PlanetScale has announced that vector support is now generally available. This feature allows vector data to be stored alongside relational MySQL data, eliminating the need for a separate vector database. The implementation is based on a fork of MySQL and includes advanced capabilities such as indexing vectors by Euclidean (L2), inner product, or cosine distance, and supports vectors up to 16,383 dimensions.

Patrick Reynolds, a software engineer at PlanetScale, stated:

"Since the open beta began, we have doubled query performance, improved memory efficiency eight times, and focused on robustness to make sure vector support is as solid as every other data type MySQL supports."

The architecture supports transactional operations, meaning that inserting, updating, and deleting vector data is immediately reflected in the index. This feature is integrated into MySQL's default storage engine, allowing for seamless interaction with queries, including JOIN and WHERE clauses.

For more details, refer to the official PlanetScale announcement and the Hacker News discussion.

Google Cloud Database Enhancements

At the Next 2025 conference, Google Cloud unveiled a variety of enhancements across its database offerings, particularly focusing on AI capabilities within AlloyDB. The integration of the open-source pgvector extension enables users to store vector embeddings directly in their databases and query them using the approximate nearest neighbor (ANN) algorithm.

With the introduction of the Scalable Nearest Neighbor (ScaNN) algorithm, Google Cloud has reported significant performance improvements—an 8x increase in vector index creation speed and a 4x increase in serving vector queries. According to Andi Gutmans from Google Cloud:

"We’re seeing thousands and thousands of customers doing vector processing."

The enhancements also include a new AlloyDB AI query engine that allows natural language queries to be embedded directly within SQL.

For further insights, see Google Cloud's official blog and the white paper on ScaNN.

AMD’s GAIA Framework

AMD has released GAIA, an open-source framework designed for running large language models (LLMs) locally on consumer hardware. GAIA supports retrieval-augmented generation (RAG) and aims to provide a solution for environments sensitive to latency or privacy concerns.

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GAIA includes a local Open-AI compatible API and tools for indexing local data sources. It allows for the inference to occur on-device, which significantly reduces latency. The framework can handle various content sources and vectorizes them for contextual querying.

Developers can access GAIA's source code on GitHub to explore its capabilities further.

MySQL and HeatWave Summit

The upcoming MySQL and HeatWave Summit will feature key speakers from Oracle and the broader database community. Notable speakers include Wim Coekaerts, Executive Vice President at Oracle, and Nipun Agarwal, Senior Vice President of MySQL Database and HeatWave Development.

The summit is designed to discuss advancements in MySQL and HeatWave, focusing on how organizations can leverage these innovations for improved database management.

For more information, visit the MySQL Event Page.

Passwordless Authentication Solutions

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By integrating passwordless authentication into web and mobile applications, organizations can significantly reduce the risk of breaches while providing users with a seamless experience. Explore MojoAuth's offerings to elevate your authentication strategies.

*** This is a Security Bloggers Network syndicated blog from MojoAuth – Go Passwordless authored by Devesh Patel. Read the original post at: https://mojoauth.com/blog/planetscale-vectors-ga-a-game-changer-for-mysql-and-ai-databases/