Stackhouse Product Overview

What is Stackhouse?

Stackhouse is an open-source, AI-native database that combines the flexibility of NoSQL with the power of SQL, vector search, and serverless compute — all in one unified system. Built in Rust, Stackhouse serves as a production-ready alternative to Supabase for modern applications requiring real-time capabilities and AI integration.

The Problem We Solve

Traditional database architectures force developers to cobble together multiple services:

Result: Stackhouse reduces infrastructure complexity from 5+ services to 1, cutting operational overhead and latency while improving developer velocity.

Core Capabilities

1. Schema-Later™ Data Store

Eliminate schema migrations and downtime. Stackhouse automatically evolves your schema as your data grows.

// Day 1: Simple document
await db.push('users', { name: 'Alice', age: 25 });

// Day 30: Add new fields — no migration needed
await db.push('users', { 
  name: 'Bob', 
  email: 'bob@example.com',
  preferences: { theme: 'dark', notifications: true }
});

Benefits:

• Zero downtime schema changes
• No migration scripts to write or maintain
• Handles nested JSON structures natively
• Development stays fast as your app grows

2. AI-Native Vector Search

Semantic search built into the core, powered by Qdrant's HNSW algorithm. No external vector database required.

// Store document with embedding
await db.vectors.insert('documents', {
  id: 'doc1',
  vector: [0.1, 0.2, 0.3, ...],  // 384+ dimensional embedding
  metadata: { title: 'Introduction to Stackhouse' }
});

// Semantic search
const results = await db.vectors.search('documents', {
  query: embedding,
  k: 10,
  filter: { category: 'tutorials' }
});

Use Cases:

• RAG (Retrieval-Augmented Generation) pipelines
• Semantic document search
• Recommendation engines
• Similarity matching
• Image search (with vision embeddings)

3. Realtime 2.0

Bidirectional live data subscriptions via WebSocket or Server-Sent Events.

const ws = new WebSocket('ws://api.stackhouse.io/v1/realtime');

ws.send(JSON.stringify({
  type: 'subscribe',
  table: 'users',
  event: '*'
}));

ws.onmessage = (event) => {
  const msg = JSON.parse(event.data);
  console.log('Live update:', msg.type, msg.record ?? msg.old_record);
};

Features:

• Sub-millisecond latency for live updates
• Multiple subscriptions per connection
• Automatic reconnection handling
• Scales to millions of concurrent connections

4. Serverless JavaScript Functions

Deploy custom business logic as JavaScript/TypeScript source and execute it via the embedded Boa engine.

# Deploy a JavaScript function
curl -X POST /v1/functions/deploy \
  -H "Content-Type: application/json" \
  -d '{
    "name": "process",
    "runtime": "javascript",
    "source_code": "exports.handler = (input) => ({ doubled: input.value * 2 });"
  }'

# Execute with data
curl -X POST /v1/functions/invoke/process \
  -d '{"value": 21}'

Benefits:

• Sandboxed execution with resource limits
• Fast cold-start times
• JavaScript/TypeScript via the embedded Boa engine
• A multi-language WASM runtime is a future roadmap item

5. PostgreSQL-Backed Storage

Stackhouse's data layer is PostgreSQL, managed through sqlx (stackhouse/src/platform/db.rs). There is no separate custom LSM storage engine in this codebase — durability, crash-safety, and WAL behavior come from Postgres itself. "Schema-Later" is implemented as dynamic DDL generation over standard Postgres tables driven by incoming JSON shape, not an alternate storage format.

6. Enterprise Security

Defense-in-depth security architecture:

API Overview

Data Operations

POST   /v1/push/:collection         # Insert document
GET    /v1/query/:collection        # Query with filters
POST   /v1/update/:collection/:id   # Update document
POST   /v1/delete/:collection/:id   # Delete document

Vector Search (proxied to Qdrant)

POST   /v1/vectors/:collection/upsert   # Upsert vector(s)
POST   /v1/vectors/:collection/search   # Semantic search
GET    /v1/vectors/:collection/info     # Collection info

There is no list-all-collections endpoint (no GET /v1/vectors).

Functions — implemented, NOT currently reachable

POST   /v1/functions/deploy         # Deploy function
POST   /v1/functions/invoke/:name   # Execute function
GET    /v1/functions               # List functions

This router exists in compute/functions.rs but is never mounted in main.rs — none of these endpoints respond over HTTP in the current build.

Realtime

WS     /v1/realtime                 # WebSocket connection
GET    /v1/stream/:collection       # SSE stream

Scale Targets

Stackhouse is designed to scale from prototype to enterprise:

Technology Stack

Use Cases

1. AI-Powered Applications

Build RAG pipelines, chatbots, and recommendation systems with native vector search.

2. Realtime Collaboration

Live document editing, collaborative whiteboards, multiplayer games.

3. Modern Web Apps

Rapid prototyping with automatic schema evolution that scales to production.

4. Content Management

Semantic search across documents, images, and multimedia content.

5. E-commerce

Personalized recommendations, visual search, real-time inventory.

Quick Start

# Clone and build
git clone https://github.com/ArjavDesa912/stackhouse.git
cd stackhouse-stack/stackhouse

# Requires a reachable Postgres and a JWT secret — there is no default
STACKHOUSE_URL=postgres://postgres:password@localhost:5432/stackhouse \
STACKHOUSE_JWT_SECRET=some-long-dev-secret \
cargo run --release -- serve

# Insert your first document
curl -X POST http://localhost:3000/v1/push/users \
  -H "Content-Type: application/json" \
  -d '{"name": "Alice", "email": "alice@example.com"}'

# Query it
curl http://localhost:3000/v1/query/users

Related Documentation

• System Design — Enterprise architecture for 100K–10M users
• API Reference — Complete REST API documentation
• Security — Security controls and hardening status

License

MIT License — see LICENSE for details.

Stackhouse — Schema-Later • AI-Native • Realtime