K8s and AKS Interviewer Questions : ChatGPT Answers

34 min readMar 11, 2023

In AKS (azure kubernets service), what’s the relationship and order between cluster, node, pod and container ?

In AKS (Azure Kubernetes Service), the relationship and order between cluster, node, pod, and container is as follows:

Cluster: A cluster in AKS is a set of nodes that are managed by Kubernetes. The cluster provides a logical representation of the underlying infrastructure and manages the deployment and scaling of containerized applications.
Node: A node is a worker machine that runs containerized applications. Each node in AKS runs one or more pods, which are the smallest deployable units in Kubernetes.
Pod: A pod is the smallest deployable unit in Kubernetes and represents a single instance of a running process in a cluster. A pod can contain one or more containers, which share the same network namespace and can communicate with each other via inter-process communication (IPC).
Container: A container is a lightweight, standalone executable package of software that includes everything needed to run an application, including code, libraries, and system tools. A pod can contain one or more containers, each running a different process.

In summary, a cluster in AKS is a logical representation of the underlying infrastructure, which contains one or more nodes. Each node runs one or more pods, which can contain one or more containers.

Is node always denotes a single machine ?

In the context of Kubernetes and AKS, a node typically refers to a single physical or virtual machine. However, it is important to note that a node can also refer to a group of machines that act as a single unit for scheduling purposes.

Kubernetes is designed to be highly scalable and can manage clusters with thousands of nodes. In such cases, a node can refer to a group of machines that work together as a single unit to schedule and run containers. This is achieved through the use of specialized software, such as kubelet, which runs on each node and communicates with the Kubernetes master to manage containers and pods.

In summary, while a node in Kubernetes and AKS typically refers to a single machine, it can also refer to a group of machines that act as a single unit for scheduling purposes.

How containers communicate with each other via inter-process communication (IPC) ? If node have multiple containers then how we identify/refer a particular container ?

Containers communicate with each other via inter-process communication (IPC) mechanisms, such as shared memory or sockets. In Kubernetes and AKS, containers within the same pod share the same network namespace and can communicate with each other via localhost, using standard inter-process communication mechanisms.

If a node has multiple containers running, each container is identified by a unique name or ID, which is generated by the container runtime. In Kubernetes and AKS, containers are typically deployed within a pod, and each pod has a unique name and IP address. To refer to a particular container within a pod, you can use the container name or ID. For example, to access a container named “my-container” within a pod named “my-pod”, you can use the following command:

kubectl exec -it my-pod -c my-container bash

This command will start an interactive shell session in the “my-container” container within the “my-pod” pod. The “-c” option is used to specify the container name.

In addition to container names, Kubernetes and AKS also support labels and selectors, which allow you to group containers based on their properties or characteristics. You can use labels and selectors to identify and refer to groups of containers within a pod or across multiple pods.

List all the content must be deployed to a container ? Is there anything common between all containers ?

The content that must be deployed to a container depends on the specific requirements of the application or service being deployed. However, there are some common elements that are typically included in a container image:

Application code and dependencies: The code for the application being deployed, along with any dependencies required to run the application, such as libraries, frameworks, and runtime environments.
Operating system: A lightweight operating system that provides the basic system resources required to run the application.
System tools and utilities: A set of system tools and utilities, such as a package manager, file system utilities, and networking tools, that are required to manage the container and perform basic system-level tasks.
Configuration files: Configuration files that are used to configure the container and the application it runs, such as environment variables, networking settings, and log settings.
Any additional assets or data: Any additional assets or data required by the application, such as static files, images, or other resources.

While the specific content deployed to a container varies depending on the application being deployed, all containers share some common characteristics, including:

Isolation: Containers provide a level of isolation between the application being deployed and the underlying host system, allowing multiple applications to run on the same host without interfering with each other.
Portability: Containers are designed to be highly portable, allowing them to be easily deployed and run on a wide range of host systems, including cloud environments and on-premises data centers.
Scalability: Containers are designed to be highly scalable, allowing applications to be easily scaled up or down based on demand.
Efficiency: Containers are lightweight and consume minimal system resources, making them highly efficient and cost-effective compared to traditional virtualization technologies.

Is it possible to have hybrid or multi cloud cluster/ pod ? If yes, then how such a setup configured ?

Yes, it is possible to have a hybrid or multi-cloud cluster/pod in Kubernetes, including in AKS. A hybrid or multi-cloud setup allows you to distribute your workloads across multiple cloud providers or on-premises data centers, providing greater flexibility and resilience.

To configure a hybrid or multi-cloud setup in AKS, you can use the following steps:

Set up clusters in each cloud provider or on-premises data center: To create a hybrid or multi-cloud setup, you need to set up Kubernetes clusters in each cloud provider or data center that you want to use. This can be done using AKS or other Kubernetes management tools.
Configure networking: To allow the clusters to communicate with each other, you need to configure networking between the different clusters. This can be done using virtual private networks (VPNs) or other networking technologies, such as service meshes.
Configure cluster federation: Kubernetes provides a feature called cluster federation, which allows you to manage multiple clusters as a single entity. You can use cluster federation to deploy and manage applications across multiple clusters, including clusters in different cloud providers or data centers.
Deploy applications: Once you have set up the clusters and configured networking and federation, you can deploy your applications across the different clusters using Kubernetes manifests or other deployment tools.

Note that setting up a hybrid or multi-cloud cluster/pod requires careful planning and configuration, as well as a deep understanding of Kubernetes networking and deployment concepts. It is recommended to seek guidance from experienced Kubernetes administrators or consultants to ensure a successful setup.

List common kubectl commands with common mistake done by developers ?

Here are some common kubectl commands used by developers along with common mistakes that are made:

kubectl get: This command is used to retrieve information about Kubernetes resources. One common mistake is forgetting to specify the resource type, such as pods or services, which can result in an error message or incorrect output.
kubectl create: This command is used to create new Kubernetes resources, such as pods or services. A common mistake is not specifying all the required parameters or options, such as the image or container name, which can result in a failed deployment.
kubectl apply: This command is used to apply changes to existing Kubernetes resources. A common mistake is not specifying the correct YAML file or configuration, which can result in an incorrect or failed deployment.
kubectl logs: This command is used to view the logs of a Kubernetes pod. A common mistake is not specifying the correct pod name or container name, which can result in an error message or incorrect output.
kubectl exec: This command is used to execute a command in a running Kubernetes container. A common mistake is not specifying the correct pod name or container name, which can result in an error message or the command being executed in the wrong container.
kubectl delete: This command is used to delete Kubernetes resources. A common mistake is not specifying the correct resource type or name, which can result in unintended resource deletion.
kubectl describe: This command is used to view detailed information about Kubernetes resources. A common mistake is not specifying the correct resource type or name, which can result in an error message or incorrect output.
kubectl rollout: This command is used to manage rolling updates to Kubernetes resources. A common mistake is not specifying the correct deployment or replica set, which can result in a failed update or unintended changes.

It’s important to double-check the command and parameters before executing them to avoid these common mistakes. Also, using the --help option with any command can provide useful information and examples to avoid making mistakes.

Where can i run kubectl command in AKS ?

You can run kubectl commands in Azure Kubernetes Service (AKS) from a command-line interface (CLI) on your local computer, or from within the Azure portal.

To run kubectl commands from your local computer, you need to install the kubectl CLI and connect it to your AKS cluster. You can follow these steps:

Install the kubectl CLI: You can download and install the kubectl CLI from the Kubernetes website. Alternatively, you can use a package manager like Homebrew on macOS or Chocolatey on Windows to install it.
Connect to your AKS cluster: To connect to your AKS cluster, you need to retrieve the cluster credentials and configure kubectl to use them. You can use the following command in the Azure CLI:

az aks get-credentials --resource-group <resource-group-name> --name <cluster-name>

This command downloads the cluster credentials and configures kubectl to use them for authentication.

3. Run kubectl commands: Once you have connected to your AKS cluster, you can run kubectl commands from your local computer using the command-line interface.

Alternatively, you can run kubectl commands from within the Azure portal by opening the Azure Cloud Shell. The Azure Cloud Shell provides a web-based command-line interface with kubectl pre-installed, so you can run kubectl commands directly from the portal without installing anything on your local computer.

To open the Azure Cloud Shell, follow these steps:

Sign in to the Azure portal and navigate to your AKS cluster.
Click on the “Cloud Shell” button in the top menu bar.
Choose the Bash or PowerShell environment and wait for the shell to initialize.
Run kubectl commands directly from the Azure Cloud Shell.

Regardless of where you run kubectl commands from, make sure you have the necessary permissions and access to the AKS cluster to avoid any authentication or authorization issues.

List most common syntax error in yaml file done by developers ?

Here are some common syntax errors in YAML files that developers may encounter:

Indentation errors: YAML syntax relies heavily on indentation, and any error in indentation can cause issues. Common mistakes include using the wrong number of spaces or tabs, or mixing spaces and tabs, which can cause the YAML file to fail to parse.
Missing or extra quotes: YAML uses quotes to denote strings, and any missing or extra quotes can cause issues. Common mistakes include forgetting to close quotes or using single quotes where double quotes are required.
Missing or extra hyphens: YAML uses hyphens to denote lists or arrays, and any missing or extra hyphens can cause issues. Common mistakes include forgetting to add a hyphen to the start of a list item, or adding a hyphen where it is not needed.
Mismatched braces: YAML uses braces to denote objects or dictionaries, and any mismatched braces can cause issues. Common mistakes include forgetting to close a brace, or adding an extra brace where it is not needed.
Incorrect data types: YAML has strict data types, and any incorrect data type can cause issues. Common mistakes include using a string where a boolean is required, or using an integer where a string is required.
Typos or misspellings: YAML is case-sensitive, and any typos or misspellings can cause issues. Common mistakes include using uppercase letters where lowercase is required, or misspelling keywords or values.
Using invalid characters: YAML has strict rules for characters that can be used in the file, and any invalid characters can cause issues. Common mistakes include using special characters, such as ampersands or dollar signs, where they are not allowed.

To avoid these common syntax errors, it’s important to double-check the YAML file before deploying it, and to use a YAML linter or validator to catch any issues before deployment.

List all the pre, current and post deployment steps needs to be done in chronological order for both aks and k8s perspective ?

Here are the pre-deployment, deployment, and post-deployment steps for deploying applications on Azure Kubernetes Service (AKS) and Kubernetes in general:

Pre-Deployment Steps:

Plan your deployment: Determine the resources required for your application, such as pods, containers, and services. Determine the optimal placement for your application within the cluster, and decide on the appropriate deployment strategy (rolling update, canary deployment, blue-green deployment, etc.).
Create or provision an AKS cluster: You can create an AKS cluster in the Azure portal, Azure CLI, or with Azure Resource Manager templates. The AKS cluster should have the appropriate resource allocation and configuration to support your application.
Prepare the application deployment manifest: Create a YAML file that describes your application deployment, including the pod template, container image, ports, volumes, and environment variables.
Build and push the container image: Use a container registry to build and store the container image for your application.
Configure secrets and config maps: Store sensitive configuration data and environment variables in Kubernetes secrets, and other configuration data in ConfigMaps.
Test the deployment locally: Test the deployment manifest and container image locally to ensure they are functioning correctly before deploying them to the AKS cluster.

Deployment Steps:

Deploy the application manifest: Use the kubectl apply command to deploy the YAML file that describes your application deployment.
Monitor the deployment: Use the kubectl rollout status command to monitor the deployment progress and ensure that the pods are running correctly.
Expose the application: Expose the application as a Kubernetes service, which provides a stable IP address and DNS name for the application.
Configure load balancing: Configure a load balancer to distribute traffic to the application pods.
Monitor the application: Use Kubernetes tools such as kubectl logs and kubectl exec to monitor and troubleshoot the application.

Post-Deployment Steps:

Scale the application: Use Kubernetes commands to scale the application up or down as needed.
Backup and restore data: Use Kubernetes tools to backup and restore application data, such as volumes and databases.
Update the application: Use the kubectl apply command to update the application manifest to deploy new versions or changes to the application.
Monitor and optimize performance: Use Kubernetes tools to monitor and optimize the performance of the application, such as scaling, resource allocation, and pod scheduling.
Cleanup: Delete any unnecessary resources, such as pods, services, and deployments, to free up resources and reduce costs.

When an API request comes to AKS, how it gets verified, routed and processed ? Provide all the flow steps with appropriate architecture diagram links ?

When an API request comes to Azure Kubernetes Service (AKS), it goes through a series of steps to get verified, routed, and processed. Here are the high-level steps:

The API request is sent to the AKS ingress controller, which is responsible for routing incoming requests to the appropriate service.
The ingress controller checks if the request matches any of the defined ingress rules. An ingress rule defines the path and host for a particular service.
If the request matches an ingress rule, the ingress controller forwards the request to the appropriate service. If the request does not match any ingress rules, the ingress controller returns an error.
The service then authenticates the request using a service account or a client certificate. The authentication process verifies that the request is coming from a trusted source.
If the request is authenticated, the service processes the request by executing the appropriate code or command.
The service returns a response to the client that made the API request.

Here is an architecture diagram that shows the flow of an API request in AKS:

(Source: https://docs.microsoft.com/en-us/azure/architecture/reference-architectures/containers/kubernetes-aks)

In this diagram, you can see that the API request comes in through the ingress controller, which then routes the request to the appropriate service. The service then authenticates the request and processes it, returning a response to the client. This diagram also shows the various components involved in an AKS cluster, such as nodes, pods, and containers.

Why service and ingress required ? Does ingress work as load balancer ?

Services and Ingress are important components in Kubernetes and Azure Kubernetes Service (AKS) for routing traffic to your application, and providing access to your application from outside the cluster.

Services provide a stable IP address and DNS name for your application, allowing other services to access it within the cluster. A service also provides load balancing functionality, distributing traffic to multiple pods behind the service. This ensures that requests are evenly distributed across the pods, improving application availability and reliability.

Ingress provides external access to your application, allowing traffic from outside the cluster to reach your service. Ingress defines the rules for how traffic should be routed to your service, based on the URL path and host name. Ingress works as a reverse proxy, routing traffic to the appropriate service based on the defined rules. Ingress can also provide load balancing functionality, distributing traffic across multiple services or pods.

So, to summarize:

Services are used for routing traffic within the cluster and providing load balancing functionality.
Ingress provides external access to your application and defines the rules for routing traffic to your service.
Ingress can provide load balancing functionality, distributing traffic to multiple services or pods.

In essence, Ingress works as a reverse proxy and can provide some basic load balancing functionality. However, for more advanced load balancing, you may need to use a separate load balancer service or solution, such as Azure Load Balancer or a third-party solution.

What are the key difference between reverse proxy and load balancer ?

A reverse proxy and a load balancer are both network components that are used to distribute traffic across servers or applications, but they differ in their primary function and the level at which they operate. Here are the key differences:

Function:

A reverse proxy is primarily used to route traffic from the Internet to an internal network. It is an intermediary server that receives requests from clients and forwards them to the appropriate servers. The primary function of a reverse proxy is to handle the protocol translation and security features, such as SSL/TLS encryption, while also improving performance by caching frequently requested content.

A load balancer, on the other hand, is designed to distribute traffic evenly across multiple servers or applications. Its primary function is to ensure that the workload is distributed evenly, helping to improve the overall availability and reliability of the system.

2. Level of operation:

A reverse proxy operates at the application layer of the network stack. It can inspect and modify the application-layer data that is being transmitted between the client and the server. Reverse proxies are typically used for web servers, email servers, and other application servers.

A load balancer, on the other hand, operates at the transport layer of the network stack. It can inspect and modify the network-layer data that is being transmitted between the client and the server. Load balancers are typically used for server clusters, virtual machine instances, and containerized applications.

3. Placement:

A reverse proxy is typically placed in front of an application server or a group of servers. This allows it to provide a single point of entry for clients and to offload some of the processing from the application servers.

A load balancer can be placed in front of a group of servers, but it can also be used to distribute traffic between different data centers or regions.

In summary, while both reverse proxies and load balancers are used to distribute traffic, they have different primary functions and operate at different levels of the network stack. Reverse proxies are used for routing and security, while load balancers are used for load distribution and availability.

Can we write/configure custom routing logic using reverse proxy ?

Yes, it is possible to write custom routing logic using a reverse proxy. Reverse proxies are designed to be flexible and extensible, allowing you to customize their behavior to meet the needs of your application.

One of the most common ways to write custom routing logic is to use a scripting language such as Lua or JavaScript to define the routing rules. Many reverse proxies, such as NGINX and HAProxy, support scripting languages and provide APIs that allow you to define custom logic for handling requests.

For example, you can use NGINX to define custom routing rules based on the request URL, HTTP headers, or other factors. You can also define rules for load balancing and caching to improve performance and reliability.

In addition to scripting, many reverse proxies also support plugins and modules that allow you to add additional functionality, such as authentication, logging, and traffic shaping.

Overall, reverse proxies provide a flexible and powerful way to customize the routing and behavior of your application, making them a key component of many modern web architectures.

Can one set external FTP location as persistent volume ?

Yes, it is possible to set an external FTP location as a persistent volume in Kubernetes. Kubernetes supports various types of persistent volumes, including FTP, which can be mounted by a container and used to store data that persists across container restarts.

To set up an external FTP location as a persistent volume, you will need to configure the FTP server to allow access to the storage location, and then create a Kubernetes PersistentVolume (PV) object that specifies the FTP server address, username, password, and file path. You can also set options such as read-only or read-write access and storage capacity.

Here is an example YAML configuration for a PersistentVolume that uses FTP as the storage backend:

apiVersion: v1
kind: PersistentVolume
metadata:
  name: ftp-pv
spec:
  capacity:
    storage: 1Gi
  accessModes:
    - ReadWriteOnce
  ftp:
    host: ftp.example.com
    user: myuser
    password: mypassword
    path: /data/myvolume

Once you have created the PersistentVolume object, you can use it to create a PersistentVolumeClaim (PVC) object, which is used by pods to request access to the persistent storage. The PVC can be mounted as a volume inside a container, allowing it to read and write data to the FTP server.

Note that using an external FTP server as a persistent volume has some limitations, such as slower performance and potential network latency issues. It is generally recommended to use a local storage backend, such as a network-attached storage (NAS) device or a cloud-based storage service, for better performance and reliability.

Why container registry needed in aks ?

A container registry is needed in AKS (Azure Kubernetes Service) for several reasons:

Storing container images: A container registry is a storage location for container images that are used to run your application in AKS. When you build your application as a container image, you can store that image in a container registry so that it can be easily distributed and deployed to AKS.
Version control and management: A container registry provides version control and management for your container images. It allows you to keep track of different versions of your images and roll back to previous versions if needed.
Security: A container registry provides a secure location to store your container images, with features such as access control, image signing, and vulnerability scanning. This helps ensure that only authorized users can access and deploy your images, and that your images are free from known security vulnerabilities.
Performance: A container registry can improve performance by caching frequently used images closer to the nodes running your application in AKS. This can help reduce the time it takes to deploy and run your application, and can also help reduce bandwidth usage.

There are several container registry options available for AKS, including Azure Container Registry (ACR), Docker Hub, and other third-party registries. Using a registry that is integrated with AKS, such as ACR, can simplify the deployment and management of your application by providing a seamless integration with AKS and other Azure services.

Can ACR container image can be access from other cloud provider kubernet service like aws?

Yes, it is possible to access container images stored in Azure Container Registry (ACR) from other cloud provider Kubernetes services like AWS.

To access the ACR container images from an AWS Kubernetes cluster, you will need to perform the following steps:

Create a service principal in Azure that has read access to the ACR registry.
Use the service principal to create a Kubernetes secret that contains the ACR login credentials.
Deploy your Kubernetes application to AWS and configure it to use the ACR secret to pull the container images.

Here are more detailed steps to perform these actions:

Create a service principal in Azure that has read access to the ACR registry. You can use the Azure CLI to create a new service principal:

javascriptCopy code
az ad sp create-for-rbac --name <service-principal-name> --role acrpull --scopes /subscriptions/<subscription-id>/resourceGroups/<resource-group-name>/providers/Microsoft.ContainerRegistry/registries/<acr-name>

2. Use the service principal to create a Kubernetes secret that contains the ACR login credentials. You can create the secret using the Azure CLI:

az acr credential show --name <acr-name> --query "passwords[0].value" | kubectl create secret docker-registry acr-secret --docker-server=<acr-name>.azurecr.io --docker-username=<service-principal-id> --docker-password=- --docker-email=<your-email>

Replace <acr-name> with the name of your ACR registry, <service-principal-id> with the client ID of the service principal, and <your-email> with your email address.

3. Deploy your Kubernetes application to AWS and configure it to use the ACR secret to pull the container images. You can specify the ACR secret as a Kubernetes imagePullSecrets in your deployment YAML file:

spec: containers: - name: my-container image: <acr-name>.azurecr.io/my-image:latest imagePullSecrets: - name: acr-secret

Replace <acr-name> with the name of your ACR registry, and <my-image> with the name of your container image.

With these steps, your AWS Kubernetes cluster should now be able to pull container images from your Azure Container Registry.

How one can update container image with latest one in ACR ?

To update a container image with the latest one in Azure Container Registry (ACR), you can perform the following steps:

Push the latest version of the container image to ACR. You can use the docker push command to push the new image to ACR. For example:

docker push <acr-name>.azurecr.io/<image-name>:latest

Replace <acr-name> with the name of your ACR registry, and <image-name> with the name of your container image.

2. Update your Kubernetes deployment to use the latest container image. You can update the deployment YAML file to specify the latest version of the container image:

spec: containers: - name: my-container image: <acr-name>.azurecr.io/<image-name>:latest

Replace <acr-name> with the name of your ACR registry, and <image-name> with the name of your container image.

3. Apply the updated deployment YAML file using the kubectl apply command:

kubectl apply -f <deployment-yaml-file>

Replace <deployment-yaml-file> with the path to your updated deployment YAML file.

Once you have performed these steps, your Kubernetes deployment should use the latest version of the container image from ACR. Note that you can also use ACR webhooks to automate the process of updating your Kubernetes deployment when a new container image is pushed to ACR.

How can we automatically update ACR container images at certain interval to take latest container image ?

To automatically update Azure Container Registry (ACR) container images at a certain interval to take the latest container image, you can use Azure Container Instances (ACI) with a scheduled task. ACI provides a service to run containers on demand without having to provision or manage any infrastructure. You can use ACI to run a container that periodically checks for new container images in ACR, and if a new image is available, it can update the deployment in your Kubernetes cluster.

Here are the high-level steps to set up this process:

Create a container image that checks for new versions of the image in ACR and updates the Kubernetes deployment if a new image is available.
Create an Azure Container Registry (ACR) to store your container image.
Create a Kubernetes deployment that uses the container image you created in step 1.
Create an Azure Container Instance (ACI) with a scheduled task to run the container image at a certain interval.
Update the scheduled task to run the container image at the desired interval.

Once you have set up this process, the container image will automatically check for new versions of the image in ACR and update the Kubernetes deployment if a new image is available.

Here is an example of how to create an ACI with a scheduled task to run a container that checks for new versions of an image in ACR:

az container create --resource-group <resource-group-name> --name <aci-name> --image <acr-name>.azurecr.io/<image-name> --registry-username <acr-username> --registry-password <acr-password> --command-line "/bin/bash -c 'while true; do kubectl set image deployment/<deployment-name> <container-name>=<acr-name>.azurecr.io/<image-name>:latest; sleep <interval>; done'" --schedule "*/<interval-in-minutes> * * * *"

Replace <resource-group-name> with the name of your resource group, <aci-name> with the name of your ACI instance, <acr-name> with the name of your ACR registry, <image-name> with the name of your container image, <acr-username> with the username for your ACR registry, <acr-password> with the password for your ACR registry, <deployment-name> with the name of your Kubernetes deployment, <container-name> with the name of the container in your Kubernetes deployment, and <interval-in-minutes> with the desired interval in minutes.

What are the main components of the master node?

In Kubernetes, the master node is responsible for managing the cluster and its various components. The main components of the master node are:

API Server: The API server is the central management point for the Kubernetes cluster. It exposes the Kubernetes API, which clients use to interact with the cluster.
etcd: etcd is a distributed key-value store that is used to store the configuration data for the cluster. It is the primary source of truth for the cluster, and all other components use it to store and retrieve data.
Scheduler: The scheduler is responsible for scheduling pods onto nodes in the cluster. It considers resource requirements, quality of service, and other constraints when making scheduling decisions.
Controller Manager: The controller manager is responsible for maintaining the desired state of the cluster. It includes a number of controllers that watch the state of the cluster and make changes as necessary to ensure that the desired state is maintained.
Cloud Controller Manager (optional): The cloud controller manager is responsible for interacting with the cloud provider’s APIs to provision and manage cloud resources. It is only present if the cluster is running on a cloud provider.

These components work together to ensure that the Kubernetes cluster is functioning properly and that workloads are running as expected.

What are the different types of multiple- container pods?

In Kubernetes, a pod is the smallest deployable unit that can be created, scheduled, and managed. It is a logical host for one or more containers, and all containers in a pod share the same network namespace, IPC namespace, and file system namespace. There are several types of multiple-container pods in Kubernetes, including:

Sidecar containers: A sidecar container is a helper container that runs alongside the main container in the same pod. It can perform tasks such as logging, monitoring, or data processing. Sidecar containers share the same network namespace as the main container, which allows them to communicate with each other over the local loopback interface.
Ambassador containers: An ambassador container is a container that acts as a proxy for a service running in another pod. It can route traffic to the service and perform load balancing, service discovery, and other functions.
Adapter containers: An adapter container is a container that transforms data between different formats or protocols. It can be used to convert data from one format to another, or to translate between different APIs.
Init containers: An init container is a special type of container that runs before the main container in a pod. It can be used to perform initialization tasks such as downloading data, configuring the environment, or waiting for other services to become available.

These different types of multiple-container pods provide flexibility and modularity to Kubernetes workloads, allowing them to be customized to meet specific requirements.

What is the Namespace? List some of practical uses cases for the same ? Is it optional ?

In Kubernetes, a Namespace is a way to divide cluster resources into logically named groups. It provides a scope for names, allowing multiple users and teams to share a cluster without interfering with each other’s resources. Each namespace can have its own policies, resource quotas, and access controls, which can be used to manage the resources within that namespace.

Some practical use cases of namespaces include:

Resource isolation: Namespaces can be used to isolate resources used by different teams or applications, which can help prevent conflicts and improve security.
Environment separation: Namespaces can be used to separate resources used for development, testing, and production, making it easier to manage and monitor resources in each environment.
Resource quotas: Namespaces can have their own resource quotas, which can be used to limit the amount of CPU, memory, and storage used by resources within that namespace.
Access control: Namespaces can be used to enforce role-based access control (RBAC) policies, which can limit who can access resources within that namespace.
Multi-tenancy: Namespaces can be used to provide a multi-tenant environment, where different teams or applications share the same cluster but are isolated from each other.

Namespaces are not optional in Kubernetes. Every resource in Kubernetes is associated with a namespace, and if no namespace is specified, the resource is created in the default namespace. However, namespaces are not mandatory for small clusters or simple use cases where resource management is not a concern.

What are the major operations of Kubelet as a node service component in Kubernetes?

Kubelet is a node service component in Kubernetes that is responsible for managing the containers running on a node. Some of the major operations of Kubelet are:

Container management: Kubelet is responsible for managing the lifecycle of containers running on the node. It starts, stops, and restarts containers as necessary, and ensures that the containers are running correctly.
Image management: Kubelet is responsible for pulling container images from a container registry, and caching them locally on the node. It also periodically checks for updated images and pulls them if necessary.
Volume management: Kubelet is responsible for managing the volumes used by containers on the node. It creates and mounts volumes, and ensures that they are available to containers as necessary.
Health monitoring: Kubelet monitors the health of the containers running on the node, and reports their status to the Kubernetes control plane. If a container becomes unhealthy, Kubelet takes appropriate action to restart the container.
Node status reporting: Kubelet reports the status of the node to the Kubernetes control plane, including the status of the containers running on the node, available resources, and any other relevant information.
Pod lifecycle management: Kubelet is responsible for managing the lifecycle of pods on the node. It starts and stops pods as necessary, and ensures that the containers within the pod are running correctly.

Overall, Kubelet is a critical component of the Kubernetes node, responsible for managing the containers and pods running on the node, and ensuring that they are healthy and functioning correctly.

List some of use cases where kubernetes is not a best fit ?

While Kubernetes is a powerful and flexible platform for container orchestration, there are some use cases where it may not be the best fit. Here are some examples:

Small-scale deployments: Kubernetes may be overkill for small-scale deployments, where a simpler container orchestration solution may be sufficient.
Stateless applications: If an application is stateless and does not require any special scaling or scheduling considerations, Kubernetes may not be necessary.
High-performance computing: Kubernetes may not be the best fit for high-performance computing workloads that require low-latency communication and specialized hardware.
Legacy applications: Applications that are not containerized and cannot be easily containerized may not be a good fit for Kubernetes.
Non-containerized workloads: Kubernetes is designed for containerized workloads, so it may not be a good fit for applications that cannot be containerized.
Limited resources: Kubernetes requires a certain level of infrastructure resources to run effectively, so it may not be a good fit for environments with limited resources.

Overall, while Kubernetes is a powerful and flexible platform, it may not be the best fit for every use case. It’s important to carefully evaluate the requirements and constraints of your specific application and infrastructure before deciding whether to use Kubernetes.

How would you use Azure Cosmos DB to store and manage data for an AKS-based application?

Azure Cosmos DB is a globally distributed, multi-model database service that is designed to provide low latency and high availability for applications that require high scalability and low latency. Here’s how you could use it to store and manage data for an AKS-based application:

Choose the appropriate Cosmos DB API: Cosmos DB supports multiple APIs, including SQL API, MongoDB API, Cassandra API, Gremlin API, and Azure Table API. Choose the API that best suits the needs of your application.
Create a Cosmos DB account: In the Azure portal, create a new Cosmos DB account and select the appropriate API and other settings based on your requirements.
Configure access: Configure access to the Cosmos DB account using the Cosmos DB connection string, which includes the endpoint URL and the authorization key.
Create a database and collection: In Cosmos DB, create a new database and collection for your AKS-based application to store and manage data. You can choose the appropriate partition key and indexing policy based on your data access patterns.
Connect your AKS-based application: In your AKS-based application, you can connect to Cosmos DB using the appropriate API and the connection string. You can then perform CRUD (create, read, update, delete) operations on the database and collection using the API.
Scale and replicate: Azure Cosmos DB supports automatic scaling and replication, so you can easily scale up or down based on your application’s needs, and replicate data across multiple regions for high availability and low latency.

By using Azure Cosmos DB to store and manage data for your AKS-based application, you can take advantage of its globally distributed architecture, low latency, and high scalability, and ensure that your application has the necessary resources to meet your customers’ needs.

What happens when a master fails in kubernets? What happens when a worker fails?

When a master node fails in Kubernetes, the following happens:

The Kubernetes control plane detects the failure of the master node.
The Kubernetes control plane elects a new master node from the available nodes in the cluster.
The Kubernetes control plane updates the cluster state and reschedules any workloads that were running on the failed master node to the new master node.
If the failed master node was the only etcd member, the etcd cluster may need to be reinitialized or restored from a backup to recover the data.

When a worker node fails in Kubernetes, the following happens:

The Kubernetes control plane detects the failure of the worker node.
The Kubernetes control plane removes the failed worker node from the cluster and updates the cluster state.
The Kubernetes scheduler reschedules any workloads that were running on the failed worker node to other available nodes in the cluster.
If the failed worker node was running a stateful workload with persistent volumes, the Kubernetes volume controller will detach the volumes from the failed node and reattach them to the new node where the workload is rescheduled.
If the failed worker node was running a DaemonSet or a hostPath volume, the Kubernetes controller manager will reschedule the workload on another available node.

By handling failures of master and worker nodes in this way, Kubernetes ensures that the cluster remains highly available and that workloads continue to run without interruption.

What is a StatefulSet in Kubernetes?

A StatefulSet is a Kubernetes controller that manages the deployment and scaling of a set of stateful pods. Unlike a Deployment, which manages a set of stateless pods, a StatefulSet is designed to manage stateful applications that require stable network identities, ordered pod initialization and termination, and persistent storage.

Each pod in a StatefulSet has a unique identity and network hostname that is based on its position in the set, which enables the pod to maintain its identity and state across rescheduling events. The StatefulSet controller also ensures that pods are started and stopped in a predictable order to maintain any dependencies between the pods.

StatefulSets are commonly used to manage stateful applications such as databases, messaging systems, and other stateful services. They are also used to manage stateful workloads that require strict ordering and persistence guarantees, such as distributed systems and machine learning workflows.

Overall, StatefulSets provide a way to manage stateful applications in Kubernetes that is more predictable and reliable than other deployment options, making it easier to operate complex stateful workloads on Kubernetes.

Give some real life examples of StatefulSet ?

Here are some real-life examples of stateful applications that can be managed using a StatefulSet in Kubernetes:

Databases: StatefulSets can be used to manage stateful databases like MySQL, PostgreSQL, MongoDB, etc. Each database instance can be managed as a unique pod with its own persistent volume and network identity.
Streaming platforms: StatefulSets can be used to manage streaming platforms like Apache Kafka, Apache Pulsar, etc. Each broker or node in the cluster can be managed as a unique pod with its own persistent volume and network identity.
Distributed systems: StatefulSets can be used to manage distributed systems like Apache ZooKeeper, Apache Cassandra, etc. Each node in the cluster can be managed as a unique pod with its own persistent volume and network identity.
Machine learning workflows: StatefulSets can be used to manage machine learning workflows that require strict ordering and persistence guarantees. Each step in the workflow can be managed as a unique pod with its own persistent volume and network identity.

Overall, StatefulSets are a powerful tool for managing stateful applications on Kubernetes, providing a reliable and predictable way to manage complex stateful workloads.

What is a DaemonSet ? List some of daemonset examples except logging ?

In Kubernetes, a DaemonSet is a type of workload that ensures that a copy of a pod is running on every node in a cluster. Here are some examples of DaemonSets, other than logging:

Monitoring agents: DaemonSets can be used to deploy monitoring agents like Prometheus Node Exporter or Datadog Agent on every node in a cluster, which can collect metrics and monitor system health.
Networking: DaemonSets can be used to deploy networking components like CNI (Container Networking Interface) plugins or load balancer agents, which can help manage networking configurations across all nodes.
Security: DaemonSets can be used to deploy security components like anti-virus agents or intrusion detection systems, which can help ensure that every node is protected from attacks.
Storage: DaemonSets can be used to deploy storage components like CSI (Container Storage Interface) plugins or storage agents, which can manage storage across all nodes and ensure that persistent volumes are accessible from every node.

Overall, DaemonSets are a powerful tool for managing workloads that need to be deployed on every node in a cluster, providing a reliable and predictable way to manage infrastructure components across a Kubernetes cluster.

What are the disadvantages of using DaemonSet ? How i can configure DaemonSet which exclude some nodes based on some condition ?

While DaemonSets are a powerful tool for managing workloads, they do have some disadvantages:

Overhead: Running a copy of a pod on every node in a cluster can be resource-intensive, which can lead to increased overhead and reduced performance.
Complexity: Managing a DaemonSet across a large cluster can be complex, requiring careful coordination and management to ensure that all nodes are running the correct version of the pod.
Maintenance: Upgrading a DaemonSet can be more complex than upgrading other types of workloads, since you need to ensure that every node is upgraded at the same time.

To exclude some nodes from a DaemonSet based on a condition, you can use a node selector or a node affinity rule. These features allow you to specify criteria for selecting or excluding nodes based on factors like node labels or node attributes. Here is an example of a DaemonSet configuration file that excludes nodes with the label “special-node”:

apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: my-daemonset
spec:
  selector:
    matchLabels:
      app: my-app
  template:
    metadata:
      labels:
        app: my-app
    spec:
      nodeSelector:
        special-node: "false"
      containers:
      - name: my-container
        image: my-image

In this example, the nodeSelector field specifies that nodes with the label "special-node" set to "false" should be selected, while nodes with the label set to "true" will be excluded from the DaemonSet. You can also use other types of node selectors or node affinity rules to exclude nodes based on other criteria, such as node attributes or availability zones.

What are the top options to organize or group nodes ?

In Kubernetes, nodes can be organized or grouped in different ways using labels and selectors. Here are some common options:

Availability zones: Nodes can be labeled with the availability zone in which they are running. This is useful for deploying applications that need to be highly available and fault-tolerant.
Roles: Nodes can be labeled with different roles based on the type of workloads they are running. For example, you might have nodes that are labeled as database servers, web servers, or worker nodes.
Hardware specifications: Nodes can be labeled based on their hardware specifications such as CPU, RAM, storage, or GPU. This is useful for deploying applications that require specific hardware resources.
Geographic location: Nodes can be labeled based on their geographic location. This is useful for deploying applications that need to be geographically distributed.

To group nodes based on these labels, selectors can be used to identify nodes that match specific criteria. For example, you can create a Kubernetes deployment or a service that selects only the nodes labeled as web servers to run a specific workload.

Which problems does a Container Orchestration solve ?

Container orchestration solves a number of problems that arise when deploying and managing containerized applications at scale. Here are some of the key problems that container orchestration addresses:

Scaling: Container orchestration enables automatic scaling of applications in response to changes in demand. This helps ensure that the application can handle increased traffic without downtime or performance degradation.
High availability: Container orchestration ensures that applications are highly available, with built-in mechanisms for detecting and recovering from failures.
Load balancing: Container orchestration provides load balancing across multiple containers or nodes, ensuring that traffic is distributed evenly and that no single node or container is overwhelmed.
Service discovery: Container orchestration enables automatic discovery of services running in the cluster, making it easy to configure and manage connections between services.
Rollouts and rollbacks: Container orchestration provides mechanisms for rolling out updates to applications in a controlled manner, with the ability to quickly roll back changes if necessary.
Resource allocation: Container orchestration enables efficient allocation of resources, with mechanisms for limiting resource usage and ensuring that applications have the resources they need to run effectively.

Overall, container orchestration makes it easier to deploy and manage containerized applications, while also ensuring that they are highly available, scalable, and performant.

Is service discovery and load balancing are interrelated terms ?

Yes, service discovery and load balancing are closely related terms in the context of container orchestration.

Service discovery refers to the process of automatically discovering and registering services in a cluster, making it easier to configure and manage connections between them. This can include things like automatically registering the IP address and port number of a service, as well as its associated metadata such as labels and annotations.

Load balancing, on the other hand, refers to the process of distributing traffic across multiple instances of a service to ensure that the workload is evenly distributed and that no single instance is overwhelmed. Load balancing can be performed at different levels, such as at the network level or at the application level, and can be implemented using different techniques such as round-robin, least connections, or IP hash.

In a container orchestration system such as Kubernetes, service discovery and load balancing are typically handled by the same component, such as the kube-proxy or an external load balancer. The service discovery component automatically registers services as they are created or deleted, and the load balancing component routes traffic to the appropriate instance of the service based on its IP address and port number.

Explain the difference between a replica set and replication controllers ?

In Kubernetes, ReplicaSets and Replication Controllers are both used for ensuring that a specified number of pod replicas are running at all times. However, there are some differences between the two:

Label selectors: ReplicaSets use a set-based selector whereas Replication Controllers use an equality-based selector. This means that ReplicaSets can support more complex selector expressions, allowing for greater flexibility in selecting pods.
Rolling updates: ReplicaSets support rolling updates, which allow for a more controlled and automated way to update a set of pods to a new version, while Replication Controllers do not.
API compatibility: ReplicaSets are the recommended way to manage replication in Kubernetes, and are considered to be the successor to Replication Controllers. ReplicaSets have a newer, more powerful API and are used by more advanced features such as Deployments and StatefulSets.
Naming conventions: ReplicaSets use the word “set” in their name (e.g. “my-replicaset”) whereas Replication Controllers use the word “controller” (e.g. “my-controller”).

In general, ReplicaSets are preferred over Replication Controllers due to their greater flexibility and support for rolling updates. However, Replication Controllers are still supported in Kubernetes and may be used in certain cases where rolling updates are not required.

I need to create additional N number of nodes for some special dates every year. How can i configure the same in kubernetes ? Every year those dates are fixed. And i don’t want to repeat same thing again and again every year.

You can create a Kubernetes cron job to run a script that scales up the number of nodes on the specified dates. The cron job can be scheduled to run every year at the same time to ensure that the additional nodes are created on those dates.

Here is an example of a script that can be used to scale up the nodes:

# set the number of nodes to scale up
node_count=5

# scale up the nodes
kubectl scale --replicas=$node_count deployment/node-deployment

You can then create a Kubernetes cron job to run the script on the specified dates:

apiVersion: batch/v1beta1
kind: CronJob
metadata:
  name: node-scale-up
spec:
  schedule: "0 0 1 1 *" # run on 1st January every year
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: node-scaler
            image: <your-image>
            command: ["/bin/bash"]
            args: ["-c", "/path/to/script.sh"]
          restartPolicy: OnFailure

This cron job will run the script on the 1st of January every year, scaling up the number of nodes as specified in the script. You can modify the schedule field to match the dates you need to scale up the nodes.

Can cron job, can scale/descale cpu/memory as well for existing nodes ?

No, CronJob is used to schedule a job to run at a specific time or interval, and it doesn’t have the capability to scale or descale CPU/memory resources for existing nodes.

Scaling or descaling the CPU/memory resources for existing nodes is generally handled by Kubernetes’ Horizontal Pod Autoscaler (HPA), which automatically scales the number of replicas of a deployment based on CPU/memory usage metrics. If you need to scale the number of nodes themselves, you can use Kubernetes’ Cluster Autoscaler, which automatically scales the number of nodes in a cluster based on resource utilization.