Ankush Madaan’s Post

Real-Time Kubernetes Monitoring in Your Terminal with Kubebox! 📊 If you're managing Kubernetes clusters, you know how critical real-time monitoring is. One tool that’s changing the game for DevOps professionals? Kubebox! 🚀 What is Kubebox? Kubebox is a terminal-based tool for monitoring Kubernetes clusters. Think of it as a "top" or "htop" for Kubernetes. It provides a live view of your cluster's health, resources, and logs—all from your terminal. It's perfect for those who want a quick, interactive view without leaving the command line. Why Use Kubebox? 1. Real-Time Metrics 📉: Monitor CPU, memory, and storage usage in real time, right from your terminal. 2. Interactive Navigation 🧭: Quickly move between namespaces, pods, and containers to get detailed insights. 3. Centralized Logs 📑: View container logs in one place, making troubleshooting fast and efficient. 4. Resource Optimization ⚙️: Spot resource-heavy pods instantly and take action to optimize your cluster’s performance. How to Get Started with Kubebox: 1. Install Kubebox (requires Node.js): npm install -g kubebox 2. Run it by connecting to your cluster: kubebox With just that, Kubebox will give you a dashboard of your Kubernetes resources and live stats, making it a must-have for quick insights and incident response. Kubebox is lightweight, visual, and fits perfectly into any DevOps toolkit for Kubernetes. Whether you're troubleshooting, optimizing resources, or just keeping an eye on your clusters, it’s a tool worth trying. #Kubernetes #DevOps #Kubebox #Monitoring #OpenSource #K8s #CloudNative #Observability

Hello All, Today I have an interesting topic to discuss. #Kubernetes has transformed how applications are deployed and managed, offering scalability, flexibility, and automation. However, there are challenges when working in multi-tenant environments or managing isolated Kubernetes clusters for development, testing, or debugging purposes. Enter #vCluster- A virtual #Kubernetes cluster solution that provides a lightweight way to manage multiple environments within a single Kubernetes cluster. To know more: https://lnkd.in/gEh54muy #k8s #kubernetes #cluster #cncf #opensource #multitenancy #costoptimization #devops

Understanding Container Types in Kubernetes ...Kubernetes doesn't directly define distinct "types" of containers, it leverages specific container patterns to achieve diverse functionalities within a pod. Let's explore these common patterns: 𝟏. 𝐈𝐧𝐢𝐭 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐬: Purpose: Execute tasks before the main application container starts. 🔶 Use Cases: - Setting up configurations - Preloading data - Checking prerequisites #️⃣ Behavior: - Run sequentially - Must complete successfully for the main container to start 𝟐. 𝐒𝐢𝐝𝐞𝐜𝐚𝐫 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐬: Purpose: Complement the main application container with additional functionalities. 🔶 Use Cases: - Logging - Monitoring - Proxy services - Security #️⃣ Behavior: - Run concurrently with the main container - Often share the same network and volume 𝟑. 𝐄𝐩𝐡𝐞𝐦𝐞𝐫𝐚𝐥 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐬: Purpose: Diagnose and troubleshoot running pods. 🔶 Use Cases: - Executing shell commands - Inspecting file systems - Running debugging tools #️⃣ Behavior: - Short-lived and temporary - Don't persist after pod termination 𝟒. 𝐌𝐮𝐥𝐭𝐢-𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫 𝐏𝐨𝐝𝐬: Purpose: Group multiple containers into a single pod for co-location and resource sharing. 🔶 Use Cases: - Microservices architectures - Complex applications requiring multiple components #️⃣ Behavior: - Share the same network namespace and IP address - Can communicate via localhost Would you like to dive deeper into a specific container pattern or explore other Kubernetes concepts? If you liked this post:  💾 Save this post for future reference 🤝 Have questions or insights? Share in the comments below! ♻️ Repost if this helped you—let’s keep supporting the DevOps community! Have anything to add? #DevOps #Containers #Microservices #CICD #SoftwareDevelopment #CloudComputing #Automation #Scalability #kubernetes #k8s

Pod: The smallest deployable unit in Kubernetes, representing a single instance of a running process in your cluster. Namespace: A way to divide cluster resources between multiple users (via resource quota) or environments (e.g., dev, test, prod). Service: An abstraction that defines a logical set of Pods and a policy by which to access them (e.g., load balancing, service discovery). Ingress: Manages external access to services in a cluster, typically HTTP, and can provide load balancing, SSL termination, and name-based virtual hosting. Volume: Allows data to persist beyond the lifetime of a Pod, ensuring that data is not lost when a Pod restarts. Secrets and ConfigMaps: Resources for storing sensitive information or configuration data, which can be mounted into Pods as files or used as environment variables. Horizontal Pod Autoscaler (HPA): Automatically scales the number of Pods in a replication controller, deployment, or replica set based on observed CPU utilization or other custom metrics. Kubectl: The command-line tool used to interact with Kubernetes clusters, allowing you to deploy applications, inspect and manage cluster resources, and view logs. Kubernetes Dashboard: A web-based user interface for Kubernetes clusters, providing a visual representation of the cluster's resources and their status. StatefulSet: Ensures stable, unique network identifiers and persistent storage for stateful applications. Deployment: Manages the rollout and updates of replicated applications, ensuring availability and scalability. DaemonSet: Ensures a copy of a pod is running on all (or a subset of) nodes, useful for cluster-wide tasks like logging or monitoring. ClusterIP: Provides internal-only connectivity to services within the cluster, ideal for inter-service communication. NodePort: Exposes a service on a static port on each node's IP, allowing external access to the service. LoadBalancer: Automatically provisions an external load balancer to expose a service to the internet. add this tooUnderstanding these concepts is essential for efficient Kubernetes cluster management and application deployment. 💡 #Kubernetes #DevOps #CloudComputing#Happy learning.

𝐏𝐨𝐬𝐭 28 🚀 𝐓𝐢𝐭𝐥𝐞: Boosting Your Kubernetes Deployments with Init Containers 🚀🛠️ 🔍 𝐈𝐬𝐬𝐮𝐞: Managing dependencies and initialisation tasks before the main application container starts in a Kubernetes Pod can be complex. Often, there is a need to perform setup operations such as fetching configuration files, waiting for services to become available, or initialising databases. 💡𝐅𝐢𝐱: Kubernetes Init Containers provide a robust solution for handling initialisation logic. They run before the main application container starts, ensuring that all necessary preconditions are met. This leads to a more reliable and efficient deployment process. 📋 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Init Containers can be configured to run before the main application container in a Pod. If there are multiple init containers, they run sequentially, each one starting only after the previous one has completed successfully. Here’s a snippet illustrating how they work. In this example, the init container runs a command to perform initialisation tasks. Only after it completes successfully does the main application container start. If the init container fails, Kubernetes will restart the Pod until the init container succeeds, ensuring reliable execution of initialisation tasks. 🎓 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠s: 1. Dependency Management: Init Containers are ideal for managing dependencies that need to be resolved before starting the main application. 2. Sequential Execution: Multiple init containers can be used to run tasks in a defined sequence, providing precise control over the initialisation process. 3. Robustness: By ensuring all initialization tasks complete successfully before the main application starts, you reduce the chances of runtime errors and increase application reliability. 4. Automatic Retry: Kubernetes handles retries automatically if an init container fails, ensuring that your Pods are only running when they are fully prepared. Using Init Containers effectively can significantly enhance the reliability and maintainability of your Kubernetes applications, making sure they are always in a ready state when launched. #Kubernetes #DevOps #InitContainers #CloudComputing #K8s #TechTips #Reliability #Initialisation #Containers 🚀🛠️🔧

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐬? Kubernetes is an open-source 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫 𝐨𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 designed to automate the deployment, scaling, and management of containerized applications. It provides a framework for deploying, managing, and scaling applications across clusters of hosts. 𝐀𝐏𝐈 𝐒𝐞𝐫𝐯𝐞𝐫 : The Kubernetes API Server acts as the frontend for the Kubernetes control plane. It exposes the Kubernetes API, which is used by internal components and external tools to communicate with the cluster. It validates and processes REST requests, and then updates the corresponding objects in etcd. 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐞𝐫: The Scheduler in Kubernetes is responsible for placing newly created pods onto nodes in the cluster. It takes into account various factors like resource requirements, hardware/software constraints, affinity, anti-affinity, and other policies to make optimal scheduling decisions. 𝐞𝐭𝐜𝐝:  etcd is a distributed key-value store used by Kubernetes to store all of its cluster data. This includes configuration data, state information, and metadata about the cluster's objects. etcd ensures consistency and helps maintain the desired state of the cluster. 𝐊𝐮𝐛𝐞𝐥𝐞𝐭: Kubelet is an agent that runs on each node in the Kubernetes cluster. It is responsible for ensuring that containers are running in a pod. Kubelet communicates with the Kubernetes API Server and manages the pods and their containers on the node. 𝐊𝐮𝐛𝐞-𝐩𝐫𝐨𝐱𝐲: Kube-proxy is a network proxy that runs on each node in the cluster. It maintains network rules on nodes, which allows communication between different pods and services within the cluster and from external clients to services within the cluster. 𝐏𝐨𝐝𝐬:  Pods are the smallest deployable units in Kubernetes and represent a single instance of an application. They can contain one or more containers that share storage and network resources and are scheduled and deployed together on the same host. 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫:  A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably across different computing environments. Containers are managed by container runtimes like Docker or containerd within Kubernetes. #kubernetes, #DevOps, #K8s, #K8s_architecture

𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 Make your setup production-ready. 👇 Kubernetes has established itself as a flexible and powerful container orchestration system. Despite the accessibility and simplicity in the initial setup, Kubernetes is much more complex in a production environment. So keep these considerations in mind: - 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆: Minimize attack surfaces and guard your apps and data. - 𝗣𝗼𝗿𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Leverage Kubernetes' self-healing and autoscaling features to keep up with your growing business needs. - 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆: Embrace automated CI/CD pipelines for faster, more reliable deployments. - 𝗗𝗶𝘀𝗮𝘀𝘁𝗲𝗿 𝗥𝗲𝗰𝗼𝘃𝗲𝗿𝘆: Reduce Mean Time to Recovery (MTTR) from hours to minutes with GitOps. - 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Monitor services pre-deployment to mitigate risks and ensure smooth operations. Kubernetes may be complex, but it becomes a powerhouse for your production needs with the right tools and processes. Chandresh Desai made a great graphic you can use as a reference. ⤵️ Follow these best practices: > Utilize namespaces for efficient team development and resource management. > Implement Role-Based Access Control (RBAC) for fine-grained access management. > Enforce network policies for secure inter-service communications. > Opt for pod-to-pod SSL traffic and protect cluster components. > Plan for stateful and stateless applications with the proper storage solutions. What are your tips for Kubernetes in production? #Devops #Observability #Kubernetes #Gitops #Cloudnative #Engineering Credit: Image from Chandresh Desai Drive ROI on tech projects with Kubernetes. Follow to learn more.

The Kubernetes Pod lifecycle: are you aware of it? In Kubernetes, the pod lifecycle refers to the different stages that a pod goes through from its creation to termination. Understanding the pod lifecycle is important for managing and troubleshooting pods effectively. Here are the main stages of the pod lifecycle in Kubernetes: 1. Pending: When a pod is created, it enters the Pending state. In this state, the Kubernetes scheduler is responsible for assigning the pod to a suitable node in the cluster. The scheduler considers factors like resource availability, node affinity, and pod anti-affinity when making this assignment. 2. Running: Once a pod is assigned to a node, it transitions to the Running state. In this state, the pod's containers are created and started, and they begin running on the assigned node. However, the containers may still be initializing or starting up, so the pod may not be fully ready to serve traffic. 3. Succeeded: If a pod completes its main task successfully and terminates, it enters the Succeeded state. This typically happens for batch jobs or one-time tasks. Once in the Succeeded state, the pod remains in this state until it is explicitly deleted. The resources occupied by the pod can be reclaimed by the system. 4. Failed: If a pod encounters an error or its containers fail to start or run, it enters the Failed state. This indicates a problem with the pod's execution. The pod will not be automatically restarted, but it can be manually deleted or recreated to address the failure. 5. Unknown: If the pod's status cannot be determined due to communication issues between the Kubernetes control plane and the pod's node, it enters the Unknown state. This can occur if the node becomes unresponsive or loses connectivity. To check the current status of a pod, you can use the "kubectl get pods" command, which displays the current status of all pods. #kubernetes #devops

🌟 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀? Discover Essential Best Practices for a More Robust and Scalable Setup! Implementing best practices in Kubernetes is key to achieving a secure, reliable, and manageable system. Let’s dive in! 1️⃣ 𝐒𝐞𝐩𝐚𝐫𝐚𝐭𝐞 𝐂𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐚𝐭𝐢𝐨𝐧 𝐛𝐲 𝐄𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭🗂️: Keep your 𝘥𝘦𝘷, 𝘴𝘵𝘢𝘨𝘪𝘯𝘨, 𝘢𝘯𝘥 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘪𝘰𝘯 configurations separate by creating unique YAML files or utilizing templating tools like Helm. 2️⃣ 𝐒𝐞𝐜𝐮𝐫𝐞 𝐒𝐞𝐧𝐬𝐢𝐭𝐢𝐯𝐞 𝐃𝐚𝐭𝐚 𝐰𝐢𝐭𝐡 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐒𝐞𝐜𝐫𝐞𝐭𝐬🔒: Never store sensitive info (passwords, API keys) directly in your config files. Use Secrets to manage sensitive data, referencing them through environment variables within your container definitions. 3️⃣ 𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 & 𝐍𝐚𝐦𝐞𝐬𝐩𝐚𝐜𝐞 𝐈𝐬𝐨𝐥𝐚𝐭𝐢𝐨𝐧📂: Use namespaces to isolate each application, especially when handling multiple teams or environments. 4️⃣ 𝐃𝐞𝐟𝐢𝐧𝐞 𝐚 𝐑𝐨𝐥𝐥𝐢𝐧𝐠 𝐔𝐩𝐝𝐚𝐭𝐞 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲🔄: Set rolling update strategies in your Deployments to manage updates with zero downtime, giving your users a seamless experience. 5️⃣ 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐀𝐜𝐜𝐞𝐬𝐬 𝐂𝐨𝐧𝐭𝐫𝐨𝐥 (𝐑𝐁𝐀𝐂)🔑: Implement fine-grained access controls with RBAC, granting each user or service account only the necessary permissions. This reinforces the Principle of Least Privilege and keeps your cluster secure. 💡 Implementing these best practices will help you get the most out of Kubernetes, whether scaling applications, managing sensitive data, or ensuring seamless deployments. Happy deploying! 🛠️ #Kubernetes #CloudNative #DevOps #DevSecOps #BestPractices #Containerization #Infrastructure #CloudComputing #YAML #TechTips

𝗪𝗵𝘆 𝗞8𝘀 (𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀) 𝗶𝘀 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗶𝗻 𝗠𝗼𝗱𝗲𝗿𝗻 𝗗𝗲𝘃𝗢𝗽𝘀 When working with Docker, I encountered several limitations that highlight the importance of a robust orchestration tool like Kubernetes. Here’s why: 𝙎𝙞𝙣𝙜𝙡𝙚 𝙃𝙤𝙨𝙩 𝘿𝙚𝙥𝙚𝙣𝙙𝙚𝙣𝙘𝙮: 𝗜𝗺𝗮𝗴𝗶𝗻𝗲 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 100 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝘀 𝗼𝗻 𝗼𝗻𝗲 𝗵𝗼𝘀𝘁, 𝗮𝗻𝗱 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 #1 𝗱𝗲𝗺𝗮𝗻𝗱𝘀 𝘀𝗼 𝗺𝗮𝗻𝘆 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝘁𝗵𝗮𝘁 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 #100 𝘀𝘁𝗿𝘂𝗴𝗴𝗹𝗲𝘀 𝘁𝗼 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻. 𝗧𝗵𝗲 𝗸𝗲𝗿𝗻𝗲𝗹 𝗰𝗼𝘂𝗹𝗱 𝗸𝗶𝗹𝗹 𝘁𝗵𝗲 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲-𝘀𝘁𝗮𝗿𝘃𝗲𝗱 𝗰𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿, 𝗱𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻. 𝗞8𝘀 𝗱𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝘀 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆 𝗮𝗰𝗿𝗼𝘀𝘀 𝗻𝗼𝗱𝗲𝘀 𝘁𝗼 𝗽𝗿𝗲𝘃𝗲𝗻𝘁 𝘀𝘂𝗰𝗵 𝗶𝘀𝘀𝘂𝗲𝘀. 1. 𝗔𝘂𝘁𝗼 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: No DevOps engineer wants to manually run 𝗱𝗼𝗰𝗸𝗲𝗿 𝗿𝘂𝗻 commands to scale applications. Kubernetes automates scaling, ensuring applications adapt seamlessly to demand. 2. 𝗔𝘂𝘁𝗼 𝗛𝗲𝗮𝗹𝗶𝗻𝗴: Containers need continuous monitoring. Kubernetes provides self-healing capabilities, automatically restarting failed containers to maintain system health. 3. 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗟𝗲𝘃𝗲𝗹 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: Docker alone doesn’t inherently support key enterprise requirements like firewalls, load balancing, or API gateways. Kubernetes integrates these elements to meet production standards. Kubernetes isn’t just a tool—it’s a necessity for ensuring scalability, reliability, and robust system management. 🔧💡 Embrace Kubernetes to move from container management to container orchestration! #Kubernetes #DevOps #CloudComputing #ContainerOrchestration #TechInsights #InfrastructureAsCode #Scalability #Automation #ITInnovation #Docker #K8s