The latest from the MySQL community

MySQL Galera Cluster reaches end of life on September 30, 2026. This guide compares five practical paths forward - MariaDB Galera Cluster, Percona XtraDB Cluster, self-managed MySQL, managed cloud MySQL and Tungsten Cluster - with the trade-offs, migration effort and long-term risks of each.

Tối ưu chỉ mục (index) trong MySQL là kiến thức cơ bản mà nhiều dev PHP mới hay bỏ qua. Có bạn dev mới ra trường nhắn tin kể cho chúng tôi về một buổi tối khá căng thẳng. Ứng dụng quản lý đơn hàng bạn viết chạy mượt suốt mấy tuần thử nghiệm với vài trăm bản ghi. Nhưng khi khách hàng thật bắt đầu dùng, dữ liệu tăng lên vài chục nghìn dòng. Trang danh sách đơn hàng bỗng dưng tải chậm tới mức khách phàn nàn. Bạn kiểm tra code PHP mãi không thấy lỗi gì. Tới khi một anh senior xem qua mới phát hiện câu truy vấn tìm đơn hàng theo mã khách chưa hề có chỉ mục. Điều này khiến MySQL phải quét qua toàn bộ bảng mỗi lần tìm kiếm. Đây là tình huống khá phổ biến ở nhiều dev PHP mới vào nghề. Chỉ mục trong MySQL là kiến thức nền tảng, nhưng lại thường bị …

MySQL Galera Cluster maintenance ends September 30, 2026, while MySQL 8.0 has already reached end of life. Learn what these overlapping lifecycle changes mean, what risks increase after support ends, and what teams should decide before the Galera EOL deadline.

The MySQL community gathered once again for the second MySQL Contributor Summit of 2026, held August 5–6 as a hybrid event from Broomfield, Colorado. Building on the momentum of the inaugural summit in May, MySQL contributors from across the MySQL ecosystem came together to share ideas, review technical proposals, and collaborate on the future of MySQL.  Over two […]

Visual Studio 2026 ships SQL projects through SSDT, and only the original project format. Here is how to configure, validate, version, deploy, and drift-check a database project without discovering the gaps during a release.

The post 13 best practices for database development in Visual Studio 2026  appeared first on Devart Blog.

The common practice is to size the Galera Cache based on write volume measured during peak load, but often it is more of a guesswork. The writeset cache capacity planning is crucial to shorten the maintenance time and avoid long state transfers while the cluster runs with reduced compute power. Now, if you could understand what’s exactly inside the cache, wouldn’t the planning be more aware as compared to only calculating the best size based on wsrep_received/replicated_bytes variables?

Similarly, while dealing with various incidents occurring in Percona XtraDB Cluster or MariaDB Galera Cluster environments, how many times did you stumble upon the fact that the GCache file (galera.cache) is a black box and you can’t inspect it in a meaningful way? 

In some scenarios, having the opportunity to see what exactly ended up in the cache file(s) could help us understand the write workload impact or what happened …

Choosing how to upgrade a MySQL database is rarely just a question of source and target versions. The best approach can depend on database size, storage engines, replication topology, available infrastructure, acceptable downtime, backup readiness, application compatibility, and rollback requirements. A small standalone database with a long maintenance window may be a good candidate for […]

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.