https://yennj12.js.org/yennj12_blog_V4/tags/autoscaling/Recent content in Autoscaling on YennJ12 Engineering BlogHugo -- gohugo.ioen-usMon, 08 Jun 2026 09:00:00 +0800https://yennj12.js.org/yennj12_blog_V4/posts/kubernetes-autoscaling-complete-guide-part1-horizontal-pod-autoscaler/Sun, 09 Nov 2025 10:00:00 +0800https://yennj12.js.org/yennj12_blog_V4/posts/kubernetes-autoscaling-complete-guide-part1-horizontal-pod-autoscaler/Part 1 of the Kubernetes Autoscaling series: Deep dive into Horizontal Pod Autoscaler (HPA) approaches, comparing resource-based, custom metrics, external metrics, and event-driven autoscaling with KEDA. Learn when to use each approach with real-world examples and production best practices.https://yennj12.js.org/yennj12_blog_V4/posts/kubernetes-autoscaling-complete-guide-part2-cluster-autoscaling/Sun, 09 Nov 2025 14:00:00 +0800https://yennj12.js.org/yennj12_blog_V4/posts/kubernetes-autoscaling-complete-guide-part2-cluster-autoscaling/Part 2 of the Kubernetes Autoscaling series: Comprehensive guide to cluster-level autoscaling covering Cluster Autoscaler, Karpenter, cloud provider-specific solutions (EKS, GKE, AKS), and emerging technologies for intelligent node provisioning and cost optimization.https://yennj12.js.org/yennj12_blog_V4/posts/kubernetes-autoscaling-complete-guide-part3-hands-on-hpa-demo/Sun, 09 Nov 2025 16:00:00 +0800https://yennj12.js.org/yennj12_blog_V4/posts/kubernetes-autoscaling-complete-guide-part3-hands-on-hpa-demo/Part 3 of the Kubernetes Autoscaling series: Hands-on tutorial demonstrating Horizontal Pod Autoscaler with a real Apache-PHP application. Includes complete AWS CDK infrastructure code, Kubernetes manifests, load testing, and step-by-step deployment guide.https://yennj12.js.org/yennj12_blog_V4/posts/kubernetes-autoscaling-complete-guide-part6-advanced-patterns/Sun, 09 Nov 2025 22:00:00 +0800https://yennj12.js.org/yennj12_blog_V4/posts/kubernetes-autoscaling-complete-guide-part6-advanced-patterns/Part 6 of the Kubernetes Autoscaling series: Advanced autoscaling patterns for stateful applications, multi-cluster deployments, cost optimization strategies, batch job scaling, and emerging technologies. Real-world architectures and production-grade implementations.https://yennj12.js.org/yennj12_blog_V4/posts/kubernetes-autoscaling-complete-guide-part7-troubleshooting-war-stories/Mon, 10 Nov 2025 00:00:00 +0800https://yennj12.js.org/yennj12_blog_V4/posts/kubernetes-autoscaling-complete-guide-part7-troubleshooting-war-stories/Part 7 of the Kubernetes Autoscaling series: Real-world production incidents, debugging workflows, common failure scenarios, and hard-learned lessons from operating autoscaling at scale. Battle-tested troubleshooting guides and postmortem analysis.https://yennj12.js.org/yennj12_blog_V4/posts/fde-interview-guide-part39-scalability-zh/Mon, 08 Jun 2026 09:00:00 +0800https://yennj12.js.org/yennj12_blog_V4/posts/fde-interview-guide-part39-scalability-zh/10,000 個內部員工用，一切都很順。 百萬外部用戶第一天上線，系統在 30 分鐘內崩潰。 「加更多機器」不是答案—— 正確的問題是：哪些地方讓你根本無法加機器？ 面試情境 面試官：「你幫一家金融公司做了內部員工 AI 助手，10,000 個內部用戶，系統很穩定。現在 CEO 決定把這個產品開放給外部客戶，目標是百萬 MAU（月活躍用戶）。你說需要重新設計架構。從哪裡開始？你會做哪些改動？為什麼？」 一、為什麼 10K → 1M 不只是「加機器」 10K 內部用戶的隱性假設（這些假設在 1M 時全部失效）： 用戶行為： ├── 行為模式可預測（9-18 點工作時間，流量曲線平滑） ├── 用量相對均勻（員工配額相似，不會有人瘋狂濫用） └── 系統問題可以容忍（內部用戶有耐心，可以接受偶爾慢） 系統設計： ├── Session State 在記憶體（少數實例，重啟少） ├── 認證：單一 LDAP/SSO（一種身份系統就夠） ├── 沒有速率限制（員工不會惡意攻擊自家系統） └── SLA：P95 < 10s 內部用戶接受 1M 外部用戶：每一個假設都被打破 假設失效 真實挑戰 系統症狀 ────────────────────────────────────────────────────────────── 行為可預測 病毒式傳播：1 小時內 100x 流量 Auto-Scale 來不及 → 503 用量均勻 惡意用戶濫用、失控的客戶端 Bug 一個用戶拖垮整個平台 Session 在記憶體 Scale-Out 後新實例找不到 Session 對話斷掉，用戶流失 無速率限制 機器人、爬蟲、Bug 迴圈呼叫 LLM 配額耗盡 → 全平台崩潰 SLA 寬鬆 外部客戶不等待，直接離開 用戶留存率崩潰 成本不計較 1M × 50 queries × $0.