A Beginner's Guide to Kubernetes Event-Driven Autoscaling (KEDA)

Introduction

Kubernetes has revolutionized the way we deploy and manage containerized applications. However, efficiently scaling workloads based on demand remains a challenge. While Kubernetes’ native Horizontal Pod Autoscaler (HPA) scales applications based on CPU and memory usage, it lacks support for external event-based triggers. This is where KEDA (Kubernetes Event-Driven Autoscaler) comes in.

KEDA extends Kubernetes' autoscaling capabilities by enabling event-driven scaling based on external sources like message queues, databases, cloud events, and custom metrics. This guide will help beginners understand KEDA and how to integrate it into their Kubernetes environments.

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What is KEDA?

KEDA is an open-source project under the Cloud Native Computing Foundation (CNCF) that provides event-driven autoscaling for Kubernetes workloads. It works alongside Kubernetes’ HPA, allowing workloads to scale based on real-time external events rather than just CPU and memory metrics.

Key Features of KEDA:

• Event-Driven Scaling: Scales applications based on external event sources such as Kafka, RabbitMQ, AWS SQS, PostgreSQL, and Azure Event Hubs.

• Efficient Resource Utilization: Pods remain at zero when idle, reducing unnecessary resource consumption.

• Seamless HPA Integration: Works with Kubernetes' native HPA to ensure efficient autoscaling.

• Flexible Scaling Policies: Define how and when your applications should scale using custom triggers.

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Why Use KEDA?

KEDA is particularly useful in scenarios where workload demand fluctuates based on external events rather than system resource usage. Here are a few common use cases:

1. Queue-Based Processing: Scale applications dynamically based on the number of messages in a queue (e.g., RabbitMQ, Azure Queue Storage, AWS SQS).

2. Database-Driven Scaling: Scale services when new records are inserted into a database.

3. IoT and Streaming Applications: Automatically scale applications based on incoming IoT data or Kafka events.

4. Event-Driven Serverless Applications: Run workloads only when events occur, reducing infrastructure costs.

Advantages of KEDA Over CPU/Memory-Based HPA and Cluster Autoscaler

KEDA provides several advantages compared to traditional CPU and memory-based Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler (CA):

1. Event-Driven Scaling Instead of Just CPU/Memory

   • HPA only scales based on CPU and memory usage (with limited support for custom metrics).

   • KEDA allows scaling based on external events such as queue length in Kafka, database row count, HTTP request rate, or even cloud events.

2. Fine-Grained Control Over Scaling

   • HPA and CA react only when resource utilization crosses thresholds.

   • KEDA allows custom triggers (e.g., "Scale up when Kafka queue has more than 100 messages").

3. Cost Efficiency

   • With HPA, pods remain active based on CPU/memory, which may lead to unnecessary costs.

   • KEDA ensures pods run only when needed, reducing costs by automatically scaling down to zero when there are no events.

4. Faster Response to Workload Spikes

   • HPA and CA rely on metric scraping, which can delay autoscaling responses.

   • KEDA reacts to real-time events, ensuring faster pod provisioning and scaling.

5. Works Alongside HPA and CA

   • You can still use HPA for CPU/memory-based autoscaling alongside KEDA for event-driven scaling.

   • Cluster Autoscaler (CA) is still needed to scale up nodes when pods need more capacity.

6. Ideal for Asynchronous and Batch Workloads

   • If your application processes messages from a queue (e.g., RabbitMQ, Kafka, Azure Event Hub), KEDA is a perfect fit.

   • For serverless applications, it provides event-driven scaling without overprovisioning resources.

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Installing KEDA

KEDA can be installed using Helm, which simplifies the deployment process.

Step 1: Add the KEDA Helm Repository

helm repo add kedacore https://kedacore.github.io/charts
helm repo update

Step 2: Install KEDA

helm install keda kedacore/keda --namespace keda --create-namespace

To verify the installation, check if the KEDA pods are running:

kubectl get pods -n keda

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Configuring KEDA for Autoscaling

Step 1: Define a TriggerAuthentication

If your external source requires authentication, you need to define a TriggerAuthentication resource.

apiVersion: keda.sh/v1alpha1
kind: TriggerAuthentication
metadata:
  name: my-trigger-auth
spec:
  secretTargetRef:
    - parameter: connectionString
      name: my-secret
      key: connectionString

Step 2: Create a ScaleObject

A ScaleObject defines the scaling behavior for your workload.

Example: Scaling a Deployment based on a RabbitMQ queue length

apiVersion: keda.sh/v1alpha1
kind: ScaleObject
metadata:
  name: rabbitmq-scaler
spec:
  scaleTargetRef:
    name: my-app
  minReplicaCount: 1
  maxReplicaCount: 10
  triggers:
    - type: rabbitmq
      metadata:
        queueName: my-queue
        mode: QueueLength
        value: "5"
      authenticationRef:
        name: my-trigger-auth

This configuration scales the my-app deployment based on the number of messages in the RabbitMQ queue.

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Monitoring KEDA

To check the status of KEDA’s scaling operations:

kubectl get scaledobjects
kubectl get hpa

To view logs:

kubectl logs -f deployment/keda-operator -n keda

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Conclusion

KEDA brings event-driven scaling to Kubernetes, making it ideal for workloads that depend on external events. By integrating with various event sources, it ensures that applications scale efficiently and cost-effectively. Whether you’re managing queue-based jobs, database-triggered workloads, or streaming applications, KEDA provides a powerful and flexible solution for scaling in response to real-world demands.

Ready to get started? Install KEDA in your cluster and experiment with different event sources to see how it can optimize your application scaling!