Top Companies interview questions and answers related to kubernetes

1. Imagine your microservices architecture in Kubernetes is experiencing high traffic. How would you handle dynamic scaling while ensuring high availability and minimal downtime?

Ans: Handling high traffic in a microservices architecture on Kubernetes requires a robust approach for dynamic scaling while maintaining high availability and minimizing downtime.

Follow are some steps that we can use to avoid this situation:

1. Enable Horizontal Pod Autoscaling (HPA)
2. Use Cluster Autoscaler
3. Zero-Downtime Deployments (Rolling Updates and Canary Deployments)
4. Service Mesh (e.g., Istio or Linkerd) for Traffic Management and Fault Tolerance
5. Load Balancing and Ingress Controllers
6. Optimize Resource Requests and Limits
7. Use Queueing and Rate-Limiting
8. Monitoring and Observability (Prometheus, Grafana, etc.)
9. Fault Tolerance and Self-Healing
10. Use Node Affinity and Pod Anti-Affinity

1. Enable Horizontal Pod Autoscaling (HPA):

Kubernetes provides Horizontal Pod Autoscaling (HPA), which automatically adjusts the number of pod replicas based on observed CPU utilization or custom metrics. Here’s how you can leverage it:

Configure Metrics: Start by ensuring that each microservice exposes useful metrics. By default, HPA uses CPU utilization, but you can configure it for memory or even custom application-level metrics (like requests per second, latency, etc.).

Set Target Utilization: Define threshold limits at which the service should scale up or down. For example, if CPU usage exceeds 70%, Kubernetes can add more replicas to handle the traffic.

kubectl autoscale deployment <microservice-name> --cpu-percent=70 --min=3 --max=10

2. Use Cluster Autoscaler:

HPA handles scaling of individual pods, but if the node capacity becomes insufficient (e.g., your node runs out of CPU or memory), you need to scale the number of nodes in your Kubernetes cluster dynamically. This is where the Cluster Autoscaler comes into play:

1. It automatically adjusts the number of nodes in a cluster based on the pending pods.
2. When the load decreases, it can also scale down the cluster to save resources.

3. Zero-Downtime Deployments (Rolling Updates and Canary Deployments):

Ensuring that scaling or new deployments don’t lead to downtime requires using Kubernetes features that provide smooth transitions:

1. Rolling Updates: Kubernetes natively supports rolling updates to gradually replace old pods with new ones. It ensures that at least a certain percentage of the application remains available during the update process.
2. Canary Deployments: You can gradually roll out a new version of your service to a subset of users and monitor it. This approach minimizes the impact of potential issues from new updates and ensures stability before scaling up.

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

4. Service Mesh (e.g., Istio or Linkerd) for Traffic Management and Fault Tolerance:

A service mesh provides powerful traffic management and resilience features that can help with high traffic and failure scenarios:

1. Traffic splitting: Dynamically route traffic between different service versions (useful for canary releases or blue-green deployments).
2. Circuit Breaking: Automatically prevent your services from overwhelming themselves or dependent services by rejecting excess traffic once a failure threshold is hit.
3. Retry and Timeout Policies: Automatically retry failed requests and control how long your services wait for a response before timing out.

5. Load Balancing and Ingress Controllers:

Load balancing ensures that incoming traffic is evenly distributed across your pods. To improve traffic handling:

1. Kubernetes LoadBalancer/Ingress: Use cloud-native load balancers or Ingress controllers to handle the distribution of external traffic to your services.
2. Sticky Sessions: If your services need session persistence, configure sticky sessions via your Ingress or LoadBalancer settings.

6. Optimize Resource Requests and Limits:

Kubernetes uses resource requests and limits to schedule pods on nodes with sufficient resources. To avoid resource starvation during high traffic:

1. Set Appropriate Requests and Limits: Ensure each microservice pod has accurate CPU and memory requests based on their average load.
2. Overprovisioning: For critical services, you may overprovision resources by setting a higher resource limit to ensure they can handle traffic spikes without waiting for autoscaling to add new replicas.

resources:
  requests:
    memory: "128Mi"
    cpu: "250m"
  limits:
    memory: "512Mi"
    cpu: "500m"

7. Use Queueing and Rate-Limiting:

If the traffic spike is sudden and large, even autoscaling may not respond fast enough. To prevent service crashes or degraded performance, you can implement:

1. Queueing: Use message queues (e.g., Kafka, RabbitMQ) between services to buffer requests during high load, ensuring requests are processed when resources are available.
2. Rate Limiting: Rate-limiting incoming traffic ensures that your services can gracefully reject excess traffic and not become overloaded.

8. Monitoring and Observability (Prometheus, Grafana, etc.):

Real-time monitoring and alerting are critical for reacting to traffic surges and potential issues:

1. Use Prometheus and Grafana for metrics collection and visualization.
2. Set up alerts for key performance indicators such as CPU usage, memory usage, request latency, and error rates.
3. With observability tools (like Jaeger or Zipkin), trace requests across microservices to identify bottlenecks and areas that need scaling or optimization.

9. Fault Tolerance and Self-Healing:

Ensure Kubernetes is configured to automatically restart failed pods or replace unhealthy ones:

1. Use liveness and readiness probes to detect when pods are not functioning correctly and need to be restarted.
2. Configure Pod Disruption Budgets (PDB) to ensure that during planned maintenance or scaling events, a minimum number of replicas are always available.

readinessProbe:
  httpGet:
    path: /health
    port: 8080
  initialDelaySeconds: 3
  periodSeconds: 5

10. Use Node Affinity and Pod Anti-Affinity

To ensure high availability, you can configure node affinity and pod anti-affinity so that pods are spread across different nodes (or even different availability zones). This ensures that even if a node or zone fails, your services remain operational.

affinity:
  podAntiAffinity:
    requiredDuringSchedulingIgnoredDuringExecution:
    - labelSelector:
        matchExpressions:
        - key: app
          operator: In
          values:
          - <microservice-name>
      topologyKey: "kubernetes.io/hostname"