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I want to register custom domains through lambdas, when I run runTask() method on fargate. How can I do it?

Assuming that you are trying to associate a domain name with a specific task that you're running in AWS Fargate and that you are running this task using a Lambda function, you can make use of the AWS SDK for JavaScript (v3 as of this writing) in your Lambda function to perform these actions programmatically.

Here are the broad steps that you need to follow:

1. Run your task - When you call the runTask() method to run your Fargate task, make sure you are using a task definition and service which are associated with the AWS Cloud Map service. You will then get the private IP address for the task from the response of the runTask() method.

2. Register your instance in AWS Cloud Map - After your task is up and running, you can register your instance in AWS Cloud Map using the private IP address from the runTask() response.

3. Create a Route53 DNS Record - After the instance has been registered, you can use this information to create a Route53 DNS record that points to your service instance.

Here's an example of how you might achieve these steps using AWS SDK for JavaScript:

const AWS = require('@aws-sdk/client-ecs');
const Route53 = require('@aws-sdk/client-route-53');
const CloudMap = require('@aws-sdk/client-servicediscovery');

const ecs = new AWS.ECSClient();
const route53 = new Route53.Route53Client();
const cloudMap = new CloudMap.ServiceDiscoveryClient();

async function registerCustomDomain() {
  const runTaskCommand = new AWS.RunTaskCommand({
    /* your task definition and service params */
  });

  const taskResponse = await ecs.send(runTaskCommand);
  const privateIpAddress = taskResponse.tasks[0].attachments[0].details.find(detail => detail.name === 'networkInterfaceId').value;

  const registerInstanceCommand = new CloudMap.RegisterInstanceCommand({
    /* your AWS Cloud Map params, and privateIpAddress for instance attributes */
  });

  await cloudMap.send(registerInstanceCommand);

  const changeResourceRecordSetsCommand = new Route53.ChangeResourceRecordSetsCommand({
    /* your Route53 params, using privateIpAddress for resource record */
  });

  await route53.send(changeResourceRecordSetsCommand);
}

registerCustomDomain().catch(console.error);

Please note that you will need to replace the placeholders with your actual values. Also, error handling and proper exception management should be considered when you implement this in a production environment. Please note that the exact details might vary depending on your use-case and infrastructure setup.

Also, remember that with this setup, you're directly exposing your tasks which can lead to some potential security risks and also does not provide the elasticity that a load balancer would provide. If your tasks need to scale, this method could quickly become unmanageable.

See this: https://docs.aws.amazon.com/cdk/v2/guide/testing.html

Accept an optional parameter to select the kind of image to instantiate. Default value alpine.

Investigate what are the best practices on CDK based projects. The stack is being built currently on a single stack call. Maybe I should split that?

I need to add an endpoint that requests the creation of a new container, using the default image on ECR. This new container needs to be available to access from outside, so I think that some rules need to be applied on networking.

Currently, when I call the API to create a new container, the Fargate task takes almost 40 seconds to be up and running. An alternative would be having a couple of instances running (autoscaling) on a service.

From ChatGPT:

Fargate tasks take almost 30 seconds to be up and running. How can I make it faster? Show me the example with cdk

To improve the startup time of your Fargate tasks, you can use the AWS Cloud Development Kit (CDK) to configure your Fargate service with a warm start strategy. This strategy keeps a certain number of tasks running at all times to reduce the delay when new tasks are needed.

Here's an example using the CDK in TypeScript:

1. Install the required dependencies:

npm install aws-cdk-lib @aws-cdk/aws-ecs @aws-cdk/aws-ec2

2. Create a new CDK stack (e.g., FargateStack.ts):

import * as cdk from 'aws-cdk-lib';
import * as ecs from 'aws-cdk-lib/aws-ecs';
import * as ec2 from 'aws-cdk-lib/aws-ec2';

export class FargateStack extends cdk.Stack {
  constructor(scope: cdk.Construct, id: string, props?: cdk.StackProps) {
    super(scope, id, props);

    const vpc = new ec2.Vpc(this, 'MyVpc'); // Replace with your VPC configuration

    // Create a Fargate service
    const fargateService = new ecs.FargateService(this, 'MyFargateService', {
      cluster: new ecs.Cluster(this, 'MyCluster', { vpc }),
      taskDefinition: new ecs.TaskDefinition(this, 'MyTaskDefinition', {
        memoryMiB: '512', // Adjust as per your requirements
        cpu: '256', // Adjust as per your requirements
      }),
      desiredCount: 1, // Number of warm tasks to keep running
    });

    // Enable service autoscaling to handle increased load
    const scaling = fargateService.autoScaleTaskCount({ maxCapacity: 10 }); // Adjust max capacity as needed

    scaling.scaleOnCpuUtilization('CpuScaling', {
      targetUtilizationPercent: 50, // Adjust the target utilization as per your needs
    });
  }
}

Make sure to replace the VPC configuration with your own.

3. In your project's main file (e.g., index.ts), create an instance of cdk.App and add the stack:

import * as cdk from 'aws-cdk-lib';
import { FargateStack } from './FargateStack';

const app = new cdk.App();
new FargateStack(app, 'MyFargateStack');
app.synth();

4. Build and deploy the CDK stack:

cdk synth
cdk deploy

By configuring the desiredCount parameter in the FargateService constructor, you can control the number of warm tasks that should be running at all times. Adjusting the memory and CPU resources in the TaskDefinition is also important to ensure optimal performance.

Additionally, the autoscaling configuration in the example enables the service to handle increased load by scaling the number of tasks based on CPU utilization. You can customize the scaling policies according to your application's requirements.

Remember to replace the placeholders and adjust the configurations to match your specific use case.

Ideally automated tests would be awesome. But also consider this: https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-sam-cli-using-debugging.html

I don't want for the container to live forever, because, money :/

2 hours seems to be fair.