Awesome Open Source
Awesome Open Source

Deploying Microservices on AWS Cloud

Table of Contents

This repo contains a simple application that consists of three microservices. The sample application uses three services:

  1. webapp: Web application microservice calls greeting and name microservice to generate a greeting for a person.

  2. greeting: A microservice that returns a greeting.

  3. name: A microservice that returns a persons name based upon {id} in the URL.

Each application is deployed using different AWS Compute options.

Build and Test Services using Maven

  1. Each microservice is in a different repo:




  2. Clone all the repos. Open each one in a separate terminal.

  3. Run greeting service: mvn wildfly-swarm:run

  4. Run name service: mvn wildfly-swarm:run

  5. Run webapp service: mvn wildfly-swarm:run

  6. Run the application: curl http://localhost:8080/


Create Docker Images

mvn package -Pdocker for each repo will create the Docker image.

By default, the Docker image name is arungupta/<service> where <service> is greeting, name or webapp. The image can be created in your repo:

mvn package -Pdocker -Ddocker.repo=<repo>

By default, the latest tag is used for the image. A different tag may be specified as:

mvn package -Pdocker -Ddocker.tag=<tag>

Push Docker Images to Registry

Push Docker images to the registry:

mvn install -Pdocker

Deployment to Docker Swarm

  1. docker swarm init

  2. cd apps/docker

  3. docker stack deploy --compose-file docker-compose.yaml myapp

  4. Access the application: curl http://localhost:8080

    1. Optionally test the endpoints:

  5. Remove the stack: docker stack rm myapp


  1. List stack:

    docker stack ls
  2. List services in the stack:

    docker stack services myapp
  3. List containers:

    docker container ls -f name=myapp*
  4. Get logs for all the containers in the webapp service:

    docker service logs myapp_webapp-service

Amazon ECS and AWS Fargate

This section will explain how to deploy these microservices using Fargate on Amazon ECS cluster.

AWS Fargate is not supported in all AWS regions. These instructions will only work in supported regions. Check the AWS’s Regions Table for details.

Deployment: Create Cluster using AWS Console

This section will explain how to create an ECS cluster using AWS Console.

Use the cluster name fargate-cluster.

Deployment: Create Cluster using AWS CloudFormation

This section will explain how to create an ECS cluster using CloudFormation.

The following resources are needed in order to deploy the sample application:

  • Private Application Load Balancer for greeting and name and a public ALB for webapp

  • Target groups registered with the ALB

  • Security Group that allows the services to talk to each other and be externally accessible

    1. Create an ECS cluster with these resources:

      cd apps/ecs/fargate/templates
      aws cloudformation deploy \
        --stack-name fargate-cluster \
        --template-file infrastructure.yaml \
        --region us-east-1 \
        --capabilities CAPABILITY_IAM
    2. View the output from the cluster:

      aws cloudformation \
        describe-stacks \
        --region us-east-1 \
        --stack-name fargate-cluster \
        --query 'Stacks[].Outputs[]' \
        --output text

Deployment: Simple ECS Cluster

This section explains how to create a ECS cluster with no additional resources. The cluster can be created with a private VPC or a public VPC. The CloudFormation templates for different types are available at

This section will create a 3-instance cluster using a public VPC:

curl -O
aws cloudformation deploy \
  --stack-name MyECSCluster \
  --template-file public-vpc.yml \
  --region us-east-1 \
  --capabilities CAPABILITY_IAM

List the cluster using aws ecs list-clusters command:

    "clusterArns": [

Deployment: Create Cluster and Deploy Services using Fargate CLI

This section explains how to create a Fargate cluster and run services on it.

  1. Download CLI from

  2. Create the LoadBalancer:

    fargate lb create \
      microservices-lb \
      --port 80
  3. Create greeting service:

    fargate service create greeting-service \
      --lb microservices-lb \
      -m 1024 \
      -i arungupta/greeting \
      -p http:8081 \
      --rule path=/resources/greeting
  4. Create name service:

    fargate service create name-service \
      --lb microservices-lb \
      -m 1024 \
      -i arungupta/name \
      -p http:8082 \
      --rule path=/resources/names/*
  5. Get URL of the LoadBalancer:

    fargate lb info microservices-lb
  6. Create webapp service:

    fargate service create webapp-service \
      --lb microservices-lb \
      -m 1024 \
      -i arungupta/webapp \
      -p http:8080 \
      -e GREETING_SERVICE_PATH=/resources/greeting \
      -e NAME_SERVICE_HOST=<lb> \
      -e NAME_SERVICE_PORT=80 \
      -e NAME_SERVICE_PATH=/resources/names
  7. Test the application:

    curl http://<lb>
    curl http://<lb>/0
  8. Scale the service: fargate service scale webapp-service +3

  9. Clean up the resources:

    fargate service scale greeting-service 0
    fargate service scale name-service 0
    fargate service scale webapp-service 0
    fargate lb destroy microservices-lb
As described at, the number of tasks using the Fargate launch type, per region, per account is 20. This limit can be increased by filing a support ticket from the AWS Console.

Deployment: Deploy Tasks and Service using ECS CLI

This section will explain how to create an ECS cluster using a CloudFormation template. The tasks are then deployed using ECS CLI and Docker Compose definitions.


  1. Install ECS CLI.

  2. Install - Perl.

Deploy the application

  1. Run the CloudFormation template to create the AWS resources:


    Launch Template

    N. Virginia (us-east-1)

    deploy to aws
  2. Run the follow command to capture the output from the CloudFormation template as key/value pairs in the file ecs-cluster.props. These will be used to setup environment variables which are used subseqently.

    aws cloudformation describe-stacks \
      --stack-name aws-microservices-deploy-options-ecscli \
      --query 'Stacks[0].Outputs' \
      --output=text | \
      perl -lpe 's/\s+/=/g' | \
      tee ecs-cluster.props
  3. Setup the environment variables using this file:

    set -o allexport
    source ecs-cluster.props
    set +o allexport
  4. Configure ECS CLI:

    ecs-cli configure --cluster $ECSCluster --region us-east-1 --default-launch-type FARGATE
  5. Create the task definition parameters for each of the service: greeting name webapp
  6. Start the greeting service up:

    ecs-cli compose --verbose \
      --file greeting-docker-compose.yaml \
      --task-role-arn $ECSRole \
      --ecs-params ecs-params_greeting.yaml \
      --project-name greeting \
      service up \
      --target-group-arn $GreetingTargetGroupArn \
      --container-name greeting-service \
      --container-port 8081
  7. Bring the name service up:

    ecs-cli compose --verbose \
      --file name-docker-compose.yaml \
      --task-role-arn $ECSRole \
      --ecs-params ecs-params_name.yaml  \
      --project-name name \
      service up \
      --target-group-arn $NameTargetGroupArn \
      --container-name name-service \
      --container-port 8082
  8. Bring the webapp service up:

    ecs-cli compose --verbose \
      --file webapp-docker-compose.yaml \
      --task-role-arn $ECSRole \
      --ecs-params ecs-params_webapp.yaml \
      --project-name webapp \
      service up \
      --target-group-arn $WebappTargetGroupArn \
      --container-name webapp-service \
      --container-port 8080

    Docker Compose supports environment variable substitution. The webapp-docker-compose.yaml uses $PrivateALBCName to refer to the private Application Load Balancer for greeting and name service.

  9. Check the healthy status of different services:

    aws elbv2 describe-target-health \
      --target-group-arn $GreetingTargetGroupArn \
      --query 'TargetHealthDescriptions[0].TargetHealth.State' \
      --output text
    aws elbv2 describe-target-health \
      --target-group-arn $NameTargetGroupArn \
      --query 'TargetHealthDescriptions[0].TargetHealth.State' \
      --output text
    aws elbv2 describe-target-health \
      --target-group-arn $WebappTargetGroupArn \
      --query 'TargetHealthDescriptions[0].TargetHealth.State' \
      --output text
  10. Once all the services are in healthy state, get a response from the webapp service:

    curl http://"$ALBPublicCNAME"
    Hello Sheldon

Tear down the resources

ecs-cli compose --verbose \
      --file greeting-docker-compose.yaml \
      --task-role-arn $ECSRole \
      --ecs-params ecs-params_greeting.yaml \
      --project-name greeting \
      service down
ecs-cli compose --verbose \
      --file name-docker-compose.yaml \
      --task-role-arn $ECSRole \
      --ecs-params ecs-params_name.yaml \
      --project-name name \
      service down
ecs-cli compose --verbose \
      --file webapp-docker-compose.yaml \
      --task-role-arn $ECSRole \
      --ecs-params ecs-params_webapp.yaml \
      --project-name webapp \
      service down
aws cloudformation delete-stack --region us-east-1 --stack-name aws-microservices-deploy-options-ecscli

Deployment: Create Cluster and Deploy Fargate Tasks using CloudFormation

This section creates an ECS cluster and deploys Fargate tasks to the cluster:


Launch Template

N. Virginia (us-east-1)

deploy to aws

Retrieve the public endpoint to test your application deployment:

aws cloudformation \
  describe-stacks \
  --region us-east-1 \
  --stack-name aws-compute-options-fargate \
  --query 'Stacks[].Outputs[?OutputKey==`PublicALBCNAME`].[OutputValue]' \
  --output text

Use the command to test:

curl http://<public_endpoint>

Deployment: Create Cluster and Deploy EC2 Tasks using CloudFormation

This section creates an ECS cluster and deploys EC2 tasks to the cluster:


Launch Template

N. Virginia (us-east-1)

deploy to aws

Retrieve the public endpoint to test your application deployment:

aws cloudformation \
  describe-stacks \
  --region us-east-1 \
  --stack-name aws-compute-options-ecs \
  --query 'Stacks[].Outputs[?OutputKey==`PublicALBCNAME`].[OutputValue]' \
  --output text

Use the command to test:

curl http://<public_endpoint>

Deployment Pipeline: Fargate with AWS CodePipeline

This section will explain how to deploy a Fargate task via CodePipeline

  1. Fork each of the repositories in the Build and Test Services using Maven section.

  2. Clone the forked repositories to your local machine:

    git clone<your_github_username>/microservice-greeting
    git clone<your_github_username>/microservice-name
    git clone<your_github_username>/microservice-webapp
  3. Create the CloudFormation stack:


    Launch Template

    N. Virginia (us-east-1)

    deploy to aws

The CloudFormation template requires the following input parameters:

  1. Cluster Configuration

    1. Launch Type: Select Fargate.

  2. GitHub Configuration

    1. Repo: The repository name for each of the sample services. These have been populated for you.

    2. Branch: The branch of the repository to deploy continuously, e.g. master.

    3. User: Your GitHub username.

    4. Personal Access Token: A token for the user specified above. Use to create a new token. See Creating a personal access token for the command line for more details.

The CloudFormation stack has the following outputs:

  1. ServiceUrl: The URL of the sample service that is being deployed.

  2. PipelineUrl: A deep link for the pipeline in the AWS Management Console.

Once the stack has been provisioned, click the link for the PipelineUrl. This will open the CodePipline console. Clicking on the pipeline will display a diagram that looks like this:

Fargate Pipeline

Now that a deployment pipeline has been established for our services, you can modify files in the repositories we cloned earlier and push your changes to GitHub. This will cause the following actions to occur:

  1. The latest changes will be pulled from GitHub.

  2. A new Docker image will be created and pushed to ECR.

  3. A new revision of the task definition will be created using the latest version of the Docker image.

  4. The service definition will be updated with the latest version of the task definition.

  5. ECS will deploy a new version of the Fargate task.

Cleaning up the example resources

To remove all the resources created by the example, do the following:

  1. Delete the main CloudFromation stack which deletes the sub stacks and resouces.

  2. Manually delete the resources which may contain content:

    1. S3 Bucket: ArtifactBucket

    2. ECR Repository: Repository


Deployment: Create EKS Cluster

Create an EKS cluster based upon Limited Preview instructions.

Deployment: Create Cluster using kops

  1. Install kops

    brew update && brew install kops
  2. Create an S3 bucket and setup KOPS_STATE_STORE:

    aws s3 mb s3://kubernetes-aws-io
    export KOPS_STATE_STORE=s3://kubernetes-aws-io
  3. Define an envinronment variable for Availability Zones for the cluster:

    export AWS_AVAILABILITY_ZONES="$(aws ec2 describe-availability-zones --query 'AvailabilityZones[].ZoneName' --output text | awk -v OFS="," '$1=$1')"
  4. Create cluster:

    kops create cluster \
      --name=cluster.k8s.local \

By default, it creates a single master and 2 worker cluster spread across the AZs.

Deployment: Standalone Manifests

Make sure kubectl CLI is installed and configured for the Kubernetes cluster.

  1. Apply the manifests: kubectl apply -f apps/k8s/standalone/manifest.yml

  2. Access the application: curl http://$(kubectl get svc/webapp -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')

  3. Delete the application: kubectl delete -f apps/k8s/standalone/manifest.yml

Deployment: Helm

Make sure kubectl CLI is installed and configured for the Kubernetes cluster. Also, make sure Helm is installed on that Kubernetes cluster.

  1. Install the Helm CLI: brew install kubernetes-helm

  2. Install Helm in Kubernetes cluster: helm init

  3. Install the Helm chart: helm install --name myapp apps/k8s/helm/myapp

    1. By default, the latest tag for an image is used. Alternatively, a different tag for the image can be used:

      helm install --name myapp apps/k8s/helm/myapp --set "docker.tag=<tag>"
  4. Access the application:

    curl http://$(kubectl get svc/myapp-webapp -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')
  5. Delete the Helm chart: helm delete --purge myapp

Deployment: Ksonnet

Make sure kubectl CLI is installed and configured for the Kubernetes cluster.

  1. Install ksonnet from homebrew tap: brew install ksonnet/tap/ks

  2. Change into the ksonnet sub directory: cd apps/k8s/ksonnet/myapp

  3. Add the environment: ks env add default

  4. Deploy the manifests: ks apply default

  5. Access the application: curl http://$(kubectl get svc/webapp -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')

  6. Delete the application: ks delete default

Deployment: Kubepack

This section will explain how to use Kubepack to deploy your Kubernetes application.

  1. Install kubepack CLI:

    wget -O pack \
      && chmod +x pack \
      && sudo mv pack /usr/local/bin/
  2. Move to package root directory: cd apps/k8s/kubepack

  3. Pull dependent packages:

    pack dep -f .

    This will generate manifests/vendor folder.

  4. Generate final manifests: Combine the manifests for this package and its dependencies and potential patches into the final manifests:

    pack up -f .

    This will create manifests/output folder with an installer script and final manifests.

  5. Install package: ./manifests/output/

  6. Access the application:

    curl http://$(kubectl get svc/webapp -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')
  7. Delete the application: kubectl delete -R -f manifests/output

Deployment: Local Dev & Test using Draft

  1. Install Draft:

    brew tap Azure/draft
    brew install Azure/draft/draft
  2. Initialize:

    draft init
  3. Create Draft artifacts to containerize and deploy to k8s:

    draft create

Following issues are identified so far:

Deployment Pipeline: AWS Codepipeline

This section explains how to setup a deployment pipeline using AWS CodePipeline.

CloudFormation templates for different regions are listed at us-west-2 is listed below.


Launch Template

Oregon (us-west-2)

deploy to aws
  1. Create Git credentials for HTTPS connections to AWS CodeCommit:

  2. Reset any stored git credentials for CodeCommit in the keychain. Open Keychain Access, search for codecommit and remove any related entries.

  3. Get CodeCommit repo URL from CloudFormation output and follow the instructions at

Deployment Pipeline: Jenkins

Create a deployment pipeline using Jenkins X.

  1. Install Jenkins X CLI:

    brew tap jenkins-x/jx
    brew install jx
  2. Create the Kubernetes cluster:

    jx create cluster aws

    This will create a Kubernetes cluster on AWS using kops. This cluster will have RBAC enabled. It will also have insecure registries enabled. These are needed by the pipeline to store Docker images.

  3. Clone the repo:

    git clone
  4. Import the project in Jenkins X:

    jx import

    This will generate Dockerfile and Helm charts, if they don’t already exist. It also creates a Jenkinsfile with different build stages identified. Finally, it triggers a Jenkins build and deploy the application in a staging environment by default.

  5. View Jenkins console using jx console. Select the user, project and branch to see the deployment pipeline.

  6. Get the staging URL using jx get apps and view the output from the application in a browser window.

  7. Now change the message in displayed from HelloHandler and push to the GitHub repo. Make sure to change the corresponding test as well otherwise the pipeline will fail. Wait for the deployment to complete and then refresh the browser page to see the updated output.

Deployment Pipeline: Gitkube

  1. Deploy the greeting service

  2. Install Gitkube:

    kubectl create -f
    kubectl --namespace kube-system expose deployment gitkubed --type=LoadBalancer --name=gitkubed
  3. Configure secret for Docker registry in the cluster:

    kubectl create secret \
      docker-registry gitkube-secret \
      --docker-server= \
      --docker-username=arungupta \
      --docker-password='<password>' \
      [email protected]
  4. Create a Remote resource manifest based upon greeting-remote.yaml

  5. Create the Remote resource:

    kubectl apply -f greeting-remote.yaml
  6. Add remote to git repo:

    git remote add gitkube `kubectl get remote greeting -o jsonpath='{.status.remoteUrl}'`

Deployment Pipeline: Spinnaker

Deployment Pipeline: Skaffold

Deployment: Canary Deployment with Istio

Istio allows the deployment of canary services. This is done by using a simple DSL that controls how API calls and layer-4 traffic flow across various services in the application deployment.

  1. Install Istio in the Kubernetes cluster:

    curl -L | sh -
    cd istio-0.7.1/
    kubectl apply -f install/kubernetes/istio.yaml
  2. Istio uses the Envoy proxy to manage all inbound/outbound traffic in the service mesh. Envoy proxy needs to be injected as sidecar into the application. So, we’ll deploy the application:

    kubectl apply -f <(istioctl kube-inject -f apps/k8s/istio/manifest.yaml)

    This will deploy the application with 3 microservices. Each microservice is deployed in its own pod, with the Envoy proxy injected into the pod; Envoy will now take over all network communications between the pods.

  3. Create route rules:

    kubectl apply -f apps/k8s/istio/route-50-50.yaml
  4. Access the application:

    curl http://$(kubectl get svc/webapp -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')

    Access the endpoint multiple times and notice how Hello and Howdy greeting is returned. Its not a round-robin but over 100 requests, 50% would be split between different greeting message.

Here are some convenient commands to manage route rules:

  1. istioctl get routerules shows the list of all route rules

  2. istioctl delete routerule <name> deletes a route rule by name

Another route with the traffic split of 90% and 10% is at apps/k8s/istio/route-90-10.yaml.

Monitoring: AWS X-Ray

  1. arungupta/xray:us-west-2 Docker image is already available on Docker Hub. Optionally, you may build the image:

    cd config/xray
    docker build -t arungupta/xray:latest .
    docker image push arungupta/xray:us-west-2
  2. Deploy the DaemonSet: kubectl apply -f xray-daemonset.yaml

  3. Deploy the application using Helm charts:

    helm install --name myapp apps/k8s/helm/myapp
  4. Access the application:

    curl http://$(kubectl get svc/myapp-webapp -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')
  5. Open the X-Ray console and watch the service map and traces.

X-Ray Service map looks like:

k8s xray service map

X-Ray traces looks like:

k8s xray traces

Monitoring: Conduit

Conduit is a small, ultralight, incredibly fast service mesh centered around a zero config approach. It can be used for gaining remarkable visibility in your Kubernetes deployments.

  1. Confirm that both Kubernetes client and server versions are v1.8.0 or greater using kubectl version --short

  2. Install the Conduit CLI on your local machine:

    curl | sh
  3. Add the conduit command into your PATH:

    export PATH=$PATH:$HOME/.conduit/bin
  4. Verify the CLI is installed and running correctly. You will see a message that says 'Server version: unavailable' because you have not installed Conduit in your deployments.

    conduit version
  5. Install Conduit on your Kubernetes cluster. It will install into a separate conduit namespace, where it can be easily removed.

    conduit install | kubectl apply -f -
  6. Verify installation of Conduit into your cluster. Your Client and Server versions should now be the same.

    conduit version
  7. Verify the Conduit dashboard opens and that you can connect to Conduit in your cluster.

    conduit dashboard
  8. Install the demo app to see how Conduit handles monitoring of your Kubernetes applications.

    curl | conduit inject - | kubectl apply -f -
  9. You now have a demo application running on your Kubernetes cluster and also added to the Conduit service mesh. You can see a live version of this app (not in your cluster) to understand what this demo app is. Click to vote your favorite emoji. One of them has an error. Which one is it? You can also see the local version of this app running in your cluster:

    kubectl get svc web-svc -n emojivoto -o jsonpath="{.status.loadBalancer.ingress[0].*}"

The demo app includes a service (vote-bot) constantly running traffic through the demo app. Look back at the conduit dashboard. You should be able to browse all the services that are running as part of the application to view success rate, request rates, latency distribution percentiles, upstream and downstream dependencies, and various other bits of information about live traffic.

You can also see useful data about live traffic from the conduit CLI.

  1. Check the status of the demo app (emojivoto) deployment named web. You should see good latency, but a success rate indicating some errors.

    conduit stat -n emojivoto deployment web
  2. Determine what other deployments in the emojivoto namespace talk to the web deployment.

    conduit stat deploy --all-namespaces --from web --from-namespace emojivoto
  3. You should see that web talks to both the emoji and voting services. Based on their success rates, you should see that the voting service is responsible for the low success rate of requests to web. Determine what else talks to the voting service.

    conduit stat deploy --to voting --to-namespace emojivoto --all-namespaces
  4. You should see that it only talks to web. You now have a plausible target to investigate further since the voting service is returning a low success rate. From here, you might look into the logs, or traces, or other forms of deeper investigation to determine how to fix the error.

Monitoring: Istio and Prometheus

Istio is deployed as a sidecar proxy into each of your pods; this means it can see and monitor all the traffic flows between your microservices and generate a graphical representation of your mesh traffic.

  1. Prometheus addon will obtain the metrics from Istio. Install Prometheus:

    kubectl apply -f install/kubernetes/addons/prometheus.yaml
  2. Install the Servicegraph addon; Servicegraph queries Prometheus, which obtains details of the mesh traffic flows from Istio:

    kubectl apply -f install/kubernetes/addons/servicegraph.yaml
  3. Generate some traffic to the application:

    curl http://$(kubectl get svc/webapp -o jsonpath='{.status.loadBalancer.ingress[0].hostname}')
  4. View the ServiceGraph UI:

    kubectl -n istio-system \
      port-forward $(kubectl -n istio-system \
        get pod \
        -l app=servicegraph \
        -o jsonpath='{.items[0]}') \
        8088:8088 &
    open http://localhost:8088/dotviz
  5. You should see a distributed trace that looks something like this. It may take a few seconds for Servicegraph to become available, so refresh the browser if you do not receive a response.

    istio servicegraph

AWS Lambda

Deployment: Package Lambda Functions

mvn clean package -Plambda in each repo will build the deployment package for each microservice.

Deployment: Test Lambda Functions Locally

Serverless Application Model (SAM) defines a standard application model for serverless applications. It extends AWS CloudFormation to provide a simplified way of defining the Amazon API Gateway APIs, AWS Lambda functions, and Amazon DynamoDB tables needed by your serverless application.

sam is the AWS CLI tool for managing Serverless applications written with SAM. Install SAM CLI as:

npm install -g aws-sam-local

The complete installation steps for SAM CLI are at

In Mac

All commands are given from apps/lambda directory.

  1. Start greeting service:

    sam local start-api --template greeting-sam.yaml --port 3001
  2. Test greeting endpoint:

  3. Start name service:

    sam local start-api --template name-sam.yaml --port 3002
  4. Test name endpoint:

  5. Start webapp service:

    sam local start-api --template webapp-sam.yaml --env-vars test/env-mac.json --port 3000
  6. Test webapp endpoint:


In Windows

Firstly start the Greeting and Name service as Mac, and then start the WebApp service using the following command

  1. sam local start-api --template webapp-sam.yaml --env-vars test/env-win.json --port 3000

  2. Test the urls above in a browser

Deployment: Debug using IntelliJ

This section will explain how to debug your Lambda functions locally using SAM Local and IntelliJ.

  1. Start functions using SAM Local and a debug port:

    sam local start-api \
      --env-vars test/env-mac.json \
      --template sam.yaml \
      --debug-port 5858
  2. In IntelliJ, setup a break point in your Lambda function.

  3. Go to Run, Debug, Edit Configurations, specify the port 5858 and click on Debug. The breakpoint will hit and you can see the debug state of the function.

Deployment: Deploy using Serverless Application Model

  1. Serverless applications are stored as a deployment packages in a S3 bucket. Create a S3 bucket:

    aws s3api create-bucket \
      --bucket aws-microservices-deploy-options \
      --region us-west-2 \
      --create-bucket-configuration LocationConstraint=us-west-2

    Make sure to use a bucket name that is unique.

  2. Package the SAM application. This uploads the deployment package to the specified S3 bucket and generates a new file with the code location:

    sam package \
      --template-file sam.yaml \
      --s3-bucket aws-microservices-deploy-options \
      --output-template-file \
  3. Create the resources:

    sam deploy \
      --template-file sam.transformed.yaml \
      --stack-name aws-microservices-deploy-options-lambda \
      --capabilities CAPABILITY_IAM
  4. Test the application:

    1. Greeting endpoint:

      curl `aws cloudformation \
        describe-stacks \
        --stack-name aws-microservices-deploy-options-lambda \
        --query "Stacks[].Outputs[?OutputKey=='GreetingApiEndpoint'].[OutputValue]" \
        --output text`
    2. Name endpoint:

      curl `aws cloudformation \
        describe-stacks \
        --stack-name aws-microservices-deploy-options-lambda \
        --query "Stacks[].Outputs[?OutputKey=='NamesApiEndpoint'].[OutputValue]" \
        --output text`
    3. Webapp endpoint:

      curl `aws cloudformation \
        describe-stacks \
        --stack-name aws-microservices-deploy-options-lambda \
        --query "Stacks[].Outputs[?OutputKey=='WebappApiEndpoint'].[OutputValue]" \
        --output text`/1

Deployment: Deploy to Serverless Application Repository

The AWS Serverless Application Repository (SAR) enables you to quickly deploy code samples, components, and complete applications for common use cases such as web and mobile back-ends, event and data processing, logging, monitoring, IoT, and more. Each application is packaged with an AWS Serverless Application Model (SAM) template that defines the AWS resources used.

The complete list of applications can be seen at

This section explains how to publish your SAM application to SAR. Detailed instructions are at

  1. Applications packaged as SAM can be published at

  2. Add the following policy to your S3 bucket:

        "Version": "2012-10-17",
        "Statement": [
                "Effect": "Allow",
                "Principal": {
                    "Service":  ""
                "Action": "s3:GetObject",
                "Resource": "arn:aws:s3:::<your-bucket-name>/*"
  3. Use sam.transformed.yaml as the SAM template

  4. Publish the application

  5. Test the application:

    curl `aws cloudformation \
      describe-stacks \
      --stack-name aws-serverless-repository-aws-microservices \
      --query "Stacks[].Outputs[?OutputKey=='WebappApiEndpoint'].[OutputValue]" \
      --output text`/1
  6. List of your published applications:

Deployment Pipeline: AWS CodePipeline

This section will explain how to deploy Lambda + API Gateway via CodePipeline.

  1. Generate new GitHub personal access token.

  2. Create CloudFormation stack:

    1. Create pipeline for greeting and name services. The default repository can be overridden to forked public repo by providing URL to the parameter Git:

      cd apps/lambda
      aws cloudformation deploy \
        --template-file microservice-pipeline.yaml \
        --stack-name lambda-microservices-greeting-pipeline \
        --parameter-overrides ServiceName=greeting GitHubOAuthToken=<github-token> \
        --capabilities CAPABILITY_IAM
      aws cloudformation deploy \
        --template-file microservice-pipeline.yaml \
        --stack-name lambda-microservices-name-pipeline \
        --parameter-overrides ServiceName=name GitHubOAuthToken=<github-token> \
        --capabilities CAPABILITY_IAM
    2. Wait for greeting and name pipelines to be created successfully. Then, create the pipeline for the webapp service:

      aws cloudformation deploy \
        --template-file microservice-pipeline.yaml \
        --stack-name lambda-microservices-webapp-pipeline \
        --parameter-overrides ServiceName=webapp GitHubOAuthToken=<github-token> \
        --capabilities CAPABILITY_IAM
  3. Get the Deployment Pipeline URL:

    aws cloudformation \
      describe-stacks \
      --stack-name lambda-microservices-greeting-pipeline \
      --query "Stacks[].Outputs[?OutputKey=='CodePipelineUrl'].[OutputValue]" \
      --output text
    aws cloudformation \
      describe-stacks \
      --stack-name lambda-microservices-name-pipeline \
      --query "Stacks[].Outputs[?OutputKey=='CodePipelineUrl'].[OutputValue]" \
      --output text
    aws cloudformation \
      describe-stacks \
      --stack-name lambda-microservices-webapp-pipeline \
      --query "Stacks[].Outputs[?OutputKey=='CodePipelineUrl'].[OutputValue]" \
      --output text
  4. Get the URL to test microservices:

    curl `aws cloudformation \
      describe-stacks \
      --stack-name aws-compute-options-lambda-greeting \
      --query "Stacks[].Outputs[?OutputKey=='greetingApiEndpoint'].OutputValue" \
      --output text`
    curl `aws cloudformation \
      describe-stacks \
      --stack-name aws-compute-options-lambda-name \
      --query "Stacks[].Outputs[?OutputKey=='nameApiEndpoint'].OutputValue" \
      --output text`
    curl `aws cloudformation \
      describe-stacks \
      --stack-name aws-compute-options-lambda-webapp \
      --query "Stacks[].Outputs[?OutputKey=='webappApiEndpoint'].OutputValue" \
      --output text`/1

    Deployment pipeline in the AWS console looks like as shown:

    Lambda Pipeline
  5. After one run of the webapp pipeline, access the endpoint:

    curl `aws cloudformation \
      describe-stacks \
      --stack-name lambda-microservices-webapp \
      --query "Stacks[].Outputs[?OutputKey=='webappApiEndpoint'].[OutputValue]" \
      --output text`/1

Deployment: Canary Deployment for Lambda Functions

The greeting service has implemented Lambda SAM Safe Deployment. By default, the function is deployed using Canary10Percent5Minutes deployment type. This means that 10% of the traffic will be shifted to the new Lambda function. If there are no errors or CloudWatch alarms are triggered, the remaining traffic is shifted after 5 minutes. This is further explained at

In the microservice-greeting repository, we prepared the greeting-sam.yaml template allows users to change the deployment types supported by safe deployment. You can update the default setting to another support deployment types.

To test the Canary deployment, please follow the following steps

  1. Fork (microservice-greeting github repository.

  2. Checkout the forked repository localy

  3. Modify the Lambda function source code src/main/java/org/aws/samples/compute/greeting/ to return response "Hi" instead of "Hello".

  4. Use the following command to commit the change.

    git add src/main/java/org/aws/samples/compute/greeting/
    git commit -m "say hi to canary"
    git push origin master
  5. Navigate to this repo, and run the following command to update CodePipeline stack for the greeting service.

    cd apps/lambda
    aws cloudformation deploy \
      --template-file microservice-pipeline.yaml \
      --stack-name lambda-microservices-greeting-pipeline \
      --parameter-overrides ServiceName=greeting GitHubOAuthToken=<github-token> GitHubSetting=OVERRIDE GitHubRepo=<forked-repo-name> GitHubOwner=<github-owner-user-name> GitHubBranch=master \
      --capabilities CAPABILITY_IAM

To checkout Canary deployment progress, navigate to AWS Console CodeDeploy Service and open Application lambda-microservices-greeting-ServerlessDeploymentApplication-<random-string> in the console. Please see the following example:

Lambda CodeDeploy

To monitor the deployment progress, select the in progress deployment link, you will see the progress like the following screenshot.

Lambda Canary

Deployment: Composition using AWS Step Functions

Monitoring: AWS X-Ray

AWS X-Ray is fully integrated with AWS Lambda. This can be easily enabled for functions published using SAM by the following property:

Tracing: Active

More details about AWS Lambda and X-Ray integration is at

Deploying the functions as explained above will generate X-Ray service map and traces.

Deployment: Remove the stack

aws cloudformation delete-stack \
  --stack-name aws-microservices-deploy-options-lambda


This sample code is made available under the MIT-0 license. See the LICENSE file.

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