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 …

The Aurora hash join feature for relational databases has been around for a while now. But unlike MySQL Block Nested Loop algorithm, an Aurora hash join only caters to a specific number of use cases. When implemented with the optimizer properly, they can provide great benefits with certain workloads. Below we’ll see a brief example of a quick win.

This new feature is available in Aurora lab mode version 1.16. Because this is a lab feature, it’s important to make sure to test your queries before upgrading, especially if you are looking to scale up to the new R4 instances before the Superbowl to avoid hitting the same problem I discuss below.

When lab mode is enabled and

hash_join

  is ON, you can verify the optimizer feature from the

optimizer_switch

 variable:

In this blog post, I’ll look at whether optimizing indexing is always the key to improving MySQL query performance (spoiler, it isn’t).

As we look at MySQL query performance, our first concern is often whether a query is using the right indexes to retrieve the data. This is based on the assumption that finding the data is the most expensive operation – and the one you should focus on for MySQL query optimization. However, this is not always the case.

Let’s look at this query for illustration:

mysql> show create table tbl G
*************************** 1. row ***************************
      Table: tbl
Create Table: CREATE TABLE `tbl` (
 `id` int(11) NOT NULL AUTO_INCREMENT,
 `k` int(11) NOT NULL DEFAULT '0',
 `g` int(10) unsigned NOT NULL,
 PRIMARY KEY (`id`),
 KEY `k_1` (`k`)
) ENGINE=InnoDB …

In this blog post, we’ll look at ways you can use MySQL 5.7 generated columns (or virtual columns) to improve query performance.

Introduction

About two years ago I published a blog post about Generated (Virtual) Columns in MySQL 5.7. Since then, it’s been one of my favorite features in the MySQL 5.7 release. The reason is simple: with the help of virtual columns, we can create fine-grained indexes that can significantly increase query performance. I’m going to show you some tricks that can potentially fix slow reporting queries with GROUP BY and ORDER BY.

The Problem

Recently I was working with a customer who was struggling with this query:

SELECT
CONCAT(verb, ' - …