Crystal Backend API

Deploy the Crystal backend service

Navigate to the crystal service repo and populate the git hash file which is required for our microservice.

cd ~/environment/ecsdemo-crystal
git rev-parse --short=7 HEAD > code_hash.txt

In the previous section, we deployed our application, test environment, frontend service, and the nodejs service.

Like we’ve done in previous sections, we will first need to create our crystal service in the ecsworkshop application.

The following command will open a prompt for us to add our service to the application.

copilot init

We will be prompted with a series of questions related to the application, environment, and the service we want to deploy. Answer the questions as follows:

  • Would you like to use one of your existing applications? “Y”
  • Which existing application do you want to add a new service to? Select “ecsworkshop”, hit enter
  • Which service type best represents your service’s architecture? Select “Backend Service”, hit enter
  • What do you want to name this Backend Service: ecsdemo-crystal
  • Dockerfile: ./Dockerfile

After you answer the questions, it will begin the process of creating some baseline resources for your service. This also includes the manifest file which defines the desired state of this service. For more information on the manifest file, see the copilot-cli documentation.

Next, you will be prompted to deploy a test environment. An environment encompasses all of the resources that are required to support running your containers in ECS. This includes the networking stack (VPC, Subnets, Security Groups, etc), the ECS Cluster, Load Balancers (if required), and more.

Type “y”, and hit enter. Given that a test environment already exists, copilot will continue on and build the docker image, push it to ECR, and deploy the backend service.

Below is an example of what the cli interaction will look like:

deployment

That’s it! When the deployment is complete, navigate back to the frontend URL and you should now see the backend Crystal service in the image.

Interacting with the application

Let’s check out the ecsworkshop application details.

copilot app show ecsworkshop

The result should look like this:

app_show

We can see that our recently deployed crystal service is shown as a Backend Service in the ecsworkshop application.

Interacting with the environment

Given that we deployed the test environment when creating our frontend service, let’s show the details of the test environment:

copilot env show -n test

env_show

We now can see our newly deployed service in the test environment!

Interacting with the crystal service

Let’s now check the status of the frontend service.

Run:

copilot svc status -n ecsdemo-crystal

svc_status

We can see that we have one active running task, along with some additional details.

Scale our task count

Let’s scale our task count up! To do this, we are going to update the manifest file that was created when we initialized our service earlier. Open the manifest file (./copilot/ecsdemo-crystal/manifest.yml), and replace the value of the count key from 1 to 3. This is declaring our state of the service to change from 1 task, to 3. Feel free to explore the manifest file to familiarize yourself.

# Number of tasks that should be running in your service.
count: 3

Once you are done and save the changes, run the following:

copilot svc deploy

Copilot does the following with this command:

  • Build your image locally
  • Push to your service’s ECR repository
  • Convert your manifest file to CloudFormation
  • Package any additional infrastructure into CloudFormation
  • Deploy your updated service and resources to CloudFormation

To confirm the deploy, let’s first check our service details via the copilot-cli:

copilot svc status -n ecsdemo-crystal

You should now see three tasks running!

Now go back to the load balancer url, and you should see the diagram alternate between the three frontend tasks.

Review the service logs

The services we deploy via copilot are automatically shipping logs to Cloudwatch logs by default. Rather than navigate and review logs via the console, we can use the copilot cli to see those logs locally. Let’s tail the logs for the crystal service.

copilot svc logs -a ecsworkshop -n ecsdemo-crystal --follow

Note that if you are in the same directory of the service you want to review logs for, simply type the below command. Of course, if yuo wanted to review logs for a service in a particular environment, you would pass the -e flag with the environment name.

copilot svc logs

Last thing to bring up is that you aren’t limited to live tailing logs, type copilot svc logs --help to see the different ways to review logs from the command line.

Next steps

We did it! We have successfully deployed a three tier, polyglot, microservice application to ECS!

Validate deployment configuration

cd ~/environment/ecsdemo-crystal/cdk

Confirm that the cdk can synthesize the assembly CloudFormation templates

cdk synth

Review what the cdk is proposing to build and/or change in the environment

cdk diff

Deploy the crystal backend service

cdk deploy --require-approval never

Code Review

As we mentioned in the platform build, we are defining our deployment configuration via code. Let’s look through the code to better understand how cdk is deploying.

Importing base configuration values from our base platform stack

Because we built the platform in its own stack, there are certain environmental values that we will need to reuse amongst all services being deployed. In this custom construct, we are importing the VPC, ECS Cluster, and Cloud Map namespace from the base platform stack. By wrapping these into a custom construct, we are isolating the platform imports from our service deployment logic.

class BasePlatform(core.Construct):

    def __init__(self, scope: core.Construct, id: str, **kwargs):
        super().__init__(scope, id, **kwargs)

        # The base platform stack is where the VPC was created, so all we need is the name to do a lookup and import it into this stack for use
        self.vpc = aws_ec2.Vpc.from_lookup(
            self, "ECSWorkshopVPC",
            vpc_name='ecsworkshop-base/BaseVPC'
        )

        # Importing the service discovery namespace from the base platform stack
        self.sd_namespace = aws_servicediscovery.PrivateDnsNamespace.from_private_dns_namespace_attributes(
            self, "SDNamespace",
            namespace_name=core.Fn.import_value('NSNAME'),
            namespace_arn=core.Fn.import_value('NSARN'),
            namespace_id=core.Fn.import_value('NSID')
        )

        # Importing the ECS cluster from the base platform stack
        self.ecs_cluster = aws_ecs.Cluster.from_cluster_attributes(
            self, "ECSCluster",
            cluster_name=core.Fn.import_value('ECSClusterName'),
            security_groups=[],
            vpc=self.vpc,
            default_cloud_map_namespace=self.sd_namespace
        )

        # Importing the security group that allows frontend to communicate with backend services
        self.services_sec_grp = aws_ec2.SecurityGroup.from_security_group_id(
            self, "ServicesSecGrp",
            security_group_id=core.Fn.import_value('ServicesSecGrp')
        )

Crystal backend service deployment code

For the backend service, we simply want to run a container from a docker image, but still need to figure out how to deploy it and get it behind a scheduler. To do this on our own, we would need to build a task definition, ECS service, and figure out how to get it behind CloudMap for service discovery. To build these components on our own would equate to hundreds of lines of CloudFormation, whereas with the higher level constructs that the cdk provides, we are able to build everything with 30 lines of code.

class CrystalService(core.Stack):

    def __init__(self, scope: core.Stack, id: str, **kwargs):
        super().__init__(scope, id, **kwargs)

        # Importing our shared values from the base stack construct
        self.base_platform = BasePlatform(self, self.stack_name)

        # The task definition is where we store details about the task that will be scheduled by the service
        self.fargate_task_def = aws_ecs.TaskDefinition(
            self, "TaskDef",
            compatibility=aws_ecs.Compatibility.EC2_AND_FARGATE,
            cpu='256',
            memory_mib='512',
        )

        # The container definition defines the container(s) to be run when the task is instantiated
        self.container = self.fargate_task_def.add_container(
            "CrystalServiceContainerDef",
            image=aws_ecs.ContainerImage.from_registry("brentley/ecsdemo-crystal"),
            memory_reservation_mib=512,
            logging=aws_ecs.LogDriver.aws_logs(
                stream_prefix='ecsworkshop-crystal'
            )
        )

        # Serve this container on port 3000
        self.container.add_port_mappings(
            aws_ecs.PortMapping(
                container_port=3000
            )
        )

        # Build the service definition to schedule the container in the shared cluster
        self.fargate_service = aws_ecs.FargateService(
            self, "CrystalFargateService",
            task_definition=self.fargate_task_def,
            cluster=self.base_platform.ecs_cluster,
            security_group=self.base_platform.services_sec_grp,
            desired_count=1,
            cloud_map_options=aws_ecs.CloudMapOptions(
                cloud_map_namespace=self.base_platform.sd_namespace,
                name='ecsdemo-crystal'
            )
        )

Review service logs

Review the service logs from the command line:

Expand here to see the solution

Review the service logs from the console:

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Scale the service

Manually scaling

Expand here to see the solution

Autoscaling

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