Kubernetes in a nutshell

In my previous blog, We learned how to deploy an API to the GCP K8s engine, Today we will learn about Kubernetes as an Overview.

Google created Kubernetes (K8s) as part of their internal infrastructure to manage the containerized applications running on their infrastructure.

Kubernetes is an open-source platform designed to automate the deployment, scaling, and management of containerized applications. It's been around for a while now and has become the standard for managing containerized applications in production environments.

What is Kubernetes? 

Kubernetes is an orchestration system that automates the deployment, scaling, and management of containers. It provides a unified platform for deploying and managing containers, making it easier for organizations to run and scale their applications. Kubernetes is highly extensible, allowing organizations to customize it to meet their specific needs.

Kubernetes (K8s) is an open-source container orchestration system for automating the deployment, scaling, and management of containerized applications. It works by using a master node to control and manage a group of worker nodes. 

How does Kubernetes work? 

Kubernetes works by dividing an application into smaller units, called containers. Each container holds a piece of an application, such as a microservice. These containers can be deployed and managed independently, making it easier to scale and manage applications. 

Kubernetes uses a declarative approach to manage containers, meaning you define what you want your application to look like and Kubernetes takes care of the rest. This makes it easy to manage complex applications, as you don't need to worry about the details of how containers are deployed and managed.. 

The main components of a Kubernetes cluster are: 

• The API server: The entry point for all administrative tasks. It exposes the Kubernetes API and communicates with the other components. 
• etcd: A distributed key-value store that stores the configuration data of the cluster. 
• The controller manager: Responsible for maintaining the desired state of the system by making changes to the actual state of the system as necessary. 
• The kubelet: Runs on each worker node and communicates with the API server. It is responsible for starting and stopping containers on the node. 
• The kube-proxy: Runs on each worker node and provides network connectivity to the containers. 

Kubernetes uses a declarative approach, where the user defines the desired state of the system in the form of manifests, and the system ensures that the actual state of the system matches the desired state. 

Why use Kubernetes? 

Kubernetes provides a number of benefits over traditional approaches to managing containers. Some of the most significant benefits include: 

• Scalability: Kubernetes makes it easy to scale your application, either by adding more containers or by increasing the resources assigned to existing containers. 
• Resilience: Kubernetes automatically monitors containers and restarts them if they fail, ensuring that your application is always available. 
• Portability: Kubernetes can run on a variety of cloud platforms, as well as on-premises. This makes it easier to move your application from one platform to another, reducing vendor lock-in. 
• Integration: Kubernetes integrates with a variety of tools and platforms, making it easier to integrate your application with other systems.

Kubernetes Architecture 

Kubernetes is based on a master-worker architecture. The master node is responsible for managing the cluster, while worker nodes run the containers. The master node communicates with worker nodes to deploy and manage containers, and to ensure that the desired state of the application is maintained.

Kubernetes uses a number of components to manage containers, including: 

• API server: The API server is the central component of the Kubernetes architecture. It provides a RESTful interface for managing the cluster, and is used by other components to interact with the cluster. 
• etcd: etcd is a distributed key-value store that Kubernetes uses to store cluster data. This data is used to ensure that the desired state of the application is maintained. 
• Scheduler: The scheduler is responsible for scheduling containers to run on worker nodes. It uses data from the etcd store to determine the optimal placement of containers. 
• Controller manager: The controller manager is responsible for managing the state of the cluster. It monitors the state of the cluster and takes action to ensure that the desired state is maintained. 
• Kubelet: The kubelet is a component that runs on each worker node. It communicates with the master node to receive instructions for deploying and managing containers.

Deploying Applications on Kubernetes

To deploy an application in a Kubernetes cluster, you would create a deployment manifest that defines the desired state of the application, such as the number of replicas and the container image to use. The Kubernetes control plane will then ensure that the actual state of the system matches the desired state by creating the necessary pods and replication controllers. 

Another example is if an application running on a node goes down, Kubernetes will automatically create a new pod to replace the failed one. Also, if the load on an application increases, Kubernetes can automatically scale the number of replicas to handle the increased load. 

Kubernetes also provides features such as service discovery and load balancing to make it easier to access applications running in the cluster, as well as rolling updates to allow for updates to be made to the system with minimal downtime. 

Overall, Kubernetes provides a powerful platform for managing containerized applications at scale, making it easier to deploy, scale, and manage applications in a production environment.

The process of deploying an application in a Kubernetes cluster involves several steps: 

• Containerizing the application: The first step is to containerize the application by creating a Docker image that includes the application code and all its dependencies. 
• Creating a deployment manifest: Once the application is containerized, you need to create a deployment manifest that defines the desired state of the application. This includes the number of replicas, the container image to use, and any environment variables or volumes that the application requires. 
• Creating a service manifest: A Service manifest defines the desired state of the service. It is responsible for the network communication between the pods and the external world. 
• Applying the manifests: The next step is to apply the manifests to the cluster. This can be done using the kubectl command-line tool, which communicates with the Kubernetes API server to create the necessary resources in the cluster. 
• Verifying the deployment: After applying the manifests, you can use the kubectl command-line tool to verify that the deployment was successful. This includes checking that the pods and replication controllers were created and that the desired number of replicas is running. 
• Updating the deployment: If you need to make changes to the deployment, such as updating the container image or changing the number of replicas, you can do so by modifying the deployment manifest and reapplying it to the cluster. Kubernetes will then update the actual state of the system to match the desired state. 
• Scaling the deployment: If the workload increases, you can scale the deployment by modifying the replicas count in the deployment manifest and reapplying it to the cluster. Kubernetes will then automatically create new pods to handle the increased load. 
• Monitoring the deployment: Monitoring the deployment, including the health and performance of the application, is important to ensure that the application is running as expected and to troubleshoot any issues that may arise. 

Overall, the process of deploying an application in a Kubernetes cluster involves containerizing the application, creating manifests, applying them to the cluster, and then monitoring and updating the deployment as needed.

How we can secure the deployment in Kubernetes

• Secure communication: Ensure that all communication between components within the cluster, as well as between the cluster and external systems, is secure. This can be done by using secure protocols such as HTTPS and securing etcd with proper authentication and authorization. 
• Network segmentation: Use network policies to segment the network and limit communication between pods and services. 
• Role-based access control (RBAC): Use RBAC to control access to the Kubernetes API, and limit the actions that users, groups, and service accounts can perform within the cluster. 
• Secrets and configMaps management: Use Kubernetes secrets and configMaps to store sensitive information such as passwords, tokens, and certificates in an encrypted form and avoid storing them in the application code. 
• Pod security policies: Use pod security policies to define the security context for pods, including setting resource limits and enabling security features such as AppArmor or SELinux. 
• Regular Auditing: Regularly audit the cluster for security risks and compliance issues, and take action as necessary. 
• Secure your nodes: Secure the nodes by using a firewall, configuring secure boot, using a trusted platform module (TPM), and securing the operating system. 
• Use security add-ons: Use security add-ons such as Kubernetes Network Policy, PodSecurityPolicy, Kubernetes Secrets, Kubernetes ConfigMaps, etc to secure your deployment. Use third-party tools: 
• Use third-party tools such as Kube-bench, Kube-hunter, etc to scan and test the cluster for vulnerabilities and misconfigurations. 

Overall, securing a deployment in Kubernetes requires a combination of different security measures to protect the communication, network, and access control, as well as the data and application running within the cluster.

This deployment file creates a deployment named "my-app" with 3 replicas and runs the container as non-root user, with read-only root file system. The environment variables, SECRET_KEY, and CONFIG_SETTINGS are set using Kubernetes Secrets and ConfigMaps respectively, to store sensitive and non-sensitive information. Also, it uses a pod security policy to set the security context of the pod.

 

Happy Coding and Keep Sharing!!

Serverless computing

Serverless computing is a cloud-based computing execution model in which the cloud provider dynamically manages the allocation of machine resources. With serverless computing, the cloud provider is responsible for provisioning, scaling, and managing the servers that run the code, rather than the user. This allows developers to focus on writing code and deploying their applications, without the need to worry about the underlying infrastructure. 

In serverless computing, the code is run in stateless compute containers that are triggered by events and automatically scaled to match the rate of incoming requests. This eliminates the need for provisioning, scaling, and maintaining servers, resulting in lower costs and increased scalability. 

Examples of serverless computing include AWS Lambda, Azure Functions, and Google Cloud Functions. These services allow developers to create and deploy their code as small, single-purpose functions, which are automatically triggered by events such as an HTTP request or a database update. 

Serverless computing is commonly used for building web and mobile backends, real-time data processing, and event-driven architectures. 

It's important to note that despite the name, there are servers still running behind the scene, the difference is that the provider manages the servers and the user only pays for the resources used (compute, storage, etc) and not for the servers.

Here are a few common use cases for serverless computing: 

• Event-driven computing: Serverless architectures are well-suited for processing events, such as changes to a database or new files being uploaded to a storage service. This allows for real-time data processing and efficient scaling. 
• APIs and Microservices: Serverless computing is often used to build and deploy APIs, as well as to run microservices. This allows for better scalability and cost management, as resources are only allocated when an API request is made or a microservice is invoked. 
• Background tasks and cron jobs: Serverless computing can be used to run background tasks, such as image processing or data analysis, which can be triggered by a schedule or a specific event. 
• Web and mobile apps: Serverless architectures can be used to build and deploy web and mobile applications, allowing for faster development, lower costs, and better scalability. 
• IoT and edge computing: Serverless computing can be used to build and deploy applications for Internet of Things (IoT) devices and for edge computing, where compute resources are located at or near the edge of a network. 
• Chatbot and voice assistants: Serverless function can be used to handle the logic of chatbot and voice assistants, this way only the necessary compute power is used when a user interacts with the chatbot or assistant.

Advantages of using serverless applications in .NET Core :

• Cost-effective: serverless architecture eliminates the need for provisioning and maintaining servers, resulting in lower costs. 
• Scalability: serverless applications can automatically scale in response to increased traffic, without the need for manual intervention. 
• Flexibility: serverless architecture allows for the deployment of small, single-purpose functions, making it easier to build and maintain a microservices-based architecture. 
• Reduced operational complexity: serverless applications are abstracted away from the underlying infrastructure, reducing the operational complexity of deploying and managing applications. 
• Improved availability: serverless applications can be designed to automatically failover to other instances in the case of a failure, improving the overall availability of the application.

Disadvantages of using serverless applications:

• Cold start: serverless applications may experience a delay in response time when they first receive a request after a period of inactivity, known as a "cold start." 
• Limited control over the underlying infrastructure: serverless applications do not provide the same level of control over the underlying infrastructure as traditional server-based applications. 
• Concurrency limitations: serverless applications may be subject to concurrency limitations, depending on the platform and the number of instances available. 
• Limited support for long-running tasks: serverless architecture is best suited for short-lived, stateless tasks, and may not be the best choice for long-running, stateful tasks. 
• Higher latency: serverless applications may experience higher latency because of the need to spin up new instances to handle incoming requests.

Here are the general steps to build a Serverless application: 

• Choose a cloud provider: There are several popular cloud providers that offer serverless computing services, such as AWS Lambda, Azure Functions, and Google Cloud Functions. Choose the one that best fits your needs and has good Java support. 
• Set up the development environment: Before you start building your serverless application, you will need to set up your development environment. This typically involves installing the necessary software and configuring your development environment. 
• Create a new function: Once your development environment is set up, you can create a new function. This typically involves specifying the function's name, the trigger that will invoke the function, and the code that will be executed when the function is invoked. 
• Write the code: Write the code for your function using Java. The code should handle the input and output of the function. Test the function: 
• Test the function locally before deploying it to the cloud. This can be done using the cloud provider's command-line tools or SDK. 
• Deploy the function: Once your function is tested and working, deploy it to the cloud provider's serverless computing service. 
• Monitor and maintain: After deploying your function, monitor and maintain it. This includes monitoring the function's performance and error logs, and making updates and fixes as necessary.

Steps to build serverless application in .NET Core

• Install the .NET Core SDK and the AWS SDK for .NET on your local machine. 
• Create a new .NET Core project using the "dotnet new" command. 
• Add the AWS Lambda NuGet package to the project. 
• Create a new class that will serve as the entry point for the Lambda function. This class should implement Amazon.Lambda.Core.ILambda function interface. 
• Add the necessary code to handle the input and output of the Lambda function in the class created in the previous step. 
• Create an AWS profile and configure the AWS SDK for .NET with the appropriate credentials. 
• Use the "dotnet lambda deploy-function" command to deploy the Lambda function to AWS. 
• Test the deployed Lambda function using the AWS Lambda console or the AWS CLI. 
• Add any other functionality, such as connecting to a database or invoking other AWS services, as needed for your application. 
• Continuously monitor and update your serverless application to ensure optimal performance and stability.

Happy Coding and Keep Sharing !!