Posts

CKAD Certification Journey — Part 3: Application Observability & Maintenance (The Reality of Running Systems) If deployment is where you introduce change, observability is where you survive that change. And here is the uncomfortable truth: Most engineers don’t fail at Kubernetes because they don’t know how to deploy — they fail because they don’t know how to understand what’s happening after deployment. This article is not a list of tools. It is a practical mental model for diagnosing real systems under pressure. ...

CKAD Certification Journey — Part 2: Application Deployment If Part 1 was about building applications correctly, this part is about something far more dangerous: 👉 Deploying them without breaking production Because here’s the uncomfortable truth: Most Kubernetes outages are not caused by Kubernetes itself — they are caused by how applications are deployed, updated, and scaled. This article is not a glossary. It’s a mental model + practical guide to how deployment actually works in real systems. ...

What Really Happens on a Kubernetes Node (Deep Dive): OverlayFS, containerd, Volumes, and the Hidden Mechanics Most Kubernetes explanations stop at Pods and containers. But the real story — the one that actually helps you debug, optimise, and reason about failures — happens inside the node. This article goes beyond the basics and explains: OverlayFS (and why it matters in real scenarios) containerd internals (beyond “it runs containers”) Image storage and caching Writable layers and their limitations Volume mounting mechanics (bind mounts) The kubelet’s role in orchestrating everything Linux primitives: namespaces & cgroups Where performance and problems actually come from 🧠 The Real Execution Flow When you create a Pod, Kubernetes does NOT “run a container”. ...

CKAD Certification Journey — Part 1: Application Design & Build Achieving the Certified Kubernetes Application Developer (CKAD) certification has been a rewarding milestone in my journey as a developer. This blog series is structured into five parts, each covering a key domain of the CKAD curriculum: Application Design & Build (this article) Application Deployment Application Observability & Maintenance Application Environment, Configuration & Security Services & Networking This first part focuses on the foundations of building applications for Kubernetes — understanding containers, how they are built and distributed, and how Kubernetes runs them efficiently. ...

From Local Dockerfile to Kubernetes Deployment: A Complete Step-by-Step Guide Introduction In modern cloud-native development, containerisation and orchestration are fundamental skills. This guide walks you through the entire lifecycle of a simple Python application — from building a Docker image locally, storing it in GitHub, pushing it to Docker Hub, and finally running it in a Kubernetes cluster from another virtual machine. By the end of this tutorial, you will have a fully reproducible workflow that mirrors real-world DevOps practices. ...

27 Development Rules for High-Performance webMethods Solutions Integration platforms rarely fail because of a single architectural mistake. Performance degradation is usually the cumulative effect of many small design and development decisions, repeated thousands or millions of times per day. The following 27 development rules were established based on real-world analysis of webMethods Integration Server solutions, production incidents, GC investigations, and performance tuning exercises. They are intended to be used as development norms, not optional tips. ...

Understanding and Tuning Garbage Collection in Java (G1GC) A practical, production-minded guide (with gc.log field reference and tuning options) Garbage Collection (GC) is one of the most misunderstood topics in the Java ecosystem. It is frequently blamed for latency spikes, slow services, and “random” performance issues—yet many teams never look at GC evidence. In integration platforms such as webMethods Integration Server, this matters even more: workloads are long-running, concurrent, and often create large volumes of short-lived objects (pipelines, documents, strings, adapter outputs, transformation data). When GC is unhealthy, the symptoms are operational: thread pool exhaustion, timeouts, throughput collapse, and unpredictable response times. ...

Below is a long-form, end-to-end technical blog post, written as if for experienced developers who want to truly understand how computers run networked applications. It’s conceptual, precise, and practical, not academic fluff. From HTTP Requests to CPU Cycles: A Practical Mental Model of Computer Systems Modern software often hides the machine behind layers of abstraction: frameworks, runtimes, containers, cloud platforms. This is powerful — but it also creates blind spots. ...

Reference Architecture: Redis + Kafka for Fast Reads and Event Propagation 1. Purpose This document describes a reference architecture where: Redis is used to accelerate synchronous read paths Kafka is used to propagate state changes asynchronously The application layer is the sole coordinator of state and events This architecture optimises for: Low latency API responses Decoupled downstream processing Independent scaling of consumers Eventual consistency across systems It does not assume microservices, CQRS, or event sourcing by default. ...

Redis + Kafka: Fast Reads with Event-Driven Change Propagation Overview Redis and Kafka are often presented together, yet they solve orthogonal problems: Redis optimises synchronous access to current state Kafka propagates asynchronous state changes to interested consumers Used together, they enable: Low-latency API responses Decoupled distribution of data changes Independent scaling of read paths and consumers This article explains how they complement each other in practice, without implying any direct coupling or “magic integration”. ...