Terraform Fundamentals: App Runner

App Runner with Terraform: A Production-Focused Guide

The relentless pressure to ship features faster often leads to technical debt in infrastructure. Teams frequently compromise on operational excellence for speed, resulting in manually managed container deployments, inconsistent scaling, and a lack of observability. This is especially true for smaller services or internal tools where a full Kubernetes cluster feels like overkill. AWS App Runner addresses this gap, providing a managed container runtime that simplifies deployment and scaling. This post details how to leverage App Runner effectively within a Terraform-centric infrastructure as code (IaC) workflow, focusing on production considerations for engineers and SREs. It assumes familiarity with Terraform and cloud concepts. App Runner fits neatly into platform engineering stacks as a self-service component, allowing developers to deploy containerized applications without needing deep infrastructure expertise.

What is "App Runner" in Terraform context?

App Runner is a fully managed service that makes it easy to quickly deploy containerized web applications and APIs at scale. Within Terraform, it’s accessed via the AWS provider and the aws_apprunner_service resource. The Terraform provider translates your declarative configuration into the necessary API calls to provision and manage the App Runner service.

The resource is relatively straightforward, but understanding its lifecycle is crucial. App Runner handles the underlying infrastructure (compute, networking, load balancing) so Terraform primarily manages the configuration of the service. Updates to the service configuration trigger rolling deployments. Terraform’s depends_on attribute is vital when integrating with other resources like IAM roles or source code repositories.

Currently, there isn’t a widely adopted, comprehensive Terraform module for App Runner. While individual resources can be modularized, a full-fledged module offering advanced features like complex networking or custom health checks is often built in-house. The official Terraform registry offers basic examples, but these are rarely sufficient for production use cases. https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/apprunner_service

Use Cases and When to Use

App Runner excels in specific scenarios:

1. Microservices Backends: Deploying stateless microservices that don’t require the complexity of Kubernetes. This is ideal for teams adopting a microservices architecture but lacking the operational overhead capacity for Kubernetes.
2. Internal Tools & Dashboards: Rapidly deploying internal applications, dashboards, or admin panels. The simplicity of App Runner significantly reduces the time to market for these tools.
3. API Gateways: Hosting lightweight API gateways or proxies. App Runner’s built-in auto-scaling and load balancing are well-suited for handling API traffic.
4. Event-Driven Applications: Deploying applications triggered by events from services like SQS or EventBridge. App Runner can scale to handle varying event loads.
5. Proof-of-Concept Deployments: Quickly validating new application ideas or architectures without the overhead of setting up a full infrastructure stack.

Key Terraform Resources

Here are eight essential Terraform resources for working with App Runner:

1. aws_apprunner_service: The core resource for defining the App Runner service.

resource "aws_apprunner_service" "example" {
  name        = "my-app-runner-service"
  runtime     = "PYTHON_3_9"
  source_configuration {
    image_repository {
      image_identifier = "public.ecr.aws/lambda/python:3.9"
      image_type       = "ECR_PUBLIC"
    }
  }
  health_check {
    path = "/health"
  }
}

1. aws_iam_role: Defines the IAM role assumed by the App Runner service.

resource "aws_iam_role" "app_runner_role" {
  name               = "app-runner-role"
  assume_role_policy = jsonencode({
    Version = "2012-10-17",
    Statement = [
      {
        Action = "sts:AssumeRole",
        Principal = {
          Service = "apprunner.amazonaws.com"
        }
      }
    ]
  })
}

1. aws_iam_policy: Grants permissions to the App Runner role.

resource "aws_iam_policy" "app_runner_policy" {
  name        = "app-runner-policy"
  description = "Policy for App Runner service"
  policy      = jsonencode({
    Version = "2012-10-17",
    Statement = [
      {
        Action = [
          "logs:CreateLogGroup",
          "logs:CreateLogStream",
          "logs:PutLogEvents"
        ],
        Resource = "arn:aws:logs:*:*:*"
      }
    ]
  })
}

1. aws_iam_role_policy_attachment: Attaches the policy to the role.

resource "aws_iam_role_policy_attachment" "app_runner_attachment" {
  role       = aws_iam_role.app_runner_role.name
  policy_arn = aws_iam_policy.app_runner_policy.arn
}

1. aws_apprunner_vpc_connector: Connects the App Runner service to a VPC.

resource "aws_apprunner_vpc_connector" "example" {
  subnet_ids         = ["subnet-xxxxxxxxxxxxxxxxx", "subnet-yyyyyyyyyyyyyyyyy"]
  security_group_ids = ["sg-zzzzzzzzzzzzzzzzz"]
  name               = "my-vpc-connector"
}

1. aws_apprunner_vpc_access: Associates the VPC connector with the App Runner service.

resource "aws_apprunner_vpc_access" "example" {
  service_arn      = aws_apprunner_service.example.arn
  vpc_connector_arn = aws_apprunner_vpc_connector.example.arn
}

1. data.aws_region: Dynamically retrieves the current AWS region.

data "aws_region" "current" {}

1. data.aws_caller_identity: Retrieves information about the current AWS account.

data "aws_caller_identity" "current" {}

Common Patterns & Modules

• Remote Backend: Always use a remote backend (S3, Terraform Cloud) for state management, especially in team environments.
• Dynamic Blocks: Utilize dynamic blocks within aws_apprunner_service for configuring multiple health check paths or environment variables.
• for_each: Deploy multiple instances of the same service with different configurations using for_each.
• Layered Architecture: Structure your Terraform code into layers: modules for reusable components (like IAM roles), and root modules for specific environments.
• Environment-Based Configuration: Use Terraform workspaces or separate directories to manage different environments (dev, staging, production).

While a comprehensive public module is lacking, consider building your own. Focus on abstracting the common configuration elements and exposing input variables for customization.

Hands-On Tutorial

This example deploys a simple "Hello, World!" Python application using App Runner.

Provider Setup:

terraform {
  required_providers {
    aws = {
      source  = "hashicorp/aws"
      version = "~> 5.0"
    }
  }
}

provider "aws" {
  region = "us-east-1" # Replace with your desired region

}

Resource Configuration:

resource "aws_iam_role" "app_runner_role" {
  name               = "app-runner-role"
  assume_role_policy = jsonencode({
    Version = "2012-10-17",
    Statement = [
      {
        Action = "sts:AssumeRole",
        Principal = {
          Service = "apprunner.amazonaws.com"
        }
      }
    ]
  })
}

resource "aws_iam_policy" "app_runner_policy" {
  name        = "app-runner-policy"
  description = "Policy for App Runner service"
  policy      = jsonencode({
    Version = "2012-10-17",
    Statement = [
      {
        Action = [
          "logs:CreateLogGroup",
          "logs:CreateLogStream",
          "logs:PutLogEvents"
        ],
        Resource = "arn:aws:logs:*:*:*"
      }
    ]
  })
}

resource "aws_iam_role_policy_attachment" "app_runner_attachment" {
  role       = aws_iam_role.app_runner_role.name
  policy_arn = aws_iam_policy.app_runner_policy.arn
}

resource "aws_apprunner_service" "example" {
  name        = "hello-world-app"
  runtime     = "PYTHON_3_9"
  source_configuration {
    image_repository {
      image_identifier = "public.ecr.aws/lambda/python:3.9"
      image_type       = "ECR_PUBLIC"
    }
    auto_deployments_enabled = false
  }
  health_check {
    path = "/health"
  }
  role_arn = aws_iam_role.app_runner_role.arn
}

Apply & Destroy:

terraform init
terraform plan
terraform apply
terraform destroy

The terraform plan output will show the resources that will be created. terraform apply will provision the App Runner service. terraform destroy will remove all created resources.

Enterprise Considerations

Large organizations should leverage Terraform Cloud/Enterprise for state locking, remote operations, and collaboration. Sentinel or Open Policy Agent (OPA) can enforce policy-as-code, ensuring compliance with security and governance standards. IAM design should follow the principle of least privilege, granting App Runner only the necessary permissions. Cost monitoring is crucial, as App Runner charges for compute and network usage. Multi-region deployments require careful consideration of data replication and latency.

Security and Compliance

Enforce least privilege using granular IAM policies. Utilize aws_iam_policy to restrict access to only the required AWS services. Implement RBAC within your Terraform Cloud/Enterprise organization. Use Sentinel policies to validate resource configurations against security best practices. Enable drift detection to identify unauthorized changes. Tag all resources for cost allocation and auditing.

Integration with Other Services

Here's how App Runner integrates with other services:

1. API Gateway: App Runner can be fronted by API Gateway for advanced routing and authentication.
2. Load Balancer: App Runner provides a built-in load balancer, but you can integrate with Application Load Balancers for more control.
3. CloudWatch: App Runner automatically sends logs to CloudWatch for monitoring and troubleshooting.
4. Secrets Manager: Store sensitive information (API keys, database passwords) in Secrets Manager and access them from your App Runner application.
5. SQS: Trigger App Runner services based on messages in SQS queues.

graph LR
    A[API Gateway] --> B(App Runner);
    C[CloudWatch] <-- B;
    D[Secrets Manager] --> B;
    E[SQS] --> B;
    F[Application Load Balancer] --> B;

Module Design Best Practices

• Abstraction: Encapsulate the App Runner service configuration within a module.
• Input Variables: Define clear and concise input variables for customization (e.g., image_uri, memory_size, cpu).
• Output Variables: Expose relevant output variables (e.g., service_url, service_arn).
• Locals: Use locals to simplify complex expressions and improve readability.
• Backends: Always use a remote backend for state management.
• Documentation: Provide comprehensive documentation for the module, including examples and usage instructions.

CI/CD Automation

Here's a GitHub Actions snippet for deploying App Runner:

name: Deploy App Runner

on:
  push:
    branches:
      - main

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: hashicorp/terraform-github-actions/init@v3
      - uses: hashicorp/terraform-github-actions/fmt@v3
      - uses: hashicorp/terraform-github-actions/validate@v3
      - uses: hashicorp/terraform-github-actions/plan@v3
      - uses: hashicorp/terraform-github-actions/apply@v3
        with:
          args: -auto-approve

Pitfalls & Troubleshooting

1. IAM Permissions: Insufficient IAM permissions are a common issue. Double-check the role and policy attachments.
2. Image Pull Errors: Ensure the container image is publicly accessible or that App Runner has access to the ECR repository.
3. Health Check Failures: Verify that the health check path is correct and that the application responds with a 200 OK status.
4. VPC Configuration: Incorrect VPC configuration can prevent App Runner from accessing resources within the VPC.
5. Deployment Rollbacks: App Runner automatically rolls back deployments if health checks fail. Monitor the deployment logs for errors.
6. Resource Limits: App Runner has resource limits (memory, CPU). Ensure your application fits within these limits.

Pros and Cons

Pros:

• Simplicity: Easy to deploy and manage containerized applications.
• Scalability: Automatic scaling based on traffic.
• Managed Service: No infrastructure management overhead.
• Cost-Effective: Pay-as-you-go pricing.

Cons:

• Limited Customization: Less control over the underlying infrastructure compared to Kubernetes.
• Vendor Lock-in: Tightly coupled to AWS.
• Debugging Challenges: Limited debugging capabilities compared to self-managed environments.
• Cold Starts: Potential for cold starts with infrequent traffic.

Conclusion

App Runner provides a compelling solution for deploying containerized applications without the operational complexity of Kubernetes. When integrated with Terraform, it enables infrastructure as code, automation, and consistent deployments. Engineers should evaluate App Runner for microservices, internal tools, and API gateways where simplicity and scalability are paramount. Start with a proof-of-concept, explore existing modules, and establish a CI/CD pipeline to unlock the full potential of this service.