Deploy Containers to AWS ECS with Fargate

AWS Fargate removes the need to manage EC2 instances by providing serverless compute for containers. Combined with ECS, Application Load Balancer, and Terraform, you get a production-ready container platform without operational overhead.

When to Use This

• You want to run containers without managing servers. See Docker Basics for container fundamentals.
• You need auto-scaling based on CPU or request count. See Load Testing for performance baselines.
• You want infrastructure as code for reproducible environments. See Terraform AWS VPC for network infrastructure as code.

Prerequisites

• AWS CLI configured with appropriate IAM permissions
• Docker installed locally
• Terraform 1.5+

Solution: Terraform + ECS Fargate

1. Terraform Infrastructure

# main.tf
terraform {
  required_providers {
    aws = { source = "hashicorp/aws", version = "~> 5.0" }
  }
}

provider "aws" { region = "us-east-1" }

# VPC and networking
module "vpc" {
  source  = "terraform-aws-modules/vpc/aws"
  version = "~> 5.0"

  name            = "ecs-vpc"
  cidr            = "10.0.0.0/16"
  azs             = ["us-east-1a", "us-east-1b"]
  private_subnets = ["10.0.1.0/24", "10.0.2.0/24"]
  public_subnets  = ["10.0.101.0/24", "10.0.102.0/24"]
}

# ECS Cluster
resource "aws_ecs_cluster" "app" {
  name = "production-cluster"
}

resource "aws_ecs_cluster_capacity_providers" "app" {
  cluster_name = aws_ecs_cluster.app.name
  capacity_providers = ["FARGATE"]

  default_capacity_provider_strategy {
    base              = 1
    weight            = 100
    capacity_provider = "FARGATE"
  }
}

# Task Definition
resource "aws_ecs_task_definition" "app" {
  family                   = "web-app"
  requires_compatibilities = ["FARGATE"]
  network_mode             = "awsvpc"
  cpu                      = "256"
  memory                   = "512"
  execution_role_arn       = aws_iam_role.ecs_exec.arn

  container_definitions = jsonencode([{
    name  = "web"
    image = "nginx:alpine"
    portMappings = [{ containerPort = 80, protocol = "tcp" }]
    logConfiguration = {
      logDriver = "awslogs"
      options = {
        awslogs-group         = aws_cloudwatch_log_group.app.name
        awslogs-region        = "us-east-1"
        awslogs-stream-prefix = "ecs"
      }
    }
  }])
}

# Service
resource "aws_ecs_service" "app" {
  name            = "web-service"
  cluster         = aws_ecs_cluster.app.id
  task_definition = aws_ecs_task_definition.app.arn
  desired_count   = 2
  launch_type     = "FARGATE"

  network_configuration {
    subnets          = module.vpc.private_subnets
    security_groups  = [aws_security_group.ecs.id]
    assign_public_ip = false
  }

  load_balancer {
    target_group_arn = aws_lb_target_group.app.arn
    container_name   = "web"
    container_port   = 80
  }
}

# Application Load Balancer
resource "aws_lb" "app" {
  name               = "app-alb"
  internal           = false
  load_balancer_type = "application"
  subnets            = module.vpc.public_subnets
  security_groups    = [aws_security_group.alb.id]
}

resource "aws_lb_target_group" "app" {
  name     = "app-tg"
  port     = 80
  protocol = "HTTP"
  vpc_id   = module.vpc.vpc_id
  target_type = "ip"
}

resource "aws_lb_listener" "app" {
  load_balancer_arn = aws_lb.app.arn
  port              = "80"
  protocol          = "HTTP"
  default_action {
    type             = "forward"
    target_group_arn = aws_lb_target_group.app.arn
  }
}

2. Deploy

terraform init
terraform apply

3. CI/CD with GitHub Actions

# .github/workflows/deploy.yml
name: Deploy to ECS

on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Configure AWS credentials
        uses: aws-actions/configure-aws-credentials@v4
        with:
          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
          aws-region: us-east-1

      - name: Login to ECR
        uses: aws-actions/amazon-ecr-login@v2

      - name: Build and push image
        run: |
          docker build -t myapp:${{ github.sha }} .
          docker tag myapp:${{ github.sha }} $ECR_REGISTRY/myapp:${{ github.sha }}
          docker push $ECR_REGISTRY/myapp:${{ github.sha }}

      - name: Update ECS service
        run: |
          aws ecs update-service \
            --cluster production-cluster \
            --service web-service \
            --force-new-deployment

How It Works

1. Fargate provisions compute on demand without managing EC2 instances
2. ALB distributes traffic across tasks and handles health checks
3. Task Definition defines the container image, resources, and networking
4. Service maintains the desired count and integrates with ALB target groups

Production Considerations

• Use Application Auto Scaling to scale tasks based on CPU or request count
• Store secrets in AWS Secrets Manager and reference them in task definitions
• Enable CloudWatch Container Insights for metrics and logging
• Use Blue/Green deployments with CodeDeploy for zero-downtime updates

Performance Tips

1. Right-size tasks based on CloudWatch metrics. Check CPU and memory utilization over 7 days and adjust:

aws cloudwatch get-metric-statistics \
  --namespace AWS/ECS \
  --metric-name CPUUtilization \
  --dimensions Name=ClusterName,Value=production-cluster \
  --start-time $(date -u -v-7d +%Y-%m-%dT%H:%M:%S) \
  --end-time $(date -u +%Y-%m-%dT%H:%M:%S) \
  --period 3600 \
  --statistics Average

2. Use ALB connection draining. Allow in-flight requests to complete:

resource "aws_lb" "app" {
  name               = "app-alb"
  internal           = false
  load_balancer_type = "application"
  subnets            = module.vpc.public_subnets
  security_groups    = [aws_security_group.alb.id]

  connection_draining        = true
  connection_draining_timeout = 30
}

3. Minimize cold starts with warm pools. Keep minimum capacity above zero:

resource "aws_appautoscaling_target" "ecs" {
  min_capacity = 2  # Keep at least 2 tasks warm
  max_capacity = 10
}