KEDA vs Karpenter - Which One to Choose

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Good day. It's Thursday, Aug. 29, and in this issue, we're covering:

• KEDA vs Karpenter - Which One to Choose ?

• Google Cloud Announces Terraform Google Provider 6.0.0

• How Monzo run migrations across 2,800 microservices

• Runtime anomaly detection in Kubernetes

• How to Create Dockerfiles with GenAI

• Terraform with GitHub Actions : How to Manage & Scale

You share. We listen. As always, send us feedback at [email protected]

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Use Case

KEDA vs Karpenter - Which One to Choose?

Kubernetes practitioners often find themselves unsure whether to choose KEDA or Karpenter, as both offer valuable but distinct approaches to scaling within their clusters.

Let’s understand the basics first..

KEDA:

Specializes in scaling your applications based on external event sources, making it perfect for workloads that need to respond to specific events like messages in a queue.

• KEDA receives events from sources like Kafka or RabbitMQ.

• The KEDA operator processes these events using its metrics adapter, controller, and scaler.

• Metrics are sent to the Kubernetes API server.

• Kubernetes API server communicates with the Horizontal Pod Autoscaler (HPA).

• HPA adjusts the number of pods in your deployments based on these metrics.

Karpenter:

Automatically provisions new nodes specifically when unscheduled pods are detected, ensuring your cluster scales efficiently by adding the exact resources needed.

• Karpenter detects unscheduled pods in the cluster.

• The Karpenter controller defines the provisioning requirements using the Provisioner CRD.

• It interacts with the cloud provider API to provision new nodes.

• Once the nodes are ready, the Kubernetes scheduler assigns the unscheduled pods to these new nodes.

Quick Setup Comparison:

KEDA Sample Configuration:

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: my-scaledobject
spec:
  scaleTargetRef:
    name: app-deployment
  triggers:
  - type: kafka
    metadata:
      bootstrapServers: my-kafka-broker:9092
      topic: my-topic
      consumerGroup: my-group
      lagThreshold: "10"

Karpenter Sample Configuration:

apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
  name: default
spec:
  cluster:
    name: top-cluster
  requirements:
    - key: "node.kubernetes.io/instance-type"
      operator: In
      values: ["m5.large", "m5.xlarge"]
  limits:
    resources:
      cpu: 1000

When to Use:

KEDA: Ideal for event-driven workloads. If your application scales based on external events or metrics like queue length, Kafka messages, or custom metrics, KEDA integrates seamlessly.

Karpenter: Perfect for dynamic infrastructure scaling. If you need to scale your cluster by adding or removing nodes based on the demands of unscheduled pods, Karpenter is your go-to.

Karpenter efficiently manages cluster capacity, ensuring the right resources are available when workloads fluctuate.

The right choice depends on the specific needs of your workloads and cluster management strategy.

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