This is the AWS CDK v2 Developer Guide. The older CDK v1 entered maintenance on June 1, 2022 and ended support on June 1, 2023.
# Example: Create an AWS Fargate service using the AWS CDK
In this example, we show you how to create an AWS Fargate service running on an Amazon Elastic Container Service (Amazon ECS) cluster that’s fronted by an internet-facing Application Load Balancer from an image on Amazon ECR.
Amazon ECS is a highly scalable, fast, container management service that makes it easy to run, stop, and manage Docker containers on a cluster. You can host your cluster on serverless infrastructure that’s managed by Amazon ECS by launching your services or tasks using the Fargate launch type. For more control, you can host your tasks on a cluster of Amazon Elastic Compute Cloud (Amazon EC2) instances that you manage by using the Amazon EC2 launch type.
In this example, we launch some services using the Fargate launch type. If you’ve used the AWS Management Console to create a Fargate service, you know that there are many steps to follow to accomplish that task. AWS has several tutorials and documentation topics that walk you through creating a Fargate service, including:
+ [How to Deploy Docker Containers - AWS](https://aws.amazon.com/getting-started/tutorials/deploy-docker-containers)
+ [Setting Up with Amazon ECS](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/get-set-up-for-amazon-ecs.html)
+ [Getting Started with Amazon ECS Using Fargate](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/ECS_GetStarted.html)
This example creates a similar Fargate service using the AWS CDK.
The Amazon ECS construct used in this example helps you use AWS services by providing the following benefits:
+ Automatically configures a load balancer.
+ Automatically opens a security group for load balancers. This enables load balancers to communicate with instances without having to explicitly create a security group.
+ Automatically orders dependency between the service and the load balancer attaching to a target group, where the AWS CDK enforces the correct order of creating the listener before an instance is created.
+ Automatically configures user data on automatically scaling groups. This creates the correct configuration to associate a cluster to AMIs.
+ Validates parameter combinations early. This exposes AWS CloudFormation issues earlier, thus saving deployment time. For example, depending on the task, it’s easy to improperly configure the memory settings. Previously, we would not encounter an error until we deployed our app. But now the AWS CDK can detect a misconfiguration and emit an error when we synthesize our app.
+ Automatically adds permissions for Amazon Elastic Container Registry (Amazon ECR) if we use an image from Amazon ECR.
+ Automatically scales. The AWS CDK supplies a method so we can auto scale instances when we use an Amazon EC2 cluster. This happens automatically when we use an instance in a Fargate cluster.
In addition, the AWS CDK prevents an instance from being deleted when automatic scaling tries to stop an instance, but either a task is running or is scheduled on that instance.
Previously, we had to create a Lambda function to have this functionality.
+ Provides asset support, so that we can deploy a source from our machine to Amazon ECS in one step. Previously, to use an application source, we had to perform several manual steps, such as uploading to Amazon ECR and creating a Docker image.
**Important**
The `ApplicationLoadBalancedFargateService` constructs we’ll be using includes numerous AWS components, some of which have non-trivial costs if left provisioned in our AWS account, even if we don’t use them. Be sure to clean up (`cdk destroy`) if you follow along with this example.
## Create a CDK project
We start by creating a CDK project. This is a directory that stores our AWS CDK code, including our CDK app.
**Example**
```
mkdir MyEcsConstruct
cd MyEcsConstruct
cdk init --language typescript
```
```
mkdir MyEcsConstruct
cd MyEcsConstruct
cdk init --language javascript
```
```
mkdir MyEcsConstruct
cd MyEcsConstruct
cdk init --language python
source .venv/bin/activate # On Windows, run '.\venv\Scripts\activate' instead
pip install -r requirements.txt
```
```
mkdir MyEcsConstruct
cd MyEcsConstruct
cdk init --language java
```
We may now import the Maven project into our IDE.
```
mkdir MyEcsConstruct
cd MyEcsConstruct
cdk init --language csharp
```
We may now open `src/MyEcsConstruct.sln` in Visual Studio.
Next, we run the app and confirm that it creates an empty stack.
```
cdk synth
```
## Create a Fargate service
There are two different ways that we can run our container tasks with Amazon ECS:
+ Use the `Fargate` launch type, where Amazon ECS manages the physical machines that oour containers are running on for us.
+ Use the `EC2` launch type, where we do the managing, such as specifying automatic scaling.
For this example, we’ll create a Fargate service running on an Amazon ECS cluster, fronted by an internet-facing Application Load Balancer.
We add the following AWS Construct Library module imports to our *stack file*:
**Example**
File: `lib/my_ecs_construct-stack.ts`
```
import * as ec2 from "aws-cdk-lib/aws-ec2";
import * as ecs from "aws-cdk-lib/aws-ecs";
import * as ecs_patterns from "aws-cdk-lib/aws-ecs-patterns";
```
File: `lib/my_ecs_construct-stack.js`
```
const ec2 = require("aws-cdk-lib/aws-ec2");
const ecs = require("aws-cdk-lib/aws-ecs");
const ecs_patterns = require("aws-cdk-lib/aws-ecs-patterns");
```
File: `my_ecs_construct/my_ecs_construct_stack.py`
```
from aws_cdk import (aws_ec2 as ec2, aws_ecs as ecs,
aws_ecs_patterns as ecs_patterns)
```
File: `src/main/java/com/myorg/MyEcsConstructStack.java`
```
import software.amazon.awscdk.services.ec2.*;
import software.amazon.awscdk.services.ecs.*;
import software.amazon.awscdk.services.ecs.patterns.*;
```
File: `src/MyEcsConstruct/MyEcsConstructStack.cs`
```
using Amazon.CDK.AWS.EC2;
using Amazon.CDK.AWS.ECS;
using Amazon.CDK.AWS.ECS.Patterns;
```
Within our stack, we add the following code:
**Example**
```
const vpc = new ec2.Vpc(this, "MyVpc", {
maxAzs: 3 // Default is all AZs in region
});
const cluster = new ecs.Cluster(this, "MyCluster", {
vpc: vpc
});
// Create a load-balanced Fargate service and make it public
new ecs_patterns.ApplicationLoadBalancedFargateService(this, "MyFargateService", {
cluster: cluster, // Required
cpu: 512, // Default is 256
desiredCount: 6, // Default is 1
taskImageOptions: { image: ecs.ContainerImage.fromRegistry("amazon/amazon-ecs-sample") },
memoryLimitMiB: 2048, // Default is 512
publicLoadBalancer: true // Default is true
});
```
```
const vpc = new ec2.Vpc(this, "MyVpc", {
maxAzs: 3 // Default is all AZs in region
});
const cluster = new ecs.Cluster(this, "MyCluster", {
vpc: vpc
});
// Create a load-balanced Fargate service and make it public
new ecs_patterns.ApplicationLoadBalancedFargateService(this, "MyFargateService", {
cluster: cluster, // Required
cpu: 512, // Default is 256
desiredCount: 6, // Default is 1
taskImageOptions: { image: ecs.ContainerImage.fromRegistry("amazon/amazon-ecs-sample") },
memoryLimitMiB: 2048, // Default is 512
publicLoadBalancer: true // Default is true
});
```
```
vpc = ec2.Vpc(self, "MyVpc", max_azs=3) # default is all AZs in region
cluster = ecs.Cluster(self, "MyCluster", vpc=vpc)
ecs_patterns.ApplicationLoadBalancedFargateService(self, "MyFargateService",
cluster=cluster, # Required
cpu=512, # Default is 256
desired_count=6, # Default is 1
task_image_options=ecs_patterns.ApplicationLoadBalancedTaskImageOptions(
image=ecs.ContainerImage.from_registry("amazon/amazon-ecs-sample")),
memory_limit_mib=2048, # Default is 512
public_load_balancer=True) # Default is True
```
```
Vpc vpc = Vpc.Builder.create(this, "MyVpc")
.maxAzs(3) // Default is all AZs in region
.build();
Cluster cluster = Cluster.Builder.create(this, "MyCluster")
.vpc(vpc).build();
// Create a load-balanced Fargate service and make it public
ApplicationLoadBalancedFargateService.Builder.create(this, "MyFargateService")
.cluster(cluster) // Required
.cpu(512) // Default is 256
.desiredCount(6) // Default is 1
.taskImageOptions(
ApplicationLoadBalancedTaskImageOptions.builder()
.image(ContainerImage.fromRegistry("amazon/amazon-ecs-sample"))
.build())
.memoryLimitMiB(2048) // Default is 512
.publicLoadBalancer(true) // Default is true
.build();
```
```
var vpc = new Vpc(this, "MyVpc", new VpcProps
{
MaxAzs = 3 // Default is all AZs in region
});
var cluster = new Cluster(this, "MyCluster", new ClusterProps
{
Vpc = vpc
});
// Create a load-balanced Fargate service and make it public
new ApplicationLoadBalancedFargateService(this, "MyFargateService",
new ApplicationLoadBalancedFargateServiceProps
{
Cluster = cluster, // Required
DesiredCount = 6, // Default is 1
TaskImageOptions = new ApplicationLoadBalancedTaskImageOptions
{
Image = ContainerImage.FromRegistry("amazon/amazon-ecs-sample")
},
MemoryLimitMiB = 2048, // Default is 256
PublicLoadBalancer = true // Default is true
}
);
```
Next, we validate our code by running the following to synthesize our stack:
```
cdk synth
```
The stack is hundreds of lines, so we won’t show it here. The stack should contain one default instance, a private subnet and a public subnet for the three Availability Zones, and a security group.
To deploy the stack, we run the following:
```
cdk deploy
```
AWS CloudFormation displays information about the dozens of steps that it takes as it deploys our app.
Once deployment completes, we have successfully created a Fargate powered Amazon ECS service to run a Docker image.
## Clean up
As a general maintenance best practice, and to minimize unnecessary costs, we delete our stack when complete:
```
cdk destroy
```