Repository
AliSQL
AliSQL is a MySQL branch originated from Alibaba Group. Fetch document from Release Notes at bottom.
- Feature
- Description
- DuckDB Storage Engine
- DuckDB Storage Engine
- Vector Index (VIDX)
- Vector Index (VIDX)
- DDL Optimization
- DDL Optimization
Specs
- FeatureDescription
- DuckDB Storage EngineDuckDB Storage Engine
- Vector Index (VIDX)Vector Index (VIDX)
- DDL OptimizationDDL Optimization
- RTO OptimizationRTO Optimization
- Replication BoostReplication Boost
- OptionDescription
- -t release\|debugBuild type
- -d <dir>Installation directory
- -g asan\|tsanEnable sanitizer (memory/thread)
- -cEnable code coverage (gcov)
- DocumentDescription
- DuckDB Integration GuideComplete guide for DuckDB storage engine
- Vector Index GuideNative vector storage and ANN search
- Release NotesWhat's new in AliSQL 8.0.44
- Setup DuckDB NodeQuick setup guide for analytics
README
<h1 align="center">AliSQL</h1>
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
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.
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200x Faster Analytics
DuckDB columnar engine delivers 200x speedup on analytical queries compared to InnoDB
</td>
<td width="33%" align="center">
Native Vector Search
Built-in HNSW algorithm supporting up to 16,383 dimensions for AI/ML workloads
</td>
<td width="33%" align="center">
100% MySQL Compatible
Use your existing MySQL tools, drivers, and SQL — zero learning curve
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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:
Contributing
AliSQL became open source in December 2025 and is actively maintained by Alibaba Cloud Database Team.
We welcome contributions of all kinds!
- Fork the repository
- Create a feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - 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
Community & Support
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<tr>
<td align="center" width="50%">
GitHub Issues
For bug reports & feature requests
</td>
<td align="center" width="50%">
Alibaba Cloud RDS
Managed DuckDB analytical instances
</td>
</tr>
</table>
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
Why AliSQL?
1
-
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
1
-
DuckDB columnar engine delivers 200x speedup on analytical queries compared to InnoDB
Native Vector Search
1
-
Built-in HNSW algorithm supporting up to 16,383 dimensions for AI/ML workloads
100% MySQL Compatible
1
-
Use your existing MySQL tools, drivers, and SQL — zero learning curve
Key Features
1
-
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
Option 1: Build from Source
1
-
# 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
1
Initialize & Start Server
1
-
# Initialize data directory ~/alisql/bin/mysqld --initialize-insecure --datadir=~/alisql/data # Start the server ~/alisql/bin/mysqld --datadir=~/alisql/data
DuckDB for Analytics
1
-
-- 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
1
-
-- 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
1
-
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
Roadmap
1
-
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
7
Contributing
1
-
AliSQL became open source in December 2025 and is actively maintained by Alibaba Cloud Database Team.
RDSAI CLI — AI-Powered Database Assistant
1
Community & Support
1
-
<table> <tr> <td align="center" width="50%">
License
2
-
AliSQL is licensed under GPL-2.0 , the same license as MySQL.
Star History
4