From Concepts to Production: Real-World Patterns, Query Plans, and What’s Next

In Part I, we introduced scoped vector search in MySQL using the MyVector plugin, focusing on how semantic similarity and SQL filtering work together.

In Part II, we explored schema design, embedding strategies, HNSW indexing, hybrid queries, and tuning — and closed with a promise to show real-world usage and execution behavior.

This final part completes the series.

Semantic Search with Explicit Scope

In real systems, semantic search is almost never global. Results must be filtered by tenant, user, or domain before ranking by …

I was able to get the MySQL MCP Server up and running and it was time to try it first time against a sample data set outside of its test suite.

Let’s start with Stack Overflow and the value of its data for modern LLMs and AI systems. As most of you already know, the questions in forums has significantly dropped since the launch of OpenAI, aka ChatGPT

Source : https://x.com/alojoh/status/2009366134513127462

This is probably true for traditional search engines like Google, although they’re already adopting built-in AI solutions. Other sources, such as Reddit, also provided valuable, quick, and easy information to the masses.

I wanted to spin a subset of StackExchange questions. These are loaded into a local MySQL server. I intended to put the MCP server to a …

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.

…

At Percona, our mission has always been to help you succeed with open source databases. We do that by listening to the community, understanding the challenges you face, and building the solutions you need. Now, after a comprehensive review of market trends and direct feedback from our customers and the MySQL community, we are excited […]

Bridging natural language processing with semi-structured data brings both opportunity and complexity.

MySQL HeatWave GenAI’s NL2SQL feature shows how natural language can simplify data interaction — even for JSON documents. Yet, because JSON embeds both data and metadata within a single column, LLMs may struggle without explicit schema cues.

By creating well-structured views that reveal JSON’s internal organization, you can transform unstructured data into a relational format the model understands — improving both query accuracy and overall usability.

This approach highlights how MySQL HeatWave continues to evolve as a powerful engine for intelligent, natural language–driven analytics.

The post Querying the Unstructured: Natural Language to SQL for JSON Data first appeared …

Having explored the innovative MySQL HeatWave technology that converts Natural Language into SQL (Ask Your Database Anything: Natural Language to SQL in MySQL HeatWave), our next article in this series, dives into a practical use case demonstrating how an AI DBA Assistant can significantly simplify your query generation workflow. In MySQL, there are 3 specialized […]

The post Let Your AI DBA Assistant Write Your MySQL Queries first appeared on dasini.net - Diary of a MySQL expert.

Natural Language to SQL in MySQL HeatWave makes data accessible to everyone, not just SQL experts. By turning plain English into optimized queries, it removes barriers and speeds up insights. While best practices help ensure accuracy, the feature already shows how LLMs are transforming the way we work with data. With the NL_SQL routine, asking your database questions is as simple as asking a colleague.

The post Ask Your Database Anything: Natural Language to SQL in MySQL HeatWave first appeared on dasini.net - Diary of a MySQL expert.

MySQL Analysis: With an AI-Powered CLI Tool

As DBAs with MySQL we often live on a Linux terminal window. We also enjoy free options when available. This post shows an approach that allows us to stay on our terminal window and still use an AI-powered tool. You can update to use other direct AI providers but I set this example up to use aimlapi.com as it brings multiple AI models to your terminal for free with limited use or very low cost for more testing.

Note: I'm not a paid spokesperson for AIMLAPI or anything - this is just an easy example to highlight the idea.

The Problem

You're looking at a legacy database with hundreds of tables, each with complex relationships and questionable design decisions made years ago. The usual process involves:

• Manual schema inspection
• Cross-referencing documentation (if it exists)
• Running multiple EXPLAIN queries
…

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 …

Yesterday I participated to the Oracle Technology Roundtable for Digital Natives in Zurich.

It was a good opportunity to learn more about AI, Cloud and HeatWave with the focus on very trendy features of this product: generative AI, machine learning, vector processing, analytics and transaction processing across data in Data Lake and MySQL databases.

It was also great to share moments with the Oracle and MySQL teams and meet customers which gave feedback and tips about their solutions already in place in this area.

I’ll try to summarize below some key take-away of each session.

Unlocking Innovation: How Oracle AI is Shaping the Future of Business (by Jürgen Wirtgen)

AI is not a new topic. But how do we …