While there is no magic bullet for MySQL 5.7 database tuning, there are a few areas that can be focused on upfront that can dramatically improve the performance of your MySQL installation. While much information has been published on this topic over the years, I wanted to break down some of the more critical settings that anyone can implement – with no guesswork required.

Depending on the version of MySQL you are running, some of the default values used in this post may differ from your install, but the premise is still largely the same. We will focus on MySQL 5.7 for the purposes of this article.

Initial MySQL performance tuning can be broken down to the following categories:

• Tuning for your hardware
• Tuning for best performance/best practices
• Tuning for your workload

MySQL 5.7 Database Tuning Tuning For Your Hardware

Depending on the hardware …

In my Webinar on Using Percona Monitoring and Management (PMM) for MySQL Troubleshooting, I showed how to use direct queries to ClickHouse for advanced query analysis tasks. In the followup Webinar Q&A, I promised to describe it in more detail and share some queries, so here it goes.

PMM uses ClickHouse to store query performance data which gives us great performance and a very high compression ratio. ClickHouse stores data in column-store format so it handles denormalized data very well. As a result, all query performance data is stored in one simple “metrics” table:

More and more people are nowadays thinking of cloud migration. The question of “Which cloud provider performs better for my MySQL workload?” is really common but cannot always be easily answered. However, there are ways to come up with an answer. This question also applies when thinking of moving to any provider, not necessarily a cloud one or DBaaS.

The Problem

The most reliable conclusion can be found if you have a testing environment that is fully identical and can produce the same amount of traffic compared to your production version. In this case, the comparison should be straightforward as what you have to do is point your testing environment against the under-evaluation (cloud) providers, and evaluate the differences in performance. But this is not always easy, as many organizations do not have such environments or it’s not always possible to replay all traffic.

The Idea

In this …

Disclaimer: The following blog post does not try to provide a solution for split-brain situations, and the example provided is for demonstrative purposes only. Inconsistencies resulting from a split-brain scenario might possibly be more complex than the one shown, so do not use the following example as a complete guide.

What is Split-Brain?

A split-brain scenario is the result of two data sets (which were originally synced) losing the ability to sync while potentially continuing to receive DMLs over the same rows and ids on both sides. This could have consequences such as data corruption or inconsistencies where each side has data that does not exist on the other side.

For example before split-brain:

After split-brain:

Node1:

Node2:

It can be seen that after the split-brain scenario there are many differences between the nodes:

• Customer row id=2 was deleted …

Percona is happy to announce the experimental release of Percona XtraDB Cluster 8.0. This is a major step for tuning Percona XtraDB Cluster to be more cloud- and user-friendly. This is the second experimental release that combines the updated and feature-rich Galera 4, with substantial improvements made by our development team.

Improvements and New Features in Percona XtraDB Cluster

Galera 4, included in Percona XtraDB Cluster 8.0, has many new features. Here is a list of the most essential improvements:

• Streaming replication to support large transactions
• The synchronization functions allow action coordination (wsrep_last_seen_gtid, wsrep_last_written_gtid, wsrep_sync_wait_upto_gtid)
• More granular and improved error logging. wsrep_debug is …

As a MySQL DBA, the databases powering your applications need to handle changing traffic workloads while remaining responsive and stable so you can deliver an excellent user experience. Optimizing MySQL performance and troubleshooting MySQL problems cost-efficiently are some of the most critical and challenging tasks for MySQL DBAs. In this presentation, we will look at specific, common MySQL problems and demonstrate how Percona Monitoring and Management (PMM), built on free and open-source software, enables you to solve these challenges.

Please join Percona CEO Peter Zaitsev on Wednesday, March 18, 2020, at 1:30 pm EDT for his webinar “Optimize and Troubleshoot MySQL Using Percona Monitoring and Management”.

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There are a lot of things I love about Prometheus; its data model is fantastic for monitoring applications and PromQL language is often more expressive than SQL for data retrieval needs you have in the observability space. One thing, though, I hate about Prometheus with a deep passion is the behavior of its rate() and similar functions, deeply rooted in the Prometheus computational model, which I was told by the development team is not likely to change.

So What’s the Problem, and Why is it Such a Big Deal?

First – the problem.  rate() functions give you the rate of change of the time series for the Interval supplied, so rate(mysql_global_status_questions[10s]) will basically give us the average number of MySQL questions over the last 10seconds. Everything is great so far.

But what if the resolution of this time series is lower than 10 seconds, for example, if we take …

In the last blog post of this series, we discussed in detail how Master Key encryption works. In this post, based on what we already know about Master Key encryption, we look into how Master Key rotation works.

The idea behind Master Key rotation is that we want to generate a new Master Key and use this new Master Key to re-encrypt the tablespace key (stored in tablespace’s header).

Let’s remind ourselves what a Master Key encryption header looks like (it is located in tablespace’s header):

From the previous blog post, we know that when a server starts it goes through all encrypted tablespaces’ encryption headers. During that, it remembers the highest KEY ID it read from all the encrypted tablespaces. For instance, if we have three tables with KEY_ID = 3 and one table with KEY ID = 4, it means that …

This talk covers some of the challenges we sought to address by creating a Kubernetes Operator for Percona XtraDB Cluster, as well as a look into the current state of the Operator, a brief demonstration of its capabilities, and a preview of the roadmap for the remainder of the year. Find out how you can deploy a 3-node PXC cluster in under five minutes and handle providing self-service databases on the cloud in a cloud-vendor agnostic way. You’ll have the opportunity to ask the Product Manager questions and provide feedback on what challenges you’d like us to solve in the Kubernetes landscape.

Please join Percona Product Manager Tyler Duzan on Wednesday, February 26, 2020, at 1 pm EST for his webinar “Building a Kubernetes Operator for Percona XtraDB Cluster”.

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This is the second part of a two-articles series. In the first part, we introduced the Common Table Expression (CTE), a new feature available on MySQL 8.0 as well as Percona Server for MySQL 8.0. In this article, we’ll present the Recursive Common Table Expression. SQL is generally poor at recursive structures, but it is now possible on MySQL to write recursive queries. Before MySQL 8.0, recursion was possible only by creating stored routines.

What is a Recursive Common Table Expression?

A recursive CTE is one having a subquery that refers to its own name. It is particularly useful in the following cases:

• To generate series
• Hierarchical or tree-structured data traversal