DEV Community

Cover image for Run event-driven workflows with Amazon EKS Blueprints, Keda and Karpenter

Run event-driven workflows with Amazon EKS Blueprints, Keda and Karpenter


  • Implement a kubernetes solution to contanerize the project that will be shared along with this problem.
  • Implement a very simple API server with the following routes that can interact with kubernetes.
    • POST /jobs (Create a new job that runs on a kubernetes cluster)
    • GET /jobs/stats (Returns aggregate job stats. Succeeded vs failed and no of retries)
    • POST /jobs/schedule (Schedule a job using cron syntax)
  • The project is wrritten in Rust and we need to containerize the project to serve following
    • Each job should spawn a docker container and run Rust function from binary script.
    • If the job fails, retry 2 times with a small time delay ideally.


  • This post demonstrates a proof-of-concept implementation that uses Kubernetes to execute code in response to an event here is API request. The workflow is powered by Keda (Kubernetes Event-driven Autoscaling) which scales out the kubernetes pods bases on incoming events such as SQS messages. After keda scaleout pods which are in pending state, Karpenter (Just-in-time Nodes for Any Kubernetes Cluster) bases on provisioners to decide scaleout more nodes
  • Keda and Karpenter AddOn with Amazon Elastic Kubernetes Service (Amazon EKS), making it easy to build event-driven workflows that orchestrate jobs running on Kubernetes with AWS services, such as AWS Lambda, HTTP API Gateway, DynamoDB and Amazon Simple Queue Service (Amazon SQS), with minimal code.
  • All AWS resources as well as kubernetes manifest and kubernetes AddOns are managed and installed using CDK (AWS Cloud Development Kit) and CDK8S (Cloud Development Kit for Kubernetes)


Table Of Contents

🚀 Solution overview

  • The API server is hosted by HTTP API gateway with lambda integration
    • POST /jobs - Lambda function send SQS message, keda scaledJob listens to the SQS queue and then scaleout job to serve the request, karpenter catches pod pending event to provision new node
    • GET /jobs/stats - Lambda function query dynamoDB table to get job aggregation stats and return the result for job succeed, failed and number of retries.
    • POST /jobs/schedule - Lambda function create eventbridge Schedule rule with Cron expression from the input of cron syntax. Eventbridge rule will send SQS message for triggering keda scaledjob

🚀 Containerize the project

  ⚡ $ tree data_processor/
  ├── Cargo.toml
  ├── Dockerfile
  └── src
  1 directory, 5 files
Enter fullscreen mode Exit fullscreen mode
  • In the there are three main tasks in this project

    1. Processing job
    2. Consume and delete SQS message
    3. Write succeed, fail and retry state to dynamodb
  • We leverage multi-stage builds in Dockerfile to optimize the image size and running process as non-root user for security.

    • First stage compile Rust source code to binary data-processor-sample
    • Last stage copy binary and define the ENTRYPOINT ["/data-processor-sample"] under USER rust so whenever the container starts, it automatically trigger processing jobs.
  • Build, tag and push image

  ⚡ $ cd data_processor/
  ⚡ $ docker build -t dozer/process-job .
  Sending build context to Docker daemon  52.74kB
  Successfully built 8b8178a8f41f
  Successfully tagged dozer/process-job:latest

  ⚡ $ docker tag dozer/process-job:latest

  ⚡ $ docker push
Enter fullscreen mode Exit fullscreen mode

🚀 Create K8S Charts for keda scaledJob, provisioner and serviceAccount

  • If you know helm chart or kustomize to provision managed kubernetes manifest at scale, CDK8S not only provide those benefits but also more
    • cdk8s is an open-source software development framework for defining Kubernetes applications and reusable abstractions using familiar programming languages here I use TypeScript
    • We can combine CDK and CDK8S to single project and apply CDK8S Charts inside the EKS cluster stacks and then just run deploy
  ⚡ $ tree aws-k8s-iac/src/cdk8s/
  ├── imports
  │   ├── k8s.ts
  │   ├──
  │   └──
  ├── karpenter-provisioner
  │   ├── dozer-job-provisoner.ts
  │   └── provisioner-constants.ts
  ├── keda
  │   └── dozer-job.ts
  ├── main.ts
  └── serviceAccount
      └── processor-job-sa.ts

  4 directories, 8 files
Enter fullscreen mode Exit fullscreen mode
  • For designing event-driven with autoscaling and cost optimization, we use Keda scaledJob with SQS triggers. In the scaledJob, we define following specs

    • jobTargetRef which contains the k8s job spec
    • pollingInterval which is the period of polling to the SQS queue
    • minReplicaCount is set to 0 means no SQS message no job
    • maxReplicaCount to control the max number of scaled jobs so first come first serve
    • triggers defines SQS target to poll for messages
    • serviceAccountName IAM role for accessing AWS resources
    • Source code: dozer-job.ts
    • Check scaledobject created
     ~ $ k get scaledjob
    dozer-job   4     aws-sqs-queue                    True    False    24h
  • For autoscaling, we use Karpenter with proper provioner spec. The provisoner includes following major spec

    • instanceProfile AWS instance profile with enough IAM permissions for nodes to join EKS cluster
    • amiFamily Use Bottlerocket for optimize AMI and security
    • subnetSelector Provide tags of EKS private subnets
    • securityGroupSelector Provide tags of EKS security group for pods communication
    • ttlSecondsAfterEmpty delete empty/unnecessary instances
    • requirements
    • Use spot instances with proper instance type
    • Taint the EKS nodes with proper key-values for the project
    • Source code: dozer-job-provisoner.ts
    • Check provisioner created
    ~ $ k get provisioner
    NAME        AGE
    dozer-job   23h
  • ServiceAccount which associates with IAM role for serviceAccount, it's best practice to provide service permissions to access AWS resources, processor-job-sa.ts

  ~ $ k get sa
  default     1         4d16h
  dozer-job   1         23h
Enter fullscreen mode Exit fullscreen mode

🚀 Create EKS Cluster and other resourses using CDK EKS blueprints

  • We use CDK typescript to provide Infrastructure as code
  ⚡ $ tree aws-k8s-iac/src/
  ├── apigw-lambda-sqs.ts
  ├── ddb.ts
  ├── ecr.ts
  ├── eks-blueprints
  │   ├── builder.ts
  │   ├── eks-blueprint.ts
  │   └── platform-user.ts
  ├── irsa.ts
  ├── lambda-handler
  │   └──
  ├── main.ts
  └── shared
      ├── configs.ts
      ├── constants.ts
      ├── environment.ts
      └── tagging.ts

  8 directories, 21 files
Enter fullscreen mode Exit fullscreen mode
  • Overview of CDK stacks
  ⚡ $ cdk ls
Enter fullscreen mode Exit fullscreen mode
  • After creating .env with CDK_DEFAULT_ACCOUNT and CDK_DEFAULT_REGION we can run cdk deploy --all to create resources. Check cloudformation

  • Overview of all components

🚀 Test API

  • Overview of HTTP API Gateway

  • Create a new job that runs on a kubernetes cluster
    • Send POST request
  curl -X POST
Enter fullscreen mode Exit fullscreen mode
  • Keda scales job and Karpenter scale node

    • New node joined
      ~ $ k get node
      NAME                                              STATUS   ROLES    AGE    VERSION
      ip-10-0-103-57.ap-southeast-1.compute.internal    Ready    <none>   52s    v1.21.13
      ip-10-0-118-81.ap-southeast-1.compute.internal    Ready    <none>   122m   v1.21.13
      ip-10-0-151-134.ap-southeast-1.compute.internal   Ready    <none>   122m   v1.21.13
    • Job created
      ~ $ k get pod --watch
      NAME                    READY   STATUS    RESTARTS   AGE
      dozer-job-kl8kd-dmq85   0/1     Pending   0          22s
      dozer-job-kl8kd-dmq85   0/1     ContainerCreating   0          23s
      dozer-job-kl8kd-dmq85   1/1     Running             0          68s
      dozer-job-kl8kd-dmq85   0/1     Completed           0          74s
      dozer-job-9qxrm-s8p6p   0/1     Pending             0          0s
      dozer-job-9qxrm-s8p6p   0/1     ContainerCreating   0          0s
      dozer-job-9qxrm-s8p6p   1/1     Running             0          2s
      dozer-job-9qxrm-s8p6p   0/1     Error               0          7s
      dozer-job-9qxrm-9m27d   0/1     Pending             0          0s
      dozer-job-9qxrm-9m27d   0/1     ContainerCreating   0          0s
      dozer-job-9qxrm-9m27d   1/1     Running             0          1s
      dozer-job-9qxrm-9m27d   0/1     Error               0          2s
      dozer-job-9qxrm-5d4vc   0/1     Pending             0          0s
      dozer-job-9qxrm-5d4vc   0/1     ContainerCreating   0          0s
      dozer-job-9qxrm-5d4vc   1/1     Running             0          2s
      dozer-job-9qxrm-5d4vc   0/1     Error               0          3s
    • Get job aggregation stats
  • Send GET request

    ~ $ curl -X GET
    {"succeedJob": 1, "jobRetry": 2, "failedJob": 3}
  • Double check dynamoDB table

    • Schedule a job using cron syntax
  • Send POST request

    curl -X POST --data '{"cron": "* * * * *"}' --header "Content-Type: application/json"
    ~ $ curl -X POST --data '{"cron": "0 7 * * MON"}' --header "Content-Type: application/json"
  • Check eventbridge schedules

  • Let the schedule of triggering per minute run for a while and then check job stats again

    ~ $ curl -X GET
    {"succeedJob": 13, "jobRetry": 12, "failedJob": 15}

🚀 Conclusion

  • This post showed how to run event-driven workflows using API request at scale on Amazon EKS, HTTP APIGW and AWS Lambda. We provided you with AWS CDK code as well as CDK8S code to create the cloud infrastructure, Kubernetes resources, and the application within the same codebase.
  • In real project, we can do some improvements
    • POST job request will base on the request ID to separate which job ID, which user triggered
    • Security the API endpoint
    • Implement CDK pipeline for the codebase

Oldest comments (0)

An Animated Guide to Node.js Event Loop

>> Check out this classic DEV post <<