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Top 10 Kubernetes Best Practices for Production Excellence


Estimated reading time: 8 minutes

Kubernetes has quickly become the standard for container orchestration, automating deployment, scaling, and operations of containerized apps. 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 a powerful way to segment your cluster resources. If you properly organize namespaces it can significantly enhance manageability, security, and resource allocation. You can consider adopting a namespace structure that reflects your application’s architecture or team divisions.

apiVersion: v1
kind: Namespace
metadata:
name: team-a

2. Resource Requests and Limits

Efficient resource management is crucial in a Kubernetes environment. Setting resource requests and limits for containers ensures optimal resource utilization and prevents resource contention. Resource requests indicate the guaranteed amount of resources a container needs, while limits prevent a container from consuming excessive resources.

Make sure to define CPU/RAM resource requests and limits diligently on all Kubernetes pods based on application metrics and pipeline stages. This allows the Kubernetes scheduler to make optimal node placement decisions. You can tune these pod/container resources iteratively.

resources:
requests:
memory: “64Mi”
cpu: “250m”
limits:
memory: “128Mi”
cpu: “500m”

3. Automate Kubernetes Deployments

Automating Kubernetes deployments through CI/CD pipelines is highly recommended over manual adhoc kubectl apply commands. Relying solely on developers running imperative kubectl leads to configuration drift across environments. Changes get made directly on critical infrastructure without reviews, testing 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 directly into specialized Pipelines offered by GitLab, GitHub Actions, Jenkins etc.

Triggers ranging from git checkins, image tagging to production alerts then kick off declarative pipelines. These systematically build, validate, rollout configuration changes into namespaces and clusters in consistent, controllable manner. Appropriate tests, quality gates and confirmatory checks execute at each pipeline stage for comprehensive governance.

4. Health Probes

Readiness and liveness probes should be configured to monitor container health metrics and catch critical issues early on. Readiness probes help indicate if an application running in pods is actually prepared to start handling user traffic after bootup. This avoids sending requests prematurely which would error out.

Liveness probes on the other hand periodically check container vitality symptoms while continually running. Things like unresponsive endpoints, database connectivity losses or even app crashes would get caught fast without overwhelming infrastructure.

Based on probe failures, Kubernetes automatically restarts affected pods after configurable intervals to self-heal faulty situations. Sophisticated mechanisms like load balancers integrate natively with probes to gracefully pull back failing instances from traffic until probes declare them ready again.

Kubernetes supports various types of health probes – HTTP endpoint checks, container process exit codes, delays to account for bootup. Make sure to set probe frequencies thoughtfully to avoid overloading systems with extra work.

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

5. Implement Pod Health Checks

Liveness probes serve as ongoing vitality checks that continuously ping endpoints on a running pod to check if key containers are still responding properly. Things like unexpected crashes, network disconnects or uncaught exceptions would get caught this way.

Based on liveness check failures exceeding defined thresholds, Kubernetes automatically terminates and restarts affected pods to self-heal.

Readiness probes help know if a pod has just freshly booted up and is truly ready to start accepting actual application traffic after deployment. Async tasks during initialization including things like loading large machine learning models or warming up connection pools can take non-trivial time in complex apps.

Readiness probes buy additional time to conclusively signal pods are fully prepared before allowing services to route traffic to the freshly deployed instances. This avoids failures arising from pods not being fully ready when asked to handle requests too early.

6. Custom Resource Definitions (CRDs)

Kubernetes allows you to extend its functionality by defining custom resources and controllers using Custom Resource Definitions (CRDs). Leveraging CRDs enables you to introduce domain-specific abstractions and automate complex tasks. This extensibility empowers you to tailor Kubernetes to the unique requirements of your applications.

apiVersion: apiextensions.k8s.io/v1
kind: CustomResourceDefinition
metadata:
name: myapp.custom.example.com
spec:
group: custom.example.com
names:
kind: MyApp
plural: myapps
scope: Namespaced
version: v1

7. Backup and Disaster Recovery

Backup essential cluster state regularly and test restores including:

Cluster resource definitions

Kubernetes secrets and keys

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. Implementing regular backups and establishing a robust disaster recovery plan are essential. You can leverage tools like Velero to perform cluster-wide backups and streamline the recovery process in the event of data loss or a catastrophic failure.

8. Update Strategies

Rolling updates and canary deployments are essential strategies for minimizing downtime during application updates. Kubernetes allows you to define update strategies in your deployment configurations, which enables a controlled and phased rollout of new versions. This makes sure that your applications remain available and responsive throughout the update process.

strategy:
type: RollingUpdate
rollingUpdate:
maxUnavailable: 1
maxSurge: 1

9. Use a container registry to store your images

A container registry provides a way to store and distribute your container images. If you use a container registry, you can ensure that your images are versioned, secure, and easily accessible. Container registries allow you to store your images in a central location, which makes it easy to share them with other users or teams. They also provide features such as access control, image scanning, and vulnerability detection, which can help you keep your images secure.

10. Use StatefulSets for stateful applications

StatefulSets provide a way to manage stateful applications such as databases and message queues. StatefulSets ensure that each instance of the application has a unique identity and persistent storage. This is important for stateful applications, which require unique identities and persistent storage 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 requires strategic adherence to kubernetes best practices. Organize namespaces for efficient resource management, set resource requests and limits, and implement health probes for reliability. Secure sensitive data with Kubernetes Secrets and embrace Horizontal Pod Autoscaling for dynamic resource adjustments.

Logging and monitoring tools enhance visibility, while controlled update strategies and Pod Disruption Budgets minimize downtime. Use Custom Resource Definitions for extensibility, and prioritize backup and disaster recovery for data protection.

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FAQs

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. Continuously tune these to balance restraints and excess provisioning.

What types of health checks help enhance application reliability?

Readiness and liveness probes should be configured to catch errors early and restart unresponsive pods automatically. Integrate them with load balancing layers for directing traffic appropriately.

How should configuration be handled in Kubernetes applications?

Decouple all configurable application parameters from container images into ConfigMaps and Secrets that get injected as environment variables or volumes. This avoids rebuilds for changes.

Why use Kubernetes namespaces?

Kubernetes namespaces logically isolate teams, applications and environments into virtual clusters for separation of concerns. Set resource quotas per namespace for better governance.

How can deployments be automated for Kubernetes services?

Containerize apps and orchestrate deployment workflows using GitOps pipelines for consistent, reproducible CD. Automate rollback procedures as well for self-healing.

Why run stateful apps on StatefulSets over Deployments?

StatefulSets maintain persistent volumes, stable network IDs, graceful deployment and scaling capabilities necessary for databases and storage services to operate correctly.

What ingress controllers offer in Kubernetes?

Ingress objects allow cleanly specifying routing rules, TLS termination, rate limiting and other edge service behaviors in a centralized way. Use managed ingress for production traffic handling.

How to schedule Kubernetes pods optimally?

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

How can Kubernetes configurations be backed up?

Essential components like cluster definitions, ConfigMaps/Secrets, PersistentVolume snapshots and databases running internally should be backed up externally routinely for DR.

What best practices help with Kubernetes logging/monitoring?

Aggregate logs centrally. Visualize time-series metrics using Prometheus & Grafana. Trace request flows with Jaeger. This observability is invaluable for Kubernetes.

<p>The post Top 10 Kubernetes Best Practices for Production Excellence first appeared on PyPixel.</p>


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