How I Made Europe Searchable From a Single Server — The Story of HydrAIDE
🚀 1. From One Server to Millions of Pages
I made millions of websites across Central and Western Europe searchable — from a single server, and I barely crossed 3% CPU load doing it. No Redis. No Kafka. No Elasticsearch. Not a trick. Not AI. Not a crawler hack. I built my own data engine.
👉 GitHub: https://github.com/hydraide/hydraide
🧭 2. Why I Had to Build It
I've been programming for 30 years. The last 10+ were deep in backend systems — high-concurrency, high-load Go services.
In 2021, we launched Trendizz.com, a startup to help businesses find their best B2B partners across Europe — through precise, micro-segmented search.
We needed to answer questions like:
"Which Hungarian companies sell bicycles, don’t use GLS shipping, and run an Unas webshop?"
To do that, we had to index millions of websites. Not just metadata — word-level matches, across layers of content.
And that’s when we realized: no database could handle it. And we didn’t have millions in infra budget. Just the need to make it work.
🧱 3. Hitting the Wall With Databases
We tried everything: SQL, NoSQL, document stores, graph DBs, even exotic stuff.
- SQL? Slows down hard after a few million records. Query optimization is its own career.
- NoSQL? Assumes everything fits in RAM. Funny, when your data hits terabytes.
- Cloud? Egress costs would’ve buried us. We wanted control — not billing tiers.
We knew we had to think differently.
Most databases are still based on early-2000s assumptions: single-core CPUs, spinning disks, batch jobs.
But today?
- SSDs are monsters.
- RAM is fast and cheap.
- CPUs love concurrency.
Why should everything live in memory? Why can’t I decide — from code — what to load, and when?
📦 4. The Power of Small Files
That’s when the idea came. What if I skipped the huge database files — and just used lots of small, precisely named binary files I could jump to instantly?
Classic databases jam everything into one massive file, causing brutal I/O overhead. In HydrAIDE, targeting a file is an O(1) operation, and SSDs return it near-instantly.
Files are minimal: no extra index, no cache, no journal. Just the data — exactly where it should be.
The beauty? You only load what you need, and it's blazing fast. And if you don’t need something anymore? Just drop it from memory. You can control all of this — directly from the SDK.
🛠️ 5. The First Prototype
I built a prototype. And it wasn’t just fast — it was shockingly memory-efficient. The engine didn’t strain — it flew.
Side note: HydrAIDE can insert or read millions of records on a single thread, near hardware speed.
📜 6. Core Principles I Never Want to Rewrite Again
I laid down a few non-negotiable rules for what this engine must be:
- Everything must be defined in code (Go SDK, extensible to other languages)
- No query language
- Realtime by default — no pub/sub middleware needed
- No persistent indexes, yet still fast as hell
- Delete means delete — no background cleanup jobs
- No orchestrator required, yet still horizontally scalable
- Nodes must be stateless
- And I have to enjoy working with it.
🐉 7. HydrAIDE Is Born
This became HydrAIDE — Hydra Adaptive Intelligent Data Engine. A system that followed my thinking, not a legacy abstraction.
Then came 2 years of focused development and testing, and the crawling of tens of millions of sites. Billions of keyword pairs, technical attributes, and signals made searchable.
Now, in July 2025, HydrAIDE is finally fully open-source — and ready.
💬 8. Early Reactions
The first public release went better than I imagined. We hit 80 GitHub stars in the first few days, and a 7-person contributor group formed almost instantly.
Brilliant devs. Smart feedback. Real commits. Huge thanks to everyone who jumped in early — you're part of this.
⏱️ 9. Where We Are Now
HydrAIDE runs on a gRPC server. With the Go reference SDK, getting started takes minutes. A Python SDK is in the works, and Node.js and Rust are next.
We’re moving fast — because you can’t chain a Hydra.
🌍 10. So How Did Europe Become Searchable?
How did I store billions of keyword relationships from tens of millions of sites?
Now you know. HydrAIDE made it possible.
- I built a headless crawler fleet, powered by real browser sessions.
- I built a blazing-fast engine that runs even in "one-server zero-footprint" setups.
- I built a frontend in Angular, backed by Go, with HydrAIDE’s reactive flow underneath.
And I did it without huge infra, no investors, no excuses — just new thinking and time.
🤝 11. Now It’s Yours Too
This engine is now open. Not a SaaS. Not a product. A working developer’s dream — now yours too.
📌 Try it, contribute, or explore:
- GitHub: https://github.com/hydraide/hydraide
- Discord: https://discord.gg/aBfAuYjR
Got questions about how it works or how to build with it? Just ask. I’ll explain everything.
Top comments (3)
I remember Google claimed that they used just a bunch of regular PCs to power their search index, at least in its early days. It's good to see that still makes sense today.
Absolutely, and that’s one of the most inspiring parts of this journey.
Page and Brin showed that the web didn’t need supercomputers just good ideas, commodity machines, and a clear architecture. HydrAIDE was built on the same belief: that modern infrastructure is overcomplicated not because it must be, but because legacy tools force us into complexity.
I didn’t want layers of orchestration, background jobs, and clustered caches. Just fast code, intentional data, and the ability to choose what lives in memory. Turns out, that’s still enough even in 2025.
Appreciate the reference. That original Google paper? Still a masterpiece.
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