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Performance Tuning

Purpose: For platform engineers, provides component-level tuning recommendations to optimize cluster throughput and latency at scale.

etcd Tuning

ParameterDefaultRecommended (Large)Effect
--quota-backend-bytes2 GB8 GBPrevents quota alarm at scale
--auto-compaction-retention5m10mReduces compaction pressure
--snapshot-count10,00050,000Fewer snapshot I/O events
Disk typeNVMe SSDetcd is latency-sensitive; p99 < 10ms required

API Server Tuning

ParameterDefaultRecommended (Large)Effect
--max-requests-inflight400800Higher concurrent read capacity
--max-mutating-requests-inflight200400Higher write throughput
--watch-cache-sizesdefaultIncrease for Pods, ServicesReduces etcd round-trips

Kubelet Tuning

ParameterDefaultRecommendedEffect
--max-pods110110Keep at validated maximum
--kube-api-qps50100Faster node status updates
--kube-api-burst100200Burst capacity for registration
--serialize-image-pullstruefalseParallel image pulls

FluxCD Tuning

ParameterDefaultRecommended (Large)Effect
--concurrent (source-controller)28Parallel source fetches
--concurrent (kustomize-controller)412Parallel reconciliations
--concurrent (helm-controller)48Parallel Helm installs
--requeue-dependency30s15sFaster dependency resolution

Platform Services

  • Kyverno: Set --backgroundScan=false for clusters above 5,000 pods; rely on admission-time enforcement
  • Prometheus: Use recording rules to pre-aggregate high-cardinality metrics
  • Loki: Increase ingester.chunk-idle-period to reduce chunk flush frequency

Applying Tuning

All tuning is applied via Kustomize overlays in the customer GitOps repository. See Customizing Services for the overlay pattern.