I’m grateful to all my professional and personal networks for this year. It has been full of tears, sweat, and blood all over my face once again. Let’s not worry about that. I want to start with a big Thank You to all of you who made this year possible.
If I look back at what stood out in 2025, just before we hit 2026.
Oracle ACE Pro
I was thrilled to be nominated to the Oracle ACE Program as an ACE Pro in April. This recognition opened doors to launch a technical blog series on vector search and AI integration with MySQL.
Project Antalya at Altinity, Inc.
We announced native Iceberg catalog and Parquet support on S3 for ClickHouse. This pushes the boundaries of what’s possible with open lakehouse analytics.
MySQL MCP Server
Introduced a lightweight, secure MySQL MCP server bridging relational databases and LLMs. Practical AI integration starts with safety and observability.
FOSDEM & MySQL’s 30th Birthday
I have one of my busiest agendas in ten years. It includes the MySQL Devroom Committee, a talk, and an O’Reilly book signing for #mysqlcookbook4e. Additionally, there are 6 talks from Altinity.
O’Reilly Recognition
After 50+ hours of flights for conferences, I came home to O’Reilly’s all-time recognition for the MySQL Cookbook. It was a moment I won’t forget.
Sailing While Working
Once again, months at sea with salt, humidity, and wind were challenging. We handled tickets, RCAs, and meetings. We even recorded a podcast on ferry maneuvering. Born to sail, forced to work, making it work anyway.
I am immensely grateful to the #MySQL, #ClickHouse, and #opensource communities. Thank you to my co-authors Sveta Smirnova and Ibrar Ahmed. I also thank my nominator, Vinicius Grippa. I appreciate the Altinity team and every conference organizer who gave me a stage this year.
Recognition is an invitation to contribute more, not a finish line. Looking forward to more open-source collaboration in 2026.
If you’re passionate about open-source databases, MySQL, ClickHouse, or AI integration, or just want to connect, reach out.
A lightweight, secure, and extensible MCP (Model Context Protocol) server for MySQL designed to bridge the gap between relational databases and large language models (LLMs).
I’m releasing a new open-source project: mysql-mcp-server, a lightweight server that connects MySQL to AI tools via the Model Context Protocol (MCP). It’s designed to make MySQL safely accessible to language models, structured, read-only, and fully auditable.
This project started out of a practical need: as LLMs become part of everyday development workflows, there’s growing interest in using them to explore database schemas, write queries, or inspect real data. But exposing production databases directly to AI tools is a risk, especially without guardrails.
mysql-mcp-server offers a simple, secure solution. It provides a minimal but powerful MCP server that speaks directly to MySQL, while enforcing safety, observability, and structure.
What it does
mysql-mcp-server allows tools that speak MC, such as Claude Desktop, to interact with MySQL in a controlled, read-only environment. It currently supports:
Listing databases, tables, and columns
Describing table schemas
Running parameterized SELECT queries with row limits
Running as either a local MCP-compatible binary or a remote REST API server
By default, it rejects any unsafe operations such as INSERT, UPDATE, or DROP. The goal is to make the server safe enough to be used locally or in shared environments without unintended side effects.
Why this matters
As more developers, analysts, and teams adopt LLMs for querying and documentation, there’s a gap between conversational interfaces and real database systems. Model Context Protocol helps bridge that gap by defining a set of safe, predictable tools that LLMs can use.
mysql-mcp-server brings that model to MySQL in a way that respects production safety while enabling exploration, inspection, and prototyping. It’s helpful in local development, devops workflows, support diagnostics, and even hybrid RAG scenarios when paired with a vector index.
Getting started
You can run it with Docker:
docker run -e MYSQL_DSN='user:pass@tcp(mysql-host:3306)/' \
-p 7788:7788 ghcr.io/askdba/mysql-mcp-server:latest
In Part 1, we introduced the MyVector plugin — a native extension that brings vector embeddings and HNSW-based approximate nearest neighbor (ANN) search into MySQL. We covered how MyVector supports scoped queries (e.g., WHERE user_id = X) to ensure that semantic search remains relevant, performant, and secure in real-world multi-tenant applications.
Now in Part 2, we move from concept to implementation:
How to store and index embeddings
How to design embedding workflows
How hybrid (vector + keyword) search works
How HNSW compares to brute-force search
How to tune for performance at scale
1. Schema Design for Vector Search
The first step is designing tables that support both structured and semantic data.
A typical schema looks like:
CREATE TABLE documents (
id BIGINT PRIMARY KEY,
user_id INT NOT NULL,
title TEXT,
body TEXT,
embedding VECTOR(384),
INDEX(embedding) VECTOR
);
Design tips:
Use VECTOR(n) to store dense embeddings (e.g., 384-dim for MiniLM).
Always combine vector queries with SQL filtering (WHERE user_id = …, category = …) to scope the search space.
Use TEXT or JSON fields for hybrid or metadata-driven filtering.
Consider separating raw text from embedding storage for cleaner pipelines.
2. Embedding Pipelines: Where and When to Embed
MyVector doesn’t generate embeddings — it stores and indexes them. You’ll need to decide how embeddings are generated and updated:
a. Offline (batch) embedding
Run scheduled jobs (e.g., nightly) to embed new rows.
Suitable for static content (documents, articles).
Can be run using Python + HuggingFace, OpenAI, etc.
# Python example
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("all-MiniLM-L6-v2")
vectors = model.encode(["Your text goes here"])
b. Write-time embedding
Embed text when inserted via your application.
Ensures embeddings are available immediately.
Good for chat apps, support tickets, and notes.
c. Query-time embedding
Used for user search input only.
Transforms search terms into vectors (not stored).
Passed into queries like:
ORDER BY L2_DISTANCE(embedding, '[query_vector]') ASC
3. Hybrid Search: Combine Text and Semantics
Most real-world search stacks benefit from combining keyword and vector search. MyVector enables this inside a single query:
SELECT id, title
FROM documents
WHERE MATCH(title, body) AGAINST('project deadline')
AND user_id = 42
ORDER BY L2_DISTANCE(embedding, EMBED('deadline next week')) ASC
LIMIT 5;
This lets you:
Narrow results using lexical filters
Re-rank them semantically
All in MySQL — no sync to external vector DBs
This hybrid model is ideal for support systems, chatbots, documentation search, and QA systems.
4. Brute-Force vs. HNSW Indexing in MyVector
When it comes to similarity search, how you search impacts how fast you scale.
Brute-force search
Compares the query against every row
Guarantees exact results (100% recall)
Simple but slow for >10K rows
SELECT id
FROM documents
ORDER BY COSINE_DISTANCE(embedding, '[query_vector]') ASC
LIMIT 5;
HNSW: Hierarchical Navigable Small World
Graph-based ANN algorithm used by MyVector
Fast and memory-efficient
High recall (~90–99%) with tunable parameters (ef_search, M)
CREATE INDEX idx_vec ON documents(embedding) VECTOR
COMMENT='{"HNSW_M": 32, "HNSW_EF_CONSTRUCTION": 200}';
Comparison
Feature
Brute Force
HNSW (MyVector)
Recall
✅ 100%
🔁 ~90–99%
Latency (1M rows)
❌ 100–800ms+
✅ ~5–20ms
Indexing
❌ None
✅ Required
Filtering Support
✅ Yes
✅ Yes
Ideal Use Case
Small datasets
Production search
5. Scoped Search as a Security Boundary
Because MyVector supports native SQL filtering, you can enforce access boundaries without separate vector security layers.
Patterns:
WHERE user_id = ? → personal search
WHERE org_id = ? → tenant isolation
Use views or stored procedures to enforce access policies
You don’t need to bolt access control onto your search engine — MySQL already knows your users.
6. HNSW Tuning for Performance
MyVector lets you tune index behavior at build or runtime:
Param
Purpose
Effect
M
Graph connectivity
Higher = more accuracy + RAM
ef_search
Traversal breadth during queries
Higher = better recall, more latency
ef_construction
Index quality at build time
Affects accuracy and build cost
Example:
ALTER INDEX idx_vec SET HNSW_M = 32, HNSW_EF_SEARCH = 100;
You can also control ef_search per session or per query soon (planned feature).
TL;DR: Production Patterns with MyVector
Use VECTOR(n) columns and HNSW indexing for fast ANN search
Embed externally using HuggingFace, OpenAI, Cohere, etc.
Combine text filtering + vector ranking for hybrid search
Use SQL filtering to scope vector search for performance and privacy
Tune ef_search and M to control latency vs. accuracy
Coming Up in Part 3
In Part 3, we’ll explore real-world implementations:
Semantic search
Real-time document recall
Chat message memory + re-ranking
Integrating MyVector into RAG and AI workflows
We’ll also show query plans and explain fallbacks when HNSW is disabled or brute-force is needed.
Semantic Search with SQL Simplicity and Operational Control
Introduction
Vector search is redefining how we work with unstructured and semantic data. Until recently, integrating it into traditional relational databases like MySQL required external services, extra infrastructure, or awkward workarounds. That changes with the MyVector plugin — a native vector indexing and search extension purpose-built for MySQL.
Whether you’re enhancing search for user-generated content, improving recommendation systems, or building AI-driven assistants, MyVector makes it possible to store, index, and search vector embeddings directly inside MySQL — with full support for SQL syntax, indexing, and filtering.
What Is MyVector?
The MyVector plugin adds native support for vector data types and approximate nearest neighbor (ANN) indexes in MySQL. It allows you to:
Define VECTOR(n) columns to store dense embeddings (e.g., 384-dim from BERT)
Index them using INDEX(column) VECTOR, which builds an HNSW-based structure
Run fast semantic queries using distance functions like L2_DISTANCE, COSINE_DISTANCE, and INNER_PRODUCT
Use full SQL syntax to filter, join, and paginate vector results alongside traditional columns
By leveraging HNSW, MyVector delivers millisecond-level ANN queries even with millions of rows — all from within MySQL.
Most importantly, it integrates directly into your existing MySQL setup—there is no new stack, no sync jobs, and no third-party dependencies.
Scoped Vector Search: The Real-World Requirement
In most production applications, you rarely want to search across all data. You need to scope vector comparisons to a subset — a single user’s data, a tenant’s records, or a relevant tag.
MyVector makes this easy by combining vector operations with standard SQL filters.
Under the Hood: HNSW and Query Performance
MyVector uses the HNSW algorithm for vector indexing. HNSW constructs a multi-layered proximity graph that enables extremely fast approximate nearest neighbor search with high recall. Key properties:
Logarithmic traversal through layers reduces search time
Dynamic index support: you can insert/update/delete vectors and reindex as needed
Configurable parameters like M and ef_search allow tuning for performance vs. accuracy
Under the Hood: HNSW and Query Performance
MyVector uses the HNSW algorithm for vector indexing. HNSW constructs a multi-layered proximity graph that enables extremely fast approximate nearest neighbor search with high recall. Key properties:
Fast ANN queries without external services
Scoped filtering before vector comparison
Logarithmic traversal through layers reduces search time
Dynamic index support: you can insert/update/delete vectors and reindex as needed
Configurable parameters like M and ef_search allow tuning for performance vs. accuracy
What’s Next
This post introduces the foundational concept of scoped vector search using MyVector and HNSW. In Part II, we’ll walk through practical schema design patterns, embedding workflows, and hybrid search strategies that combine traditional full-text matching with deep semantic understanding — using nothing but SQL.
I’m incredibly honored to share some exciting news—I’ve been recognized as an Oracle ACE Pro by Oracle!
This recognition is deeply meaningful to me, not just as a personal milestone but as a reflection of the ongoing work I’ve poured into the database community for over three decades. It’s also a reminder of how powerful open collaboration, curiosity, and mentorship can be in shaping both a career and a community.
What Is the Oracle ACE Program?
For those unfamiliar, the Oracle ACE Program recognizes individuals who are not only technically skilled but also passionate about sharing their knowledge with the wider community. It celebrates those who contribute through blogging, speaking, writing, mentoring, and engaging in forums or user groups.
The program has multiple tiers: ACE Associate, Oracle ACE, ACE Pro, and ACE Director. Each level reflects a growing commitment to community contribution and leadership. Being named an Oracle ACE Pro places me among a diverse, global group of technologists who are actively shaping the future of Oracle technologies—and open-source ecosystems alongside them.
From MySQL to ACE: A Journey Rooted in Community
My journey with data began over three decades ago, and it’s taken me across continents, companies, and countless events. My early days were steeped in MySQL—performance tuning, operations, scaling architectures—and I quickly discovered that the greatest impact didn’t come from just solving problems, but from sharing the solutions.
Since then, my path has included global roles in consulting, support, and engineering leadership. I’ve had the opportunity to speak at international conferences, publish books like the MySQL Cookbook (4th Edition), and contribute to countless community efforts in the MySQL and opensource database ecosystems.
Recognition such as Most Influential in the Database Community (Redgate 100) and MySQL Rockstar have meant a lot—but being named an Oracle ACE Pro is especially meaningful. It represents a bridge between the worlds of open source and enterprise and affirms that collaboration across ecosystems is not only possible—it’s essential.
What This Recognition Means to Me
This isn’t just about a title or a badge. To me, becoming an Oracle ACE Pro is about continuing the mission—to share what I’ve learned, amplify others doing amazing work, and give back to the communities that have shaped my path.
I’ve always believed that technical excellence must go hand in hand with generosity. Whether it’s mentoring a young DBA, helping a team scale their architecture, or writing about real-world database design challenges, the point has never been visibility—it’s always been about value.
And that’s what this recognition reflects: not just what I’ve done, but what I hope to keep doing for the next generation of data professionals.
Looking Ahead
This milestone energizes me even more to keep contributing—not just within the Oracle ecosystem but across the open-source database space. I’ll continue speaking at events, writing, mentoring, and building resources that help engineers build better, faster, and more resilient systems.
I’m also excited about promoting hybrid data architectures combining MySQL, opensource, and cloud-native technologies. This is where the industry is heading, and I’m committed to helping folks navigate that evolving landscape with clarity and confidence.
Gratitude and Community
I want to thank Oracle for running a program that not only recognizes technical contributions, but also community-driven spirit. And a heartfelt thank you to the MySQL community, open-source contributors, and peers I’ve had the privilege of working alongside over the years.
You’ve all helped shape my thinking, my work, and my growth. I stand on the shoulders of a global community, and this milestone belongs to all of us.
Let’s Stay Connected
If you’re building something, learning something, or just curious about databases, I’d love to hear from you. Whether it’s MySQL performance, opensource design, or data architecture strategy, reach out. Let’s keep learning, building, and sharing—together.
And if you’re interested in becoming part of the Oracle ACE community, feel free to ping me. I’m always happy to share what I’ve learned and help others navigate that journey.
A Note About What’s Coming
As part of my role and responsibilities as an Oracle ACE Pro, I’ll be launching a new series of technical blog posts in the coming months. These will explore cutting-edge topics including:
• AI/ML and LLMs (Large Language Models)
• Vector Search and database integration
• Real-world use cases at the intersection of AI and relational databases
These areas are rapidly evolving, and I’m excited to share practical, hands-on insights on how they tie into modern data architecture—especially within the Oracle and open-source ecosystems.
Disclaimer: The views and opinions I’ll be sharing in upcoming posts are my own and do not necessarily reflect those of Oracle or any other organization. Content will be independent, community-driven, and based on real-world experience.
Stay tuned—and if you have specific questions or topics you’d like to see covered, feel free to reach out!
Thanks for reading—and here’s to the next chapter in our database story.
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