Interview Questions for Container Orchestration (Kubernetes)

Kubernetes is a powerful container orchestration system. Think of it as a sophisticated operating system for your containers, automating deployment, scaling, and management. Its core components work together seamlessly to achieve this.

• Nodes: These are the worker machines in your cluster, where your containers actually run. Each node typically has a Kubelet (the agent that communicates with the control plane), a container runtime (like Docker or containerd), and a kube-proxy (for networking).
• Control Plane: The brain of the operation. It manages the cluster’s state and directs the nodes. Key components include the etcd database (for storing cluster state), the kube-apiserver (the main API for interacting with the cluster), the kube-scheduler (responsible for assigning Pods to Nodes), and the kube-controller-manager (managing various controllers that ensure the desired state of the cluster).
• Pods: The smallest deployable units in Kubernetes. A Pod is a group of one or more containers that share resources and a network namespace. They are ephemeral; if a Pod fails, Kubernetes automatically recreates it.
• Services: Abstract representations of a set of Pods. They provide a stable IP address and DNS name for accessing your application, regardless of which specific Pods are currently running.
• Namespaces: Used to logically divide a cluster into isolated environments. This helps in managing different teams, applications, or environments within the same cluster.

Imagine a bustling city: Nodes are like individual buildings, the control plane is the city hall managing everything, Pods are individual apartments, Services are the street addresses, and Namespaces are different neighborhoods.

The Kubernetes control plane is the central brain of your cluster, responsible for managing all aspects of the cluster’s operation. It’s not something you directly interact with on individual nodes; instead, it’s a set of services running on one or more master nodes (often in a highly available configuration).

• etcd: A distributed key-value store that acts as the cluster’s persistent data store. It holds all the crucial configuration and state information about your cluster.
• kube-apiserver: The primary API endpoint for interacting with the Kubernetes cluster. All requests to manage resources (creating, deleting, updating Pods, Deployments, etc.) go through the kube-apiserver.
• kube-scheduler: This component is responsible for deciding on which node to run a newly created Pod, considering factors like resource availability and constraints.
• kube-controller-manager: A critical component that runs various controller processes, ensuring the desired state of the cluster. These controllers handle things like replication controllers, endpoints, services, and node lifecycle management. It’s like a constantly running watchdog, making sure everything is working as expected.

Think of the control plane as an air traffic control tower for your application’s containers, ensuring they’re deployed correctly, scaled appropriately, and have the resources they need.

Pods, Deployments, and StatefulSets are all Kubernetes resources, but they serve different purposes and have distinct characteristics.

• Pods: The smallest and most basic deployable units. They represent a single instance of your application. They’re short-lived and can be recreated automatically if they fail. Pods don’t have inherent identity beyond their IP address.
• Deployments: Used to manage the desired state of a set of Pods. They ensure that a specified number of Pods are running at any given time, automatically handling creation, updates, rollbacks, and scaling. They are declarative; you specify the desired state, and Kubernetes works to achieve and maintain it.
• StatefulSets: Similar to Deployments, but designed for applications that require persistent storage and stable network identities. They guarantee that Pods are created in a specific order, maintain their persistent volumes across restarts, and have stable network identities (like a persistent hostname).

Example: A simple web server could be deployed as a Deployment, while a database server (requiring persistent storage) would be better suited for a StatefulSet.

Think of Pods as individual workers, Deployments as a team leader ensuring a certain number of workers are always working, and StatefulSets as a team of specialists, each with their own dedicated desk and tools.

Kubernetes uses Services for service discovery. A Service acts as an abstraction over a set of Pods, providing a stable IP address and DNS name regardless of the underlying Pods’ changes (e.g., restarts, scaling).

When you create a Service, Kubernetes automatically updates the endpoints (a list of the currently running Pods belonging to that Service). Clients can then use the Service’s IP address or DNS name to access the Pods, without needing to know the individual Pod IPs, which are dynamic.

Example: You have a Deployment of 3 web server Pods. A Service is created, pointing to this Deployment. Clients can now access your web application via the Service’s IP, and Kubernetes ensures the requests are routed to one of the three running Pods. If a Pod fails and is replaced, the Service automatically updates its endpoints to reflect the new Pod.

This eliminates the need for complex configuration management and ensures a stable and robust way to access your applications.

Kubernetes networking is crucial for enabling communication between Pods and external clients. Services and Ingress are key components.

• Services: As discussed before, Services provide a stable IP and DNS name for accessing a set of Pods. They use kube-proxy on each node to route traffic to the correct Pods.
• Ingress: An Ingress controller is a reverse proxy that sits at the edge of your cluster, routing external traffic to different Services based on rules defined in the Ingress resource. It allows you to manage multiple services (e.g., web, API) behind a single IP address, often with features like SSL termination, load balancing, and URL rewriting.

Example: You have a web service and an API service. An Ingress controller can route traffic to example.com to your web service and api.example.com to your API service, providing a cleaner and more secure external access point. The Ingress controller handles SSL termination, potentially load balancing requests across multiple instances of the web service.

Think of Services as internal street addresses within your cluster, and Ingress as the main gate to your city, managing all external traffic.

Kubernetes offers several resource scheduling strategies to ensure your Pods are deployed efficiently and effectively.

• Default Scheduler: This is the built-in scheduler that considers resource requests, affinities, anti-affinities, taints, and tolerations to place Pods on Nodes.
• Priorities and Weights: You can assign priorities and weights to Pods, enabling more critical Pods to be scheduled first.
• Node Affinities and Anti-Affinities: You can define rules specifying which Nodes Pods should be scheduled on (affinities) or should avoid (anti-affinities), based on Node labels.
• Taints and Tolerations: Taints mark a Node with certain characteristics, and tolerations allow Pods to run on Nodes with specific taints. This is useful for isolating sensitive workloads or managing system-level constraints.

Example: A high-priority database Pod might have a node affinity for nodes with specific storage capabilities, ensuring it’s placed on a suitable Node. Conversely, a less critical application might have tolerations for taints marking nodes as having lower-performance hardware.

By carefully configuring scheduling strategies, you can optimize resource utilization and ensure your application runs reliably and efficiently.

Managing persistent storage in Kubernetes involves using Persistent Volumes (PVs) and Persistent Volume Claims (PVCs).

• Persistent Volumes (PVs): These represent actual storage resources (e.g., a cloud disk, a local disk, a network storage device) available in your cluster. They are provisioned independently from Pods.
• Persistent Volume Claims (PVCs): These are requests for storage from Pods. They specify the storage requirements (size, access modes, etc.). Kubernetes matches PVCs with available PVs.

Example: You create a PV representing a 10GB cloud disk. A stateful application (like a database) then creates a PVC requesting 10GB of storage. Kubernetes will bind the PVC to the available PV, providing persistent storage to the application, even if the Pod is restarted or rescheduled.

Different storage providers (like cloud providers, network storage solutions) integrate with Kubernetes, providing diverse options for persistent storage. The choice depends on your application’s needs and infrastructure.

Kubernetes namespaces are essentially virtual clusters within a single Kubernetes cluster. Think of them as dividing your cluster into logically separate sections, each with its own set of resources like pods, services, and deployments. This allows you to isolate different teams, projects, or environments within the same physical cluster, improving organization and resource management.

• Isolation: Teams can work independently without interfering with each other’s resources.
• Resource Quotas: You can set resource limits (CPU, memory) for each namespace, preventing one project from hogging all the resources.
• Access Control: Namespaces enhance security by allowing granular control over who can access and manage resources within a specific namespace.

Example: A company might have separate namespaces for development, testing, staging, and production environments. This keeps each environment distinct and prevents accidental deployment of code from one environment to another.

Kubernetes secrets management involves securely storing sensitive information like passwords, API keys, and certificates within the cluster. It’s crucial to avoid hardcoding these secrets directly into your application code, as this poses a significant security risk. Kubernetes provides mechanisms to manage these secrets securely and make them available to your applications without exposing the actual values.

Kubernetes uses Secrets objects to store this sensitive data. These objects are encrypted both at rest and in transit. There are several ways to manage secrets effectively:

• Kubernetes Secrets: The built-in mechanism, suitable for simple scenarios. However, managing and rotating secrets directly within Kubernetes can become complex for larger deployments.
• External Secret Management Tools: Tools like HashiCorp Vault, AWS Secrets Manager, or Azure Key Vault provide more robust features for secret lifecycle management, including versioning, auditing, and access control. These tools integrate with Kubernetes via dedicated plugins or operators.

Example: Imagine a database connection string. Instead of embedding it in your application’s code, you store it as a Kubernetes Secret. Your application then mounts this secret as a volume, accessing the connection string securely without ever exposing the raw value in your code.

Troubleshooting Kubernetes deployments requires a systematic approach. Start by understanding the problem’s scope and then use the available tools to investigate. Here’s a general troubleshooting framework:

1. Check the deployment status: Use kubectl describe deployment to get detailed information about your deployment, including its status, events, and pod status.
2. Inspect the pods: Use kubectl get pods -n to see the status of the pods. Look for errors, restarts, or pending status. Use kubectl describe pod for detailed pod information, including logs and events.
3. Examine the logs: Access your application logs using kubectl logs . These logs provide valuable insights into your application’s behavior.
4. Check the events: Kubernetes records events that can pinpoint problems. Use kubectl get events -n to review these events.
5. Use kubectl debug: For more advanced troubleshooting, the kubectl debug command allows you to temporarily access a pod’s shell for direct investigation.
6. Monitor resource utilization: Use tools like kubectl top pods or your cluster’s monitoring system to check CPU, memory, and network usage. Resource constraints can lead to deployment issues.

Example: If a deployment is stuck in the pending state, check the pod’s events for clues. Errors might indicate image pull issues, resource constraints, or problems with the deployment configuration.

Monitoring and logging are crucial for understanding the health and performance of your Kubernetes applications. Several tools and strategies exist:

• Logging:
  • Centralized Logging Systems: Tools like Elasticsearch, Fluentd, and Kibana (EFK stack) or the more modern Loki and Grafana stack provide centralized logging and log aggregation capabilities. This allows you to collect, analyze, and search logs from all your pods in a single place.
  • Kubernetes Logging Operators: Operators like the Fluent Bit operator simplify the process of configuring and managing logging. They automate the deployment and management of logging agents within your cluster.
• Monitoring:
  • Prometheus and Grafana: Prometheus is a powerful time-series database, while Grafana provides a user-friendly dashboard for visualizing metrics. Together, they provide comprehensive monitoring of various aspects of your cluster, including resource utilization, application performance, and service health.
  • Cloud-Native Monitoring Tools: Cloud providers like AWS, Google Cloud, and Azure offer their own Kubernetes monitoring solutions that often integrate well with their other services.

Example: Using Prometheus and Grafana, you can create dashboards to visualize CPU utilization of your pods, response times of your services, and other critical metrics, helping you proactively identify performance bottlenecks and potential problems.

Kubernetes Custom Resource Definitions (CRDs) allow you to extend the Kubernetes API by defining your own custom resources. Think of it as adding new building blocks to your Kubernetes system. This is highly valuable when you need to manage complex applications or infrastructure components that don’t fit neatly into standard Kubernetes objects like Deployments or StatefulSets.

CRDs define the schema (structure and type of data) for your custom resources. Once defined, you can create, manage, and manipulate these resources using kubectl, just like you would manage standard Kubernetes objects. This allows you to create highly customized and automated workflows.

Example: You might create a CRD to represent a database instance, defining fields like database type, version, and storage capacity. You could then use this CRD to easily provision and manage database instances within your Kubernetes cluster using custom controllers.

Scaling applications in Kubernetes involves increasing or decreasing the number of pods running your application to meet the demand. Here are common approaches:

• Horizontal Pod Autoscaler (HPA): HPA automatically scales your deployments based on metrics like CPU utilization or custom metrics. It’s a highly effective way to automatically adjust the number of pods based on real-time demand.
• Manual Scaling: You can manually scale your deployments using kubectl scale deployment --replicas=. This is useful for quick adjustments or when fine-grained control is needed.
• Cluster Autoscaler: This scales the entire Kubernetes cluster by adding or removing nodes based on demand. It ensures that there are enough resources to accommodate all running pods.

Example: An e-commerce application might experience a surge in traffic during peak shopping hours. HPA automatically scales up the number of application pods to handle the increased load, ensuring responsiveness and avoiding service disruptions. After the peak, it scales down again to save resources.

Managing Kubernetes configurations and updates involves using various techniques to ensure your cluster remains stable, secure, and up-to-date. Here are key approaches:

• Configuration Management Tools: Tools like Ansible, Puppet, or Chef can automate the deployment and management of Kubernetes configurations. They allow you to define your infrastructure as code, ensuring consistency and repeatability.
• GitOps: GitOps uses Git as the single source of truth for your Kubernetes configurations. Changes are made through Git commits, triggering automated deployments to your cluster. This provides excellent version control and auditing capabilities.
• Kubernetes Operators: Operators automate the deployment, upgrades, and management of complex applications and services. They provide a declarative way to manage stateful applications, databases, and other complex infrastructure components.
• Rolling Updates and Rollbacks: Kubernetes allows for rolling updates, gradually replacing old pods with new ones, minimizing disruption. If problems arise, you can easily rollback to the previous version.

Example: Using GitOps, a change to your application’s deployment configuration is committed to a Git repository. A tool like Argo CD detects the change, automatically builds a new container image, and performs a rolling update to deploy the new version to your cluster.

Kubernetes offers several deployment strategies to ensure smooth application updates with minimal downtime. Let’s explore two popular ones: Rolling Updates and Blue/Green Deployments.

Rolling Updates: This is a gradual process. Imagine updating your software on your phone – you don’t replace the entire app at once. Rolling updates are similar. Kubernetes gradually replaces old pods (containers running your application) with new ones containing the updated image. It ensures some old pods are always running, minimizing disruption. A few pods are updated at a time, and if an issue arises, the update can be rolled back. The process is controlled by parameters like maxSurge (how many additional pods can be created during the update) and maxUnavailable (how many pods can be unavailable during the update).

Blue/Green Deployments: This is a more drastic but safer approach. Think of it like having two identical production environments: ‘blue’ (your current live application) and ‘green’ (your updated version). You deploy your updated application to the ‘green’ environment. Once testing and validation are complete, you switch traffic from ‘blue’ to ‘green’, making the updated application live. If there are issues, you quickly switch back to ‘blue’. This minimizes risk as the old version is always readily available.

In summary: Rolling updates are more incremental and resource-efficient, while blue/green deployments are faster and safer but consume more resources. The best strategy depends on your application’s needs and risk tolerance.

Container image security is paramount in Kubernetes. A compromised image can lead to significant security breaches. Here’s a multi-layered approach:

• Use a secure registry: Store your images in a private registry like Google Container Registry (GCR), Amazon Elastic Container Registry (ECR), or Azure Container Registry (ACR). These registries offer access control and authentication.
• Image scanning: Integrate automated image scanning tools, such as Trivy, Clair, or Anchore, into your CI/CD pipeline. These tools analyze your images for vulnerabilities and malware before they’re deployed.
• Secure your registry access: Restrict access to your registry using RBAC (Role-Based Access Control) in Kubernetes and ensure only authorized users or services can pull images.
• Immutable images: Treat your container images as immutable artifacts. Once an image is built and tested, avoid modifying it directly. If changes are needed, create a new image version.
• Regular updates: Regularly update your base images and application dependencies to patch vulnerabilities. Consider using a policy to automatically update images based on vulnerability severity.
• Pod Security Policies (PSPs) or Pod Security Admission (PSA): These features (PSA is the replacement for PSPs in newer Kubernetes versions) allow you to define security constraints for pods, such as which capabilities they can have, which syscalls they can use, and which security contexts are allowed. This helps prevent compromised containers from escalating their privileges.

By combining these methods, you can create a robust container image security posture in your Kubernetes environment.

Role-Based Access Control (RBAC) is the mechanism in Kubernetes that regulates access to resources. Think of it like assigning different security clearances in a government setting – different users have different permissions. RBAC allows you to define roles, which specify permissions (like ‘read,’ ‘write,’ ‘create,’ ‘delete’), and then bind those roles to users or groups (either human users or service accounts). This ensures that only authorized users can perform specific actions within the cluster.

For instance, a developer might only have permission to deploy applications to a specific namespace, while an administrator might have cluster-wide access. RBAC promotes granular control and helps prevent unauthorized access and modification of resources.

kubectl create role ... and kubectl create rolebinding ... are the commands used to create roles and bind them to users/service accounts respectively. These commands define who can do what within the Kubernetes environment, helping to establish and maintain a secure and manageable cluster.

Network policies in Kubernetes control the network traffic flow between pods within the cluster. They allow you to define rules that dictate which pods can communicate with each other based on various criteria, such as labels, namespaces, and ports. This is crucial for security and isolation.

For example, you might create a network policy to allow only database pods to communicate with application pods on a specific port, preventing unauthorized access to the database. This enhances security and reduces the attack surface.

Implementing network policies involves defining rules using YAML configurations. A network policy typically includes selectors (to identify target pods) and rules (defining allowed traffic). You can use kubectl apply -f network-policy.yaml to apply the policy. Effective use of network policies improves the security and maintainability of your Kubernetes cluster by segregating network traffic and enhancing security.

Troubleshooting in Kubernetes often requires a systematic approach. My experience involves using several tools and techniques:

• kubectl describe: This is my go-to command for getting detailed information about any Kubernetes resource (pods, deployments, services, etc.). It often reveals the root cause of issues.
• kubectl logs: This command fetches the logs from running containers, providing valuable insight into application behavior and potential errors.
• kubectl get events: This command displays events related to pods and other resources, highlighting failures and other noteworthy occurrences.
• Kubernetes dashboard: The built-in dashboard provides a visual interface for monitoring the cluster’s health and managing resources. It’s great for a high-level overview.
• Monitoring tools: Tools like Prometheus and Grafana provide rich metrics and visualizations, making it easier to identify bottlenecks and performance issues.
• Debugging tools: Depending on the programming language, debuggers like delve (for Go) can be used to debug applications running within containers.

In addition to these, I use a systematic approach. I start with the most basic checks, like verifying resource limits, checking logs for errors, and examining pod status. If the problem is more complex, I leverage monitoring tools to gather more detailed information before digging deeper. This combination of tools and methods allows me to solve various issues efficiently.

Kubernetes Operators are essentially custom controllers that extend Kubernetes to manage complex applications. They automate the deployment, scaling, and lifecycle management of these applications, simplifying operations and ensuring consistent configurations.

Imagine a database like PostgreSQL. Manually setting up replication, backups, and upgrades can be complex. An Operator handles all of this. It understands the internals of the application and provides high-level abstractions. You interact with the application using custom resources, and the Operator takes care of the low-level details. They handle complex configuration tasks, rolling upgrades, health checks, and even self-healing.

Operators are written using the Operator SDK (Software Development Kit), which provides tools and frameworks to simplify the development process. This allows for seamless integration of custom application management logic into the Kubernetes environment.

Helm charts are packages that contain pre-configured Kubernetes manifests. Think of them as templates for deploying applications. They simplify the process of deploying and managing complex applications in Kubernetes by providing a structured way to define, install, and upgrade your applications.

Instead of manually creating numerous YAML files for deployments, services, etc., you define these in a Helm chart. A chart typically includes templates, values (customizable parameters), and other supporting files. You can then use Helm to install the chart, customizing the values as needed. This significantly reduces the complexity of managing multiple Kubernetes resources.

I use Helm extensively for deploying applications. It speeds up the deployment process and ensures consistency across various environments (development, staging, production). The ability to version and manage charts using Helm makes updating and managing applications a streamlined process.

For example, helm install my-app my-chart would install a chart named my-chart with the release name my-app. Using Helm allows for declarative management and simplified lifecycle management of applications within Kubernetes.

I’ve had extensive experience working with various cloud provider Kubernetes offerings, including Google Kubernetes Engine (GKE), Azure Kubernetes Service (AKS), and Amazon Elastic Kubernetes Service (EKS). Each platform offers a managed Kubernetes experience, abstracting away much of the underlying infrastructure management, but they differ in strengths and features.

For example, GKE excels with its strong integration with other Google Cloud Platform (GCP) services, making it ideal for organizations heavily invested in the GCP ecosystem. Its Autopilot feature simplifies cluster management significantly. AKS, on the other hand, provides seamless integration with Azure Active Directory, simplifying authentication and authorization for enterprise deployments. Its Azure Policy integration offers robust governance capabilities. Finally, EKS offers a highly compatible Kubernetes experience, aligning closely with the upstream Kubernetes project, which is beneficial for users seeking maximum portability and control. I’ve used all three in production environments, tailoring my approach to the specific needs of each project and leveraging the unique advantages each platform offers.

In one project, we chose GKE for its seamless integration with Cloud SQL and other GCP services, drastically simplifying database management and improving overall performance. In another project, the client’s existing investment in Azure made AKS the natural choice; the streamlined integration with Azure Active Directory simplified user management and security considerably.

My experience with CI/CD pipelines for Kubernetes deployments is extensive. I’ve worked with various tools, including Jenkins, GitLab CI, and Argo CD, to automate the entire deployment lifecycle. A typical pipeline would involve building container images, pushing them to a registry (like Docker Hub, Google Container Registry, or Amazon Elastic Container Registry), and then deploying them to Kubernetes using tools like kubectl or Helm.

The key is to ensure that the process is automated, reliable, and repeatable. This includes automated testing at various stages (unit, integration, and end-to-end), rollback strategies in case of failure, and monitoring the deployed application for performance and stability. I typically leverage infrastructure-as-code tools like Terraform or Pulumi to manage the Kubernetes infrastructure itself as part of the CI/CD process, ensuring consistency and reproducibility across different environments (development, staging, and production).

For instance, in a recent project, we utilized GitLab CI to build and test our application, then used Helm to package and deploy it to a Kubernetes cluster hosted on EKS. We implemented canary deployments to minimize the risk of disrupting live services and used automated rollback procedures to swiftly revert to a previous version if any issues arose. This allowed for frequent, safe deployments, accelerating our development cycle.

Ensuring high availability and fault tolerance in Kubernetes clusters requires a multi-layered approach. At the infrastructure level, we ensure multiple availability zones (AZs) are used for all nodes. Kubernetes itself provides features like replication controllers and deployments to ensure multiple copies of our application pods are running across different nodes and AZs.

Further, using StatefulSets allows managing stateful applications, ensuring data persistence even in case of node failures. We utilize service meshes like Istio or Linkerd to provide service discovery, traffic management, and resilience features, allowing for graceful degradation and fault tolerance within the application itself. Horizontal Pod Autoscaling (HPA) dynamically adjusts the number of pods based on resource utilization or custom metrics, scaling up during peak demand and scaling down during low traffic.

Beyond this, robust monitoring and logging are crucial. Tools like Prometheus, Grafana, and Elasticsearch allow us to track the health of our cluster and applications, identifying potential issues before they impact users. Regular health checks and automated remediation strategies are also implemented. For example, we might configure self-healing mechanisms that automatically restart failed pods or replace unhealthy nodes.

Kubernetes security is paramount. My approach incorporates several best practices, beginning with securing the cluster itself. This includes using strong authentication methods (like RBAC), limiting access to the cluster only to authorized personnel and services, and regularly patching the Kubernetes control plane and worker nodes. We also employ network policies to restrict communication between pods, reducing the attack surface.

At the application level, we use security contexts to define the permissions and capabilities of individual pods, adhering to the principle of least privilege. Container images are scanned for vulnerabilities using tools like Trivy or Clair, and only trusted images from verified sources are used. Secrets management is crucial; sensitive information like database credentials or API keys are never hardcoded into applications but managed securely using Kubernetes Secrets or dedicated secret management solutions.

Regular security audits and penetration testing are essential to identify and address vulnerabilities proactively. We leverage tools like Falco for runtime security monitoring, detecting suspicious activity within the cluster. Adopting a zero-trust security model, where every connection is verified, is also a core element of our approach.

Optimizing Kubernetes resource utilization is critical for cost efficiency and performance. Careful resource requests and limits defined for pods are fundamental. Over-provisioning resources wastes money, while under-provisioning can lead to performance bottlenecks. We use resource analysis tools to gain insights into pod resource consumption, identifying potential areas for optimization.

Vertical pod autoscaling (VPA) can automatically adjust resource requests and limits based on observed usage patterns, ensuring pods have the resources they need without over-allocating. Right-sizing nodes is another key aspect. We avoid oversized nodes that are underutilized and consolidate workloads where possible to improve density. Efficient scheduling strategies and using node selectors and taints/tolerations to place pods on specific nodes with the right capabilities are crucial.

Furthermore, we regularly review and refine our deployments to ensure they are efficiently using resources. This might involve optimizing application code, reducing the number of unnecessary containers or services, or migrating to more efficient container images. We constantly monitor resource utilization metrics and proactively address any identified inefficiencies.

I have extensive experience with advanced Kubernetes features, including Horizontal Pod Autoscaling (HPA) and Pod Disruption Budgets (PDB). HPA automatically scales the number of pods based on CPU utilization, memory consumption, or custom metrics. For example, I’ve used HPA to dynamically adjust the number of pods in a web application based on the request rate, ensuring responsiveness during peak traffic periods. This prevents performance degradation and ensures scalability.

Pod Disruption Budgets (PDB) allow us to control the maximum number of pods that can be evicted at any given time. This is especially important for stateful applications where disruption can lead to data loss or service interruption. By setting a PDB, we can ensure that only a limited number of pods are deleted at once during deployments or node maintenance, minimizing the impact on the application’s availability.

Beyond HPA and PDB, I’ve worked with features like Network Policies for granular network control, Ingress controllers for managing external access, and custom resource definitions (CRDs) for extending Kubernetes functionality to manage application-specific resources. I’ve used these advanced features to build robust, scalable, and reliable Kubernetes deployments in various projects.