AliSQL

Alibaba's Enterprise MySQL Branch with DuckDB OLAP & Native Vector Search

Battle-tested in Alibaba's production environment, powering millions of databases

Features • Quick Start • Docs • Roadmap • Contributing

简体中文 | English

Why AliSQL?

AliSQL brings enterprise-grade capabilities to MySQL, combining the reliability of InnoDB OLTP with DuckDB's blazing-fast analytics and native vector search — all through familiar MySQL interfaces.

200x Faster Analytics DuckDB columnar engine delivers 200x speedup on analytical queries compared to InnoDB

Native Vector Search Built-in HNSW algorithm supporting up to 16,383 dimensions for AI/ML workloads

100% MySQL Compatible Use your existing MySQL tools, drivers, and SQL — zero learning curve

Key Features

Feature	Description	Status
DuckDB Storage Engine	Columnar OLAP engine with automatic compression, perfect for analytics workloads	Available
Vector Index (VIDX)	Native vector storage & ANN search with HNSW, supports COSINE & EUCLIDEAN distance	Available
DDL Optimization	Instant DDL, parallel B+tree construction, non-blocking locks	Planned
RTO Optimization	Accelerated crash recovery for faster instance startup	Planned
Replication Boost	Binlog Parallel Flush, Binlog in Redo, large transaction optimization	Planned

Quick Start

Option 1: Build from Source

# Clone the repository
git clone https://github.com/alibaba/AliSQL.git
cd AliSQL

# Build (release mode)
sh build.sh -t release -d ~/alisql

# Install
make install

Option 2: Set Up a DuckDB Analytical Node

Step-by-step guide: How to set up a DuckDB node

Initialize & Start Server

# Initialize data directory
~/alisql/bin/mysqld --initialize-insecure --datadir=~/alisql/data

# Start the server
~/alisql/bin/mysqld --datadir=~/alisql/data

Usage Examples

DuckDB for Analytics

-- Create an analytical table with DuckDB engine
CREATE TABLE sales_analytics (
    sale_date DATE,
    product_id INT,
    revenue DECIMAL(10,2),
    quantity INT
) ENGINE=DuckDB;

-- Run complex analytics (200x faster than InnoDB!)
SELECT
    DATE_FORMAT(sale_date, '%Y-%m') as month,
    SUM(revenue) as total_revenue,
    COUNT(*) as transactions
FROM sales_analytics
GROUP BY month
ORDER BY total_revenue DESC;

Vector Search for AI Applications

-- Create a table with vector column
CREATE TABLE embeddings (
    id INT PRIMARY KEY,
    content TEXT,
    embedding VECTOR(768)  -- 768-dimensional vectors
) ENGINE=InnoDB;

-- Create HNSW index for fast ANN search
CREATE VECTOR INDEX idx_embedding ON embeddings(embedding);

-- Find similar items using cosine distance
SELECT id, content,
       COSINE_DISTANCE(embedding, '[0.1, 0.2, ...]') as distance
FROM embeddings
ORDER BY distance
LIMIT 10;

Build Options

Option	Description	Default
-t release|debug	Build type	debug
-d <dir>	Installation directory	/usr/local/alisql
-g asan|tsan	Enable sanitizer (memory/thread)	disabled
-c	Enable code coverage (gcov)	disabled

Prerequisites: CMake 3.x+, Python 3, GCC 7+ or Clang 5+

Roadmap

Q4 2025  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
         [x] DuckDB Storage Engine  [x] Vector Index (VIDX)   [x] Open Source

2026     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
         [ ] DDL Optimization       [ ] RTO Optimization      [ ] Replication Boost
             - Instant DDL              - Fast Crash Recovery     - Binlog Parallel Flush
             - Parallel B+tree          - Minimize RTO            - Binlog in Redo
             - Non-blocking Locks                                 - Large TX Optimization

Documentation

Document	Description
DuckDB Integration Guide	Complete guide for DuckDB storage engine
Vector Index Guide	Native vector storage and ANN search
Release Notes	What's new in AliSQL 8.0.44
Setup DuckDB Node	Quick setup guide for analytics

External Resources:

• MySQL 8.0 Documentation
• DuckDB Official Docs
• Detailed Article (Chinese)

Contributing

AliSQL became open source in December 2025 and is actively maintained by Alibaba Cloud Database Team.

We welcome contributions of all kinds!

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

For bugs and feature requests, please use GitHub Issues.

Related Tools

RDSAI CLI — AI-Powered Database Assistant

RDSAI CLI is a next-generation, AI-powered CLI that transforms how you interact with AliSQL and MySQL databases. Describe your intent in natural language, and the AI agent handles the rest.

# Install
curl -LsSf https://raw.githubusercontent.com/aliyun/rdsai-cli/main/install.sh | sh

# Connect and ask in natural language
rdsai --host localhost -u root -p secret -D mydb
mysql> analyze index usage on users table
mysql> show me slow queries from the last hour
mysql> why this query is slow: SELECT * FROM users WHERE name LIKE '%john%'

Key Features:

• Natural language to SQL conversion (English/中文)
• AI-powered query optimization and diagnostics
• Execution plan analysis with Ctrl+E
• Multi-model LLM support (Qwen, OpenAI, DeepSeek, Anthropic, etc.)
• Performance benchmarking with automated analysis

👉 Get Started with RDSAI CLI

Community & Support

GitHub Issues For bug reports & feature requests Open an Issue

Alibaba Cloud RDS Managed DuckDB analytical instances Learn More

License

AliSQL is licensed under GPL-2.0, the same license as MySQL.

See the LICENSE file for details.

Star History

Made with care by Alibaba Cloud Database Team

GitHub • MySQL • DuckDB