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. …

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 …

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