Top 10 Kubernetes Best Practices for Production Excellence

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KubernetesIt has become the standard for orchestrating containerized apps, automating their deployment, scaling and operation. However, running performant, resilient Kubernetes infrastructure requires experience – misconfigurations can easily happen.

In this Blog Post, we’ll cover Kubernetes best practices refined from real-world experience on what works well when operating large Kubernetes clusters, hundreds of deployments, and mission-critical distributed systems.

Kubernetes Best Practices

Let’s have a look at the 10 Kubernetes best practices that you can adapt for optimizations:

1. Namespace Organization

Kubernetes Namespaces are an effective way to segment cluster resources. When namespaces are organized correctly, they can improve resource allocation, manageability, and security. You can consider adopting a namespace structure that reflects your application’s architecture or team divisions.

apiVersion: v1
Kind: Namespace
  name: team-a

2. Resource Requests Limits

In a Kubernetes-based environment, efficient resource management is essential. Setting resource limits and requests for containers allows for optimal resource usage and prevents resource contention. Resource requests are the guaranteed amount of resource a container will need, while limits prevent excessive resource consumption.

Don’t forget to define CPU/RAM resources and limits for all Kubernetes containers based on pipeline stages and application metrics. This allows the Kubernetes Scheduler to make the best node placement decisions. You can tune pod/container resource iteratively.

    memory: "64Mi"
    cpu: "250m"
    memory: "128Mi"
    cpu: "500m"

3. Automate Kubernetes Deployments

Kubernetes deployments can be automated through CI/CD pipelines. This is a better option than manually applying kubectl commands. Relying on developers to run imperative kubectl can lead to configuration drift between environments. Changes are made directly to critical infrastructure without any testing, reviews or reproducibility.

Instead, all application deployments – whether monoliths or microservices – should be wrapped into automated git-based CI/CD workflows as much as possible. This means integrating Kubernetes yaml Manifests into specialized Pipelines offered GitLab GitHub Actions Jenkins etc.

Declarative pipelines are triggered by triggers such as git checkins and image tagging. These pipelines systematically build, verify, and rollout changes to namespaces and Clusters in a controlled, consistent manner. At each stage of the pipeline, appropriate tests, quality gates, and confirmatory checks are executed.

4. Health Probes

Readiness and liveness probing should be configured in order to monitor container health metrics, and catch critical issues as early as possible. Readiness probes can help determine if a pod-based application is ready to handle user traffic immediately after bootup. This helps to avoid sending requests too early, which could cause errors.

Liveness probes, on the other hand, periodically check container vitality symptoms as they continue to run. The infrastructure would not be overwhelmed by things like unresponsive app endpoints, database connectivity problems or even app crashes.

Kubernetes restarts pods based on probe failures at configurable intervals in order to self-heal. Sophisticated mechanisms such as load balancers are integrated natively with the probes to gracefully remove failing instances from traffic, until probes declare that they are ready again.

Kubernetes supports various types of health probes – HTTP endpoint checks, container process exit codes, delays to account for bootup. Set probe frequencies carefully to avoid overworking systems.

    path: /healthz
    port: 8080
  initialDelaySeconds: 3
  periodSeconds: 3

5. Implement pod health checks

Liveness probes are vitality checks that continually ping endpoints in a running pod. This is to ensure that containers are still responding correctly. This way, things like unexpected crashes and network disconnections or uncaught errors would be caught.

Kubernetes will automatically terminate and restart affected pods when liveness check failures exceed defined thresholds. This is to allow pods to self-heal.

Readiness probes let you know if the pod has just been booted and is really ready to accept application traffic. Async initialization tasks, such as loading machine learning models and warming up connection pools, can be time-consuming for complex apps.

Readiness probes give services more time to confirm that pods are ready before they can route traffic to newly deployed instances. This will prevent failures due to pods not being ready when they are asked to process requests too early.

6. Custom Resource Definitions

Kubernetes lets you extend its functionality using Custom Resource Definitions. CRDs allow you to automate complex tasks and introduce domain-specific abstractions. This extensibility allows you to customize Kubernetes according to the specific requirements of your application.

kind: CustomResourceDefinition
    plural: myapps
  version: v1

7. Backup and Disaster Recovery

Backup the essential state of the cluster regularly and test restores, including:

  • Cluster resource definitions
  • Secrets and keys to Kubernetes
  • PersistentVolumes – with snapshots
  • Database backups running in Kubernetes

Well, it’s obvious to protect your data and applications from loss is a critical consideration in any production environment. It is essential to implement regular backups, and create a robust disaster-recovery plan. You can use tools such as Velero to perform cluster wide backups and streamline recovery in the event of a catastrophic failure or data loss.

8. Update Strategies

Rolling updates and canary releases are essential strategies that minimize downtime when updating applications. Kubernetes enables you to define update strategy in your deployment configurations. This allows a controlled and phased release of new versions. This ensures that your applications are available and responsive during the update process.

    maxUnavailable: 1
    maxSurge: 1

9. Use a container registrar to store your images

A container registry is a convenient way to store and share your container images. You can use a registry to ensure that your images will be versioned, secured, and easily accessible. Container registries let you store your images centrally, making it easy to share with other users and teams. They offer features like access control, scanning images, and vulnerability detection to help you secure your images.

10. StatefulSets can be used to stateful applications

StatefulSets are a way of managing stateful applications, such as databases and queues. StatefulSets provide persistent storage and a unique identifier for each instance of an application. This is crucial for stateful applications that require unique identifiers and persistent storage in order to function properly

For stateful applications like databases, configure StatefulSets instead of Deployments – these maintain persistent volumes, graceful deployments, ordered scaling, stable network IDs and startup/teardown procedures across pod restarts and cluster maintenance events.

Final Thoughts

In conclusion, optimizing Kubernetes is a strategic adherence to best practices. Organize namespaces to optimize resource management. Set resource requests and limitations, and implement health probing for reliability. Kubernetes secrets will help you secure sensitive data and Horizontal Pod Autoscaling will allow dynamic resource adjustments.

Controlled update strategies and Pod disruption budgets reduce downtime. Custom Resource Definitions can be used to extend the system, and backup and disaster recover are important for data security.

Read More:


  1. How do you optimize Kubernetes Pod resources?

    Define CPU/RAM requests and limits thoughtfully based on profiling each application’s runtime behavior across various load levels. These should be continuously tuned to balance constraints and excessive provisioning.

  2. What types health checks can improve application reliability and security?

    Readiness and liveness probing should be configured so that errors are caught early and pods which are not responsive can be restarted automatically. Integrate with load balancing layer to direct traffic accordingly.

  3. How should Kubernetes application configuration be handled?

    Decouple application parameters that can be configured from container images. Instead, use ConfigMaps (or Secrets) and Volumes. These are injected in the form of environment variables or volumes. This prevents rebuilds when changes are made.

  4. Why use Kubernetes namespaces?

    Namespaces in Kubernetes are used to logically separate teams, applications, and environments. Set resource quotas per namespace for better governance.

  5. How can Kubernetes services be deployed automatically?

    Using GitOps pipelines, containerize apps and orchestrate workflows for reproducible, consistent CD. Automate rollback procedures and self-healing.

  6. Why use StatefulSets instead of Deployments when running stateful apps?

    StatefulSets maintains persistent volumes, stable Network IDs, gracious deployment and scaling abilities necessary for databases and storage service to operate correctly.

  7. What are the ingress controllers in Kubernetes offering?

    Ingress objects provide a central way to specify routing rules, TLS terminations, rate limiting, and other edge service behavior. Use managed ingress to handle production traffic.

  8. How to schedule Kubernetes clusters optimally

    Affinity/anti-affinity rules allow sophisticated pod spreading across topology domains for high availability along with attracting pods to common nodes.

  9. How can Kubernetes Configurations be backed up?

    For DR, it is important to regularly back up essential components, such as databases, cluster definitions and PersistentVolume Snapshots.

  10. What are some best practices to help with Kubernetes monitoring/logging?

    Centrally aggregate logs. Visualize time-series metrics using Prometheus & Grafana. Jaeger can be used to trace request flows. This observability for Kubernetes is invaluable.

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