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Peter Gebri for HydrAIDE

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đŸ± Schrödinger’s Index: It Only Exists When You Observe It and Only the Way You Observe It – in the HydrAIDE Engine

#go #database #opensource #webdev

🔍 We have no indexes, but...

We have no indexes. Then, the moment you ask for them, they suddenly exist — but only for as long as needed, and only in the way you observe them. đŸ€Ż WTF?! This is the real Schrödinger’s Index. It’s only there when you look at it, and only in the form you see it.

This isn’t clickbait — it’s quantum mechanics in action 😄

Previously, I wrote about how I made millions of European websites searchable from a single server using HydrAIDE.: https://dev.to/hydraide/how-i-made-europe-searchable-from-a-single-server-the-story-of-hydraide-432h

Now, I’ll show you how HydrAIDE solved memory management and SSD issues that most databases suffer from due to old-fashioned indexing strategies, thanks to Schrödinger’s Indexes.

❓ What Is Schrödinger’s Index?

Schrödinger’s Index is a completely unique development in the HydrAIDE data engine — born from a different way of thinking.

In most databases, indexes work like this:

  • Separate persistent index structures (B-Tree/GIN/Hash) are built alongside tables and stored on disk.
  • Every INSERT/UPDATE/DELETE updates the data and all related indexes.
  • Indexes require separate maintenance (rebuild/vacuum/analyze, updating statistics).
  • Maintenance consumes memory (buffer cache), storage, and CPU via background processes.

This causes problems like:

  • Write amplification: higher write latency and lower throughput with heavy writes.
  • Extra storage cost: data stored multiple times (data + multiple indexes) = bigger SSD bills.
  • Locks and contention: large table/index updates can cause blocking and wait times.
  • Operational overhead: index rebuilds, DDL changes, migrations reduce performance or require maintenance windows.
  • Over-indexing risk: poorly chosen or outdated indexes still consume resources without helping.

🕰 Looking Back: Why Always On-Disk Indexes?

I get why this was necessary in the early 2000s. Back then, with slow HDDs, file-based indexing solved a real problem. But it’s not the early 2000s anymore — it’s been 25 years. Today’s M.2 SSD speeds are insane, and RAM is on another level entirely. So why cling to the old ways?

When building HydrAIDE, I decided to start from scratch — focusing on modern hardware and possibilities — and lay entirely new foundations, including a new indexing strategy.

⚡ How It Works in HydrAIDE

Unlike traditional databases, HydrAIDE doesn’t create physical indexes for querying by ID, value, or metadata. No data duplication to SSDs — the design makes it unnecessary.

When HydrAIDE loads a swamp into memory (a small, targeted “mini-database”), it creates a map[key]value structure in Go. Go maps return values at brutal speed via direct lookups — if you know the key.

But what if you don’t know the key, and instead want sorted data from a swamp — say, by insertion time descending, or sorted by value?

This is where Schrödinger’s Index comes in: on the first such query, the HydrAIDE engine temporarily sorts the data from the base map into a slice in memory (with a pointer to the value and metadata) according to your requested criteria — and returns the results immediately.

So, the sorted key set ONLY EXISTS IF YOU ASK FOR IT — and only in the way you observe it.

The built “index” lives only in memory. When HydrAIDE — per your settings — clears the swamp from memory, the index disappears with it. No SSD duplication, and memory stays clean.

Sorting is lightning-fast and highly efficient in Go and HydrAIDE, so in practice, these temporary indexes won’t burden your system.

đŸš« No Background Index Processes

In HydrAIDE, there are no background processes managing indexes — because there’s no traditional indexing at all. That’s why HydrAIDE has virtually no idle CPU or RAM usage, yet still performs its job effectively — just not via the “traditional” route.

✅ Why It’s Not Clickbait

The index truly exists only at the moment of observation — and only in the form you observe it. 😄 Before and after that? It could be anything 😂

This system has been a game-changer for me. Without Schrödinger’s Indexes, I couldn’t have handled Europe’s web content index on a single server and served searches from it. I’m convinced this technical approach works for everything from the smallest projects to the largest ones. For reactive projects where data changes rapidly (making constant indexing a heavy burden), it’s outright essential.

💬 Your Turn

What do you think? Do you like the idea of Schrödinger’s Index? Would you use HydrAIDE as your database? I’m happy to answer questions in the comments.

You can try the engine in action on our GitHub repo. A 2‑minute install and Schrödinger’s Index comes alive:
https://github.com/hydraide/hydraide

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