The latest from the MySQL community

NOTE: We are back after a summer break! Hope you enjoyed the beach and long summer nights.

As part of building the MySQL Community, we are going to publish a curated overview of MySQL and database news and events that you might have missed over the last period.

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Enjoy!

MySQL News:

Note: Aggregated MySQL news can be found at Planet for MySQL Community and Planet MySQL (Oracle curated)

More to Explore: What’s New on Planet MySQL
TL;DR - Scott Stroz and team are doing major …

MySQL HeatWave now offers more control and transparency around maintenance. With Deferred Maintenance and Maintenance Events, customers can better align database maintenance with their business schedules and operational processes to reduce disruption. Customers can now temporarily disable disruptive maintenance which requires system reboots. To ensure the ongoing security of customer environments, zero-downtime security patches will continue to be applied regularly.  Additionally, […]

Synthetic benchmarks have their place, but I have always preferred working with real data. Not client production data — that stays private — but publicly available datasets that reflect the messy shapes, skewed distributions, and indexing challenges you encounter in the wild.

Synthetic benchmarks have their place, but I have always preferred working with real data. Not client production data — that stays private — but publicly available datasets that reflect the messy shapes, skewed distributions, and indexing challenges you encounter in the wild.

Planet MySQL has always been about discovering what’s happening across the MySQL community. Now, there’s even more to explore. We’ve added new ways to discover the projects and products that make up the broader MySQL ecosystem, find upcoming MySQL events, and search the wealth of content shared by the community. At the center of these […]

Our first post showed MySQL 9.7 with one change: mark a table ENGINE=DuckDB and its analytical queries run in DuckDB instead of InnoDB. The question we kept getting after that was about replication. Can you keep a normal InnoDB primary for the writes, and run a replica where the big tables are ENGINE=DuckDB? Then the heavy reports run on a column store, and ordinary MySQL replication keeps it current. No export job. No second database to sync by hand.

So we tried it. The first run failed, and it failed in a way that is easy to miss: the replica took every transaction, reported success, and stored nothing. We tracked down why, fixed it, and the whole test suite passes now. This post is what we tested, how we checked it, the bug we found, and where it stands.

It’s still an experiment, not production software. The code and the test harness are on GitHub under GPLv2: …

I recently had the pleasure of presenting Planning your upgrade to MySQL 9.7 at COSCUP 2026 in Taipei, Taiwan. COSCUP (Conference for Open Source Coders, Users and Promoters) is one of the largest open source conferences in Asia, and the community there is always engaged and technically sharp.

I recently had the pleasure of presenting Planning your upgrade to MySQL 9.7 at COSCUP 2026 in Taipei, Taiwan. COSCUP (Conference for Open Source Coders, Users and Promoters) is one of the largest open source conferences in Asia, and the community there is always engaged and technically sharp.

High availability keeps MySQL and MariaDB applications running through routine local failures, while disaster recovery restores service after a site or regional outage. This article explains how RTO, RPO, distance, synchronous replication and asynchronous replication shape each strategy, and how Continuent Tungsten Cluster combines local HA with multi-site DR.

We ran DuckDB MySQL storage engine at scale factor 500. It is around 500 GB of raw TPC-H, three billion lineitem rows  on an 80-core server with 187 GB of RAM. Three engines on the same box: InnoDB, our MySQL+DuckDB engine, and plain DuckDB as the reference.

Here is what came out. InnoDB finished 18 of the 22 queries and spent more than 28 hours of query time on them. Four never finished. Our engine ran all 22 in about three minutes. It loaded the data 25 times faster than InnoDB, and it used 5 times less disk. On the queries it stays close to plain DuckDB, and on a few it is ahead.

It’s still an experiment, not production software. Code and the benchmark harness are on GitHub under GPLv2: https://github.com/Percona-Lab/ducksdb-mysql-engine.

The machine, and how we ran it

• One server, 80 cores, 187.5 GB RAM.
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