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AliSQL Deep Dive: Alibaba’s Supercharged MySQL with DuckDB & Vector Search

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AliSQL Deep Dive: Alibaba’s MySQL Branch, Reshaping the Enterprise Database Experience

In the fast-evolving world of database technology, are you still grappling with the limitations of traditional MySQL in large-scale, high-performance scenarios? Do you long for a database solution that is both familiar and stable, yet packed with powerful analytical capabilities and modern features? Today, we will take an in-depth look at a critical project deeply customized and open-sourced by Alibaba – AliSQL. It is not merely a fork of MySQL; it is the crystallized wisdom forged in the crucible of ultra-large-scale production environments.

This article will provide you with a comprehensive understanding of AliSQL’s core features, its future roadmap, and how to get started quickly, offering a real, in-depth, and practical technical perspective.

Article Core Summary

AliSQL is Alibaba’s enterprise-grade database branch, deeply customized based on official MySQL 8.0.44. It integrates DuckDB as a native storage engine for lightweight analytics and has planned features like native vector processing supporting up to 16,383 dimensions, second-scale lock-free DDL, and end-to-end RTO optimization. It is designed to deliver extreme performance, stability, and functional extensions for large-scale applications.

What Exactly is AliSQL?

Simply put, AliSQL can be thought of as a “supercharged version” of MySQL. It originates from the official MySQL code, undergoing long-term, deep optimization and functional extension by Alibaba’s database team, and is widely used in production environments for core Alibaba Group businesses like Taobao, Tmall, and Alipay. This means you get not just an open-source database, but a mature product tested by the traffic peaks of one of the world’s top e-commerce platforms.

Its core objective is very clear: while maintaining perfect compatibility and user experience with MySQL, it aims to provide superior performance, higher stability, and richer functionality for enterprise-grade application scenarios characterized by large scale, high concurrency, and high availability.

Version Foundation: Based on MySQL 8.0.44

The current Long-Term Support (LTS) version of AliSQL is 8.0.44, which is entirely built upon the stable official release of MySQL 8.0.44. This ensures users can seamlessly access all the modern features of MySQL 8.0, such as window functions, Common Table Expressions (CTE), JSON enhancements, role-based management, while also enjoying the additional “performance and feature buffs” from Alibaba.

AliSQL’s Core Features: Beyond Compatibility

Revolutionary Feature: Integrated DuckDB Storage Engine

If you think AliSQL is just patching up MySQL, think again. It introduces a groundbreaking feature: integrating DuckDB as a native storage engine.

What does this mean for users?

  1. Unified Operational Experience: You don’t need to learn a new query language or management tools. You can create, query, and manage tables using the DuckDB engine with standard SQL statements, just like you would with ordinary MySQL tables (e.g., InnoDB tables). This significantly lowers the barrier to data analysis.
  2. Built-in Lightweight Analytical Power: DuckDB is renowned as the “SQLite for analytics,” famous for its excellent OLAP (Online Analytical Processing) performance. Through AliSQL, you can easily perform real-time analytical queries on your transactional data (InnoDB) within the same database instance, eliminating complex, high-latency ETL and data synchronization processes.
  3. Rapid Service Deployment: AliSQL makes deploying a service node with DuckDB capabilities exceptionally straightforward. This is an extremely attractive solution for scenarios that need to quickly inject ad-hoc querying, report generation, and other analytical capabilities into an application.

This integration marks the introduction of HTAP (Hybrid Transactional/Analytical Processing) capability in an exceptionally elegant way into the classic MySQL ecosystem.

The Future Blueprint: AliSQL’s Evolution Roadmap

AliSQL’s planned features clearly point to the forefront of modern database technology. The following planned functionalities demonstrate its commitment to solving deep-seated pain points in enterprise applications.

1. Vector Storage: Enabling Databases to “Understand” AI

In the era of explosive growth in AI applications, databases need to store and retrieve not just structured numbers and text, but also high-dimensional vectors (used to represent the semantics of text, images, audio) generated by AI models.

  • Quantifiable Metric: AliSQL plans to natively support enterprise-grade vector processing for up to 16,383 dimensions. This dimension is sufficient for the vast majority of complex embedding models.
  • High-Performance Retrieval: By integrating a highly optimized HNSW (Hierarchical Navigable Small World) algorithm, AliSQL aims to provide ultra-fast Approximate Nearest Neighbor (ANN) search, which is core to building AI-driven applications like semantic search, personalized recommendation, and image retrieval.
  • Seamless Developer Experience: Most excitingly, developers can perform these AI vector operations entirely using standard SQL interfaces. This means you could implement intelligent recommendations with a familiar query like SELECT … ORDER BY vector_distance(…) LIMIT 10, greatly simplifying the development stack for AI applications.

2. DDL Optimization: Ending “Change Fear”

Performing online schema changes (DDL) has long been a nightmare for DBAs, especially on tables with hundreds of GB or even TB of data. Traditional ALTER TABLE operations can lead to prolonged table locking, replication lag, and ultimately, business impact.

AliSQL’s DDL optimization strategy aims to fundamentally change this landscape:

  • Enhanced Instant DDL: Enables more types of schema changes to be completed truly “instantly.”
  • Parallel B+tree Construction: Accelerates index creation processes.
  • Non-blocking Lock Mechanism & Real-time DDL Apply: Minimizes impact on live operations and virtually eliminates replication lag caused by DDL. This will make database schema iteration as agile and safe as deploying application code.

3. RTO Optimization: Drastically Reducing Failure Recovery Time

RTO (Recovery Time Objective) is a key metric for measuring database reliability. AliSQL plans deep optimization of the end-to-end recovery path after an instance crash.

  • Clear Objective: Accelerate the instance startup process, significantly shorten RTO, and ensure services can be quickly restored after a failure. This is crucial for enterprises pursuing high availability and business continuity.

4. Replication Optimization: Ensuring a Data “Highway”

In master-replica replication architectures, large transactions or DDL operations are prone to causing replication lag. AliSQL’s replication optimization plan employs several technological innovations to ensure smooth data synchronization:

  • Binlog Parallel Flush: Increases log write throughput.
  • Binlog in Redo: Optimizes the logging mechanism.
  • Specialized Optimizations for Large Transactions and DDL: Work together to significantly boost replication throughput and minimize lag.

Hands-On Guide: How to Get Started with AliSQL Quickly?

Now that you understand AliSQL’s powerful capabilities, you might be eager to try it. Next, we provide a clear, verifiable build and installation guide.

Prerequisites

Before starting the compilation, ensure your system meets the following basic requirements, which are explicitly listed in the source file:

  • Build Tool: CMake version 3.x or higher.
  • Scripting Language: Python3.
  • Compiler: A compiler supporting the C++17 standard, such as GCC 7+ or Clang 5+.

Step-by-Step Build and Installation Tutorial

Here is the complete process based on the official build.sh script:

# Step 1: Obtain the Source Code
# Clone the official AliSQL repository to your local machine
git clone https://github.com/alibaba/AliSQL.git
# Navigate into the project directory
cd AliSQL

# Step 2: Execute the Build
# Option A: Build a release version for production environments
# Use -t to specify the build type as 'release', -d to specify the installation directory
sh build.sh -t release -d /your/custom/install/path

# Option B: Build a version for development and debugging
sh build.sh -t debug -d /your/custom/install/path

# Step 3: Install
# After the build completes, run 'make install' to install the server to the directory specified in the previous step
make install

Build Script Options Explained

To make the build process more flexible, the build.sh script provides several parameters you can combine according to your needs:

  • -t release|debug: This is the most important option, determining the build type. release is the optimized production version, while debug includes debugging information for development.
  • -d <dest_dir>: Specifies the installation directory. If not specified, it defaults to /usr/local/alisql or the current user’s $HOME/alisql directory.
  • -s <server_suffix>: Adds a suffix to the installed server executable, facilitating coexistence of multiple versions (e.g., mysqld-alisql-dev).
  • -g asan|tsan: Enables advanced debugging tools; asan detects memory errors, tsan detects thread data races.
  • -c: Enables the GCC code coverage testing tool (gcov).
  • -h, --help: Displays the complete help information.

Frequently Asked Questions (FAQ)

To present information more clearly, we’ve organized several key questions in a Q&A format:

Q1: What is the relationship between AliSQL and official MySQL? Can I migrate seamlessly?
A: AliSQL is a branch (fork) of MySQL, fully compatible with MySQL’s protocol, syntax, and clients. For the vast majority of applications, migrating from MySQL to AliSQL can be smooth and seamless. Your existing code, drivers, and operational tools can continue to be used. The most significant difference is that you gain additional performance enhancements and extended features.

Q2: After integrating the DuckDB engine, how do I use it?
A: The usage is very straightforward. When creating a table, simply specify the storage engine as ENGINE = DUCKDB. For example:

CREATE TABLE my_analytics_table (
    id BIGINT,
    data VARCHAR(255),
    metric DOUBLE
) ENGINE = DUCKDB;

Afterward, you can perform complex analytical queries on it using SQL, just like querying a regular MySQL table. AliSQL handles all the underlying integration details.

Q3: When will I be able to use the “planned” features like Vector Storage and DDL Optimization?
A: The Vector Storage, DDL Optimization, RTO Optimization, and Replication Optimization mentioned in the article are all planned features on AliSQL’s public roadmap. They represent the core direction of the project’s development. For specific release timelines of these features, it is recommended to follow the official AliSQL GitHub repository release page and changelogs. These plans demonstrate AliSQL’s commitment to solving core enterprise pain points.

Q4: Where can I get support if I encounter issues?
A: AliSQL has an active open-source community and commercial support channels:

  • Open Source Community: You can submit bug reports or feature requests via GitHub Issues. The project is actively maintained by engineers at Alibaba Group.
  • Commercial Product: If you are using the Alibaba Cloud RDS for MySQL service, you can opt for the “DuckDB-based Analytical Instance” instance, which comes with a fully managed, enterprise-grade service and support.
  • Regarding DuckDB: For in-depth questions about the DuckDB storage engine itself, you can refer to its official support options.

Q5: I want to contribute code to AliSQL. How should I proceed?
A: Contributions are welcome! AliSQL has been fully open-source since December 2025. The standard contribution process is as follows:

  1. Fork the official repository to your GitHub account.
  2. Create a new feature branch based on your forked repository.
  3. Make your code changes on this branch, ensuring appropriate tests are added or pass.
  4. Submit a Pull Request (PR) to the official repository and wait for review by the core maintainers.

Conclusion: Why Choose AliSQL?

Through this deep dive, we can clearly see AliSQL’s value proposition. It is not an experimental project but a production-grade solution validated by ultra-large-scale, ultra-high-concurrency business scenarios.

Choosing AliSQL means choosing a:

  • Proven Stable Foundation: Inherits all the strengths of MySQL, augmented by Alibaba’s extreme optimization experience.
  • Future-Oriented Architecture: Prepares for HTAP and AI-Native database capabilities by integrating DuckDB and planning vector processing.
  • Pursuit of Extreme Performance: From DDL and replication to failure recovery, every optimization targets the core performance bottlenecks of enterprise applications.
  • Open and Trustworthy: Open-sourced under the GPL-2.0 license, with a transparent development process and an active community, offering a clear and credible technical roadmap.

Whether you aim to push the performance limits of your existing MySQL clusters or explore the new possibilities of built-in analytics and AI vector search, AliSQL presents a highly attractive starting point. It proves that through continuous, deep innovation, classic database kernels can still exhibit powerful vitality in the cloud and AI era.

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