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SwarnDB
Vector database that thinks in graphs
SwarnDB is a Rust vector database that adds a virtual graph layer and 15+ built-in vector math operations on top of HNSW search. Query vectors, traverse the relationships SwarnDB computes for you, and run analytics like k-means or PCA, all in one engine.
Hey PH folks, this is Chirotpal, and I created SwarnDB.
SwarnDB is a vector database written in Rust that does not stop at nearest-neighbor search. It computes the relationships between your vectors automatically and exposes them as a graph you can traverse. So you get vector search, graph traversal, and 15+ vector math operations (k-means, PCA, SLERP interpolation, cone search, drift detection, maximal marginal relevance, ghost-vector detection, and more) in one engine, instead of stitching three databases together.
What's in this release (v1.0.3):
- 2,398 queries per second at 98% recall on DBpedia 1M (1536 dim) with default HNSW parameters on a 32-core box, 8 concurrent searcher threads. Sub-7ms p99.
- File-based bulk ingestion: stage a `.npy` or flat `.f32` file on disk, point the server at it, and the server memory-maps it directly. Working memory stays bounded by the index being built, not by the input file size. Loading a million vectors no longer balloons your container.
- Plain HNSW collections become queryable within seconds of the server opening its ports.
- Multi-collection databases load collections in parallel at startup.
- Transparent crash recovery: incremental delta replay if a snapshot exists, full write-ahead log replay otherwise, both happen automatically before traffic resumes.
- Operational endpoints for Kubernetes orchestrators: /healthz, /readyz, /startupz, plus a global /recovery_status and per-collection /persistence_status.
- Async and sync Python clients with identical method names and return types.
- Bulk inserts produce checkpoints and a resume token so interrupted loads pick up from the last committed batch.
- Multi-arch Docker image (linux/amd64 + linux/arm64) on Docker Hub.
- Pure-Python wheel on PyPI for Linux x86_64, Linux ARM64, macOS Apple Silicon, and Windows x86_64.
Try it in 30 seconds:
- `pip install swarndb`
- `docker run -d -p 8080:8080 -p 50051:50051 sarthiai/swarndb`
Source: github.com/SarthiAI/SwarnDB
Benchmarks: github.com/SarthiAI/SwarnDB/blob/main/docs/benchmarks.md
License: Elastic License 2.0 (free for production use).
I will be in the thread all day. Would love to hear: what are you building with vector search today, and where does the lack of a relationship/graph layer hurt? Also happy to dive into the benchmark methodology, the persistence model, or the SDK design if anyone wants the deep end.
About SwarnDB on Product Hunt
“Vector database that thinks in graphs”
SwarnDB was submitted on Product Hunt and earned 4 upvotes and 1 comments, placing #69 on the daily leaderboard. SwarnDB is a Rust vector database that adds a virtual graph layer and 15+ built-in vector math operations on top of HNSW search. Query vectors, traverse the relationships SwarnDB computes for you, and run analytics like k-means or PCA, all in one engine.
On the analytics side, SwarnDB competes within Developer Tools, Artificial Intelligence, GitHub and Database — topics that collectively have 1M followers on Product Hunt. The dashboard above tracks how SwarnDB performed against the three products that launched closest to it on the same day.
Who hunted SwarnDB?
SwarnDB was hunted by Chirotpal. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of SwarnDB including community comment highlights and product details, visit the product overview.
Chirotpal