惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

J
Java Code Geeks
量子位
MongoDB | Blog
MongoDB | Blog
N
Netflix TechBlog - Medium
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
B
Blog
A
About on SuperTechFans
腾讯CDC
The GitHub Blog
The GitHub Blog
云风的 BLOG
云风的 BLOG
雷峰网
雷峰网
Last Week in AI
Last Week in AI
H
Help Net Security
WordPress大学
WordPress大学
博客园 - 司徒正美
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
H
Hackread – Cybersecurity News, Data Breaches, AI and More
T
Tailwind CSS Blog
博客园 - 【当耐特】
S
SegmentFault 最新的问题
美团技术团队
M
MIT News - Artificial intelligence
L
LangChain Blog
博客园 - 聂微东

DEV Community

Authentication Security Deep Dive: From Brute Force to Salted Hashing (With Java Examples) Why AI Systems Don’t Fail — They Drift Spilling beans for how i learn for exam😁"Reinforcement Learning Cheat Sheet" I Replaced Chrome with Safari for AI Browser Automation. Here's What Broke (and What Finally Worked) How Python Borrows Other People's Work The $40 Architecture: Processing 1 Billion API Requests with 99.99% Uptime Vibe Coding: A Workflow Guide (From Zero to SaaS) Most webhook security guides protect the wrong side. The scary part is delivery. Headless CMS for TanStack Start: Build a Blog with Cosmic EU Age Verification App "Hacked in 2 Minutes" — What Actually Happened Comfy Cloud’s delete function does not actually remove files Running AI Models on GPU Cloud Servers: A Beginner Guide Event-driven media intelligence with AWS Step Functions and Bedrock I scored 500 AI prompts across 8 quality dimensions — here's what broke How to Call Google Gemini API from Next.js (Free Tier, No Backend Needed) The Portal Protocol: Reclaiming Human Connection in the Age of AI How to Fix Your Team's Scattered Knowledge Problem With a Self-Hosted Forum Intro to tc Cloud Functors: A Graph-First Mental Model for the Modern Cloud Designing Multi-Tenant Backends With Both Ownership and Team Access I Built a Neumorphic CSS Library with 77+ Components — Here's What I Learned PostgreSQL Performance Optimization: Why Connection Pooling Is Critical at Scale Cómo construí un SaaS multi-rubro para gestionar expensas en Argentina con FastAPI + Vue 3 🚀 I Built an Ethical Hacking Scanner Tool – Open Source Project I Replaced /usage and /context in Claude Code With a Single Statusline A Pythonic Way to Handle Emails (IMAP/SMTP) with Auto-Discovery and AI-Ready Design I Collected 8.9 Million Polymarket Price Points — Here's What I Found About How Markets Really Move EcoTrack AI — Carbon Footprint Tracker & Dashboard Everyone's Using AI. No One Agrees How. 5 self-hosted ebook managers worth trying in 2026 Building Your First AI Agent with LangChain: From Chatbot to Autonomous Assistant
The lift n' shift
Mariano Barc · 2026-05-08 · via DEV Community

This is the continuation of "The script that refused to stay small".

The conversation had already been happening for months: three data centers, rising costs, aging hardware, and a growing sense that the whole setup was one incident away from becoming a liability.

The decision came quickly after the "kalima" event: everything had to move.

What followed was not elegant, as the platform team scrambled to pull off a lift & shift:

  • hypervisor VMs mapped 1:1 into cloud instances
  • network rules replicated (and occasionally guessed)
  • storage reattached, sometimes awkwardly
  • timelines optimistic, then revised, then ignored

Most applications... just came along for the ride whilst Marta watched all of this unfold.

Her service was, technically, just another VM in the inventory. It could have been copied over, like everything else, but the lift and shift was rushed and was accumulating tech debt almost by definition. So she made a different call.

Instead of following the 1:1 VM migration path, she opted into an early container platform the company had been experimenting with—something not yet standard, slightly under-documented, but good enough.

Not because she was trying to be innovative, but because her system made it easy.


Containerizing the application turned out to be almost trivial.

Quarkus already produced a lean runtime, with the TPF pipeline living inside a single process with well-defined boundaries. There were no hidden dependencies on the host, no fragile startup scripts, no tight coupling to the underlying machine.

Marta wrote a Dockerfile, wired a couple of environment variables, and that was… mostly it.

The hardest part wasn’t the application, but everything around it:

  • getting the right network access
  • aligning with security policies
  • making sure it could talk to the same external systems as before

So while most systems were being translated from one VM to another, Marta’s was quietly being repackaged.

And once it ran, something became clear:

  • It was still a monolith
  • Steps still called each other directly, in-process
  • The pipeline still lived entirely inside a single runtime

Only the execution environment had changed.


That was the first quiet lesson TPF taught the team: you can move where something runs without changing how it behaves.

A few weeks later, someone asked Marta how long her migration had taken; she hesitated (because the honest answer sounded wrong: “About a day. Maybe two, if you count the networking issues.”


Photo credit: Javier Mediavilla E, CC BY-SA 2.5 https://creativecommons.org/licenses/by-sa/2.5, via Wikimedia Commons