Amazon ECS Workshop > Deploying Microservices to ECS > Node.js Backend API

Node.js Backend API

cdk
ecs-cli fargate mode

Validate deployment configuration

cd ~/environment/ecsdemo-nodejs/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 Nodejs 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')
        )

Nodejs 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 NodejsService(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(
            "NodeServiceContainerDef",
            image=aws_ecs.ContainerImage.from_registry("brentley/ecsdemo-nodejs"),
            memory_reservation_mib=512,
            logging=aws_ecs.LogDriver.aws_logs(
                stream_prefix='ecsworkshop-nodejs'
            )
        )
        
        # 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, "NodejsFargateService",
            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-nodejs'
            )
        )

Review service logs

Review the service logs from the command line:

Expand here to see the solution

Review the service logs from the console:

Expand here to see the solution

Scale the service

Manually scaling

Expand here to see the solution

Autoscaling

Expand here to see the solution

Why autoscale?

Well, to put it simply - it’s either a human scales the service, or the orchestrator.
- If we choose to do it manually, this means that as load increases, we need to stop what we are doing to scale the service to meet the load (and not to mention that we have to eventually scale back down once the load clears). This can be tedious and painful, hence why autoscaling exists.
- If we let the orchestrator handle the scaling in and out for the service, we can focus on continuous improvement, and less on operational heavy lifting. In order to get autoscaling setup, one first needs to know what metric to use as the decision to autoscale. Some example metrics for scaling are CPU utilization, memory utilization, and queue depth.

Setup Autoscaling in the code

Using the editor of your choice, open ‘~/environment/ecsdemo-nodejs/cdk/app.py’ in the cdk directory.
Search for Enable Service Autoscaling to find the code that will enable autoscaling for the service.

Remove the comments (#) from the code for self.autoscale and below, once you remove them, it should look like the following:

# Enable Service Autoscaling
self.autoscale = self.fargate_service.auto_scale_task_count(
min_capacity=1,
max_capacity=10
)

self.autoscale.scale_on_cpu_utilization(
"CPUAutoscaling",
target_utilization_percent=50,
scale_in_cooldown=core.Duration.seconds(30),
scale_out_cooldown=core.Duration.seconds(30)
)

Code Review

To start modeling our autoscaling logic, we first set what our upper and lower bounds are. This ensures that we will always be at a minimum of 1 task, and a maximum of 10 tasks.
```
# Enable Service Autoscaling
self.autoscale = self.fargate_service.auto_scale_task_count(
min_capacity=1,
max_capacity=10
)
```
When the ECS service is deployed, Cloudwatch metrics such as CPU utilization are enabled by default. We are going to take advantage of that metric and use it as our scaling target. In this method, we are setting what our target cpu utilization percent is, and how long in between scale activities we want to wait before it adds/removes another task.
```
self.autoscale.scale_on_cpu_utilization(
"CPUAutoscaling",
target_utilization_percent=50,
scale_in_cooldown=core.Duration.seconds(30),
scale_out_cooldown=core.Duration.seconds(30)
)
```

Deploy Autoscaling

Now that you have the autoscaling code in place, let’s deploy it!
Let’s see a diff of our present state, vs the proposed changes to our environment. Run the following:
```
cdk diff
```
You should see the additon of two resources (image below). ECS is utilizing the Application Autoscaling service to manage the scaling of ECS tasks. In short, this will create a target tracking policy, which will set the desired target for scaling in and out (in this case, CPU utilization), and attach it to the ECS service.

Deploy time!
```
cdk deploy --require-approval never
```

Load test

In order to introduce load to the Nodejs Fargate service, we need to have the ability to reach its service endpoint. Since the service is in a private subnet, we will use the EC2 instance that was deployed within the same VPC. The instance was created in the Platform/Build Environment steps.

Once you have deployed the autoscaling, copy the instance id created during the platform deployment and get into temporary ec2 using the SSM agent or use the following code:

ec2InstanceId=$(aws cloudformation describe-stacks --stack-name ecsworkshop-base --query "Stacks" --output json | jq -r '.[].Outputs[] | select(.OutputKey |contains("StressToolEc2Id")) | .OutputValue')
aws ssm start-session --target "$ec2InstanceId"

Once you are in the ec2 instance, generate the load test for the nodejs service.
```
siege -c 100 -i http://ecsdemo-nodejs.service:3000&
```
While siege is running in the background, either navigate to the console or monitor the autoscaling from the command line in a new cloud9 terminal.

Command Line

Console

Let’s bring up the NodeJS Backend API!

Deploy our NodeJS Backend Application:

cd ~/environment/ecsdemo-nodejs
envsubst < ecs-params.yml.template >ecs-params.yml

ecs-cli compose --project-name ecsdemo-nodejs service up \
    --create-log-groups \
    --private-dns-namespace service \
    --enable-service-discovery \
    --cluster-config container-demo \
    --vpc $vpc

Here, we change directories into our nodejs application code directory. The envsubst command templates our ecs-params.yml file with our current values. We then launch our nodejs service on our ECS cluster (with a default launchtype of Fargate)

Note: ecs-cli will take care of building our private dns namespace for service discovery, and log group in cloudwatch logs.

View running container, and store the output of the task id as an env variable for later use:

ecs-cli compose --project-name ecsdemo-nodejs service ps \
    --cluster-config container-demo

task_id=$(ecs-cli compose --project-name ecsdemo-nodejs service ps --cluster-config container-demo | awk -F \/ 'FNR == 2 {print $1}')

We should have one task registered.

Check reachability (open url in your browser):

alb_url=$(aws cloudformation describe-stacks --stack-name container-demo-alb --query 'Stacks[0].Outputs[?OutputKey==`ExternalUrl`].OutputValue' --output text)
echo "Open $alb_url in your browser"

This command looks up the URL for our ingress ALB, and outputs it. You should be able to click to open, or copy-paste into your browser.

View logs:

# Referencing task id from above ps command
ecs-cli logs --task-id $task_id \
    --follow --cluster-config container-demo

To view logs, find the task id from the earlier ps command, and use it in this command. You can follow a task’s logs also.

Scale the tasks:

ecs-cli compose --project-name ecsdemo-nodejs service scale 3 \
    --cluster-config container-demo
ecs-cli compose --project-name ecsdemo-nodejs service ps \
    --cluster-config container-demo

We can see that our containers have now been evenly distributed across all 3 of our availability zones.