In a recent post, The Quirks of Index Maintenance in Open Source Databases, I compared the IO load generated by open source databases while inserting rows in a table with many secondary indexes. Because of its change buffer, InnoDB was the most efficient solution. However, that’s not the end of the story. Evolution of the […]

“ Of course, in practice, no query optimizer is perfect and there will be edge cases where the way a query is written will impact its performance.”

Q1. What are your current responsibilities as Principal Member of Technical staff?

Kaan Kara : I am contributing as the tech lead for query execution in HeatWave. My main responsibility is implementing new features in HeatWave, maintaining its stability, and supporting our customers with their HeatWave-related use cases.

Q2. Let´s talk about improving database query execution time. The way a query is written has a massive impact on its performance, and developers often face hurdles in structuring them optimally. What is your take on this?

Kaan Kara : SQL is a declarative language. That means, in ideal terms, the database optimizer should produce the best …

Announcing Vitess 23.0.0 # We’re excited to release Vitess 23.0.0 — the latest major version of Vitess — bringing new defaults, better operational tooling, and refined metrics. This release builds on the strong foundation of version 22 and is designed to make deployment and observability smoother, while continuing to scale MySQL workloads horizontally with confidence. ✅ Why This Release Matters # For production users of Vitess, this release is meaningful in several ways:

Subtitle: Schema design, embedding workflows, hybrid search, and performance tradeoffs explained.

Quick Recap from Part 1

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
…

Data masking lets you hide sensitive fields (emails, credit-card numbers, job titles, etc.) while keeping data realistic for reporting, support, or testing. It is particularly useful when you collaborate with external entities and need to share your data for development reasons. You also need to protect your data and keep your customers’ privacy safe. Last […]

On October 10th, 2025, we released MySQL 9.5, the latest Innovation Release. As usual, we released bug fixes for 8.0 and 8.4 LTS, but this post focuses on the newest release. In this release, we can see contributions related to Connector J and Connector Net, as well as to different server categories. Connector / J […]

Explore the top 50 MySQL interview questions and answers fit both for newbies and experienced devs. Get prepared with real-world MySQL queries.

The post Top 50 MySQL Interview Questions and Answers for Every Skill Level  appeared first on Devart Blog.

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.