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Kubernetes autoscaler scaling pods based on Queue length. AWS SQS is only supported at present. The resource is called WPA.
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Latest commit 3361037 Nov 24, 2019

Worker Pod Autoscaler

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Scale kubernetes pods based on the Queue length of a queue in a Message Queueing Service. Worker Pod Autoscaler automatically scales the number of pods in a deployment based on observed queue length.

Currently the supported Message Queueing Services is only AWS SQS. There is a plan to integrate other commonly used message queing services.

Install the WorkerPodAutoscaler


Running the below script will create the WPA CRD and start the controller. The controller watches over all the specified queues in AWS SQS and scales the Kubernetes deployments based on the specification.

export AWS_REGIONS='ap-south-1,ap-southeast-1'
export AWS_ACCESS_KEY_ID='sample-aws-access-key-id'
export AWS_SECRET_ACCESS_KEY='sample-aws-secret-acesss-key'

Note: IAM policy required is this.

Verify Installation

Check the wpa resource is accessible using kubectl

kubectl get wpa


Do install the controller before going with the example.

  • Create Deployment that needs to scale based on queue length.
kubectl create -f artificats/example-deployment.yaml
  • Create WPA object (example-wpa) that will start scaling the example-deployment based on SQS queue length.
kubectl create -f artifacts/example-wpa.yaml

This will start scaling example-deployment based on SQS queue length.

Why make a separate autoscaler CRD ?

Kubernetes does support custom metric scaling using Horizontal Pod Autoscaler. Before making this we were using HPA to scale our worker pods. Below are the reasons for moving away from HPA and making a custom resource:

TLDR; Don't want to write and maintain custom metric exporters? Use WPA to quickly start scaling your pods based on queue length with minimum effort (few kubectl commands and you are done !)

  1. No need to write and maintain custom metric exporters: In case of HPA with custom metrics, the users need to write and maintain the custom metric exporters. This makes sense for HPA to support all kinds of use cases. WPA comes with queue metric exporters(pollers) integrated and the whole setup can start working with 2 kubectl commands.

  2. Different Metrics for Scaling Up and Down: Scaling up and down metric can be different based on the use case. For example in our case we want to scale up based on SQS ApproximateNumberOfMessages length and scale down based on NumberOfEmptyReceives. This is because if the worker jobs watching the queue is consuming the queue very fast, ApproximateNumberOfMessages would always be zero and you don't want to scale down to 0 in such cases.

  3. Fast Scaling: We wanted to achieve super fast near real time scaling. As soon as a job comes in queue the containers should scale if needed. The concurrency, speed and interval of sync have been made configurable to keep the API calls to minimum.

  4. On-demand Workers: min=0 is supported. It's also supported in HPA.


WPA Resource

kind: WorkerPodAutoScaler
  name: example-wpa
  minReplicas: 0
  maxReplicas: 10
  targetMessagesPerWorker: 2
  deploymentName: example-deployment

WPA Controller

$ bin/darwin_amd64/workerpodautoscaler run --help
Run the workerpodautoscaler

  workerpodautoscaler run [flags]

  workerpodautoscaler run

      --aws-regions string            comma separated aws regions of SQS (default "ap-south-1,ap-southeast-1")
  -h, --help                          help for run
      --kube-config string            path of the kube config file, if not specified in cluster config is used
      --resync-period int             sync period for the worker pod autoscaler (default 20)
      --sqs-long-poll-interval int    the duration (in seconds) for which the sqs receive message call waits for a message to arrive (default 20)
      --sqs-short-poll-interval int   the duration (in seconds) after which the next sqs api call is made to fetch the queue length (default 20)
      --wpa-threads int               wpa threadiness, number of threads to process wpa resources (default 10)


  • Decide a tag and bump up the tag here and create and merge the pull request.

  • Get the latest master code.

git clone
cd k8s-worker-pod-autoscaler
git pull origin master

  • Build and push the image to Note: practodev push access is required.
git fetch --tags
git tag v0.2.2
make push
  • Create a Release in Github. Refer this and create a release. Release should contain the Changelog information of all the issues and pull request after the last release.

  • Publish the release in Github 🎉

  • For first time deployment use this.

  • For future deployments. Edit the image in deployment with the new tag.

kubectl edit deployment -n kube-system workerpodautoscaler


It would be really helpful to add all the major message queuing service providers. This interface implementation needs to be written down to make that possible.

  • After making code changes, run the below commands to build and run locally.
$ make build
making bin/darwin_amd64/workerpodautoscaler

$ bin/darwin_amd64/workerpodautoscaler run --kube-config /home/user/.kube/config


Thanks to kubernetes team for making crds and sample controller

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