Alexander Smagin

🚀 𝙈𝙖𝙜𝙚𝙣𝙩𝙤 𝘾𝙧𝙤𝙣 𝙈𝙖𝙣𝙖𝙜𝙚𝙢𝙚𝙣𝙩 𝙞𝙣 𝙇𝙤𝙘𝙖𝙡 𝙀𝙣𝙫𝙞𝙧𝙤𝙣𝙢𝙚𝙣𝙩𝙨 𝙪𝙨𝙞𝙣𝙜 𝘿𝘿𝙀𝙑 𝖨𝗇 𝖬𝖺𝗀𝖾𝗇𝗍𝗈, 𝖼𝗋𝗈𝗇 𝗃𝗈𝖻𝗌 𝖺𝗋𝖾 𝖼𝗋𝗂𝗍𝗂𝖼𝖺𝗅 𝖿𝗈𝗋 𝗍𝖺𝗌𝗄𝗌 𝗌𝗎𝖼𝗁 𝖺𝗌 𝗋𝖾𝗂𝗇𝖽𝖾𝗑𝗂𝗇𝗀, 𝗌𝖾𝗇𝖽𝗂𝗇𝗀 𝖾𝗆𝖺𝗂𝗅𝗌, 𝖺𝗇𝖽 𝖼𝗅𝖾𝖺𝗇𝗂𝗇𝗀 𝗅𝗈𝗀𝗌. 𝖧𝗈𝗐𝖾𝗏𝖾𝗋, 𝖾𝗇𝖺𝖻𝗅𝗂𝗇𝗀 𝖼𝗋𝗈𝗇 𝗈𝗇 𝗅𝗈𝖼𝖺𝗅 𝖾𝗇𝗏𝗂𝗋𝗈𝗇𝗆𝖾𝗇𝗍𝗌 𝖼𝖺𝗇 𝗅𝖾𝖺𝖽 𝗍𝗈 𝗉𝖾𝗋𝖿𝗈𝗋𝗆𝖺𝗇𝖼𝖾 𝗂𝗌𝗌𝗎𝖾𝗌, 𝖻𝖾𝖼𝖺𝗎𝗌𝖾 𝖬𝖺𝗀𝖾𝗇𝗍𝗈 𝗍𝗋𝗂𝗀𝗀𝖾𝗋𝗌 𝖼𝗋𝗈𝗇 𝗈𝗇 𝖾𝗏𝖾𝗋𝗒 𝗋𝖾𝗊𝗎𝖾𝗌𝗍, 𝗉𝗈𝗍𝖾𝗇𝗍𝗂𝖺𝗅𝗅𝗒 𝖼𝖺𝗎𝗌𝗂𝗇𝗀 𝖼𝗈𝗇𝖿𝗅𝗂𝖼𝗍𝗌 𝖺𝗇𝖽 𝗌𝗅𝗈𝗐𝗂𝗇𝗀 𝖽𝗈𝗐𝗇 𝖽𝖾𝗏𝖾𝗅𝗈𝗉𝗆𝖾𝗇𝗍. 🚀 Best Practice with DDEV and n98-magerun2: 1️⃣ Install n98-magerun2 in your project: ddev exec bash -c "sudo wget -q https://lnkd.in/dQhA4_54 -O n98-magerun2 && sudo chmod +x n98-magerun2" 2️⃣ Verify installation: • ddev exec ./n98-magerun2 --version • ddev exec ./n98-magerun2 sys:cron:list 3️⃣ Execute a specific cron job manually: ddev exec ./n98-magerun2 sys:cron:run <job_code> 𝑲𝒆𝒚 𝑻𝒂𝒌𝒆𝒂𝒘𝒂𝒚𝒔: ✔ ./n98-magerun2 explicitly tells the shell to use the executable in the current directory. ✔ Without ./, the shell searches $PATH, resulting in “command not found”. ✔ Manual cron execution ensures local testing is predictable, efficient, and aligned with production behavior. #Magento #DDEV #Docker #CronJobs #PHP #EnterpriseMagento #WebDevelopment #DevBestPractices

13

🐳 Tackling Node.js Deployment Chaos with Docker 🚀 "Why does my Node.js app run perfectly on dev but crash in production?" 😩 If you've ever battled dependency mismatches, environment variables gone wrong, or port conflicts in your Node.js deployments, you're not alone. This is a classic DevOps headache! 😓 The Challenge: Dockerizing a Node.js App Imagine this: You've built a slick Node.js application. It works flawlessly on your local machine. But when deploying to servers, issues explode—missing modules from npm, incompatible Node versions, or ports that refuse to expose properly. Scaling? Forget it; manual setups lead to inconsistencies, security risks, and hours of debugging. In enterprise scenarios, like those in KodeKloud's Docker challenges, you might need to: Create a custom Dockerfile for the app. Manage dependencies with npm install. Expose specific ports (e.g., 3000). Ensure the container runs the app reliably, without root privileges. Without Docker, this turns into a nightmare of manual configs and "it works on my machine" excuses. 🐛 💡 How Docker Solves It Docker transforms this mess into a streamlined process! By packaging your Node.js app, its dependencies, and runtime into a single container, Docker ensures: Consistency: Same behavior across dev, staging, and prod. No more version drifts! ✅ Portability: Build once, deploy anywhere—with proper port mapping. 🌍 Efficiency: Lightweight containers use fewer resources than VMs. 💸 Security: Run as non-root, isolate dependencies. 🛡️ In a real task (like Level 4, Task 4 from KodeKloud), you'd write a Dockerfile like: FROM node:14 WORKDIR /app COPY . /app RUN npm install EXPOSE 3000 CMD ["node", "app.js"] Then build and run: docker build -t my-node-app . and docker run -p 3000:3000 my-node-app. Boom—deployed! ⚡ 📊 The Impact Aspect Without Docker With Docker Deployment Time Hours of setup ⏳ Minutes ⚡ Consistency Variable 😕 Guaranteed 🌟 Scaling Manual pain 😫 Effortless 📈 Debugging Endless logs 🕵️ Isolated & quick 🔧 🚀 Facing Similar Issues? If you're struggling with Dockerizing Node.js apps (or any Docker challenges), check out this repo for step-by-step solutions: https://lnkd.in/duWF3swQ. It covers everything from basics to enterprise-level tasks, including this exact Node.js dockerization! What's your toughest Docker problem? Share in the comments! 👇 #Docker #DevOps #NodeJS #Containerization #KodeKloud #TechTips #SoftwareEngineering

1

🐳 Docker: Orchestrating Multi-Container Stacks with Ease! 🔗 "How do I deploy a web app with DB without endless debugging?" 😫 In DevOps, setting up interdependent services (web app + database) manually leads to port clashes, data persistence woes, and deployment failures—scaling to production is a nightmare. 😫 The Challenge: Multi-Container Stack Deployment Deploy a Node.js web app with MongoDB: Run separate containers, link them via networks. Mount volumes for DB data. Handle dependencies and ports. Ensure they communicate seamlessly. This is the core of KodeKloud's Level 4 Task 3: Multi-Container Stack with Docker Compose. 💡 Docker Compose's Seamless Solution Docker Compose defines everything in a YAML file, launching the stack with one command. Repo Example (Task 3): docker-compose.yml: version: '3' services: web: image: node:14 ports: - "3000:3000" depends_on: - db db: image: mongo:latest volumes: - db-data:/data/db volumes: db-data: Run docker-compose up—app and DB live, connected, and persistent! 📊 Docker Compose vs. Manual Orchestration Compose simplifies what manuals complicate. Traditional commands are fragmented and error-prone, 😓 while Compose unifies with YAML. 🚀 Manual linking risks isolation issues, but Compose auto-networks services. 🔗 Data loss is common manually, yet Compose volumes ensure persistence. 📈 🚀 Struggling with Multi-Container Deploys? Explore the KodeKloud Docker Solutions repo for Task 3's full walkthrough in Level 4! https://lnkd.in/duWF3swQ Your multi-container horror story? Share below! 👇 #DockerCompose #DevOps #Containerization #Microservices #TechTips

1

The Project: Mistral-7B-Debug-Fixer I fine-tuned Mistral-7B-v0.1 to act as a specialized "Debug Expert." Unlike a general assistant, this model is trained specifically to ingest raw error logs and stack traces to provide a structured 4-step analysis: 1️⃣ Root Cause: Identifying exactly where the logic failed. 2️⃣ The "Why": Explaining the underlying system or language mechanics. 3️⃣ The Fix: Providing the corrected code block. 4️⃣ Prevention: Strategy to avoid the same bug in the future. The Challenge: Hardware Constraints Mistral-7B usually needs ~28GB of VRAM to train. By implementing QLoRA (4-bit Quantized Low-Rank Adaptation), I slashed the memory footprint by over 70%, allowing the entire training pipeline to run stably on local consumer hardware. Technical Highlights: Optimization: Used bitsandbytes (NF4) with Double Quantization and bfloat16 to maintain high precision while staying within local GPU limits. Targeting: Injected LoRA adapters into all linear layers (q_proj, gate_proj, etc.) to ensure the model effectively captured complex debugging patterns. Local Inference: Built a custom CLI with TextStreamer for real-time, 100% private, and zero-latency debugging sessions. This project proves that with the right optimization techniques like PEFT, you don't need a server farm to build high-performance, private, and specialized AI tools. GitHub: https://lnkd.in/e9QKt_Zd #LocalAI #QLoRA #Mistral7B #MachineLearning #Python #RTX4060 #OpenSource #FineTuning #AIEngineering

51

4 Comments

EKS (Kubernetes) or ECS 🤔 Here's the problem: Many engineers are slightly confused to make a choice. It's not their fault. It's not an easy choice 💯 Especially when the requirements are not very clear. Which is usually the case with the customer anyways 😁 But generally speaking. If you see high scaling, portability demands and complex compliance requirements, then EKS is a good choice 😃 For teams new to K8s, its a tough choice. ECS on the other hand can also handle scale. Its easy to manage. Aligns very well with smaller teams, teams with less devops experience. But portability and fulfilling compliance requirements can be challenging. You are free to make your choice, but you are not free from the consequences of your choice. So ask right questions, it will help you make the right choice 🤠 Thats it for today. Hope this is useful. A repost helps ♻️ P.S. What are you using right now? And are you happy with your choice?

65

12 Comments

Kubernetes feels like alphabet soup? Try this mental model - who decides, who does, who remembers, who talks. Who decides (control plane): • API Server = front door for every request • Scheduler = picks the node for each pod • Controller Manager = keeps desired state true Who remembers: • etcd = the cluster’s memory (state) Who does (data plane): • Kubelet = runs pods on the node + reports status • Container Runtime = actually executes containers • Kube Proxy = routes traffic to the right pod • Pod = smallest deployable unit Who talks: • kubectl = your remote control Quick debug flow: “Pod not starting?” → Check if Pod exists in API Server → See if Scheduler assigned a node → Verify Kubelet health on that node → Inspect Events and logs with kubectl. Save this for your next on-call. If you want more practical breakdowns (and to build a strong cloud portfolio), follow me and check out my ACMP program. 𝐃𝐌 me "roadmap" if you're serious about your cloud career and ready to fast-track your results. Follow Muhammad Mobeen Tahir for more tips and insights into AWS Cloud

20

2 Comments

Over the past few days, I have been exploring system design from the ground up and started by building my own HTTP load balancer entirely in Golang. Q. What exactly is Load Balancer? Well a load balancer sits between client and multiple backend servers, distributing incoming requests efficiently to ensure no single server is overwhelmed with huge traffic which may cause downtime and bad user experience due to high response time. It ensures high availability, scalability and fault tolerance in distributed systems. Let's dig deep into my implementation: I began with the classic round-robin algorithm, which sequentially forwards each request to a different backend in rotation. While this approach ensures a fair distribution, it does not adapt to real time performance variations across servers. To address this, I implemented the least response time algorithm, which continuously measures backend latency and dynamically routes requests to the server responding fastest on average. To make the system concurrent I ensured thread safe by using "sync.Mutex" to implement mutex locks to synchronize concurrent access to HTTP reverse proxy that forwards requests to a pool of backends. Additionally, I implemented SSL redirection and secure forwarding headers to guarantee that all client communication is routed through HTTPS securely. In upcoming weeks I plan to implement more reliable health checks, connection pooling, sticky sessions to make the load balancer more reliable and performative. Github :- https://lnkd.in/dw5Zwehr

19

2 Comments

If you ever encountered a situation where Plesk-related operations like backups, extension uninstallations, or file operations hang indefinitely, the root cause could be a broken or missing filemng binary. In this article, we’ll explain why it happens, how to diagnose it, and how to fix it by manually extracting the correct binary from a Plesk RPM package. https://lnkd.in/dNcu33Nc

1

🚀 DevOps Productivity Hacks – Because We Automate Everything, Right? ⚙️💡 Being a DevOps Engineer isn’t just about automating infrastructure—it's about automating your workflow to stay productive and sane. Here are my top 5 productivity tips to keep things running smoother than a well-tuned CI/CD pipeline! 🏗️💨 🛠️ 1. Automate the Boring Stuff If you’re doing it twice, it’s once too many! Whether it’s writing a script for log analysis 📝🔍 or automating deployments 🚀, let Ansible, Terraform, or Bash handle the mundane while you focus on innovation. 📜 2. Master the Art of the Checklist SREs love runbooks, and so should you! Whether it’s debugging an issue or onboarding a new service, checklists make sure nothing slips through the cracks. Bonus: Use Markdown + Git for version-controlled SOPs! 📑✔️ 🎯 3. Deep Work > Multi-Tasking Managing CI/CD, monitoring, cloud infra, and security simultaneously? 🤯 Nope! Block time for focused work and batch similar tasks together—your brain (and your cluster) will thank you. 🧠🔒 ⚡ 4. Keyboard Shortcuts & CLI Power Moves Stop clicking around like it’s 2005. Alias your most-used commands, master tmux, and use tools like fzf for blazing-fast navigation. Pro DevOps move? Write a script to generate your aliases dynamically. 🚀⌨️ ☕ 5. Take Breaks, Seriously! Yes, Kubernetes is cool, but so is sleep. A fresh mind troubleshoots better and automates smarter. Use Pomodoro 🍅, go for a walk, or—controversial take—close your laptop for an hour. 😴 What’s your go-to productivity hack? Drop it in the comments! ⬇️🔥

1

🏗️ 𝗧𝗵𝗲 𝗔𝗪𝗦 𝗪𝗲𝗹𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗧𝗵𝗲 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝗱𝗼𝗻’𝘁 𝗳𝗮𝗶𝗹 𝘄𝗵𝗲𝗻 𝗶𝘁 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 If you work with AWS, one mindset separates beginners from real cloud architects: 𝗗𝗲𝘀𝗶𝗴𝗻 𝗳𝗶𝗿𝘀𝘁, 𝗱𝗲𝗽𝗹𝗼𝘆 𝘀𝗲𝗰𝗼𝗻𝗱. And the best design tool we have is the 𝗔𝗪𝗦 𝗪𝗲𝗹𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝗲𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸. It is not just a checklist, it is a way of thinking about reliability, security, cost, performance, and operations as one unified system. Here is the practical breakdown that every engineer, architect, and tech leader should know. 🔐 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 Most breaches do not come from hackers, they come from misconfigurations. AWS pushes you to design for identity-first security, minimal privileges, encrypted data, and strong detection controls. Key focus areas:  • IAM roles and least-privilege  • Encryption everywhere  • Centralized secrets  • Continuous monitoring with CloudTrail and GuardDuty ⚡ 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 Cloud is not about more power. It is about the right power at the right time. AWS encourages you to scale out, not up, and use managed services whenever possible. Focus areas:  • Autoscaling  • Caching  • Selecting the right compute model (Lambda, Fargate, EC2, Graviton)  • Choosing the right DB engine for the workload 💰 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 The biggest cloud bill problems are not technical, they are architectural. AWS Well Architected helps you design for efficiency from day one by focusing on:  • Right sizing instances  • Stopping idle resources  • S3 lifecycle rules  • Graviton adoption  • Using serverless where possible 𝗖𝗹𝗼𝘂𝗱 𝗰𝗼𝘀𝘁 𝗶𝘀 𝗮 𝗱𝗲𝘀𝗶𝗴𝗻 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗮 𝗳𝗶𝗻𝗮𝗻𝗰𝗲 𝗶𝘀𝘀𝘂𝗲. 🛡️𝗥𝗲𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 Failures will happen in production. Good architecture makes sure users never notice. Reliability is about distributing workloads across Availability Zones (AZs), automating recovery, and eliminating single points of failure. Focus areas:  • Multi AZ deployment  • Health checks and auto healing  • Backup and restore procedures  • Chaos testing 🔄 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗘𝘅𝗰𝗲𝗹𝗹𝗲𝗻𝗰𝗲 This pillar turns teams from reactive to proactive. AWS emphasizes observability, automation, and continuous improvement so you can detect issues before customers do. Key practices:  • Metrics and dashboards  • Distributed tracing  • Automated deployments  • Runbooks and postmortems 🎯 𝗙𝗶𝗻𝗮𝗹 𝘁𝗵𝗼𝘂𝗴𝗵𝘁 Cloud success is not about running workloads on AWS. It is about designing them the right way. The Well Architected Framework gives you a blueprint that scales with your system and your career. If you want to build systems that survive traffic spikes, failures, cost pressure, and security threats, this is where you start.

17

1 Comment

𝗘𝘃𝗲𝗿 𝘄𝗼𝗻𝗱𝗲𝗿𝗲𝗱 𝘄𝗵𝘆 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗽𝗼𝗱𝘀 𝗱𝗼𝗻’𝘁 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝗿𝘂𝗻 𝗼𝗻 𝘁𝗵𝗲 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗽𝗹𝗮𝗻𝗲 𝗻𝗼𝗱𝗲? By default, the control plane (or master node) does have the capacity to run workloads — but it’s intentionally kept clear. The reason is simple: there’s a taint applied to the control plane node: 𝙣𝙤𝙙𝙚-𝙧𝙤𝙡𝙚.𝙠𝙪𝙗𝙚𝙧𝙣𝙚𝙩𝙚𝙨.𝙞𝙤/𝙘𝙤𝙣𝙩𝙧𝙤𝙡-𝙥𝙡𝙖𝙣𝙚:𝙉𝙤𝙎𝙘𝙝𝙚𝙙𝙪𝙡𝙚 You can see it in the kubectl describe nodes output I’ve attached. This taint tells the Kubernetes scheduler “don’t schedule regular workloads here.” Why does this matter? The control plane is where all the cluster’s critical components run — the API server, scheduler, controller manager, etc. If you overload it with application pods, you risk starving those core services of resources. That’s why worker nodes handle your actual workloads, while the control plane focuses on keeping the whole system healthy and running. If you really need to (like for testing, dev clusters, or edge cases), you can run pods on the control plane — but you’ll have to remove the taint or add a toleration. Just be mindful of the risks. Bottom line: Keeping the control plane separate is one of the small details that makes Kubernetes so resilient in production. Follow for more such insightful posts. #Kubernetes #CloudNative #DevOps #Containers #K8s #CNCF #Cloud #Taints #Tolerations

16

Hit this wall while deploying kube-prometheus-stack on GKE Autopilot using Terraform: "Error: services is forbidden: User cannot create resource "services"  in API group "" in the namespace "kube-system": GKE Warden authz  [denied by managed-namespaces-limitation]" So I had a question, "if Google manages it, why can't I monitor it?" Here's the deeper story behind this error: - The Security Architecture GKE Warden (Google's admission controller) actively blocks access to managed namespaces. This isn't just a permission issue, it's architectural isolation. - What Was Actually Happening: kube-prometheus-stack was trying to: Create ServiceMonitor resources in kube-system. Deploy services to scrape kubelet metrics . Access node-level endpoints for system component monitoring. Install admission webhooks that conflict with GKE's own webhooks. - The Real Problem: Traditional Prometheus assumes you OWN the infrastructure. It wants to: Monitor etcd directly. Scrape scheduler metrics. Access controller-manager endpoints. Create services in system namespaces. But in Autopilot, these components don't even exist in your cluster, they're abstracted away in Google's managed control plane. So the Shift was this; Traditional K8s: "I need to monitor everything I'm responsible for" GKE Autopilot: "I only monitor what I actually control" - The Solution I Implemented: Instead of fighting the architecture, I listened to the GKE Warden: # Disabled system monitoring nodeExporter: { enabled: false } kubeControllerManager: { enabled: false } # Enabled application monitoring kubeStateMetrics: { enabled: true } # Added custom scrape configs for container metrics additionalScrapeConfigs: [...] So the error wasn't just about permissions, it revealed a fundamental misunderstanding of the platform. GKE Autopilot isn't "Kubernetes with restrictions"; it's "Kubernetes as a Service" where the service boundary is clearly defined. I believe your monitoring strategy should match your operational responsibility and Google really tried to enforce that. Some might think Google was just being a "Strict Parent" with GKE Autopilot, but I see it differently. I think they were saying: "Let us worry about keeping Kubernetes alive. You just worry about what you're running on it." That’s not restriction, that’s clarity. And it forces your monitoring strategy to align with what you actually own.

15

5 Comments

🧩 AzerothCore in Kubernetes: scalability and flexibility After the Docker environment, I went further and moved AzerothCore to Kubernetes to gain flexibility, fault tolerance, and ease of service management. 🔹 What was the goal Separate components (AuthServer, WorldServer, MySQL) into separate sub-projects Automate restart and configuration management Prepare the foundation for CI/CD and scaling ⚙️ Technologies used K3s (lightweight Kubernetes distribution for VPS) kubectl, Helm PersistentVolume / PersistentVolumeClaim for databases ConfigMap and Secrets for configurations 🔹 How I deployed it: 1️⃣ Installed K3s curl -sfL https://get.k3s.io | sh - 2️⃣ Created manifests for MySQL and AzerothCore mysql-deploy.yaml — Deployment + PVC azerothcore-multi.yaml — with database dependency Added Service and ConfigMap for network access and configuration 3️⃣ Launched and tested kubectl apply -f mysql-deploy.yaml kubectl apply -f azerothcore-multi.yaml ✅ Result Now the server can be scaled, updated, and restored automatically. And most importantly, the infrastructure has become repeatable and portable. #Kubernetes #DevOps #AzerothCore #GameServers #Automation #Grafana #Prometheus

1

#11Kubernetes ⚓ 𝐇𝐨𝐫𝐢𝐳𝐨𝐧𝐭𝐚𝐥 𝐏𝐨𝐝 𝐀𝐮𝐭𝐨𝐬𝐜𝐚𝐥𝐞𝐫 (𝐇𝐏𝐀): 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐢𝐧 𝐊𝐮𝐛𝐞𝐫𝐧𝐞𝐭𝐞𝐬 🚀 ❓ 𝐇𝐏𝐀 (𝐇𝐨𝐫𝐢𝐳𝐨𝐧𝐭𝐚𝐥 𝐏𝐨𝐝 𝐀𝐮𝐭𝐨𝐬𝐜𝐚𝐥𝐞𝐫): In K8s, application traffic is never constant — it can spike unexpectedly or drop at any time. Manually scaling pods is not practical. This is where HPA comes in. HPA automatically adjusts the number of pods based on resource usage, such as CPU and memory. When usage increases, pods scale up. When usage decreases, pods scale down. 🍽 𝐇𝐨𝐰 𝐀 𝐑𝐞𝐬𝐭𝐚𝐮𝐫𝐚𝐧𝐭 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐬 𝐇𝐏𝐀? Imagine a restaurant at different times of the day. In K8s terms: • 𝑷𝒐𝒅𝒔 → Chefs preparing food • 𝑫𝒆𝒑𝒍𝒐𝒚𝒎𝒆𝒏𝒕 → Manager deciding how many chefs are needed • 𝑺𝒆𝒓𝒗𝒊𝒄𝒆 → Waiter distributing orders • 𝑴𝒆𝒕𝒓𝒊𝒄𝒔 𝑺𝒆𝒓𝒗𝒆𝒓 → Observes how busy the kitchen is • 𝑯𝑷𝑨 → Smart manager who adds more chefs when the kitchen is busy and reduces them when it’s quiet 👉 Lunch rush? Add more chefs (scale up) 👉 Late night? Reduce chefs (scale down) This ensures smooth service without overworking resources. 🔍 𝐓𝐨𝐩𝐢𝐜𝐬 𝐂𝐨𝐯𝐞𝐫𝐞𝐝: 👉 What is Horizontal Pod Autoscaler (HPA)? 👉 Why autoscaling is important in Kubernetes 👉 CPU and Memory-based scaling 👉 Deploying an application with resource requests 👉 Creating HPA using YAML manifests 👉 Generating load and observing autoscaling 👉 Understanding complete architecture and flow 💻𝐁𝐥𝐨𝐠: https://lnkd.in/gZkZwr94 🔗𝐑𝐞𝐩𝐨: https://lnkd.in/gBbBfY-D 🔗𝐊𝟖𝐬 𝐑𝐞𝐩𝐨: https://lnkd.in/g7Qw94YE In this project, I’ve shared a step-by-step, hands-on implementation to understand how Kubernetes automatically scales applications based on real-time demand using HPA. If you find this helpful, feel free to share it with your network ♻️ Happy Scaling 🚀 #Kubernetes #K8s #DevOps #CloudNative #Containers #Autoscaling #HPA #KubernetesLearning #BeginnerFriendly

44

Ildar is a full-stack .NET developer with 13 years of experience. His goal? To land a remote job at an international company. But there was a challenge… While Ildar is technically strong, technical interviews in English—especially system design discussions—were holding him back. The biggest struggle? ✅ Using the right technical vocabulary ✅ Explaining LeetCode & system design solutions clearly ✅ Thinking out loud in English To tackle this, we focused on: 💡 Breaking down a Booking.com system design problem step by step 💡 Practicing real-world interview discussions (not just coding) 💡 Expanding his technical vocabulary with homework and feedback 🔥 After one session, Ildar improved his ability to describe system design trade-offs confidently in English. We’re continuing this journey with more LeetCode deep dives & structured system design discussions. Want to improve your technical interview skills in English? Let’s talk! 🚀 https://lnkd.in/e5uBv3UQ

8

Zustand: My New Go-To for Simplified State Management! State management in JavaScript apps can be complex. Recently, I built an application and decided to try Zustand – and I absolutely loved it! Zustand is a lightweight, fast, and incredibly simple state management solution for React. Unlike heavier alternatives, it cuts down on boilerplate significantly. You just define your store, and components directly use the state they need, leading to super clean and simplified code. I was amazed at how quickly I could integrate it and how much cleaner my components felt. It's truly a game-changer for developer experience and keeping your codebase lean. If you want to explore Zustand and learn more about its practical application, refer to this article of mine where I've provided a detailed example! https://lnkd.in/eE5Dntvu #Zustand #ReactJS #StateManagement #FrontendDevelopment #WebDev #JavaScript

7