Shaurya Singh’s Post

🚀 Enhancing Kubernetes Cluster Performance with Descheduler 🌐 In dynamic Kubernetes environments, maintaining optimal resource utilization and performance can be challenging. One powerful tool to address this is the Kubernetes Descheduler. Here’s how you can leverage it to improve your cluster efficiency: 1. Pod Eviction Based on Node Conditions: The Descheduler can evict pods from nodes that are under high resource pressure or have specific taints. This helps to redistribute workloads and ensure more balanced resource utilization across your cluster. 2. Node Affinity and Anti-Affinity: If your nodes have specific labels or taints, the Descheduler can help in rebalancing pods according to the updated affinity/anti-affinity rules, ensuring that your workload placement adheres to your intended topology. 3. Resource Defragmentation: Over time, the allocation of resources across your nodes can become fragmented. The Descheduler helps in redistributing pods to defragment resource usage, making it easier to accommodate new pods and scale applications efficiently. 4. Evicting Pods Based on Age: For clusters with long-running workloads, some pods may become stale or less optimal for the current state of the cluster. The Descheduler can evict these older pods to make room for newer, more efficient deployments. 5. Evicting Pods with Noisy Neighbors: If certain pods are causing performance degradation for others on the same node (the "noisy neighbor" problem), the Descheduler can help by evicting and redistributing these pods to maintain overall cluster performance. Implementing the Descheduler can lead to significant improvements in your Kubernetes cluster management by continuously optimizing pod placement and resource utilization. 🔗 Learn more about the Kubernetes Descheduler and how to implement it: What strategies do you use to optimize your Kubernetes clusters? Share your experiences and tips below! 💡 🔁 Consider a Repost if this is useful. #Kubernetes #Descheduler #CloudComputing #DevOps #ClusterManagement #TechTips #Automation #DevOpsEngineer

Hello LinkedIn connection! Pods are the smallest deployable unit of Kubernetes where we can run our applications. Scheduling in Kubernetes is a core component as it aims to schedule the pod to a correct and available node. If you want to understand why Pods are placed onto a particular Node and what is scheduling how it is work and Key Features of k8s scheduler, This content is for u to enhanced your knowledge. What is Kube-scheduler? The Kubernetes scheduler is a control plane process which assigns Pods to Nodes. At the heart of Kubernetes scheduling lies kube-scheduler, a critical component responsible for making intelligent decisions about where to place pods based on resource requirements, constraints, and optimization goals. It acts as the brain of the cluster, ensuring optimal resource utilization and workload distribution. The Kubernetes scheduler’s task is to ensure that each pod is assigned to a node to run on. At a high-level K8s scheduler works in the following way: 📍When new pods are created, they’re added to a queue. 📍The scheduler continuously takes pods off that queue and schedules them on the nodes. Key Features of kube-scheduler: [1] Prioritize: Pod priority is a method that allows scoring nodes that meet the conditions and helps find the most appropriate node for a Pod. [2] Resource-aware Scheduling: kube-scheduler evaluates the resource requirements of pods and makes placement decisions based on available resources on nodes, ensuring efficient resource utilization across the cluster. [3] Affinity and Anti-affinity: It supports affinity and anti-affinity rules, allowing users to specify preferences or constraints for pod placement based on factors such as node labels, pod labels, or other pod attributes. [4] Topology-aware Scheduling: kube-scheduler is aware of the topology of the cluster, considering factors such as node locality, inter-node communication latency, and network bandwidth when making scheduling decisions. [5] Custom Scheduling Policies: Users can define custom scheduling policies and priorities to influence pod placement decisions, ensuring that critical workloads receive preferential treatment based on business requirements. #kubernetes #kubernetescluster #kubernetesmanagement #devopscommunity #devops #containerorchestration #linkdinfamily 😊

Post 9 Title: Optimising Kubernetes Resource Management with kubectl top pods command 🚀 Issue: 🤔 In Kubernetes environments, understanding resource utilisation is crucial for maintaining optimal performance and stability. However, gaining insights into the resource consumption of individual pods can be challenging, especially in large-scale deployments. Root Cause: ⚠️ Traditional monitoring tools may not provide granular insights into pod-level resource utilisation, making it difficult to pinpoint problematic pods and even the problem of installing the monitoring tools at early stages. Fix: 🔧 The kubectl top pods command provides a solution by offering real-time resource usage statistics for pods running in a Kubernetes cluster. By leveraging this command, one can easily monitor CPU and memory utilisation of individual pods, allowing for proactive resource management and optimisation. Example: 💻 Suppose we have a Kubernetes cluster hosting a microservices-based application. One of the pods in the cluster starts exhibiting high CPU usage, impacting the overall performance of the application. To diagnose the issue, we use the kubectl top pods command: $ kubectl top pods This command displays resource usage statistics for all pods in the default namespace, including CPU and memory utilisation. By analysing the output, we identify the problematic pod consuming excessive CPU resources. Learnings: 🌟 kubectl top pods empowers DevOps teams with real-time insights into pod-level resource utilisation, enabling proactive monitoring and troubleshooting. Let's leverage this powerful command to streamline resource management and drive continuous improvement in our Kubernetes environments! #DevOps #Kubernetes #ResourceManagement

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

📄 Mastering Kubernetes: Essential Commands for Every DevOps Pro 📄 Just came across a fantastic Kubernetes cheat sheet, and it’s a must-have for anyone looking to sharpen their command-line skills in Kubernetes. Here are some of the essential commands it covers: Object Creation: Quickly create and manage pods, deployments, services, and config maps using kubectl apply with YAML files or direct commands. Monitoring and Scaling: Check CPU and memory usage of nodes and pods, and easily scale deployments with kubectl scale. Deployment Management: Master commands for updating images, rolling back to previous revisions, and viewing deployment history. ConfigMaps and Secrets: Simplify environment variable management by creating config maps and secrets directly from files or literals. Pod Management: From viewing detailed pod information to accessing logs and executing commands within pods, these commands cover it all. Advanced Resource Management: Includes insights on managing DaemonSets, StatefulSets, Jobs, CronJobs, and Network Policies for specialized workloads. For those diving into DevOps or aiming to level up in Kubernetes, keeping these commands at hand can make managing clusters a breeze. Let’s connect if you’re into container orchestration or want to share tips on Kubernetes! #Kubernetes #DevOps #CloudNative #Containerization #Automation #CheatSheet #TechTips

Understanding Kubernetes Namespaces and DaemonSets In the world of container orchestration, Kubernetes provides powerful tools to efficiently manage workloads, optimize resources, and maintain organization across environments. Two such key features are Namespaces and DaemonSets—both critical for building scalable and secure applications. Namespaces: Organize Your Cluster Namespaces are like virtual walls within your Kubernetes cluster, allowing you to segregate resources for different teams, projects, or environments. Whether you're managing dev, staging, or production workloads, namespaces ensure: Better resource isolation Simplified access control Organized resource management Using namespaces, we can allocate quotas, assign role-based access, and prevent one workload from interfering with another. DaemonSets: Deploy Everywhere DaemonSets ensure that a copy of a specific pod runs on every node (or selected nodes) in the cluster. This is invaluable for: Running monitoring agents (e.g., Prometheus, Fluentd) Deploying security agents or log collectors Supporting infrastructure-level operations As new nodes are added to the cluster, DaemonSets automatically deploy the required pods, ensuring seamless scaling. --- Why These Matter Together When combined, Namespaces and DaemonSets enable better multi-tenancy and infrastructure management. For example, you can: 1. Deploy a logging DaemonSet (e.g., Fluentd) for specific namespaces to streamline resource monitoring. 2. Create separate namespaces for each team while ensuring a uniform deployment of security agents across the cluster. With these tools, Kubernetes empowers teams to build robust, secure, and highly organized environments. Always use resource quotas and labels in namespaces to enhance control, and schedule DaemonSets wisely to avoid overloading nodes. What are your best practices for Namespaces and DaemonSets? Share in the comments! #Kubernetes #DevOps #ContainerOrchestration #Namespace

🚀 𝗗𝗮𝘆 𝟮𝟲: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗚𝘂𝗶𝗱𝗲 🛠️ Today, we dive into the depths of Kubernetes, exploring advanced topics that every DevOps engineer should master. This guide will help you understand the intricate details of Kubernetes architecture, deployment strategies, and best practices for managing containerized applications at scale. Key Highlights: - Detailed exploration of Kubernetes architecture and components. - Advanced deployment strategies using Helm charts. - Best practices for scaling applications in Kubernetes. - In-depth guide to Kubernetes networking and security. - Practical tips for optimizing performance and resource management. Sample Commands: # Deploy a Kubernetes application kubectl apply -f your-app.yaml # Scale a deployment kubectl scale deployment your-deployment-name --replicas=3 # View logs of a specific pod kubectl logs your-pod-name # Access a running pod kubectl exec -it your-pod-name -- /bin/bash # List all resources in a namespace kubectl get all -n your-namespace Credit - Sagar Choudhary Download the complete guide to get all the insights and step-by-step instructions! #DevOps #SRE #Kubernetes #CloudNative #Containers #Microservices #DevOpsLife #DevOpsCommunity #K8s #KubernetesGuide #TechLearning #AdvancedKubernetes #CloudInfrastructure #ContainerOrchestration #TechSkills #Automation #CI/CD #CloudComputing #SoftwareDevelopment #IT #TechCommunity --- Feel free to modify or add any specific points or commands as needed!

🚀 What is Kubernetes (k8s)? 🚀 Kubernetes, commonly known as k8s, is an open-source container orchestration platform that automates the deployment, scaling, and management of containerized applications. Originally developed by Google and inspired by their internal system, Borg, Kubernetes has become the de facto standard for managing containers at scale in production environments. 🔹 Understanding Kubernetes Architecture: 1. Control Plane: The Brain of Kubernetes API Server: Acts as the central communication hub, allowing all components and external tools to interact with the Kubernetes cluster. It serves as the frontend for the Kubernetes control plane. Scheduler: Responsible for assigning newly created Pods to available nodes based on resource requirements and other constraints, ensuring efficient resource utilization. Controller Manager: Runs various controllers, such as the Node Controller, which monitors the health of nodes, and the ServiceAccount Controller, which manages service accounts and access control. etcd: A distributed key-value store that holds the entire state of the Kubernetes cluster, including configuration data, secrets, and service discovery information. It’s critical for the cluster’s resilience and consistency. 2. Nodes: The Workers of Kubernetes Pods: The smallest deployable unit in Kubernetes, a Pod encapsulates one or more containers that share the same network namespace and storage. Each Pod is assigned a unique IP address, and all containers within a Pod can communicate with each other via localhost. Kubelet: The primary agent that runs on each node, ensuring that the containers described in the Pod specs are running and healthy. It constantly communicates with the control plane to maintain the desired state of applications. Kube Proxy: A network proxy that runs on each node, responsible for maintaining network rules that allow communication to your Pods from network sessions inside or outside of your cluster. It routes traffic to the appropriate container based on IP address and port. 🔹 Why Kubernetes? Kubernetes abstracts the underlying infrastructure, allowing developers and operators to focus on deploying applications rather than managing the complexities of individual containers. It offers features like self-healing, load balancing, horizontal scaling, and automated rollouts and rollbacks, making it a cornerstone of modern DevOps practices and cloud-native architectures. Despite its initial learning curve, understanding Kubernetes is a game-changer for deploying and managing scalable applications in production environments. #Kubernetes #ContainerOrchestration #CloudComputing #DevOps #SystemDesign #CloudNative #InterviewTips #Coding Picture credits : ByteByteGo

By leveraging 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐨𝐛𝐣𝐞𝐜𝐭𝐬 effectively, Kubernetes ensures a smooth, scalable, and reliable update process for your applications. These are the challenges used to solve by Deployment object: #️⃣ #️⃣ 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 𝐚𝐧𝐝 𝐭𝐡𝐞𝐢𝐫 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬: #️⃣ #️⃣ 🔶 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐃𝐨𝐰𝐧𝐭𝐢𝐦𝐞: Use rolling updates or blue-green deployments. 🔶  𝐅𝐚𝐢𝐥𝐞𝐝 𝐔𝐩𝐝𝐚𝐭𝐞𝐬: Rollback using kubectl rollout undo. 🔶 𝐋𝐨𝐧𝐠 𝐔𝐩𝐝𝐚𝐭𝐞 𝐃𝐮𝐫𝐚𝐭𝐢𝐨𝐧𝐬: Adjust maxUnavailable and maxSurge parameters for faster updates. 🔶  𝐓𝐫𝐚𝐟𝐟𝐢𝐜 𝐑𝐨𝐮𝐭𝐢𝐧𝐠 𝐈𝐬𝐬𝐮𝐞𝐬: Use Service objects or ingress controllers to manage traffic properly. 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

🚀 Kubernetes Node Scheduling Simplified: Taints & Tolerations, Node Selectors, and Affinities! 🚀 As Kubernetes practitioners, understanding pod scheduling is critical to optimizing workload distribution and resource utilization. Let’s explore and compare three key concepts that guide pod-to-node placement: 🛠 Taints & Tolerations What they do: Ensure that certain nodes accept only specific pods. How it works: Nodes are "tainted" (e.g., key=value:NoSchedule), and only pods with a matching toleration can schedule there. Use case: Reserve nodes for critical workloads or isolate nodes for testing. 💡 Analogy: Think of taints as "Do Not Disturb" signs, and tolerations as special permissions to enter. 📌 Node Selectors What they do: Match pods to nodes with specific labels (e.g., nodeSelector: {"disktype": "ssd"}). How it works: It's a basic key-value matching mechanism. Use case: Assign workloads to nodes based on simple attributes like hardware or location. 💡 Analogy: A straightforward "You must have this feature to qualify" filter. 🌐 Node Affinity & Anti-Affinity What they do: Provide advanced scheduling rules based on node labels, supporting both soft (preferred) and hard (required) constraints. Affinity: Attract pods to nodes with specific labels. Anti-affinity: Keep pods away from nodes with specific labels. Use case: Ensure high availability (spread workloads across zones) or optimize performance (schedule workloads near dependent resources). 💡Quirky Analogy: Think of affinity as "I’d like to be close to you" and anti-affinity as "Let’s maintain a healthy distance." Mastering these concepts ensures you can effectively manage workloads, optimize resources, and maintain application reliability in Kubernetes clusters. #Kubernetes #DevOps #Containerization #LearningJourney