Autoscaling Search and Analytics in Kubernetes: The OpenSearch Operator Advantage
summary
A concrete autoscaling pipeline ties the OpenSearch Operator to GitOps and monitoring: OpenSearch Operator uses a smart scaler and pre‑drain to avoid red clusters, metrics come from a Prometheus exporter and Prometheus rules (example thresholds set to 6 and 11, 1 minute evaluation), Alertmanager webhooks feed Argo Events into Argo Workflows that commit replica changes back to Git, and Argo CD applies them. Demo runs on K3s/Docker Desktop with a 6‑node cluster, successfully scaled replicas (3→2) without data loss.
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chapters (10)
- 0:00Autoscaling OpenSearch intro
- 1:04Scaling OpenSearch challenges
- 3:11Operator features and benefits
- 5:59GitOps integration
- 10:13Monitoring with Prometheus/Grafana
- 14:07Automated scaling with alerts
- 15:11Argo Events for scaling
- 18:48Live autoscaling demo
- 26:51Cost benefits of autoscaling
- 27:33Q&A
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