Leonhard Pang

⚠️ The ESO (External Secrets Operator) for #Kubernetes project is on hold. No new releases. No patches. No container images. Why? Because the maintainer team is too small and burned out. Only one active maintainer is left. When he was on vacation, nothing moved forward. The project still accepts community PRs, but there is no official support. No Slack. No GitHub Discussions. And NO new versions until at least five active maintainers step up. If your company uses External Secrets in production, now is the time to contribute. Do not just think about money or donations. What matters more is time. Ask your company if you can contribute during work hours. This is not just someone else’s problem. ESO is one of the standard tools to get secrets from stores like AWS Secrets Manager, Azure Key Vault or HashiCorp Vault into a Kubernetes cluster. Without more people, the project might move into maintenance mode or even be archived. The message from the maintainers is clear: Either we grow the team or we scale back expectations. What do you think? Does your company rely on External Secrets? Would you or your team be ready to help maintain it? Repost this to increase the reach of this important message. 👉 Follow the thread on GitHub: https://lnkd.in/dFT5RXJx 👉 Ready to contribute? Fill out the google forms here: https://lnkd.in/dq8ubA7r If you ask why I cannot help here: I already contribute my free available time as maintainer to the #Prometheus and #Grafana community.

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🚨 When PostgreSQL Vacuum Operations Brought Down a $2.3M E-commerce Platform Ever wondered what happens when routine database maintenance turns into a production nightmare? In my latest blog, I share a critical incident where PostgreSQL vacuum operations nearly destroyed our e-commerce platform during Black Friday, causing: • $10,000 revenue loss per minute • 15,000 abandoned carts • Response times spiking from 50ms to 15 seconds But more importantly, I reveal: ✅ A comprehensive diagnostic toolkit with 10 battle-tested scripts ✅ Real-world implementation strategy that saved our platform ✅ Advanced monitoring solutions for preventing vacuum conflicts ✅ Production-ready deployment guidelines 🔑 Key Takeaways: • Why default autovacuum settings fail in high-traffic applications • How to predict and prevent vacuum-related outages • Advanced techniques for PostgreSQL performance optimization 💡 For DBAs, Developers, and Architects who want to prevent their own 3 AM crisis calls. Read the full story here: https://lnkd.in/gxN_6kyW #PostgreSQL #DatabaseOptimization #DevOps #TechnicalLeadership #ProductionIncidents #PerformanceTuning What lessons have you learned from your database crisis moments? Share your experiences below! 👇

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Everybody says not to start with Kubernetes but to paraphrase Kelsey Hightower, "Kubernetes is the new Linux", so it's important to know how to build on it. Here's how I would build a future-proof Kubernetes cluster on AWS and GitHub that will allow you to iterate quickly and scale up when you need to. I like to think of Kubernetes-based infrastructure in three layers. They need to be deployed in this order: 1) Physical infrastructure layer (a set of nodes running Kubernetes) 2) Application infrastructure layer (the services you need to run your apps, things like: cert managers, ingress controllers, etc) 3) Application layer (your actual apps you want to run) Here's the high-level recipe: 1) Start by deploying AWS EKS and its requisite resources with Terraform. This should be a single Terraform module in a GitHub repo with a GitHub Action for automatically running `terraform apply` when you push a commit to main. 2) Use Terraform to bootstrap FluxCD on your cluster to allow you to run "GitOps". This can be in the same module as the above just add the right dependencies. 3) Store your application infrastructure services as Helm charts in a GitHub repo. Use FluxCD HelmRelease resources to automatically sync these charts to your cluster (this is the GitOps part). This should include things like AWS External Secrets Operator, Nginx Ingress Controller, etc. 4) Containerize your app components and add GitHub Actions to their repos to build and publish images to ECR when you commit to main. 5) Write Helm charts for each of your app's components (frontend, backend, queue, DB, etc) and add HelmRelease resources for them. 6) Add ImageUpdateAutomation resources to scan ECR and update your Helm chart values for each of your app components. This will trigger a deployment from the HelmRelease setup we did in step 5. This can be a lot of work and there are lots of details but the end result is: - To deploy your latest code to the cluster all you have to do is commit to the main branch. - To update a Helm chart all you have to do is commit to the main branch and bump the version tag in the HelmRelease resource. - To scale up your apps just edit one value in their HelmRelease. - To scale up your cluster just change one number in the Terraform values. - To debug your system just use standard `kubectl` commands that AI knows well. - All of this infrastructure and configuration is well versioned and centralized in a single repo so you can add branch protections and prevent drift. This might be overkill for your vibecoded todo app but for any system requiring serious infrastructure considerations I'd argue its better to pay this cost upfront before your customers come knocking about downtime and latency issues later.

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Kubernetes isn't hard - building dynamic, scalable and stable infrastructure is. Kubernetes isn't hard - Changing legacy people processes and breaking down silos is. Kubernetes isn't hard - learning new patterns is. Kubernetes is not very hard standalone but implementing in an environment that isn't conducive to cloud native patterns can be messy and complicated. Don't let people blow smoke about how it's overly complex - by itself - it isn't. Kubernetes does a great job of of simplifying the management of the complexity - but make no mistake - none of the problems you are solving are by any means "easy". When people start using declarative infrastructure platforms for the first time they don't realize the hard part is front loaded so these decisions never need to come up post deployment. This means it's always harder to deploy then it is to maintain. Once you make the decisions - you never have to make them again. But before you start - You better know exactly what you want. So "I just want to get thing running" is a better pain-point than "I just wish this thing would keep working so I can sleep a full night". The problem isn't that you need to learn Kubernetes. The problem is you need to know your environment and how Kubernetes can (or can't) help.

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Automating infrastructure can be a huge challenge, but a solid CI/CD pipeline makes all the difference. That's why I'm kicking off a new blog series on Medium showing you how to build a robust CI/CD pipeline for infrastructure and application management using Google Cloud Build! 🚀 In Part 1, we're setting the stage by covering the essential groundwork. This is a practical, step-by-step guide designed to help you 𝙗𝙪𝙞𝙡𝙙 𝙖 𝙨𝙤𝙡𝙞𝙙 𝙛𝙤𝙪𝙣𝙙𝙖𝙩𝙞𝙤𝙣 𝙛𝙤𝙧 𝙮𝙤𝙪𝙧 𝘿𝙚𝙫𝙊𝙥𝙨 𝙬𝙤𝙧𝙠𝙛𝙡𝙤𝙬 using 𝘾𝙡𝙤𝙪𝙙 𝘽𝙪𝙞𝙡𝙙. Ready to start? Read the full post and get building! 👇 https://lnkd.in/gaQc2bqb Stay tuned for Part 2, where we'll dive into how to use Cloud Build to automate the deployment of an application to Cloud Run. 🚀 #CICD #DevOps #GoogleCloud #CloudBuild #CloudRun #InfrastructureAsCode #IaC #Automation #CloudComputing #DevSecOps

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Kubernetes is no longer just a tactical tool; it’s a critical foundation for innovation. That’s why our 2025 State of Production Kubernetes matters. Now in its fifth year, this report has become a trusted resource for understanding how real organizations scale, modernize, and operate Kubernetes in production. It’s based on independent research with 455 enterprise and public sector users, and the insights are grounded, strategic, and incredibly relevant. If you’re making decisions around AI, platform engineering, or infrastructure complexity, I highly recommend giving this a read: https://lnkd.in/gjNWvp39 #Kubernetes #KubernetesManagement #AI #PlatformEngineering #VirtualMachines

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If you’ve worked with Apache Kafka, you know the trade-offs: Blazing-fast local disk writes but costly replication across zones. Tiered storage for long retention but complexity in managing hot vs cold data. The Kafka community has been asking: Can we make object storage the first-class durable store without losing Kafka’s semantics? That’s where Diskless Topics (KIP-1150 & KIP-1163) by Aiven started the journey — rethinking Kafka’s durability around object storage as the source of truth. Now comes Diskless 2.0 — a unified, zero-copy approach that: ✅ Removes parallel “disk vs tiered” estates ✅ Simplifies the produce/consume path ✅ Enables zero-copy migration (flip a topic from classic to diskless without reprocessing) ✅ Cuts costs by eliminating cross-AZ replication and reducing broker disk reliance This isn’t just an incremental patch. It’s a paradigm shift in how we think about Kafka in the cloud era — where durability and economics favor object storage, and Kafka brokers can finally be “lighter.” Of course, challenges remain: -Latency sensitivity (classic still wins for ultra-low-latency cases) -Compaction and deletion semantics (work in progress) -Operational readiness for large clusters But that’s the beauty of open source — progress through community iteration. And Diskless 2.0 feels like a community win, not just a vendor push. 👉 Dive deeper: Read the KIP-1150 proposal here: fandf.co/3VlQxW1 Explore Aiven’s take on Diskless 2.0: fandf.co/46pczw0 What do you think? Could Diskless 2.0 become the default way to run Kafka in the next 3–5 years, or will classic/tiered still dominate latency-critical workloads? Kudos to Aiven for releasing Diskless 2.0 and sponsoring this post. #Kafka #Sponsored #Aiven

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“Most people quit learning Kubernetes because they start in the wrong place.” Kubernetes is powerful, but without a clear roadmap, it’s overwhelming. I’ve tries to break down the journey of starting with kubernetes into 10 clear phases that is from foundational Linux and container skills, to mastering deployments, scaling, networking, and advanced operations. Whether you’re setting up your first local cluster or running production workloads in the cloud, this roadmap will guide you and keep you focused on the essentials while building practical, hands-on skills. It’s not about memorizing every kubectl command, but it’s about understanding how Kubernetes works and applying it confidently in real-world projects. 💡 Save this post and start your Kubernetes learning journey step-by-step. 📌 Comment “K8s” if you want the full resource list.

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