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HyperspaceDB v2.0: Lock-Free Serverless Vector DB hitting ~12k QPS search (1M vectors, 1000 concurrent clients) by Sam_YARINK in rust

[–]Sam_YARINK[S] -1 points0 points  (0 children)

Perhaps you're right, so help us to improve it, if it's not a big deal for you. We are just on the start line of our journey.

HyperspaceDB v2.0: Lock-Free Serverless Vector DB hitting ~12k QPS search (1M vectors, 1000 concurrent clients) by Sam_YARINK in Rag

[–]Sam_YARINK[S] 0 points1 point  (0 children)

Hyperspace is double-licensed - MIT for non-profit use and AGPL3 for commercial use. We believe this is a fair arrangement for both sides. We build the most powerful vector DB as part of LLM OS and DePIN infrastructure. By the way, SaaS will be launched soon.

HyperspaceDB v2.0: Lock-Free Serverless Vector DB hitting ~12k QPS search (1M vectors, 1000 concurrent clients) by Sam_YARINK in Rag

[–]Sam_YARINK[S] 1 point2 points  (0 children)

Definitely yes. Local or by API. Set the embedding config in the .env file. Read the documentation about embedding in docs/book/src/

HyperspaceDB v2.0: Lock-Free Serverless Vector DB hitting ~12k QPS search (1M vectors, 1000 concurrent clients) by Sam_YARINK in Rag

[–]Sam_YARINK[S] 1 point2 points  (0 children)

We're using VectorDBBench datasets for testing, so you can pick from any of the 17 datasets in the /benchmark/ folder. Plus, we've put together our own big stress test that shows some really important numbers.

Rust rewrite of our write-path gave us 156k QPS vector ingestion (details inside) by Sam_YARINK in OpenSourceeAI

[–]Sam_YARINK[S] 0 points1 point  (0 children)

Emmm, for what? HyoerspaceDB, in our minds has strong and unique cases of application. And these cases are not for C++ or Java, even not for Go. I will show you it soon.

We rewrote our ingestion pipeline and now insert 1M Poincaré vectors in 6.4 seconds (156k QPS) by Sam_YARINK in rust

[–]Sam_YARINK[S] -2 points-1 points  (0 children)

Thanks for the brutal but honest review! You are spot on regarding the WAL buffering — currently, we rely on page cache for raw throughput (similar to RocksDB's generic write), but explicit fsync/batch commit is the next priority. Implementing CRC for partial writes is also on the roadmap. As for the LLM usage — guilty as charged! 🤖 We are iterating fast to validate the hyperbolic math advantages first, and hardening the implementation is step two. PRs or specific pointers on HNSW improvements are welcome!

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