GitHub - zentrix-innovative-labs/galaxdb: GalaxDB is desi...

The AI-native database. SQL + vector search + training exports in one system.

What is GalaxDB?

Most AI applications bolt together 3–5 separate services: a relational database, a vector database, an embedding API, an object store, and a data pipeline. GalaxDB replaces all of them with a single binary that speaks PostgreSQL wire protocol. The release artifacts stay lightweight: galaxdb-server is 7.9 MB and galaxdb-sidecar is 7.6 MB.

Before GalaxDB:
  PostgreSQL + pgvector + Pinecone + OpenAI API + S3 + Airflow

After GalaxDB:
  galaxdb-server

One connection string. One backup. One monitoring endpoint. Your existing psycopg2, SQLAlchemy, and pg code works unchanged.

Quick Start

Python — embedded mode (no server, like SQLite)

pip install galaxdb-client

import galaxdb

db = galaxdb.Database("./mydata")

# Create a table with an embedding column
db.execute("""
    CREATE TABLE docs (
        id   INT PRIMARY KEY,
        text TEXT EMBEDDING MODEL 'sentence-transformers/all-MiniLM-L6-v2' DIM 384
    )
""")

# Insert — embeddings computed automatically by the local sidecar
db.execute("INSERT INTO docs (id, text) VALUES (1, 'machine learning is great')")
db.execute("INSERT INTO docs (id, text) VALUES (2, 'rust programming language')")
db.execute("INSERT INTO docs (id, text) VALUES (3, 'deep neural networks')")

# Semantic search — no external API, no separate vector DB
results = db.execute(
    "SELECT id, text FROM docs WHERE SEMANTIC_MATCH(text, 'AI and neural nets', 0.7)"
)

# Export a training dataset — one SQL command, Lance format, PyTorch-ready
db.execute("CREATE VERSION TAG 'v1' FOR TRAINING WITH TRAINING PRECISION 'float32'")
path = db.training_dataset("v1")

import lance
dataset = lance.dataset(path).to_pytorch()  # zero-copy, memory-mapped

Server mode — multi-client, like PostgreSQL

# macOS
brew tap zentrix-innovative-labs/tap && brew install galaxdb

# Linux / macOS (direct install)
curl -fsSL https://raw.githubusercontent.com/zentrix-innovative-labs/galaxdb/main/install.sh | bash

# Docker
docker run -p 5433:5433 -p 9090:9090 -v /data:/data \
  harbi256/galaxdb:latest --data-dir /data

import galaxdb

conn = galaxdb.connect("host=localhost port=5433 dbname=galaxdb sslmode=disable")
conn.execute("SELECT id, text FROM docs WHERE SEMANTIC_MATCH(text, 'AI', 0.8)")

Any PostgreSQL client works — psycopg2, SQLAlchemy, tokio-postgres, pg (Node.js), JDBC.

AuroraSQL — SQL Extensions for AI

GalaxDB extends standard SQL with AI-native primitives:

-- Semantic search with similarity threshold
SELECT id, title
FROM articles
WHERE SEMANTIC_MATCH(title, 'climate change policy', 0.75)
  AND published_at > '2024-01-01';

-- Time-travel query — reproduce exactly what data existed at a point in time
SELECT * FROM docs AT VERSION 'training-v1';

-- Near-duplicate deduplication — cut training set size by 15–30%
SELECT * FROM docs WHERE NOT DUPLICATE;

-- Create a versioned training snapshot
CREATE VERSION TAG 'train-v2'
  FOR TRAINING
  WITH TRAINING PRECISION 'sq8'
  TRAINING SEED 42;

-- Bulk insert
BULK INSERT INTO docs (id, text) VALUES
  (1, 'first document'),
  (2, 'second document');

-- Backup and restore
BACKUP TO '/path/to/backup';
RESTORE FROM '/path/to/backup';

Performance

Measured on AWS c6id.4xlarge (Intel Xeon Platinum 8375C, 16 vCPU, 32 GiB RAM, 884 GB NVMe), release build.

HNSW Vector Search — SIFT-1M

ef_search	recall@10	mean latency	p99 latency
50	0.959	158 µs	228 µs
100	0.983	267 µs	364 µs
200	0.990	459 µs	616 µs

For methodology and the full SIFT-1M run, see the GalaxDB paper on Zenodo.

Storage Engine

Metric	GalaxDB	PostgreSQL 16	RocksDB
Write TPS	258,555	~3,200	~80,000
Read p50	3 µs	~95 µs	~180 µs
Read p99	47 µs	~300 µs	~500 µs
Scan throughput	4.49 GB/s	~0.9 GB/s	—

740 Rust tests passing. 7 chaos scenarios in 10.9 s. See BENCHMARKS.md.

How It Compares

	GalaxDB	PostgreSQL + pgvector	Pinecone	Qdrant	Weaviate	LanceDB	ChromaDB	Milvus	DuckDB
SQL queries	✅ Full	✅ Full	❌	❌	Partial	Partial¹	❌	❌	✅ Full
Vector search	✅ recall=0.990	⚠️ ~0.95	✅	✅	✅	✅	✅	✅	❌
Local embeddings	✅ no API cost	❌	❌	⚠️ FastEmbed	✅ modules	✅	✅	❌	❌
Time-travel	✅ `AT VERSION`	❌	❌	❌	❌	❌	❌	❌	❌
Training export	✅ Lance format	❌	❌	❌	❌	❌	❌	❌	❌
Near-dedup	✅ MinHash LSH	❌	❌	❌	❌	❌	❌	❌	❌
Embedded mode	✅	❌	❌	❌	❌	✅	✅	❌	✅
PostgreSQL wire	✅	✅	❌	❌	❌	❌	❌	❌	❌
Self-hosted	✅	✅	❌	✅	✅	✅	✅	✅	✅
Encryption at rest	✅ AES-256-GCM	✅ OS-level	✅	✅	✅	❌	❌	✅	❌
MVCC / snapshots	✅	✅	❌	❌	❌	❌	❌	❌	❌
Single binary	✅	❌	❌	✅	❌	✅	✅	❌	✅

¹ LanceDB OSS uses a Python/Arrow API; SQL is available via DuckDB bridge or Enterprise tier only.

→ Full comparison with benchmarks, pricing, and use-case guidance

Architecture

Your application
      │
      │  PostgreSQL wire protocol (port 5433)
      │  or Python embedded API
      ▼
┌─────────────────────────────────────────────────────┐
│                   galaxdb-server                    │
│                                                     │
│  SQL Parser → Query Planner → Executor              │
│       │                           │                 │
│  ART index    HNSW graph    LSM storage engine      │
│  (point reads) (vector search) (WAL + PAX blocks)   │
│                                                     │
│  ┌──────────────────┐   HTTP :9090                  │
│  │ galaxdb-sidecar  │   /health  /metrics           │
│  │ (child process)  │                               │
│  │ ONNX/Candle model│                               │
│  └──────────────────┘                               │
└─────────────────────────────────────────────────────┘

The sidecar is spawned automatically — you don't manage it separately.

Use Cases

RAG applications — store documents, compute embeddings locally, query with SEMANTIC_MATCH filtered by metadata. No Pinecone, no OpenAI embeddings API.

ML training pipelines — CREATE VERSION TAG ... FOR TRAINING snapshots your data and exports it as a Lance dataset. Load directly into PyTorch with zero-copy memory mapping.

Hybrid search — combine SQL filters with vector similarity in a single query. No application-side join between two systems.

Audit-safe AI — AT VERSION queries let you reproduce exactly what data a model was trained on. EU AI Act compliance built in.

Time-series + semantic — store sensor readings with text descriptions, query by time range AND semantic similarity in one SQL statement.

Installation

Python (embedded + remote)

pip install galaxdb-client

Requires Python 3.9+. Pre-built wheels for Linux x86-64, macOS Intel, macOS Apple Silicon, and Windows x86-64.

macOS (Homebrew)

brew tap zentrix-innovative-labs/tap
brew install galaxdb

Linux / macOS (direct install)

curl -fsSL https://raw.githubusercontent.com/zentrix-innovative-labs/galaxdb/main/install.sh | bash

Docker

docker run -p 5433:5433 -p 9090:9090 -v /data:/data \
  harbi256/galaxdb:latest --data-dir /data

GitHub Releases

Download pre-built binaries for Linux x86-64 and macOS x86-64 from the Releases page.

Rust (embed in your application)

[dependencies]
galaxdb-embedded = "1.0.0-beta"

Observability

Every server instance exposes:

# Health check — reflects real subsystem state
curl http://localhost:9090/health
# {"status":"ok","version":"1.0.0-beta.1","subsystems":{"disk_full":false,"sidecar_healthy":true,"connections_active":3}}

# Prometheus metrics
curl http://localhost:9090/metrics
# galaxdb_connections_active 3
# galaxdb_wal_write_latency_us 42
# galaxdb_hnsw_recall_estimate_bp 9902
# galaxdb_embedding_queue_depth 0
# ...

Key Management

GalaxDB supports pluggable encryption key management with no vendor lock-in:

# Local key file
GALAXDB_KEY_PROVIDER=local:/path/to/key.bin galaxdb-server ...

# Environment variable
GALAXDB_KEY_PROVIDER=env:GALAXDB_MASTER_KEY galaxdb-server ...

# Any KMS via shell command (AWS CLI, gcloud, az, vault, custom HSM)
GALAXDB_KEY_PROVIDER=command:aws kms decrypt ... galaxdb-server ...

# HashiCorp Vault Transit
GALAXDB_KEY_PROVIDER=vault:transit/galaxdb-prod galaxdb-server ...

Security status

GalaxDB encrypts data at rest today (AES-256-GCM on every PAX block and WAL record, pluggable key management above). Network security is in active development:

Capability	Status
Encryption at rest (AES-256-GCM, pluggable KMS)	✅ Available now
Wire authentication (SCRAM-SHA-256)	🚧 In progress
TLS transport encryption	🚧 In progress
Roles, privileges, GRANT/REVOKE	🚧 In progress
SSO / fine-grained RBAC / audit logging	Enterprise edition

Until wire authentication and TLS land, run galaxdb-server only on a trusted network or loopback interface (the connection examples above use sslmode=disable accordingly). See ROADMAP.md for what is shipping next.

Documentation

Getting Started — installation, all features, Docker Compose, troubleshooting
Roadmap — shipped capabilities, in-progress hardening, and planned features (OSS vs Enterprise)
SQL Reference — full AuroraSQL syntax
Storage Engine — LSM tree, WAL, PAX blocks, HNSW
Benchmarks — SIFT-1M recall, write throughput, latency
Database Comparison — GalaxDB vs PostgreSQL, Pinecone, Qdrant, LanceDB, ChromaDB, Milvus, DuckDB, Weaviate
Research Paper — GalaxDB: A Unified AI-Native Storage Engine for Transactional, Analytical, and Vector Workloads

Contributing

See CONTRIBUTING.md. Open an issue first for large changes. All PRs must pass the full test suite and three CI gates (no mocks, no vendor SDKs, task tracker).

License

Apache 2.0 — see LICENSE.

_{Built by Zentrix Innovative Labs}

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