# HelixDB — OLTP Graph-Vector Database Built in Rust

> An open-source OLTP database combining graph and vector capabilities in a single engine, built in Rust on object storage for AI-native applications that need both relationship traversal and similarity search.

## Install

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# HelixDB — OLTP Graph-Vector Database Built in Rust

## Quick Use
```bash
# Install via cargo or download binary
cargo install helix-db
# Start the server
helix-db start --data-dir ./data
# Connect and run queries
helix-db query "CREATE NODE user { name: 'Alice' }"
```

## Introduction
HelixDB is an open-source database that unifies graph and vector storage in a single engine. Written in Rust and built on object storage, it is designed for AI-native applications that need both relationship traversal (graph queries) and similarity search (vector queries) without running separate databases. It targets OLTP workloads where low latency matters.

## What HelixDB Does
- Stores and queries graph data with nodes, edges, and properties
- Provides vector similarity search for embeddings and feature vectors
- Combines graph traversal and vector search in unified queries
- Runs on object storage for cost-effective, scalable persistence
- Delivers low-latency OLTP performance from a Rust-native engine

## Architecture Overview
HelixDB is written entirely in Rust for memory safety and performance. The storage layer sits on object storage (S3-compatible), which decouples compute from storage and enables cost-effective scaling. The query engine supports a custom query language for graph traversal operations (node/edge creation, path finding, pattern matching) and vector operations (nearest-neighbor search, cosine similarity). An in-memory cache layer handles hot data for sub-millisecond latency on frequently accessed nodes and vectors.

## Self-Hosting & Configuration
- Install via Cargo or download pre-built binaries for Linux and macOS
- Configure data directory for local storage or S3-compatible object storage
- Tune cache size and index parameters for your workload profile
- Expose the query endpoint on a configurable port
- CLI tools included for database management, backup, and migration

## Key Features
- Unified graph and vector storage in a single database engine
- Rust-native implementation for high performance and memory safety
- Object storage backend for scalable and cost-effective persistence
- Combined graph traversal and vector similarity queries
- Lightweight deployment with no JVM, Python, or external dependencies

## Comparison with Similar Tools
- **Neo4j** — graph-only database without native vector support; HelixDB combines both
- **Milvus** — vector-only database without graph capabilities
- **Qdrant** — vector database; does not support graph traversal queries
- **DGraph** — distributed graph database but requires more infrastructure
- **SurrealDB** — multi-model database; HelixDB focuses on the graph-vector intersection with Rust performance

## FAQ
**Q: What query language does HelixDB use?**
A: It uses a custom query language designed for combined graph and vector operations.

**Q: Can I use HelixDB for RAG applications?**
A: Yes. Store document embeddings as vectors and use graph edges to model document relationships and metadata.

**Q: Does it support ACID transactions?**
A: Yes. HelixDB provides transactional guarantees for OLTP workloads.

**Q: What object storage backends are supported?**
A: Any S3-compatible storage including AWS S3, MinIO, and Cloudflare R2.

## Sources
- https://github.com/HelixDB/helix-db

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Source: https://tokrepo.com/en/workflows/asset-1495b254
Author: AI Open Source