The latest from the MySQL community

At Percona Live Europe 2026, Dmitry Lenev and I presented JavaScript in My Database? Over My Dead Query, the story of component_js_lang, the V8-based JavaScript engine for Percona Server for MySQL 8.4. That experience told us few things:

• The presentation was seen as “for developers”
• What most DBA or DevOps heard was “JavaScript in the database” = “application logic in the database”

This let most of the people pictured developers shipping their business logic into stored procedures. Order workflows, pricing rules, REST calls, all running inside mysqld. 

That picture is wrong, and in this post I want to replace it with the right one, using the same four demos we showed on stage.

The fear is reasonable, the target is wrong

DBAs have earned their skepticism. Many of us have inherited a schema where thousands of lines of stored …

Over the period, we have witnessed how MySQL versioning evolved from 8.0 to 9.7 LTS, and now it has entered a new era: a calendar-based model.

Let’s first take a brief look at how MySQL versioning has evolved over time.

• Phase 1 – MySQL 8.0 (2018–2026) used a traditional single-track release model, with all new features and bug fixes rolled into the same 8.0 release line.
• Phase 2 – Oracle launches an Innovative/LTS dual-track model (Mid 2023). Innovation releases focus on new features and changes to short-lived performance on a quarterly basis. The LTS (Long Term Stable) release is steadier and has a long lifespan, with a cycle of 5 years of Premier Support + 3 years of Extended Support, and it typically launches every 2 years.
• Phase 3 – There are chains of Innovation releases following (8.1, …

Step 3: Why my sysbench-trained build loses and how to do it right.

Given the topic complexity and the length of this article I have split it in 3 three different blog-post:

1. What is PGO
2. How PGO it works
3. Why my sysbench-trained build loses and how to do it right.

Three compounding reasons:

1. Uncovered code gets pessimized.
   Sysbench-tpcc touches a narrow slice of mysqld.
   Every function with zero counts is treated as cold: GCC optimizes it for size, skips inlining, and shoves it into cold sections.
   But at runtime I still execute plenty of code my training never touched, such as purge, flushing, stats …

Step 2: How it works

Given the topic complexity and the length of this article I have split it in 3 three different blog-post:

1. What is PGO
2. How PGO it works
3. Why my sysbench-trained build loses and how to do it right.

How PGO works: PGO is a two-pass build. First pass compiles with instrumentation (-fprofile-generate): every basic block and branch gets a counter.
You run a training workload, counters are dumped to profraw files.
Second pass recompiles using those counts to drive inlining decisions, branch layout (hot path falls through, cold path jumps away), hot/cold function splitting, code ordering for icache/iTLB locality, …

Given the topic complexity and the length of this article I have split it in 3 three different blog-post:

1. What is PGO
2. How PGO it works
3. Why my sysbench-trained build loses and how to do it right.

Step 1: What is PGO

PGO (Profile-Guided Optimization) is a two-pass compilation technique. 

First, we build the program with instrumentation that adds counters to every branch, loop, and call site.
Next, we run it against a representative training workload so these counters can record which code paths are actually hot versus cold.
We then recompile the program using that data to drive the compiler’s decisions.

With this profiling …

Percona Toolkit’s pt-online-schema-change (pt-osc) has long been the preferred solution for performing online schema changes with minimal downtime. Its chunk-based copy algorithm is designed to adapt dynamically to the workload, making it suitable for very large tables in production environments.

However, under specific conditions, one of its optimization mechanisms can become counterproductive.

During a customer engagement involving Percona XtraDB Cluster (PXC), we investigated a case where running pt-online-schema-change with a selective –where clause caused severe Galera Flow Control and general overhead, effectively stalling the cluster for an extended period. The issue was not caused by the schema change itself, but by the interaction between:

• adaptive chunk auto-resizing
• highly selective copying of the newest rows
• clustered replication in Galera
• InnoDB …

MySQL 9.7, a Long-Term Support (LTS) release, incorporates a variety of potential features spanning across multiple technical domains. This article covers some of the primary features introduced and evaluates their practical utility within the MySQL database environment.

Following the End-of-Life (EOL) status of MySQL 8.0, this subsequent LTS release is designed to provide enhanced stability alongside significant architectural innovations.

Let’s discuss each of these features below with some examples and usage.

Flow-control monitoring in Group Replication

Flow control monitoring has been improved and provides more granularity by introducing the additional status variables listed below.

• Gr_flow_control_throttle_count : It denotes the number of transactions that have been throttled.
• Gr_flow_control_throttle_time_sum :It denotes the time in microseconds that transactions have been …

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. …

tidesdb-mysql is an experimental build that was developed to verify how TidesDB, the LSM-tree key/value engine, can work with MySQL 9.7 as a storage engine. The current build is v0.2.4, and it’s an experiment, not a finished product. So you can use it in your tests if you also want to try TidesDB with MySQL and compare with MariaDB

Why we made it

There was already a way to use TidesDB from SQL. It’s TideSQL, which loads the engine into MariaDB as ha_tidesdb, and it works fine. But it doesn’t work with MySQL. So we wanted TidesDB to work with MySQL 9.7.

MariaDB and MySQL share a lot of history, but they are not the same. We couldn’t just recompile the MariaDB plugin against MySQL headers and call it done. The one thing that stayed put through all of it was TidesDB itself, doing exactly what it does anywhere else. Only the server wrapped around was changed. In result we got our implementation, so if you’re …

Having a separate DR cluster for production databases is a modern day requirement or necessity for tech and other related businesses that rely heavily on their database systems. Setting up such a [DC -> DR] topology for Percona XtraDB Cluster (PXC), which is a virtually- synchronous cluster, can be a bit challenging in a complex Kubernetes environment.

Here, Percona Operator for MySQL comes in handy, with a minimal number of steps to configure such a topology, which ensures a remote side backup or a disaster recovery solution.

So without taking much time, let’s see how the overall setup and configurations look from a practical standpoint.

PXC Cross-Site/Disaster Recovery

DC Configuration

1) Here we have a three-node PXC cluster running on the DC side.

shell> kubectl get pods -n pxc
NAME                                               READY   STATUS …