MySQL Enterprise Edition includes powerful audit filtering capabilities, but writing audit filter JSON by hand can be tedious and error-prone. The JSON model is flexible, which is exactly what makes it useful, but it also means that a small typo, a missing event class, or an incorrectly assigned user can change what does or does […]

For over 30 years, MySQL has grown through the contributions, feedback, and collaboration of a global community of developers, database administrators, customers, partners, educators, and open source advocates. That community has helped make MySQL one of the world’s most widely used open source databases. As the ecosystem continues to grow and evolve, so do the opportunities for collaboration.  Over the past year, we have […]

MySQL HeatWave Service (MHS) on Oracle Cloud Infrastructure (OCI) already provides multiple ways to create a new DB system, such as restoring from a backup, using Point-in-Time Recovery (PITR), or importing data from Object Storage. However, when creating a new DB system from an existing DB system, especially in another region, the process required several sequential […]

This lesson should have been learned with the CREATE TABLE of death, but it is worth a refresh.

Do not uselessly grant CREATE and ALTER TABLE

The reason I am posting this reminder is that another crashing bug related to DDL came to my attention.  This bug is only fixed in a recent version of MySQL (probably not affecting 5.6 and 5.7), so if you are running the latest 8.0 or 8.4, you should

On April 21st, 2026, we released MySQL 9.7.0, the latest Long-Term Support release. As always, we are grateful to the MySQL community for helping improve MySQL with bug reports, patches, pull requests, and continued feedback. Community contributions help make MySQL better for everyone, and we are happy to recognize the contributors whose work was included […]

Understanding the New Cadence: Quarterly CPUs, Targeted CSPUs, and Transitioning to Calendar Versioning MySQL is updating its release model to make releases easier to understand, plan for, and follow: The goal is not simply to change the number on a release. The goal is to give users, DBAs, developers, Linux distributions, cloud platforms, and ecosystem […]

Managing data retention policies is one of the most common operational tasks in MySQL.

Applications continuously generate transactional, audit, logging, telemetry, and event data. Over time, these tables can grow to billions of rows, causing:

• Larger backups
• Longer recovery times
• Reduced buffer pool efficiency
• Slower index maintenance
• Increased storage costs
• Degraded query performance

To address these problems, organizations typically implement retention policies based on dates or timestamps. Examples include deleting events older than 90 days or purging session data older than 30 days and so forth. The deleted data can then eventually be archived somewhere else, like in another DBMS or on external files.

One of the most widely used tools for implementing these policies in MySQL ecosystems is pt-archiver, part of the Percona Toolkit. …

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It is time to talk again about Flow control and group replication. This time with a special eye on the use of Group Replication in the Kubernetes context. In this article we will dig a bit on how it works and what are the various side effects. 

The problem

Recently I was refining the calculation I use in the MySQL calculator for Operator given I was constantly encountering a very serious problem with the Percona Server Operator.

The problem is that when the deployment was/is serving a high level of traffic, it will, no matter what, end up in getting OMMKill by the K8 system. 

This because the pod was gradually consuming more and more memory, reaching the memory limit set in the CR specification. 

Now let me clarify a few things, to get straight to the facts.

…

Let’s be honest: migrating a relational database to Kubernetes sounds fantastic in a whiteboard meeting, but the reality of day-two operations is a completely different story.

When moving MySQL to Kubernetes, the ultimate goal is simple: identify a safe, performant set of configuration values for your database pods. But where do you start? Usually, you look at your overall node resources say, a machine with 16 CPUs and 64GB of RAM.

In the old bare-metal days, you'd apply the standard rules of thumb:

• Set innodb_buffer_pool_size to 60-80% of total RAM to maximize caching.

• Allocate 1 innodb_buffer_pool_instances per 1GB of buffer pool.

• Match innodb_io_capacity to your drive speeds.

If you try applying these legacy rules in Kubernetes, your pod won't survive. …