[H] HyperspaceDB

Fastest Vector Database for Hierarchical & Flat Data written in Rust.
HyperspaceDB natively supports both the Poincaré ball model (for hierarchies) and Euclidean space (for standard OpenAI/BGE embeddings), delivering extreme performance through specialized SIMD kernels.

🚀 Key Features

⚡️ Extreme Performance: Built with Nightly Rust and SIMD intrinsics for maximum search throughput.
📐 Cognitive Math Engine: Hyperbolic HNSW optimized for the Poincaré and Lorentz metrics, and O(N) Wasserstein-1 logic.
📦 Compression: Integrated ScalarI8 and Binary quantization reduces memory footprint by 87% to 98%.
🧵 Async Write Pipeline: Decoupled ingestion with a background indexing worker and WAL for 10x faster inserts.
🖥️ Mission Control TUI: Real-time terminal dashboard for monitoring QPS, segments, and system health.
🕸️ Edge Ready: WASM compilation target allows running the full DB in browser with Local-First privacy and IndexedDB persistence.
🛠️ Runtime Tuning: Dynamically adjust ef_search and ef_construction parameters via gRPC on-the-fly.
🏙 Multi-Tenancy: Native SaaS support with namespace isolation (user_id) and billing stats.
🔁 Replication: Leader-Follower architecture with Anti-Entropy catch-up for high availability.
⚖️ Cognitive Math & Tribunal Router: Native SDK utilities for calculating geometric trust scores on graphs to detect LLM hallucinations.
📡 Memory Reconsolidation: Trigger AI sleep mode natively within the DB to restructure vectors via Flow Matching / Riemannian SGD.

🛠 Architecture

HyperspaceDB follows a Persistence-First, Index-Second design:

gRPC Request: Insert/Search commands arrive via a high-performance Tonic server.
WAL & Segmented Storage: Every insert is immediate persisted to a Write-Ahead Log and a memory-mapped segmented file store.
Background Indexer: The HNSW graph is updated asynchronously by a dedicated thread-pool, ensuring 0ms search blocking.
Snapshots: Real-time graph topology is periodically serialized using rkyv for near-instant restarts.

🏃 Quick Start

1. Build and Start Server

Make sure you have just and nightly rust installed.

cargo build --release
./target/release/hyperspace-server

2. Launch Dashboard

./target/release/hyperspace-cli

3. Use Python SDK

pip install ./sdks/python

from hyperspace import HyperspaceClient

client = HyperspaceClient("localhost:50051")
client.insert(vector=[0.1]*8, metadata={"category": "tech"})
results = client.search(vector=[0.11]*8, top_k=5)

📊 Performance Benchmarks

Tested on M4 Pro (Emulated), 1M Vectors (8D)

Insert Throughput: ~156,000 vectors/sec (Sustained)
Search Latency: ~2.47ms (156,000 QPS) @ 1M scale
Storage Efficiency: Automatic segmentation + mmap

"The 1 Million Challenge"

HyperspaceDB successfully handles 1,000,000 vectors with zero degradation compared to traditional vector DBs, maintaining 156,000 QPS at the 1M scale.

HyperspaceDB Documentation