MySQL 8.0 vs. 8.4 vs. 9.2: Comparing Features, LTS vs. Innovation, and the Power of Vectors for AI

MySQL Version Comparison: 8.0.41 vs. 8.4.4 vs. 9.2 – Which One Should You Choose?

Choosing the right MySQL version is crucial for ensuring database stability, performance, and long-term support. With the release of MySQL 8.0.41, 8.4.4, and 9.2, developers and database administrators face a key decision: should they stick with a stable long-term support (LTS) version or explore the latest innovations?

MySQL 8.0.41 and 8.4.4 are both LTS releases, designed for production environments that require reliability and extended support. Meanwhile, MySQL 9.2 falls under the "Innovation Release" category, offering cutting-edge features but with a shorter support cycle.

In this article, we’ll break down the differences between these versions, highlighting their key features, improvements, and considerations to help you make an informed choice. Whether you're upgrading an existing system or starting fresh, understanding these distinctions can help you optimize your database strategy.

As of March 2025, MySQL has released several versions, each introducing distinct features and improvements. Here's a comparison of MySQL versions 8.0.41, 8.4.4, and 9.2.0:

Key Considerations:

Long-Term Support (LTS) vs. Innovation Releases: LTS versions like 8.0.41 and 8.4.4 are designed for production environments requiring extended support and stability. Innovation releases, such as 9.2.0, introduce new features more rapidly but have shorter support cycles and may include experimental functionalities.

Feature Adoption: If your environment benefits from the latest features and you can accommodate potential changes, innovation releases offer early access to advancements. However, for critical systems where stability is paramount, LTS versions are recommended.

Upgrade Path: Before upgrading, review the release notes and documentation to understand the changes and assess their impact on your applications. This ensures compatibility and optimal performance.

For detailed information, refer to the official MySQL documentation and release notes.

With the introduction of the VECTOR data type, MySQL has taken a significant step toward supporting AI-driven applications. This feature enables efficient storage and retrieval of high-dimensional vector embeddings, which are crucial for machine learning, recommendation systems, and natural language processing tasks.

While MySQL 8.0.41 and 8.4.4 focus on stability and long-term support, MySQL 9.2 embraces innovation by introducing native vector support. This makes it a compelling choice for AI developers who need fast and scalable similarity searches.

If you’re working on AI-powered applications—such as semantic search, image recognition, or recommendation engines—leveraging the VECTOR type in MySQL 9.2 can significantly streamline your workflow. Below is a sample implementation demonstrating how to store and query vector embeddings using MySQL’s VECTOR type:

Sample Code: Storing and Querying Vectors in MySQL 9.2

1. Create a Table with VECTOR Column

CREATE TABLE ai_embeddings (

    id INT AUTO_INCREMENT PRIMARY KEY,

    embedding VECTOR(3) NOT NULL -- 3D vector example

);

2. Insert Vector Data (Example: Word Embeddings or Image Features)

INSERT INTO ai_embeddings (embedding) 

VALUES (VECTOR([0.12, 0.87, 0.45])), 

       (VECTOR([0.34, 0.56, 0.78]));

3. Perform a Nearest Neighbor Search (Similarity Search)

SELECT id, embedding 

FROM ai_embeddings 

ORDER BY DOT_PRODUCT(embedding, VECTOR([0.30, 0.60, 0.75])) DESC

LIMIT 1;

This query retrieves the most similar vector using dot product similarity, which is commonly used in recommendation systems and AI search applications.

By incorporating vector capabilities, MySQL 9.2 enhances its role in AI development, making it easier to integrate machine learning models with traditional databases. If your project involves AI, consider MySQL’s innovation releases to take advantage of these advanced features while balancing performance and scalability.

KVRocks - The fascinating Redis Replacement from the Apache project

Kvrocks is an advanced, open-source, distributed key-value store that extends the functionalities of traditional key-value databases by integrating the simplicity of Redis with the robustness of RocksDB. Developed as an Apache project, Kvrocks combines the high-performance capabilities of RocksDB, a leading embedded database engine, with the rich, in-memory data structure features found in Redis.

At its core, Kvrocks leverages RocksDB’s log-structured merge-tree (LSM-tree) architecture to offer efficient write operations and high compression rates, addressing common challenges associated with persistent storage. This architecture enables Kvrocks to handle large volumes of data and achieve high throughput, making it suitable for scenarios requiring both high-speed access and persistent storage.

Kvrocks is designed with a focus on high availability and scalability. It supports various data distribution strategies, including sharding, to manage large datasets across multiple nodes. The system’s architecture incorporates a distributed design that enables horizontal scaling, facilitating seamless expansion as data and request volumes increase.

In terms of API compatibility, Kvrocks provides a Redis-compatible interface, allowing for straightforward migration from Redis to Kvrocks. This compatibility ensures that existing Redis clients and applications can leverage Kvrocks without extensive modifications.

Furthermore, Kvrocks includes features for data replication and fault tolerance, using mechanisms such as master-slave replication and automatic failover to maintain data integrity and availability. These features are crucial for ensuring continuous operation in distributed environments. 

The replication uses a MySQL like binlog mechanism that helps relay changes to multiple layers of replicas from a single source and thus allows the data to be replicated into cluster nodes near or far. 

Overall, Kvrocks represents a sophisticated blend of Redis’s in-memory data handling and RocksDB’s persistent storage capabilities, offering a powerful solution for modern data management needs in distributed and high-throughput contexts.

eXistDB - The Open Source native XML Database

So, a bit of history to start with... XML or Extensible Markup Language is a subset of SGML that gained popularity in late 90's and beginning of the next decade. That's when SOAP was considered the best way to implement SOA and it made you use XML. That was well before JSON was ubiquitous and BSON was known to most. Software AG were the first to come up with a native XML data store called Tamino XML Server. It was rightly timed and feature rich, the open source community realized there was a need for something that offered similar functionality in the open source world. eXistDB was created.

While one might think of eXistDB as something similar to CouchDB, i.e. a document store with a RESTful API. But eXistDB has to offer a lot more in terms of application development. It allows ease of development and deeper integration into applications via support for XSLT and hence can deliver the documents in a formatted state, thus taking the processing burden off from the front end application and reduce the amount of data exchange required.

OpenSource Software Comparison Database

If you have ever looked up a comparison of software, a number of sites show up with a side by side omparison. However, there is one that stands out where the comparison data is crowd sourced and isn't coming from the website management team. Secondly, the UI is slick and offers a decent categorized catalog of softwares to look up and compare.

Here is the link 🔗

Please do beowse and share your thoughts.

ScyllaDB's newest selling proposition

So, all the Cassandra users probably know by now that ScyllaDB provides a great drop-in replacement for Cassandra and not just that, it does so with guaranteed response times and improved deployment density.

The latest release from ScyllaDB boasts another interesting feature, in addition to the standard Cassandra interface it now offers a full-flegedged DynamoDB API.

 So, if you have an application that's write-heavy and is getting very expensive, it may be time to either redesign your application or consider switching to ScyllaDB with DynamoDB support.

In case you are wondering how it's done, Scylla University has got a training course for exactly that, and can be found here

New MySQL Terminology for High Availability

Kenny Gryp is a good friend who is working for Oracle these days and is really really good with MySQL. He recently wrote a great article about something that is changing for all the right reasons, namely the terms you use to refer to a master or slave server (speaking customarily, being an old timer).

The new nomenclature for #mysql replication is good, and will take some time to get used to but this is a good time to get it over with.

The article is available here on the MySQL High Availability Blog

Yes, Another Open Source Database Blog !

Every professional is a unique individual, with a potentially unique perspective about what they have been up to in their professional and personal life and hence can always provide value to their readers through their original content or by augmenting content from other experts with their opinions and thoughts.

Having been involved with large scale database work during my previous job engagements, I have had some interesting experiences. I used to have a blog that's been lost during a country move where the custom domain was lost. It wasn't focused on databases, but this one is different as it is focused on databases.

 The fact that computers can store and retrieve data at a very high rate and usually don't forget any of they fasinated me when I first learned that in early nineties. So, from dBase 3 Plus and FoxPro 🦊 to MySQL, Oracle, SQL Server, MongoDB and Couchbase I have had a chance to use a lot of data stores, some as a developer and others in more if an administrative role.

So, thanks for visiting and read on, hope to see you around often !