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Kubernetes Cost Reduction Techniques

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  Each technique enables organizations to optimize Kubernetes usage and minimize expenses 𝟭. 𝗥𝗶𝗴𝗵𝘁-𝘀𝗶𝘇𝗶𝗻𝗴 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀: Analyze app resource usage, adjust CPU/memory as needed. Avoid over-provisioning to save costs 𝟮. 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗡𝗼𝗱 & 𝗣𝗼𝗱 𝗔𝘂𝘁𝗼 𝗦𝗰𝗮𝗹𝗶𝗻𝗴: Enable cluster auto-scaling and use Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) to add or remove nodes / pods based on resource utilization, reducing idle costs 𝟯. 𝗣𝗼𝗱 𝗗𝗶𝘀𝗿𝘂𝗽𝘁𝗶𝗼𝗻 𝗕𝘂𝗱𝗴𝗲𝘁 (𝗣𝗗𝗕): Set up PDBs to control how many pods of a specific deployment or replica set can be down simultaneously during disruptions, ensuring high availability without overprovisioning 𝟰. 𝗡𝗼𝗱𝗲 𝗧𝗮𝗶𝗻𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗧𝗼𝗹𝗲𝗿𝗮𝘁𝗶𝗼𝗻:  Taint nodes for workload-specific delays, prioritize critical tasks on untainted nodes, and use cheaper tainted nodes for less critical tasks 𝟱. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 𝗥𝗲𝗴𝗶𝘀𝘁𝗿𝘆 & 𝗜𝗺𝗮𝗴𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻:
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𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗔𝗣𝗜 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲

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This multi-layered system, intricately designed, consists of three main segments - Built-in Resources, Aggregated APIs, and Custom Resources. Let's take a brief dive into these sections!  ➡️ 𝘽𝙪𝙞𝙡𝙩-𝙞𝙣 𝙍𝙚𝙨𝙤𝙪𝙧𝙘𝙚𝙨  These include foundational elements like Pods, Nodes, and more. They act like the backbone of your K8s architecture.  🔸 Pods: These are the smallest deployable units in Kubernetes, encapsulating one or more containers. They provide a way to run and manage containers efficiently.  🔸 Nodes: The physical or virtual machines that form the cluster's infrastructure. Nodes host and run the pods, making them an essential part of the K8s ecosystem. ➡️ 𝘼𝙜𝙜𝙧𝙚𝙜𝙖𝙩𝙚𝙙 𝘼𝙋𝙄𝙨  Here we have the "apiservices", acting like connectors. They are quite versatile and are used for bridging gaps and connecting resources.  🔸 apiservices: These serve as endpoints for accessing aggregated resources that span multiple API groups. They enable seamless communic
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𝐂𝐨𝐝𝐢𝐧𝐠 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬

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𝐂𝐨𝐝𝐢𝐧𝐠 𝐩𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐞𝐧𝐡𝐚𝐧𝐜𝐞 𝐨𝐮𝐫 “𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐨 𝐦𝐚𝐩 𝐚 𝐧𝐞𝐰 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐭𝐨 𝐚𝐧 𝐚𝐥𝐫𝐞𝐚𝐝𝐲 𝐤𝐧𝐨𝐰𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦.” Here, gathered around 20 of these coding problem patterns that I believe can help anyone learn these beautiful algorithmic techniques and make a real difference in the coding interviews. The idea behind these patterns is that once you’re familiar with a pattern, you’ll be able to solve dozens of problems with it. So, without further ado, let me list all these patterns: 1. 𝐒𝐥𝐢𝐝𝐢𝐧𝐠 𝐖𝐢𝐧𝐝𝐨𝐰 2. 𝐈𝐬𝐥𝐚𝐧𝐝𝐬 (𝐌𝐚𝐭𝐫𝐢𝐱 𝐓𝐫𝐚𝐯𝐞𝐫𝐬𝐚𝐥) 3. 𝐓𝐰𝐨 𝐏𝐨𝐢𝐧𝐭𝐞𝐫𝐬 4. 𝐅𝐚𝐬𝐭 & 𝐒𝐥𝐨𝐰 𝐏𝐨𝐢𝐧𝐭𝐞𝐫𝐬 5. 𝐌𝐞𝐫𝐠𝐞 𝐈𝐧𝐭𝐞𝐫𝐯𝐚𝐥𝐬 6. 𝐂𝐲𝐜𝐥𝐢𝐜 𝐒𝐨𝐫𝐭 7. 𝐈𝐧-𝐩𝐥𝐚𝐜𝐞 𝐑𝐞𝐯𝐞𝐫𝐬𝐚𝐥 𝐨𝐟 𝐚 𝐋𝐢𝐧𝐤𝐞𝐝𝐋𝐢𝐬𝐭 8. 𝐓𝐫𝐞𝐞 𝐁𝐫𝐞𝐚𝐝𝐭𝐡-𝐅𝐢𝐫𝐬𝐭 𝐒𝐞𝐚𝐫𝐜𝐡 9. 𝐓𝐫𝐞𝐞 𝐃𝐞𝐩𝐭𝐡 𝐅𝐢𝐫𝐬𝐭 𝐒𝐞𝐚𝐫𝐜𝐡 10. 𝐓𝐰𝐨 𝐇𝐞𝐚𝐩𝐬 11. 𝐒𝐮𝐛𝐬𝐞𝐭𝐬 12. 𝐌𝐨𝐝𝐢𝐟𝐢𝐞𝐝 𝐁𝐢𝐧𝐚𝐫𝐲 𝐒𝐞𝐚𝐫𝐜𝐡
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Funtionnal Programming

Java Functionnal programming notes :    
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Linux Directory Structure

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Basics of Linux Directory Structure In Linux OS, each file has its own importance. It means, every file is a part of a specific concern a user can set. This nature of Linux provides the higher flexibility for users like beginners, as well as provides more configurable options for advanced system administration users. In this paper, we are going to discuss shortly the directory structure of Linux. We will try to describe what a directory is used for, by reading this article, we can decide and assess the security fit falls according to different  directories.   The Root Directory Every other directory resides under the Root directory. You can call it the starting point of Linux directory structure. Please note that there is a difference between the System Root Directory and User Root Directory . The System Root Directory is one, under which you see all the other essential directory structure (simply speaking), and the User Root Directory is the one, which may exist unde
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