In this blog post, I’ll look at how you can analyze raw MySQL query logs with ClickHouse.

For typical query performance analyses, we have an excellent tool in Percona Monitoring and Management. You may want to go deeper, though. You might be longing for the ability to query raw MySQL “slow” query logs with SQL.

There are a number of tools to load the MySQL slow query logs to a variety of data stores. For example, you can find posts showing how to do it with LogStash. While very flexible, these solutions always look too complicated and limited in functionality to me.   

By far the best solution to parse and load MySQL slow query logs (among multiple log types supported) is Charity …

In this blog post,  I’ll look at how to catch slow and frequent queries with ProxySQL.

More and more people are using ProxySQL because it is a great tool and it can help DBAs a lot. But many people do not realize that it is more powerful than it looks. It has many features and possibilities. I am going to show you one of my favorite “tricks” / use cases.

There are plenty of blog posts explaining how ProxySQL works. I am not going to that again. Instead, let’s jump straight to the point. There is a table in ProxySQL called “stats.stats_mysql_query_digest”. It is one of my favorite tables because it basically records all the queries that were running against ProxySQL. Without collecting any queries on the MySQL server, I can find …

In this blog post, I’ll look at how to execute a MySQL single table Point-In-Time Recovery.

I recently wrote a blog post describing a different way of doing Point-In-Time Recovery (PITR). If you want to know the step by step, please visit the mentioned blog post. Here is a quick summary of the approach:

1. Restore the backup on the desired server
2. Create a fake master
3. Copy all relevant binlogs to the fake master
4. Configure server from the first step as a slave from a fake master

In addition to the above steps, there is a similar approach …

The ability to restore MySQL logical backups is a significant part of disaster recovery procedures. It’s a last line of defense.

Even if you lost all data from a production server, physical backups (data files snapshot created with an offline copy or with Percona XtraBackup) could show the same internal database structure corruption as in production data. Backups in a simple plain text format allow you to avoid such corruptions and migrate between database formats (e.g., during a software upgrade and downgrade), or even help with migration from completely different database solution.

Unfortunately, the restore speed for logical backups is usually bad, and for a big database it could require days …

Welcome to the first interview blog for the upcoming Percona Live 2018. Each post in this series highlights a Percona Live 2018 featured talk that will be at the conference and gives a short preview of what attendees can expect to learn from the presenter.

This blog post highlights Bryana Knight, Platform Engineer at GitHub. Her talk is titled Scaling a High-Traffic Database: Moving Tables Across Clusters. Facing an immediate need to distribute load, GitHub came up with creative ways to move a significant amount of traffic off of their main MySQL cluster – with no user impact. In our conversation, we …

Please join Percona’s Principal Support Engineer, Sveta Smirnova, as she presents “Basic Internal Troubleshooting Tools for MySQL Server” on Thursday, February 15, 2018, at 10:00 am PST (UTC-8) / 1:00 pm EST (UTC-5).

Register Now

MySQL Server has many built-in troubleshooting tools. They are always available and can provide many insights on what is happening internally. Many graphical tools, such as Percona Monitoring and Management …

While preparing a post on the design of ZFS based servers for use with MySQL, I stumbled on the topic of fsync call performance. The fsync call is very expensive, but it is essential to databases as it allows for durability (the “D” of the ACID acronym).

Let’s first review the type of disk IO operations executed by InnoDB in MySQL. I’ll assume the default InnoDB variable values.

The first and most obvious type of IO are pages reads and writes from the tablespaces. The pages are most often read one at a time, as 16KB random read operations. Writes to the tablespaces are also typically 16KB random operations, but they are done in batches. After every batch, fsync is called on the tablespace file handle.

To avoid partially written pages in the tablespaces (a source of data corruption), InnoDB performs a doublewrite. During a doublewrite operation, a batch of dirty pages, from 1 to about 100 pages, is …

Percona has revealed the line-up of in-depth tutorials for the Percona Live 2018 Open Source Database Conference, taking place April 23-25, 2018 at the Santa Clara Convention Center in Santa Clara, Calif. Secure your spot now with Advanced Registration prices (available until March 4, 2018). Sponsorship opportunities for the conference are still available.

Percona Live 2018 Open Source Database Conference is the premier open source database event. The theme for the upcoming conference is “Championing Open Source Databases,” with a range of topics on …

In this blog post, I’ll present the ProxySQL query cache functionality. This is a query caching mechanism on top of ProxySQL. As there are already many how-tos regarding the ProxySQL prerequisites and installation process, we are going to skip these steps. For those who are already familiar with ProxySQL query cache configuration, let’s go directly to the query rules and the performance results.

Before talking about the ProxySQL query cache, let’s take a look at other caching mechanisms available for MySQL environments.

MySQL query cache is a query caching mechanism – deprecated as of MySQL 5.7.20 and removed in MySQL 8.0 – on top of MySQL itself (based on the official MySQL documentation).

The MySQL query cache stores the text of a SELECT statement together with the corresponding result sent to the client. If an identical statement is …

In this blog post, I’ll look into four ways MySQL executes GROUP BY. 

In my previous blog post, we learned that indexes or other means of finding data might not be the most expensive part of query execution. For example, MySQL GROUP BY could potentially be responsible for 90% or more of the query execution time. 

The main complexity when MySQL executes GROUP BY is computing aggregate functions in a GROUP BY statement. How this works is shown in the documentation for UDF Aggregate Functions. As we see, the requirement is that UDF functions get all values that constitute the single group one …