Kubernetes for site reliability engineers (SREs)

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Kubernetes is a critical platform for modern service reliability engineering. As an SRE, Kubernetes gives you tools to automate reliability practices, observe system health, and manage operational risks.

This page explains how Kubernetes fits into the work of site reliability engineers (SREs) and highlights common patterns, tools, and best practices.

Why SREs use Kubernetes

SREs use Kubernetes to:

  • Implement self-healing patterns for workloads
  • Automate scaling based on demand
  • Manage rollout and rollback strategies for minimizing disruption
  • Gain visibility into infrastructure, platform, and application health
  • Optimize resource usage for reliability and cost-efficiency
  • Define and monitor service level objectives (SLOs) for Kubernetes-based services
  • Respond effectively to incidents with Kubernetes-native tooling
  • Implement robust disaster recovery and backup strategies

What SREs manage in Kubernetes

As an SRE, you typically:

  • Monitor node, Pod, and Service health at scale
  • Define alerting and SLOs for Kubernetes services
  • Implement observability pipelines (metrics, logs, traces)
  • Tune scaling and resource allocations to meet reliability targets
  • Manage rollout/rollback policies for Deployments and StatefulSets
  • Investigate and respond to production incidents involving Kubernetes workloads
  • Manage cluster upgrades and their impact on reliability
  • Participate in capacity planning across Kubernetes clusters
  • Define and test backup and restore processes

You also collaborate closely with platform engineering and application teams to ensure reliability best practices are embedded into Kubernetes configurations.

Common tools for SREs

  • kubectl: Core CLI for inspecting Pods, nodes, and Services
  • Prometheus / Grafana: Metrics collection and visualization
  • Alertmanager: Manage alerts and on-call workflows
  • Loki / Fluent Bit / EFK stack: Log aggregation and querying
  • Jaeger / OpenTelemetry: Distributed tracing across Kubernetes workloads
  • k9s: Terminal UI for live Kubernetes troubleshooting
  • Argo Rollouts / Flagger: Advanced deployment and release strategies
  • Velero: Backup and restore tooling for Kubernetes resources and persistent volumes

Common questions SREs ask

  • How do I observe the health of Kubernetes services and nodes?
  • What metrics and events should I monitor for reliability risks?
  • How can I define and measure SLOs in Kubernetes environments?
  • How do I implement safe rollout and rollback patterns for Kubernetes Deployments and StatefulSets?
  • How do I optimize resource requests and limits to improve reliability?
  • What are common failure modes in Kubernetes and how do I mitigate them?
  • How can I automate detection and remediation of common issues?
  • How do I manage backup and restore workflows in Kubernetes?

Best practices for SREs

  • Use Kubernetes-native probes (liveness, readiness) to drive reliability decisions
  • Implement robust observability pipelines for metrics, logs, and traces
  • Monitor key Kubernetes health signals (node readiness, Pod restarts, container OOM kills, resource saturation)
  • Define clear SLOs and SLIs for Kubernetes-based services
  • Tune HPA/VPA (Horizontal/Vertical Pod Autoscaler) policies carefully to balance reliability and efficiency
  • Implement progressive delivery patterns (canary, blue/green) for reducing release risk
  • Regularly validate backup and restore procedures at the Kubernetes level
  • Document common troubleshooting playbooks for Kubernetes incident response
  • Participate in postmortems and use reliability learnings to improve Kubernetes platform and practices

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