Question

What are your best practices for container orchestration with Kubernetes when dealing with high-availability applications?

This question would give insights into strategies and best practices to ensure high availability in Kubernetes environments. Topics might include failover handling, setting up redundancy, deploying across multiple zones or regions, readiness and liveness probes, and auto-scaling to manage loads properly.

Answer 1

To ensure a highly available application in Kubernetes for critical applications, the following best practices should be followed:

Redundancy Across Nodes: Configure multiple replicas for your application and deploy them on different nodes of the cluster to avoid a single point of failure. The Kubernetes ReplicaSets or Deployments can be used for the management of pod replicas.
Readiness and Liveness Probes: The readiness probes should be configured to route traffic only to healthy pods and liveness probes that can automatically restart failed containers.
Enable Horizontal Pod Autoscaling: To dynamically scale the number of pods based on metrics like CPU or memory usage to automatically handle traffic spikes.
Distribute Across Zones: Utilize the Kubernetes Node Affinity and Pod Anti-Affinity rules to actively distribute workloads across availability zones for fault tolerance.
Resource Requests and Limits: Define the CPU and memory requests/limits in pod specifications to avoid resource contention and ensure predictable performance.
Load Balancers and Ingress Controllers: Leverage external load balancers and Kubernetes Ingress to distribute traffic while accessing pods even in updates.
Disaster Recovery Plan: Backup stateful applications with Persistent Volume Snapshots or third-party tools, such as Velero.
Monitoring and Alerting: Integrate tools, including Prometheus, Grafana, and Kubernetes-native alerts, monitoring cluster health and responding proactively to failures.