Narendra Reddy Palavelli

🔍 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬: 5 𝐓𝐫𝐨𝐮𝐛𝐥𝐞𝐬𝐡𝐨𝐨𝐭𝐢𝐧𝐠 𝐓𝐫𝐢𝐜𝐤𝐬 𝐄𝐯𝐞𝐫𝐲 𝐃𝐞𝐯𝐎𝐩𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰 Kubernetes is a crucial infrastructure for modern businesses, but its complexity can lead to time-consuming troubleshooting, requiring mastery of key techniques for smooth operations. 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐟𝐢𝐯𝐞 𝐞𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 𝐭𝐫𝐨𝐮𝐛𝐥𝐞𝐬𝐡𝐨𝐨𝐭𝐢𝐧𝐠 𝐭𝐫𝐢𝐜𝐤𝐬 𝐞𝐯𝐞𝐫𝐲 𝐃𝐞𝐯𝐎𝐩𝐬 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐬𝐡𝐨𝐮𝐥𝐝 𝐡𝐚𝐯𝐞 𝐢𝐧 𝐭𝐡𝐞𝐢𝐫 𝐭𝐨𝐨𝐥𝐤𝐢𝐭: ✅𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐁𝐚𝐬𝐢𝐜𝐬: 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐞 𝐚𝐧𝐝 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐥𝐨𝐠𝐬 When something goes wrong in your Kubernetes cluster, the first step should always be to gather as much information as possible. 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐝𝐞𝐬𝐜𝐫𝐢𝐛𝐞: The command provides detailed information about a resource, including events, state, and potential issues, providing a starting point for problem diagnosis. 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐥𝐨𝐠𝐬: Use this to access container logs directly. It's essential when you're debugging application-specific issues, allowing you to see what might be happening inside the container itself. These simple commands often provide enough insight to pinpoint the root cause of an issue quickly. ✅𝐔𝐬𝐞 𝐇𝐞𝐚𝐥𝐭𝐡 𝐏𝐫𝐨𝐛𝐞𝐬 𝐭𝐨 𝐘𝐨𝐮𝐫 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 Kubernetes health probes are essential for monitoring applications and debugging issues related to Kubernetes' health checks, ensuring pods behave as expected. ✅𝐂𝐡𝐞𝐜𝐤 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐐𝐮𝐨𝐭𝐚𝐬 𝐚𝐧𝐝 𝐋𝐢𝐦𝐢𝐭𝐬 Sometimes, your application is functioning fine, but it's starved for resources. Kubernetes allows you to set resource quotas and limits for CPU and memory, and if your pod exceeds these limits, it may be throttled or even evicted. ✅𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐃𝐞𝐛𝐮𝐠𝐠𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐤𝐮𝐛𝐞𝐜𝐭𝐥 𝐞𝐱𝐞𝐜 Networking issues can be particularly tricky to debug in Kubernetes. When you're unsure if your pod can reach another service, use kubectl exec to run commands inside the pod, just as you would on a local machine.. ✅𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝 𝐏𝐨𝐝 𝐒𝐜𝐡𝐞𝐝𝐮𝐥𝐢𝐧𝐠 𝐈𝐬𝐬𝐮𝐞𝐬 Kubernetes pods may be stuck in a Pending state due to insufficient resources or node constraints. Check the events section for issues related to CPU/memory or node affinity. 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 Kubernetes is recommended for organizations for scalability and stability, while effective troubleshooting and debugging tools can improve system dependability and user experience. Is your team ready to tackle Kubernetes issues with confidence? #Devops #Kubernetes #Debuggingkubernetes

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𝗥𝘂𝗻 𝗟𝗲𝗮𝗱𝗲𝗿 𝗘𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗔𝗺𝗼𝗻𝗴 𝗣𝗼𝗱𝘀 𝗶𝗻 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗟𝗲𝗮𝗱𝗲𝗿 𝗘𝗹𝗲𝗰𝘁𝗶𝗼𝗻: Some applications require specific processes to run only on a single Pod instance to avoid duplicate tasks. Kubernetes Deployments with multiple Pod replicas require a leader election process outside the Pods to ensure only one Pod performs these tasks. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: 🔶  𝙎𝙘𝙝𝙚𝙙𝙪𝙡𝙚𝙙 𝙏𝙖𝙨𝙠𝙨: Only one Pod should run batch processing, cache updates, etc. 🔶  𝙇𝙚𝙖𝙙𝙚𝙧-𝙊𝙣𝙡𝙮 𝙎𝙚𝙧𝙫𝙞𝙘𝙚𝙨: Sending notifications or triggering specific APIs that shouldn't be called multiple times. 𝘒𝘶𝘣𝘦𝘳𝘯𝘦𝘵𝘦𝘴 𝗟𝗲𝗮𝘀𝗲 𝘖𝘣𝘫𝘦𝘤𝘵 𝘤𝘰𝘮𝘦𝘴 𝘵𝘰 𝘵𝘩𝘦 𝘳𝘦𝘴𝘤𝘶𝘦!! Here's how leader election works using Leases: 🔶  𝙇𝙚𝙖𝙨𝙚 𝘾𝙧𝙚𝙖𝙩𝙞𝙤𝙣: The first Pod creates a Lease object using configurations such as Lease name, Pod name & namespace and updates the current timestamp along with the Lease duration. 🔶  𝙇𝙚𝙖𝙨𝙚 𝙏𝙧𝙖𝙘𝙠𝙞𝙣𝙜: The Lease object keeps track of the current leader Pod's name. 🔶  𝙇𝙚𝙖𝙙𝙚𝙧 𝙍𝙚𝙣𝙚𝙬𝙖𝙡: If the Leader Pod is still active, it renews the lease by updating the timestamp before it expires. 🔶  𝙇𝙚𝙖𝙙𝙚𝙧 𝙀𝙡𝙚𝙘𝙩𝙞𝙤𝙣: If the timestamp isn't updated, inactive replicas race to acquire the lock with its identity and assume the leader role. This process leverages the 𝙖𝙩𝙤𝙢𝙞𝙘 𝙣𝙖𝙩𝙪𝙧𝙚 𝙤𝙛 𝙆𝙪𝙗𝙚𝙧𝙣𝙚𝙩𝙚𝙨 operations to ensure that no two replicas can acquire the Lease simultaneously by keeping track of the 𝘳𝘦𝘴𝘰𝘶𝘳𝘤𝘦𝘝𝘦𝘳𝘴𝘪𝘰𝘯 field. Read more and Code snippet: https://lnkd.in/gatVJGzP & https://lnkd.in/gWNGht3J #Kubernetes #LeaderElection #Containerization

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In #India, the average salary of a Kubernetes developer is ₹27.3lakhs, while in the #USA it ranges between 123K USD to 156k USD. What do you think of Kubernetes? Is it overrated or an in-demand global skill? Let us know in the comments below. #Kubernetes #Kubernetesdeveloper #softwaredeveloper #upskill #salary #payscale #softwaredevelopment #onlinelearning #onlinecourses #certification #skilldevelopment

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Running LLM workloads at scale could be complex🤯, but Kubernetes & #Ray make it easier, efficient & reliable😇 Join Sudhanshu P. at #CloudAIProMeetup by Cloud Native Gurugram as he shows how to run LLMs workloads on #Kubernetes☸️ RSVP here👇 https://lnkd.in/dYfpsyGJ

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𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗢𝗽𝗲𝗻 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 (𝗢𝗖𝗜, 𝗖𝗥𝗜, 𝗖𝗡𝗜, 𝗖𝗦𝗜, 𝗦𝗠𝗜, 𝗖𝗜𝗟) Kubernetes, the de facto standard for container orchestration, relies on a robust ecosystem of open standards. These standards are developed and maintained by the community and define how different components interact within the system 𝗞𝗲𝘆 𝗯𝗲𝗻𝗲𝗳𝗶𝘁𝘀 🔶 𝙄𝙣𝙩𝙚𝙧𝙤𝙥𝙚𝙧𝙖𝙗𝙞𝙡𝙞𝙩𝙮: Different vendors can create products adhering to the same standard, ensuring seamless integration. 🔶 𝙁𝙡𝙚𝙭𝙞𝙗𝙞𝙡𝙞𝙩𝙮: You can choose components and plugins from various vendors based on your specific needs. 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 : 🔶 𝙊𝙥𝙚𝙣 𝘾𝙤𝙣𝙩𝙖𝙞𝙣𝙚𝙧 𝙄𝙣𝙞𝙩𝙞𝙖𝙩𝙞𝙫𝙚 (𝙊𝘾𝙄): Defines a standard for container image formats and runtimes. Popular examples include 𝘋𝘰𝘤𝘬𝘦𝘳, 𝘊𝘙𝘐-𝘖, 𝘢𝘯𝘥 𝘙𝘒𝘛 images. 🔶 𝘾𝙤𝙣𝙩𝙖𝙞𝙣𝙚𝙧 𝙍𝙪𝙣𝙩𝙞𝙢𝙚 𝙄𝙣𝙩𝙚𝙧𝙛𝙖𝙘𝙚 (𝘾𝙍𝙄): Establishes a consistent way for Kubernetes to interact with different container runtimes. CRI utilizes a plugin architecture, allowing support for various Runtime options like 𝘊𝘙𝘐-𝘖, c𝘰𝘯𝘵𝘢𝘪𝘯𝘦𝘳𝘥 etc 🔶 𝘾𝙤𝙣𝙩𝙖𝙞𝙣𝙚𝙧 𝙉𝙚𝙩𝙬𝙤𝙧𝙠 𝙄𝙣𝙩𝙚𝙧𝙛𝙖𝙘𝙚 (𝘾𝙉𝙄): Provides a specification and toolset for configuring network interfaces within containers. This plugin architecture enables Kubernetes to integrate with various network plugins like 𝘍𝘭𝘢𝘯𝘯𝘦𝘭, 𝘊𝘢𝘭𝘪𝘤𝘰, 𝘢𝘯𝘥 𝘞𝘦𝘢𝘷𝘦 𝘕𝘦𝘵. 🔶 𝘾𝙤𝙣𝙩𝙖𝙞𝙣𝙚𝙧 𝙎𝙩𝙤𝙧𝙖𝙜𝙚 𝙄𝙣𝙩𝙚𝙧𝙛𝙖𝙘𝙚 (𝘾𝙎𝙄): Facilitates communication between Kubernetes and diverse storage providers. Storage providers can develop CSI plugins that are compatible with various container orchestration systems. Examples 𝘈𝘞𝘚 𝘌𝘭𝘢𝘴𝘵𝘪𝘤 𝘉𝘭𝘰𝘤𝘬 𝘚𝘵𝘰𝘳𝘢𝘨𝘦, 𝘕𝘦𝘵𝘈𝘱𝘱 etc. 🔶 𝙎𝙚𝙧𝙫𝙞𝙘𝙚 𝙈𝙚𝙨𝙝 𝙄𝙣𝙩𝙚𝙧𝙛𝙖𝙘𝙚 (𝙎𝙈𝙄): Defines a standard for service meshes on Kubernetes, allowing consistent interaction with different service mesh implementations like 𝘐𝘴𝘵𝘪𝘰, 𝘓𝘪𝘯𝘬𝘦𝘳𝘥 etc 🔶 𝘾𝙤𝙣𝙩𝙖𝙞𝙣𝙚𝙧 𝙄𝙢𝙖𝙜𝙚 𝙇𝙞𝙗𝙧𝙖𝙧𝙮 (𝘾𝙄𝙇): This set of specifications defines how container images are stored, indexed, and distributed. Major container registries like 𝘋𝘰𝘤𝘬𝘦𝘳 𝘏𝘶𝘣, 𝘘𝘶𝘢𝘺.𝘪𝘰, 𝘢𝘯𝘥 𝘏𝘢𝘳𝘣𝘰𝘳 leverage CIL for sharing and reusing container images across various platforms and environments. #kubernetes #Containerization #Cloud

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🚀 Excited to share our latest blog post! "PostgreSQL Automatic failover with repmgr" In this blog , Bhupathi Shameer Kumar explores the detailed process of automating failover with repmgr and retain the old primary back. Here is the blog https://lnkd.in/gwKPCQE3 🚀 Aiming New Postgres Horizons 🚀 Stay tuned !! For more info, reach us at OpenSource DB or https://opensource-db.com/ #repmgr #OSDB #OpenSourceDB #Failover #Retain #Primary #PostgreSQL

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Postgres Women India Thank you Bhupathi Shameer Kumar for sharing the blog "PostgreSQL Automatic failover with repmgr". This blog covers: Seamless promotion of standby nodes Step by step failover process Ensuring minimal downtime #postgresql #womenintech #databasemanagement #opensource #failover #repmgr #careers #dba #highavailability

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🌟 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝗬𝗼𝘂𝗿 𝗖𝗜/𝗖𝗗 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝘄𝗶𝘁𝗵 𝗙𝗹𝘂𝘅 🌟 In today’s fast-paced tech landscape, GitOps has revolutionized how we manage and deploy applications. One tool that has stood out in enabling seamless GitOps workflows is Flux! 🚀 🔑 𝗪𝗵𝗮𝘁 𝗺𝗮𝗸𝗲𝘀 𝗙𝗹𝘂𝘅 𝗮𝘄𝗲𝘀𝗼𝗺𝗲? 👉 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆 𝗠𝗮𝗱𝗲 𝗘𝗮𝘀𝘆: Flux ensures your Kubernetes cluster is always in sync with your Git repository, offering reliable and automated deployments. 👉 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝗮𝘁 𝗜𝘁𝘀 𝗖𝗼𝗿𝗲: Every deployment is driven by Git, bringing the transparency and auditability we all love. 𝗥𝗲𝗮𝗹-𝗧𝗶𝗺𝗲 𝗦𝘆𝗻𝗰: Automatically updates your cluster with any Git changes. No manual intervention needed. 👉 𝗪𝗶𝗱𝗲 𝗘𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: Works beautifully with Helm, Kustomize, and your existing CI/CD setup. 👉 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: With FluxCD, teams can collaborate more effectively by managing their deployments through pull requests. This not only improves visibility but also fosters a culture of shared ownership over the deployment process. 💡 Whether you’re deploying microservices or managing complex environments, Flux brings consistency, security, and simplicity to the table. Are you ready to embrace the GitOps approach and let Flux handle the heavy lifting? Let’s discuss how it can elevate your cloud-native projects! 🌐 #GitOps #FluxCD #Kubernetes #DevOps #CloudNative

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Cost-Based Node Group Ranking for Cluster Autoscaler in Kubernetes 💡 Managing heterogeneous Kubernetes clusters—those with different types of nodes (like GPUs, standard nodes, etc.)—presents a challenge for the Cluster Autoscaler (CA). In these clusters, the autoscaler traditionally selects nodes somewhat randomly when scaling up, which can lead to inefficiencies, such as provisioning expensive nodes for simple tasks. In my article, I explore how Kubernetes' Cluster Autoscaler now incorporates a cost-based node group ranking to make smarter decisions when expanding the cluster. This optimization helps ensure that the most cost-effective node type is selected based on the estimated cost and resource requirements, leading to more efficient scaling and cost savings. Key takeaways from the article: - Estimating the cost of different node types in a cluster. - Selecting the best node pool based on cost and pod resource requests. - Introducing the NodeUnfitness metric to factor in node size preferences. - Optimized ranking formula that balances cost with resource fragmentation and cluster size. This approach enhances autoscaling decisions, improving resource management and driving down operational costs for Kubernetes users. 👉 Read the full article here: https://lnkd.in/gyBiir5j #Kubernetes #ClusterAutoscaler #CostOptimization #CloudComputing #DevOps #KubernetesOptimization

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Discover the power of #Rust in K8s controller development with 👋Sangam B. at #KCDHyderabad! Learn how to leverage the kube-rs library to build robust and efficient controllers, harnessing Rust's type safety and concurrency features. #Kubernetes #RustLang #DevOps

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Kubernetes Hindi Bootcamp - Part 1 with Kubesimplify and Saiyam Pathak Here are the key takeaways: Container Fundamentals: A Journey from Monolithic to Microservices 🚀 Chapter 1: Understanding the Shift 🚀 - Transitioning from monolithic applications to microservices. - Traditional Virtual Machines (VMs) to efficient containers. - Containers as Linux processes using namespaces. - Benefits of containers: easier deployment and solving dependency issues. 🖥️ Chapter 2: Virtual Machines and Container 🖥️ - Traditional servers and VMs: limitations in resource utilization and maintenance. - Evolution to containers for better resource management and isolation. - Containers meeting modern application needs. 🔧 Chapter 3: Hands-On with Docker 🔧 - Practical experience with Docker: running a simple Docker container (nginx). - Understanding containers as isolated Linux processes. - Exploring process IDs, namespaces, and cgroups. 🌐 Chapter 4: Introduction to Kubernetes 🌐 - Managing numerous containers across different environments. - Kubernetes automates deployment, scaling, and operations of application containers. - Overview of Kubernetes architecture, components, orchestration, auto-scaling, and self-healing. 📊 Chapter 5: Practical Example with Kubernetes 📊 - Setting up a Kubernetes cluster using a playground environment. - Running workloads and checking their status. - Learning Kubernetes commands (kubectl) and basic operations. 🔍 Chapter 6: Understanding Kubernetes Internals 🔍 - In-depth look at Kubernetes management of containers. - Role of components like pause containers. - Kubernetes' use of Linux namespaces and cgroups for isolation. - Benefits of Kubernetes' approach to resource management and scaling. 📈 Chapter 7: Advanced Topics and Next Steps 📈 - Introduction to advanced Kubernetes topics: networking, security, and monitoring. - Encouragement to explore more, practice with resources, and join the community for further learning. Here is the reference link to the video : https://lnkd.in/dzPWkugv Join the journey to mastering containers and Kubernetes. Let's build the future of software together! #Containers #Kubernetes #Microservices #Docker #TechEvolution #DevOps #CloudComputing

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𝗦𝘁𝗼𝗽 𝘁𝗵𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗖𝗼𝗻𝗳𝘂𝘀𝗶𝗼𝗻! 𝗔 𝗤𝘂𝗶𝗰𝗸 𝗚𝘂𝗶𝗱𝗲 𝘁𝗼 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗖𝗜/𝗖𝗗 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 𝗧𝘆𝗽𝗲𝘀 🚀 ✔️ Infra Pipeline ✔️ App Pipeline ✔️ Mobile SDK Pipeline ✔️ Data Pipeline ✔️ IOT Pipeline ✔️ Firmware Pipeline 👉 𝗜𝗻𝗳𝗿𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 This pipeline is used for automated creation and provisioning of infrastructure resources with IAC (Infrastructure-as-Code) tools like Terraform, Pulumi, Cloudformation etc. the resources are created in Cloud providers. It helps to maintain uniformity of resources created across different environments like dev, qa, uat, prod etc. 👉 𝗔𝗽𝗽 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 Used to build, test and package the application code for Frontend and Backend apps. The programming languages include Java, Python, ReactJs, NodeJs, AngularJs etc. The packaged code is known as artifacts (jar, war, docker images etc) and they are stored and maintained in Artifact registries like Jfrog, Nexus, ECR etc. 👉 𝗠𝗼𝗯𝗶𝗹𝗲 𝗦𝗗𝗞 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 This is similar to app pipeline but the programming language and the testing tools differs. The programming languages include IOS, Android, Flutter etc. The packaged code is known as artifacts (.ipa, .apk files) and they are stored in package managers like CocoaPods for iOS and Gradle for Android. The mobile app is built on top of the functionalities provided by the SDKs. 👉 𝗗𝗮𝘁𝗮 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 The pipeline is used for receiving the data from various sources, transforming it and storing them into the databases and datalakes. This is created by connecting different services together like for example AWS Glue, Lambda, Step functions, Message brokers etc to achieve the consistency and reliability of data quality. The pipelines jobs use programming languages like Python, Java and R. 👉 𝗜𝗢𝗧 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 This is used to handle the collection and processing of data, events and messages generated by IOT (Internet of Things) devices. The IOT devices uses varying communication protocols and send high volumes of data in different formats. Similar to data pipelines, the IOT pipeline consists of combination of many services like for example IOT topics, gateways, lambda functions, message brokers etc. Usually the IOT pipelines acts as first interface to process the data in real-time or batches from IOT devices and send it to the data pipelines for transformation and storing to databases & datalake. 👉 𝗙𝗶𝗿𝗺𝘄𝗮𝗿𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 This is used to build, test and package the firmware code for the embedded and IOT devices. The programming languages include C++, Embedded C, Rust etc. Most of the time the code has to be compiled on the specific Linux processor architecture (like ARM, x86) as per the requirement of the hardware device on which it would be flashed to run. Follow me for simplified learning and best practices on DevSecOps 🙂 Save for future reference. 🔁 Consider a Repost if this is useful. #devops #cicd #pipelines #terraform #awscommunity

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Excited to share our latest blog on setting up On-Prem Kubernetes using Kubespray! Check it out now! #Kubernetes #Kubespray #OnPrem #CloudComputing #EuphoricThought #ITSolutions #TechBlog #DevOps #Containerization #IndiaTech

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In the world of Kubernetes observability, knowing how to use metrics effectively is crucial for keeping everything running smoothly. One key metric to understand is node_cpu_seconds_total. Let’s break it down and see how you can calculate the CPU usage percentage. 🔍 What is node_cpu_seconds_total? The node_cpu_seconds_total metric tracks the total amount of CPU time consumed in seconds by your CPU cores. This includes different modes like user, system, idle, and more. It’s a cumulative metric, meaning it increases over time, and it’s essential for monitoring the CPU usage of your nodes. 🤔 Why Does It Matter? Understanding CPU usage helps you: • 🏎️ Monitor Performance: Ensure your applications aren’t overloading the CPU. • 🧠 Plan Capacity: Decide if you need more resources or if you can optimize what you have. • 🔧 Troubleshoot: Identify performance bottlenecks related to CPU usage. 📐 Calculating CPU Usage Percentage Here’s how you can calculate the CPU usage percentage: 1. Collect CPU Time Data: Gather node_cpu_seconds_total values for all CPU modes (user, system, idle) at two different times. 2. Calculate Total CPU Time: • Sum up the values of all CPU modes at each time point. • Find the difference between these sums to get the total CPU time over the period. 3. Calculate Idle CPU Time: • Find the difference in the node_cpu_seconds_total{mode="idle"} values over the same period. 4. Compute CPU Usage Percentage: • Subtract the idle CPU time from the total CPU time to get the active CPU time. • Divide the active CPU time by the total CPU time to get the usage ratio. • Multiply by 100 to convert the ratio into a percentage. 🧮 Here’s the formula: CPU Usage Percentage = 100 × (1 - (Δ Idle CPU Time / Δ Total CPU Time)) 📊 Example Calculation Let’s look at a quick example: Time 1: • User: 100 seconds • System: 50 seconds • Idle: 200 seconds Time 2 (5 seconds later): • User: 110 seconds • System: 55 seconds • Idle: 205 seconds Steps: 1. Total CPU Time: 📊 Δ Total CPU Time = (110 + 55 + 205) - (100 + 50 + 200) = 370 - 350 = 20 seconds 2. Idle CPU Time: 📂 Δ Idle CPU Time = 205 - 200 = 5 seconds 3. Active CPU Time: 🔍 Δ Active CPU Time = Δ Total CPU Time - Δ Idle CPU Time = 20 - 5 = 15 seconds 4. CPU Usage Percentage: 📈 CPU Usage Percentage = 100 × (1 - (5 / 20)) = 100 × (1 - 0.25) = 100 × 0.75 = 75% 🏁 Conclusion Understanding and effectively using the node_cpu_seconds_total metric can significantly boost your ability to monitor and optimize your Kubernetes environments. By calculating the CPU usage percentage, you ensure your applications run efficiently and smoothly. Stay tuned for more insights on leveraging Kubernetes metrics for better observability and performance! 🚀 #Kubernetes #Observability #DevOps

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Delving deep into Hosted Control Plane options for Kubernetes akin to those provided by cloud giants such as EKS, GKE, and AKS, we've stumbled upon a treasure trove of tools like Kamaji, KCP, and Hypershift. Our team is currently in the trenches, evaluating these tools for the deployment of a valued customer. Stay tuned for more updates on our journey navigating the realm of Hosted Control Plane solutions for Kubernetes. Exciting times lie ahead! #Kubernetes #CloudNative #HostedControlPlane #openshift #cloud

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𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬: 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫 𝐎𝐫𝐜𝐡𝐞𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧 In today's fast-paced cloud-native world, Kubernetes has become the de-facto standard for container orchestration. Whether you're running microservices, managing complex workloads, or scaling applications, Kubernetes offers unparalleled flexibility and efficiency. 🔑 𝑲𝒆𝒚 𝑩𝒆𝒏𝒆𝒇𝒊𝒕𝒔 𝒐𝒇 𝑲𝒖𝒃𝒆𝒓𝒏𝒆𝒕𝒆𝒔: 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐒𝐜𝐚𝐥𝐢𝐧𝐠: Easily scale your applications up or down based on demand. 𝐒𝐞𝐥𝐟-𝐡𝐞𝐚𝐥𝐢𝐧𝐠: Automatically restarts failed containers and replaces them with healthy instances. 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Optimize your infrastructure and ensure high availability with minimal effort. 𝐌𝐮𝐥𝐭𝐢-𝐜𝐥𝐨𝐮𝐝 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: Run your workloads across on-premise, public cloud, or hybrid environments. 💡 As businesses continue to embrace digital transformation, understanding Kubernetes can be a game-changer for both developers and operations teams alike. Want to explore more about Kubernetes? Let's connect and discuss how this technology can help your business or projects thrive! Contact Delphic (South East Asia) at www.delphic.my Contact Delphic (South Asia) at www.delphic.in #Kubernetes #CloudNative #DevOps #Containerization #TechInnovation #DelphicGlobal #Delphic

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