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

Assignment

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.

Abstract

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)

TL;DR

Open Table Of Contents

🚀 Solution overview
🚀 Containerize the project
🚀 Create K8S Charts for keda scaledJob, provisioner and serviceAccount
🚀 Create EKS Cluster and other resourses using CDK EKS blueprints
🚀 Test API
🚀 Conclusion

🚀 Solution overview

Bootstrapping clusters with EKS Blueprints, the cluster includes AddOns such as Keda, Karpenter and others
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

Source code

⚡ $ tree data_processor/
data_processor/
├── Cargo.toml
├── Dockerfile
├── README.md
└── src
    └── main.rs
1 directory, 5 files

In the main.rs 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 123456789012.dkr.ecr.ap-southeast-1.amazonaws.com/dozer/process-job:latest

⚡ $ docker push 123456789012.dkr.ecr.ap-southeast-1.amazonaws.com/dozer/process-job:latest

🚀 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/
aws-k8s-iac/src/cdk8s/
├── imports
│   ├── k8s.ts
│   ├── karpenter.sh.ts
│   └── keda.sh.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
```
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
NAME        MAX   TRIGGERS        AUTHENTICATION   READY   ACTIVE   AGE
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
NAME        SECRETS   AGE
default     1         4d16h
dozer-job   1         23h
```

🚀 Create EKS Cluster and other resourses using CDK EKS blueprints

We use CDK typescript to provide Infrastructure as code

⚡ $ tree aws-k8s-iac/src/
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
│   └── index.py
├── main.ts
└── shared
    ├── configs.ts
    ├── constants.ts
    ├── environment.ts
    └── tagging.ts

8 directories, 21 files

Overview of CDK stacks

⚡ $ cdk ls
DevEksCluster
DevEksCluster/sin-DozerDDB
DevEksCluster/sin-DozerECR
DevEksCluster/sin-DozerProcessData
DevEksCluster/sin-d1-dev-eks-blueprint
DevEksCluster/sin-DozerIRSA

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 https://ww47is207g.execute-api.ap-southeast-1.amazonaws.com/jobs
200

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 https://ww47is207g.execute-api.ap-southeast-1.amazonaws.com/jobs/stats
{"succeedJob": 1, "jobRetry": 2, "failedJob": 3}

Double check dynamoDB table

Schedule a job using cron syntax

Send POST request

curl -X POST https://ww47is207g.execute-api.ap-southeast-1.amazonaws.com/jobs/schedule --data '{"cron": "* * * * *"}' --header "Content-Type: application/json"
200

~ $ curl -X POST https://ww47is207g.execute-api.ap-southeast-1.amazonaws.com/jobs/schedule --data '{"cron": "0 7 * * MON"}' --header "Content-Type: application/json"
200

Check eventbridge schedules

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

~ $ curl -X GET https://ww47is207g.execute-api.ap-southeast-1.amazonaws.com/jobs/stats
{"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