the event like AWS Lambda function.

Assume you want to build a sample Event-driven architecture using Amazon SNS as a simple pub/sub. In order to build such architecture, we will need:

• An Amazon SNS topic to publish events to an Amazon SQS queue subscriber.
• An Amazon SQS queue which subscribe to SNS topic.

Asking Amazon Q Developer to create a terraform code for the above use case, it generated a code that can get us started in seconds. The code includes:

• Create an SNS Topic and SQS queue
• Subscribe SQS queue to SNS topic
• AWS IAM policy to allow SNS to write to SQS

4. Container Orchestration (Amazon ECS Fargate)

Now to our next use case, we want to build an Amazon ECS cluster with Fargate. Before we start, we will ask Amazon Q Developer about Amazon ECS and the difference between using Amazon EC2 or Fargate option.

Amazon Q Developer not only provides details about Amazon ECS capabilities and well-defined description about the difference between Amazon EC2 and Fargate as compute options, and also a guide when to use what to help in decision making process.

Now, we will ask Amazon Q Developer to suggest a terraform code to create an Amazon ECS cluster with Fargate as the compute option.

Amazon Q Developer suggested the key resources in the Terraform code for the ECS cluster, the resources are:

• An Amazon ECS Cluster.
• A sample task definition for an Nginx container with all the required configuration for the container to run.
• ECS service with Fargate as launch type for the container to run.

5. Secure Machine Learning Workflows

Now let us explore final use case on setting up Machine Learning workflow. Assuming I am new to ML on AWS, I will first try to understand the best practices and requirements for ML workflows. We can ask Amazon Q Developer to get the recommendations for the resources, security policies etc.

Amazon Q Developer provided recommendations to provision SageMaker resources in private VPC, implement authentication and authorization using AWS IAM, encrypt data at rest and in transit using AWS KMS and setup secure CI/CD. Once I get the recommendations, I can identify the resources which I need to build MLOps workflow.

Amazon Q Developer suggested resources such as a SageMaker Studio instance, model registry, endpoints, version control, CI/CD Pipeline, CloudWatch etc. for ML model building, training and deployment using SageMaker. Now I can start building Terraform templates to deploy these resources. To get the code recommendations, I can ask Amazon Q Developer to give a Terraform snippet for all these resources.

Conclusion

In this blog post, we showed you how to use Amazon Q Developer, a generative AI–powered assistant from AWS, in your IDE to quickly improve your development experience when using an infrastructure-as-code tool like Terraform to create your AWS infrastructure. However, we would like to highlight some important points:

• Code Validation and Testing: Always thoroughly validate and test the Terraform code generated by Amazon Q Developer in a safe environment before deploying it to production. Automated code generation tools can sometimes produce code that may need adjustments based on your specific requirements and configurations.
• Security Considerations: Ensure that all security practices are followed, such as least privilege principles for IAM roles, proper encryption methods for sensitive data, and continuous monitoring for security compliance.
• Limitations of Amazon Q Developer: While Amazon Q Developer provides valuable assistance, it may not cover all edge cases or specific customizations needed for your infrastructure. Always review the generated code and modify it as necessary to fit your unique use cases.
• Updates and Maintenance: Infrastructure and services on AWS are continuously evolving. Make sure to keep your Terraform code and the use of Amazon Q Developer updated with the latest best practices and AWS service changes.
• Real-Time Assistance: Use Amazon Q Developer in your IDE to enhance generated code as it provides real-time recommendations based on your existing code and comments. The suggestions are based on your existing code and comments, which are in this case generated by Amazon Q Developer.

To get started with Amazon Q Developer for debugging today navigate to Amazon Q Developer in IDE and simply start asking questions about debugging. Additionally, explore Amazon Q Developer workshop for additional hands-on use cases.

For any inquiries or assistance with Amazon Q Developer, please reach out to your AWS account team.

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