Q: How do you implement autoscaling based on custom metrics (e.g., queue depth)?
Use KEDA (Kubernetes Event-Driven Autoscaling). KEDA supports 50+ built-in scalers:
#Kubernetes #Additional Kubernetes Scenarios (Q101-Q200) #L2 #Container Orchestration #K8s
🎙️ Candidate Opening & Architectural Context
""In our production Kubernetes clusters running microservices on EKS/AKS, this was a classic operational challenge. When addressing this question, I walk the interviewer through our production incident runbook: isolating the blast radius, checking diagnostic logs and metrics, and applying a safe fix.""
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🛠️ Production Runbook & Step-by-Step Resolution
1️⃣
Production Solution & Architecture
Use KEDA (Kubernetes Event-Driven Autoscaling). KEDA supports 50+ built-in scalers: This scales the worker deployment based on SQS queue depth — 1 pod per 5 messages. Scales to zero when queue is empty.
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
name: worker-scaler
spec:
scaleTargetRef:
name: worker-deployment
triggers:
- type: aws-sqs-queue
metadata:
queueURL: https://sqs.us-east-1.amazonaws.com/123/my-queue
queueLength: "5"
💡 The Senior SRE Gold Nugget (Key Architectural Takeaway)
"Pro-Tip: Use KEDA (Kubernetes Event-Driven Autoscaling). KEDA supports 50+ built-in scalers:."
⚡ 60-Second Elevator Pitch Talking Points
- Immediate Triage: Use KEDA (Kubernetes Event-Driven Autoscaling). KEDA supports 50+ built-in scalers:
- Run targeted verification commands before modifying configuration.
- Automate permanent guardrails (CI check, alerts, IaC policy) to prevent recurrence.
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