Kubernetes for DBAs

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Kubernetes is increasingly used to run database workloads in modern cloud-native platforms. As a DBA, understanding how Kubernetes works—and how it affects database operations—is key to ensuring performance, reliability, and recoverability.

This page explains how Kubernetes fits into the work of database administrators (DBAs) and highlights common patterns, tools, and best practices.

Why DBAs use Kubernetes

DBAs use Kubernetes to:

  • Automate the deployment and scaling of database instances
  • Run high availability database clusters with built-in failover and recovery
  • Manage database storage dynamically through Kubernetes APIs
  • Leverage Kubernetes-native backup and disaster recovery workflows
  • Monitor database workloads with integrated observability tools
  • Collaborate with platform teams to tune Kubernetes resources for optimal database performance

What DBAs manage in Kubernetes

As a DBA, you typically:

  • Manage StatefulSets that run database instances
  • Monitor PersistentVolumes (PV) and PersistentVolumeClaims (PVC) for database storage
  • Collaborate on StorageClass configurations to meet performance needs
  • Ensure proper resource requests and limits for database Pods
  • Participate in backup and restore operations integrated with Kubernetes
  • Tune database workloads for Kubernetes scheduling and performance patterns
  • Monitor database-specific metrics alongside Kubernetes-level observability
  • Assist in managing database security and access control within Kubernetes

You may also interact with Kubernetes-native database operators (such as the EDB Postgres operator, PGD operator, or CNPG) that automate much of this lifecycle.

Common tools for DBAs

  • kubectl: Core CLI for inspecting StatefulSets, Pods, PVCs, and Services
  • Helm: Deploy and manage Helm charts for database operators and workloads
  • Kubernetes-native operators: Manage database lifecycle (EDB Postgres operator, PGD operator, CNPG, etc.)
  • Prometheus / Grafana: Visualize database and Kubernetes metrics
  • Velero: Backup and restore Kubernetes resources and persistent volumes
  • Postgres-specific tools (pgBackRest, pgMonitor, pg_stat_statements): Tune and monitor database performance

Common questions DBAs ask

  • How do I manage database storage in Kubernetes?
  • How do StatefulSets affect database identity, scheduling, and recovery?
  • How do I tune resource requests and limits for optimal database performance?
  • How do I configure and monitor database backups through Kubernetes?
  • How do I observe database metrics alongside Kubernetes cluster metrics?
  • How does Kubernetes scheduling affect database replication and high availability?
  • What Kubernetes events should I monitor to detect database issues?
  • How do I coordinate database upgrades with Kubernetes workflows?

Best practices for DBAs

  • Work closely with platform teams to configure StorageClasses optimized for database performance
  • Understand the behavior of StatefulSets and how they interact with PersistentVolumes
  • Set explicit CPU and memory resource requests and limits to avoid contention and evictions
  • Use Kubernetes-native backup mechanisms that support PVC-level backups and snapshots
  • Monitor database Pods and PVC health regularly using Kubernetes and database tools
  • Configure readiness and liveness probes for database Pods carefully—these impact failover behavior
  • Validate that Kubernetes nodes and storage meet latency and throughput requirements for your database workloads
  • Participate in testing Kubernetes cluster upgrades to ensure compatibility with database operators and workloads

Next steps

Explore additional role-based guides:

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