Event Driven Autoscaling on Kubernetes with KEDA Scalars

The developer at Mystique Unicorn are interested in building their application using event-driven architectural pattern to process streaming data. For those who are unfamiliar, An event-driven architecture uses events to trigger and communicate between decoupled services and is common in modern applications built with microservices. An event is a change in state, or an update, like an item being placed in a shopping cart on an e-commerce website.

In this application, they will have their physical stores, send a stream sales and inventory related events to a central location(a message queue), where multiple downstream systems are consuming these events. The consumers are running on kubernetes pods.

Native kubernetes Horizontal Pod Autoscaler (HPA) scales resources based on a provided metric (usually CPU or Memory utilization), however it does not have native support for event-sources(yet). As such, there is a delay in scale-up of new Pods while the event source gets populated with events. In this case the queue length increases to undesirable levels before new consumer pods are added to the deployment. AWS SQS exposes a metric ApproximateNumberOfMessagesVisible - Which is the number of messages available for retrieval from the queue. They are interested in using metrics like this to scale the consumers.

Can you help them to solve this problem?

🎯 Solutions

Kubernetes has native support metrics APIs¹. We need a mechanism to scale our deployments using custom metrics from say Cloudwatch or message queue length to be fed into this API. There are quite a few ways of doing this, One method is to use AWS Cloudwatch Metrics Adapter ^2,3. Unfortunately, This adapter does not seem to be actively developed anymore. That last commits were 2 years back.

Introducing KEDA: Kubernetes-based Event-Driven Auto-Scaler⁴ - KEDA is a single-purpose, lightweight, open-source component that allows you to easily scale Kubernetes applications based on metrics coming from a variety of cloud providers and other technologies that are not supported out-of-the-box. KEDA works alongside standard Kubernetes components like the HPA and can extend its functionalities by allowing it to scale according to the number of events in the event source.In this demo, We will learn how we can use KEDA to auto-scale our consumer pods based on message queue depth.

We will build a EKS cluster with a managed node groups running 2 t2.medium nodes. We will also have a producer deployment writing messages to a SQS queue. A consumer running as deployment will process those messages and store them on S3. Here are the attributes of cluster that are baked into the CDK stacks.

EKS Cluster - Our primary cluster with 2 managed node groups.
SQS Queue - A standard SQS queue with a visibility timeout of 30 seconds, This allows our consumer 30 seconds to successfully process message and delete them from the queue.
Sales Events Bucket - Persistent storage for the consumer to store the events.
Producer - A deployment running an generic container python:3.8.10-alpine. The producer code is pulled from this github directly. It will produce 1 message every 2 seconds and runs to produce a maximum of 10000 messages. Being a deployment, it will be restarted and goes on to produce the next batch of 10000 messages.
Consumer - A deployment running generic container python:3.8.10-alpine. The consumer code is pulled from this github directly. Every 10 seconds it will process messages in batches of 5. The incoming messages will be stored persistently in Sales Events Bucket. It will process a maximum of 10000 messages.

Note:
1. Both the producer and consumer use AWS IAM Roles for Service Accounts(IRSA)⁵ to interact with AWS resources. If you are unfamiliar with IRSA, pods require privileges to access AWS resources like SQS, S3, RDS etc. We can create a IAM role with limited permission and enable kubernetes service account used by the application to assume that role. This enables us to provision fine-grained permissions for each of our application groups. In this case, the producer has only access to one SQS queue; Whereas the consumer has access to SQS and to S3
2. As there are still some rough edges to get KEDA working with AWS IRSA⁵(Check out this github issue), we will have to use the helm cli to install Keda to apply the patch. I will show you what needs to be done during the stack deployments.

🧰 Prerequisites

This demo, instructions, scripts and cloudformation template is designed to be run in us-east-1. With few modifications you can try it out in other regions as well(Not covered here).
- 🛠 AWS CLI Installed & Configured - Get help here
- 🛠 AWS CDK Installed & Configured - Get help here
- 🛠 Python Packages, Change the below commands to suit your OS, the following is written for amzn linux 2
  - Python3 - yum install -y python3
  - Python Pip - yum install -y python-pip
  - Virtualenv - pip3 install virtualenv

⚙️ Setting up the environment

Get the application code

git clone https://github.com/miztiik/scale-eks-with-keda
cd scale-eks-with-keda

🚀 Prepare the dev environment to run AWS CDK

We will use cdk to make our deployments easier. Lets go ahead and install the necessary components.

# You should have npm pre-installed
# If you DONT have cdk installed
npm install -g aws-cdk

# Make sure you in root directory
python3 -m venv .venv
source .venv/bin/activate
pip3 install -r requirements.txt

The very first time you deploy an AWS CDK app into an environment (account/region), you’ll need to install a bootstrap stack, Otherwise just go ahead and deploy using cdk deploy.

cdk bootstrap
cdk ls
# Follow on screen prompts

You should see an output of the available stacks,

eks-cluster-vpc-stack
eks-cluster-stack
ssm-agent-installer-daemonset-stack
sales-events-bkt-stack
sales-events-producer-stack
sales-events-consumer-stack
eks-keda-stack

🚀 Deploying the application

Let us walk through each of the stacks,
- Stack: eks-cluster-vpc-stack To host our EKS cluster we need a custom VPC. This stack will build a multi-az VPC with the following attributes,
  - VPC:
    - 2-AZ Subnets with Public, Private and Isolated Subnets.
    - 1 NAT GW for internet access from private subnets
  Initiate the deployment with the following command,
```
cdk deploy eks-cluster-vpc-stack
```
  After successfully deploying the stack, Check the Outputs section of the stack.
- Stack: eks-cluster-stack As we are starting out a new cluster, we will use most default. No logging is configured or any add-ons. The cluster will have the following attributes,
  - The control pane is launched with public access. i.e the cluster can be access without a bastion host
  - c_admin IAM role added to aws-auth configMap to administer the cluster from CLI.
  - One managed EC2 node group created from a launch template
    - It create two t3.medium instances running Amazon Linux 2
    - Auto-scaling Group with 2 desired instances.
    - The nodes will have a node role attached to them with AmazonSSMManagedInstanceCore permissions
  In this demo, let us launch the EKS cluster in a custom VPC using AWS CDK. Initiate the deployment with the following command,
```
cdk deploy eks-cluster-stack
```
  After successfully deploying the stack, Check the Outputs section of the stack. You will find the *ConfigCommand* that allows yous to interact with your cluster using kubectl
- Stack: ssm-agent-installer-daemonset-stack This EKS AMI used in this stack does not include the AWS SSM Agent out of the box. If we ever want to patch or run something remotely on our EKS nodes, this agent is really helpful to automate those tasks. We will deploy a daemonset that will run exactly once? on each node using a cron entry injection that deletes itself after successful execution. If you are interested take a look at the deamonset manifest here stacks/back_end/eks_cluster_stacks/eks_ssm_daemonset_stack/eks_ssm_daemonset_stack.py. This is inspired by this AWS guidance⁷.
  
  Initiate the deployment with the following command,
```
cdk deploy ssm-agent-installer-daemonset-stack
```
  After successfully deploying the stack, You can use connect to the worker nodes instance using SSM Session Manager⁸.
- Stack: sales-events-bkt-stack
  
  This stack will create the s3 bucket. We will add a bucket policy to delegate all access management to be done by access points. Although not required for this demo, we may use it in the future.
  
  Initiate the deployment with the following command,
```
cdk deploy sales-events-bkt-stack
```
  After successfully deploying the stack, Check the Outputs section of the stack. You will find the SalesEventsBucket.
- Stack: sales-events-producer-stack
  
  We need an SQS queue for our producer to ingest message, So we will start by creating an SQS queue with the following attributes.
  - Source Queue: reliable_q - Producers will send their messages to this queue.
  - Any new message will be hidden(DelaySeconds) for 2 seconds
  - New message will be hidden²(DelaySeconds) for 2 seconds
  - To ensure messages are given enough time to be processed by the consumer, the visibility timeout is set to 30 seconds.
  - No Dead-Letter-Queue(DLQ) is set, If you are interested in knowing more about DLQ, check out this demo3.
  Now that we have the queue, lets discuss the producer.
  - Namespace: sales-events-producer-ns - We start by creating a new namespace. As this will be the usual case, where producers will be residing in their own namespace.
  - Deployment: sales-events-producer - This stack will create a kubernetes deployment within that namespace with 1 replica running the vanilla container python:3.8.10-alpine. The producer code is pulled using wget <URL> from the container CMD. If you are interested take a look at the producer code here stacks/back_end/eks_sqs_producer_stack/lambda_src/stream_data_producer.py. At this moment you have two customization possible. They are all populated with defaults, They can be modified using pod environment variables
    - TOT_MSGS_TO_PRODUCE- Use this to define the maximum number of messages you want to produce per pod lifecycle. If you want to produce a maximum of 1000. As the pod exits successfully upon generating the maximum messages. Kubernetes will restart the pod automatically and triggering the next batch of 1000 messages. _Defaults to 10000.
    - WAIT_SECS_BETWEEN_MSGS - Use this to vary the message ingestion rate. If you want higher number of messages, reduce this to lower values. Setting it to 0 will have a very high rate of ingest. Defaults to 2 (waits for 2 seconds between burst of message ingestion)
  Finally, although not mentioned explicitly, It is quite possible to increase the replicas to generate more messages to the queue. ^{_{TODO:Another interesting feature to add to the producer: Deliberately generate duplicate messages.}}
  
  Initiate the deployment with the following command,
```
cdk deploy sales-events-producer-stack
```
  After successfully deploying the stack, Check the Outputs section of the stack. You will find the ReliableMessageQueue and ReliableMessageQueueUrl resource. You should be able to run kubectl command to list the deployment kubectl get deployments -n sales-events-producer-ns.
- Stack: sales-events-consumer-stack
  
  Just like our producer, the consumer will also be running as a deployment. We can make a case for running a kubernetes Job⁴ or even a CronJob⁵. I would like to reserve that for a future demo, as the cronjob is only stable in kubernetes v1.21. Let us take a closer look at our deployment.
  - Namespace: sales-events-consumer-ns - We start by creating a new namespace. As this will be the usual case, where consumers will be residing in their own namespace.
  - Deployment: sales-events-consumer - This stack will create a kubernetes deployment within that namespace with 1 replica running the vanilla container python:3.8.10-alpine. The consumer code is pulled using wget <URL> from the container CMD. If you are interested take a look at the consumer code here stacks/back_end/eks_sqs_consumer_stack/lambda_src/stream_data_consumer.py. At this moment you have few customization possibles. They are all populated with defaults, They can be modified using pod environment variables.
    - MAX_MSGS_PER_BATCH- Use this to define the maximum number of messages you want to get from the queue for each processing cycle. For example, Set this value to 10, if you want to process a batch of 10 messages . Defaults to 5.
    - TOT_MSGS_TO_PROCESS - The maximum number of messages you want to process per pod. The pod exits successfully upon processing the maximum messages. Kubernetes will restart the pod automatically and initiating the next batch of messages to process. Defaults to 10000.
    - MSG_POLL_BACKOFF - Use this to define, how often you want the consumer to poll the SQS queue. This is really important to avoid being throttled by AWS when there are no messages. This parameter only comes into effect only when there are no messages in the queue. I have implemented a crude back-off that will double the wait time for each polling cycle. It starts by polling after 2, 4, 8...512secs. It goes up to a maximum of 512 and resets to 2 after that. Defaults to 2.
    - MSG_PROCESS_DELAY - Use this to define the wait time between messaging processing to simulate realistic behaviour. Set this to 30 if you want to wait 30 seconds between every processing cycle. Defaults to 10
  Initiate the deployment with the following command,
```
cdk deploy sales-events-consumer-stack
```
  After successfully deploying the stack, Check the Outputs section of the stack. You should be able to run kubectl command to list the deployment kubectl get deployments -n sales-events-consumer-ns.
- Stack: eks-keda-stack
  
  There are many ways to deploy KEDA to our EKS cluster - Helm⁹, YAML file etc. We want our KEDA to be able to interact with AWS to get the SQS metrics¹⁰ like ApproximateNumberOfMessagesVisible. To do this, we need to bootstrap the keda-operator service account with an IAM Role annotation⁵.
  
  To do that, we will use this stack to create the following resources,
  - Namespace: keda - A dedicated namespace for all KEDA resources.
  - AWS IAM Role:
    - Permissions for CloudWatch and SQS, (As of now it is extremely permissive, we should scope it down further; topic for another blog incoming)
    - The KEDA service account should be able to assume this IAM Role. We will add the OIDC provider trust relationship to do that. A sample of the trust relationship is shown below,
```
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Principal": {
        "Federated": "arn:aws:iam::111122223333:oidc-provider/oidc.eks.us-east-1.amazonaws.com/id/9FBA1ECBCD00CB8AEF0D8"
      },
      "Action": "sts:AssumeRoleWithWebIdentity",
      "Condition": {
        "StringEquals": {
          "oidc.eks.us-east-1.amazonaws.com/id/9FBA1ECBCD00CB8AEF0D8:sub": "system:serviceaccount:keda:keda-operator"
        }
      }
    }
  ]
}
```
  - Service Account: keda-operator. This service account will be annotated with the IAM Role arn created in the previous step. An example annotation looks like this,
    
    eks.amazonaws.com/role-arn = arn:aws:iam::111122223333:role/eks-keda-stack-kedasvcaccntrole
  Note: KEDA is evolving very fast and there is possibility that some of the APIs and kubernetes object names might change when it graduates, refer to the docs if you run into issues.
  
  Initiate the deployment with the following command,
```
cdk deploy eks-keda-stack
```
  After successfully deploying the stack, Check the Outputs section of the stack.
- Install KEDA on EKS
  
  We are all set to install KEDA on our EKS cluster. To allow KEDA to take advantage of the previously created keda namespace and keda-operator service account, we will use the helm cli to install KEDA. If you do not have helm in your local environment, use these instructions¹¹ to get it. As of writing this demo, KEDA 2.3.0 is the latest version, update the KEDA_VERSION variable if needed.
```
# Ref: https://keda.sh/docs/2.3/deploy/
KEDA_VERSION=2.3.0

helm install keda kedacore/keda \
    --version ${KEDA_VERSION} \
    --set serviceAccount.create=false \
    --set serviceAccount.name=keda-operator \
    --set  podSecurityContext.fsGroup=1001 \
    --set podSecurityContext.runAsGroup=1001 \
    --set podSecurityContext.runAsUser=1001  \
    --namespace keda
```
  The additional instructions for the security context allows KEDA to use the IRSA mounted secrets, as this is a patch for a known github issue around mounting secrets and permissions to access them.
  
  After successful deployment, confirm your KEDA operator is able to see the AWS secrets
```
helm list -A
kubectl get pods -n keda
kubectl describe pods keda-operator-7d697b9c5b-nvbmj -n keda | grep -i aws   # ensure IRSA annotation working.
```
  Expected Outputs:
```
 ]# kubectl get pods -n keda
 NAME                                             READY   STATUS    RESTARTS   AGE
 keda-operator-6c859bf568-ztgv2                   1/1     Running   0          21h
 keda-operator-metrics-apiserver-d4d44789-tqctz   1/1     Running   0          21h
 ]# kubectl describe pods keda-operator-6c859bf568-ztgv2 -n keda | grep -i aws
       AWS_DEFAULT_REGION:           us-east-1
       AWS_REGION:                   us-east-1
       AWS_ROLE_ARN:                 arn:aws:iam::111122223333:role/eks-keda-stack-kedasvcaccntrole
       AWS_WEB_IDENTITY_TOKEN_FILE:  /var/run/secrets/eks.amazonaws.com/serviceaccount/token
       /var/run/secrets/eks.amazonaws.com/serviceaccount from aws-iam-token (ro)
   aws-iam-token:
```
- Deploy SQS Consumer Scalar
  
  Now that we have all the necessary pieces to deploy our consumer scaler. Here is my manifest for the scalar.The same yaml is also in the repo under the directory stacks/back_end/keda_scalers/keda-sqs-consumer-scalar-with-irsa.yml Let us walk through the specification.
```
---
apiVersion: keda.sh/v1alpha1 # https://keda.sh/docs/2.0/concepts/scaling-deployments/
kind: ScaledObject
metadata:
  name: sales-events-consumer-scaler
  namespace: sales-events-consumer-ns
  labels:
    app: sales-events-consumer
    deploymentName: sales-events-consumer
spec:
  scaleTargetRef:
    kind: Deployment
    name: sales-events-consumer
  minReplicaCount: 1
  maxReplicaCount: 50
  pollingInterval: 30
  cooldownPeriod: 500
  triggers:
    - type: aws-sqs-queue
      metadata:
        queueURL: https://sqs.us-east-2.amazonaws.com/111122223333/reliable_message_q
        queueLength: "50"
        awsRegion: "us-east-2"
        identityOwner: operator
---
```
  KEDA SQS Scalar¹² is a kubernetes object of kind ScaledObject. We provide the target deployment to scale with min, max values. We also need to specify the queueURL, queueLength and awsRegion. You should be able to find the ReliableMessageQueueUrl from the sales-events-producer-stack outputs.
  
  Once you have updated those values to match your environment, lets deploy this manifest,
  
  Setup Kubeconfig: You should be able to find the kubeconfig command in the output sections of this stack: eks-cluster-stack.
```
# kubeconfig command is in the cfn output of eks-cluster-stack
aws eks update-kubeconfig \
   --name c_1_event_processor \
   --region us-east-2 \
   --role-arn arn:aws:iam::11112223333:role/eks-cluster-stack-cAdminRole
```
  Deploy KEDA Scalar:
```
$] kubectl apply -f stacks/back_end/keda_scalers/keda-sqs-consumer-scalar-with-irsa.yml
scaledobject.keda.sh/sales-events-consumer-scaler created
```
  That is it!. We have setup our consumer to scale automatically based on custom metrics. Let us check if everything is working as expected.

🔬 Testing the solution

As the producer and consumer deployments will be automatically started by the kubernetes cluster. We can check the scalar in action using the following commands. You can watch the scaler in action,

Check Consumer Scalar:

$]kubectl get hpa -w -n sales-events-consumer-ns
NAME                                    REFERENCE                          TARGETS           MINPODS   MAXPODS   REPLICAS   AGE
keda-hpa-sales-events-consumer-scaler   Deployment/sales-events-consumer   22500m/50 (avg)   1         50        4          5m18s
keda-hpa-sales-events-consumer-scaler   Deployment/sales-events-consumer   22250m/50 (avg)   1         50        4          5m21s
keda-hpa-sales-events-consumer-scaler   Deployment/sales-events-consumer   29667m/50 (avg)   1         50        3          5m37s

Another way to check the events,

kubectl get events -n sales-events-consumer-ns | grep horizontalpodautoscaler
12m         Normal   SuccessfulRescale   horizontalpodautoscaler/keda-hpa-sales-events-consumer-scaler   New size: 4; reason: external metric AWS-SQS-Queue-reliable_message_q(&LabelSelector{MatchLabels:map[string]string{scaledObjectName: sales-events-consumer-scaler,},MatchExpressions:[]LabelSelectorRequirement{},}) above target
15m         Normal   SuccessfulRescale   horizontalpodautoscaler/keda-hpa-sales-events-consumer-scaler   New size: 3; reason: All metrics below target
5m12s       Normal   SuccessfulRescale   horizontalpodautoscaler/keda-hpa-sales-events-consumer-scaler   New size: 3; reason: All metrics below target
4m10s       Normal   SuccessfulRescale   horizontalpodautoscaler/keda-hpa-sales-events-consumer-scaler   New size: 2; reason: All metrics below target
3m55s       Normal   SuccessfulRescale   horizontalpodautoscaler/keda-hpa-sales-events-consumer-scaler   New size: 3; reason: external metric AWS-SQS-Queue-reliable_message_q(&LabelSelector{MatchLabels:map[string]string{scaledObjectName: sales-events-consumer-scaler,},MatchExpressions:[]LabelSelectorRequirement{},}) above target
97s         Normal   SuccessfulRescale   horizontalpodautoscaler/keda-hpa-sales-events-consumer-scaler   New size: 4; reason: external metric AWS-SQS-Queue-reliable_message_q(&LabelSelector{MatchLabels:map[string]string{scaledObjectName: sales-events-consumer-scaler,},MatchExpressions:[]LabelSelectorRequirement{},}) above target

You may face an error on the AWS GUI. For example, You may not be able to see workloads or nodes in your AWS Management Console. Make sure you using the same user/role you used to deploy the cluster. If they are different then you need to update the console user to kubernetes configmap.

Check Sales Events Consumer: Our deployment of the consumer should already be running and consuming messages from our queue and writing them to our S3 bucket SalesEventsBucket.

kubectl get deployments -n sales-events-consumer-ns

Expected Output,

$]kubectl get deployments -n sales-events-consumer-ns
NAME                    READY   UP-TO-DATE   AVAILABLE   AGE
sales-events-consumer   3/3     3

kubectl get pods -n sales-events-consumer-ns

Expected Output,

NAME                                     READY   STATUS    RESTARTS   AGE
sales-events-consumer-67c774c996-4smnw   1/1     Running   0          10m
sales-events-consumer-67c774c996-94svr   1/1     Running   0          3h10m
sales-events-consumer-67c774c996-9m4wb   1/1     Running   0          98m

Check SQS Queue:

You can also check the SQS console to verify the scaling in action. Check out the Monitoring tab of your queue. I have been running my cluster for quite some time with the scalar in action. Your view might be different than the one shown here, Here in the below screenshot, you can notice that the total number messages does not increase beyond a level as the scalar starts more consumers to process those messages.
Check S3 Data Bucket for processed events:

Navigate to SalesEventsBucket in S3 Console, Here you can notice that the events are stored under two prefixes sale_event or inventory_event. As an example, here under the inventory_event prefix you will find the files received by our consumer function. You can use S3 select to view the files or download them and view them locally.

📒 Conclusion

Here we have demonstrated how to use KEDA to scale our kubernetes deployments using customer metrics events. As KEDA also exposes metrics to Prometheus, You can extend this solution to easily scrape the KEDA metrics to Prometheus and monitor them accordingly.
🧹 CleanUp

If you want to destroy all the resources created by the stack, Execute the below command to delete the stack, or you can delete the stack from console as well
- Resources created during Deploying The Application
- Delete CloudWatch Lambda LogGroups
- Any other custom resources, you have created for this demo
```
# Delete from cdk
cdk destroy

# Follow any on-screen prompts

# Delete the CF Stack, If you used cloudformation to deploy the stack.
aws cloudformation delete-stack \
  --stack-name "MiztiikAutomationStack" \
  --region "${AWS_REGION}"
```
This is not an exhaustive list, please carry out other necessary steps as maybe applicable to your needs.

📌 Who is using this

This repository aims to show how to use KEDA to new developers, Solution Architects & Ops Engineers in AWS. Based on that knowledge these Udemy course #1, course #2 helps you build complete architecture in AWS.

💡 Help/Suggestions or 🐛 Bugs

Thank you for your interest in contributing to our project. Whether it is a bug report, new feature, correction, or additional documentation or solutions, we greatly value feedback and contributions from our community. Start here

Name		Name	Last commit message	Last commit date
Latest commit History 13 Commits
.github		.github
images		images
stacks		stacks
.gitignore		.gitignore
Makefile		Makefile
README.md		README.md
app.py		app.py
cdk.json		cdk.json
keda_crd_cleanup.md		keda_crd_cleanup.md
requirements.txt		requirements.txt
setup.py		setup.py

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Event Driven Autoscaling on Kubernetes with KEDA Scalars

🎯 Solutions

🧰 Prerequisites

⚙️ Setting up the environment

🚀 Prepare the dev environment to run AWS CDK

🚀 Deploying the application

🔬 Testing the solution

📒 Conclusion

🧹 CleanUp

📌 Who is using this

💡 Help/Suggestions or 🐛 Bugs

👋 Buy me a coffee

📚 References

🏷️ Metadata

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Event Driven Autoscaling on Kubernetes with KEDA Scalars

🎯 Solutions

🧰 Prerequisites

⚙️ Setting up the environment

🚀 Prepare the dev environment to run AWS CDK

🚀 Deploying the application

🔬 Testing the solution

📒 Conclusion

🧹 CleanUp

📌 Who is using this

💡 Help/Suggestions or 🐛 Bugs

👋 Buy me a coffee

📚 References

🏷️ Metadata

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