Minimize Docker Image Size for Production

Your containers are carrying large amounts of bloat eating away at your budget, But no-one at work talks about it, because "it works". However, it's a bottleneck in every part of your deployment process. > Build & deployment times are higher. > Scans/tests run for longer. > Threat surfaces expand. Just one tweak can change all of this though. Multistage builds. This is exactly what happened during my oversight of a microservices platform. > Images went from 99MB -> just 5MB. > Disk usage went from 462MB -> 16MB. Have a read of my blog below on it, where I shared more details 👇 . ------- ♻️ Share if helpful #devops #docker #containers

𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐬 𝐦𝐚𝐤𝐞 𝐭𝐡𝐢𝐧𝐠𝐬 𝐜𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐭… 𝐮𝐧𝐭𝐢𝐥 𝐭𝐡𝐞𝐲 𝐝𝐨𝐧’𝐭. If your Docker container works locally but fails in production, it’s usually not Docker’s fault. It’s the environment. Different base images. Missing env variables. Incorrect volume mounts. Secrets not injected properly. Same container. Different reality. 💡 𝐖𝐡𝐚𝐭 𝐈’𝐯𝐞 𝐥𝐞𝐚𝐫𝐧𝐞𝐝: • Build minimal, predictable images • Never use 𝐥𝐚𝐭𝐞𝐬𝐭 in production • Ensure environment parity • Validate runtime configs early Docker gives you consistency — but only if your ecosystem is consistent too. Because in real systems… 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐬 𝐝𝐨𝐧’𝐭 𝐟𝐚𝐢𝐥 𝐚𝐥𝐨𝐧𝐞. What’s the most unexpected issue you’ve faced in production? 👇 #Docker #DevOps #Kubernetes #SRE #CloudComputing

20-minute builds kill productivity. Your team waits. Context switches. Loses flow. ⏱️ Multi-stage Docker + BuildKit caching + parallel jobs = 4-minute builds. Here's the recipe 👇 https://lnkd.in/dg74xRCy #CI_CD #DevOps #Docker #BuildOptimization

We test deployments. We test APIs. We test features. But we don’t test failure. We assume: “If something breaks, Kubernetes will handle it. ”Will it? What happens when: • A pod is deleted mid-traffic • A node becomes unavailable • Resources get exhausted Does your system recover… or just restart? Because restart ≠ recovery. Recovery means: 👉 Services stabilize 👉 Dependencies reconnect Most systems don’t fail in staging. They fail under real-world chaos. And unless you test that chaos, you’re not validating your system — you’re validating your assumptions. This is where DevOpsArk Chaos Testing comes in. It doesn’t just break your system. It systematically validates resilience. Because the goal isn’t to simulate failure. The goal is to prove your system can survive it. If your system can recover predictably, you can trust it in production. If not — you’ve just found a failure before your users do. #ChaosEngineering #Kubernetes #DevOps #SRE #Reliability

I revisited one of the most misunderstood (yet critical) concepts in Kubernetes: Liveness vs Readiness Probes. I realized something important — most of us use probes, but don’t fully understand how they behave over time. Here’s the clarity I gained : Everything in Kubernetes is a loop Both liveness and readiness probes run continuously, independently, and in parallel. Readiness Probe = Traffic Control - Starts checking after its own initial delay - If it fails → Pod is removed from Service endpoints - If it recovers → traffic resumes - No restart involved Liveness Probe = Self-Healing - Also runs on its own schedule - If it fails repeatedly → container is restarted - Keeps your app from staying in a broken state. Key Insight: A Pod can be: - Running but NOT ready (no traffic) - Running and ready (serving traffic) - Restarting (liveness failure) Common Misconception: “Pod removed” ≠ Pod deleted It simply means → no traffic is routed, but the container is still running and being monitored. After Restart: Same Pod, same IP Probes reset Readiness starts from scratch again Big Lesson Kubernetes isn’t just orchestrating containers — it’s continuously observing, deciding, and correcting state in loops. #Kubernetes #DevOps #CloudNative #SRE #KubernetesProbes #LivenessProbe #ReadinessProbe #Microservices #PlatformEngineering #CloudComputing #LearningInPublic

💡 Not every deploy is zero-downtime. ⚙️ Our backend deploys take the old pod down before bringing the new one up. The service is unreachable for 30 seconds to 2 minutes during each deploy. Why? Because the alternative (rolling updates) was causing rollout timeouts and unpredictable failures. We chose a brief, predictable gap over an unpredictable rolling deploy.  The trade-off: ✅ Cost: schedule deploys outside peak traffic. ✅ Benefit: every deploy finishes cleanly, no flaky half-states.  Engineering is often less about "what's the best pattern" and more about "what works for this system." #SoftwareEngineering #DevOps #Engineering #Kubernetes #K8s #CloudNative #SRE #PlatformEngineering

🚀 The Kubernetes Lifecycle in a Nutshell Ever wondered how your code actually lives in production? This photo perfectly captures the "parenting" hierarchy of the K8s ecosystem. * 👴 Deployment: The wise strategist. It doesn't get its hands dirty with individual pods; it just ensures the ReplicaSet is doing its job and manages the rollout of new versions. * 👨💼 ReplicaSet: The middle manager. Its sole purpose in life is to make sure the exact number of Pods you requested are up and running at all times. * 🧔 Pod: The actual host. It provides the environment (network, storage) for our little ones to thrive. * 👶 Container: The core unit. This is where your application actually breathes, packaged with everything it needs to run. It’s all fun and games until a Liveness Probe fails and someone starts crying! 🛠️📦 #Kubernetes #DevOps #CloudNative #Docker #SoftwareEngineering #TechHumor #K8s #InfrastructureAsCode #DevSecOps

Most Kubernetes content is too obvious. Deployments. Services. Ingress. Repeat. The interesting stuff is the layer after that. I just wrote about 7 Kubernetes features that feel like cheats once you discover them: - Ephemeral containers - Startup probes - Topology spread constraints - TTL cleanup for finished Jobs - Indexed Jobs - Priority Classes - Pod Disruption Budgets These are not "Kubernetes basics." They are the features that make you stop and say: "Wait. Kubernetes can already do that?" My top 3 from the list: 1. Ephemeral containers for debugging distroless pods 2. Startup probes for slow-booting apps 3. Topology spread constraints for real HA That’s the kind of stuff readers remember because they learned one concrete new thing today. Article link(𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗮𝗻𝗱 𝗥𝗲𝗮𝗱!): https://lnkd.in/g4WRmhbx Which Kubernetes feature felt like a cheat the first time you used it? #Kubernetes #DevOps #PlatformEngineering #SRE #CloudNative

Platform engineering sounds great in theory, but without the right guardrails, it can quickly turn into chaos. Too much freedom slows teams down, and too many restrictions kill developer experience. Finding the balance is where the real challenge lies. In this session, Rajan Sharma shares how to design platform guardrails in Kubernetes that actually help teams move faster instead of blocking them. It is about creating systems that enable developers, not control them, while still keeping reliability, security, and scale in check. If you are building or working on platform engineering teams, this is something you should not miss. 📅 May 2, 2026 📍 CogNerd #Kubernetes #PlatformEngineering #DevOps #CloudNative #Kubesimplify

[40/100] Ever faced this issue , works perfectly on one system… but breaks on another? 👀 That’s where containerization comes in. It packages your application with all its dependencies, so it runs the same everywhere, no environment issues. Build once, run anywhere… that’s the real power behind modern development. #Docker #DevOps #Containerization #SoftwareDevelopment #Backend #TechExplained #SystemDesign