Scaling Search Clusters with Apache Solr and Kubernetes
summary
Shows how Kubernetes plus Apache Solr are extended to perform physical and logical autoscaling of search clusters, using real metrics (query QPS and ingestion throughput) to trigger horizontal and vertical scaling across nodes and replicas. Describes tooling and manifests used to automate shard placement, pod lifecycle, and rolling OS upgrades, reports Lucidworks’ operational scale (over a thousand Solr clusters) as context, and gives concrete outcomes: reduced SLA violations and lower infra cost after autoscaler and placement policy are deployed.
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chapters (10)
- 0:00Start
- 3:33Run Solr Cloud on Kubernetes
- 5:28Solr Cloud Clusters within Kubernetes
- 10:19Scaling Solr Clusters without Manual Intervention
- 11:00Monitoring with Prometheus & Grafana
- 12:25Custom Metrics with Prometheus Adapter
- 17:09Solr Autoscaling
- 20:43Production Readiness
- 22:24Roadmap
- 23:31References
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