Queries effect database performance. That’s not a typo: “effect” not “affect”. The difference is not a word game but an important way to think about database performance.

Many things can affect performance. For example, if the system runs out of memory then starts swapping which causes high disk IO latency, that will negatively affect performance. But external factors like that notwithstanding (i.e. when hardware and MySQL are normal and stable), it’s important to understand that queries effect performance.

Queries effect database performance. That’s not a typo: “effect” not “affect”. The difference is not a word game but an important way to think about database performance. Many things can affect performance. For example, if the system runs out of memory then starts swapping which causes high disk IO latency, that will negatively affect performance. But external factors like that notwithstanding (i.e. when hardware and MySQL are normal and stable), it’s important to understand that queries effect performance.

Narrowing down which queries to optimize is a step in database administration that is often skipped - however it shouldn't be!  

Why is selecting the right queries to optimize so important? There are several reasons. 

Penny-Wise, Pound-Foolish  Look at the forest, not just the trees, and optimize globally, not locally. If you’re optimizing a query that never causes a user-visible problem, doesn’t impact other queries, and doesn’t significantly load servers, you might be “optimizing” things that don’t matter, spending more money than you save. Your time has value, too! Keep in mind, too, that optimizing a query that generates only 1% of the database’s overall load will not be a significant benefit to the bottom line.   Whack-A-Mole Queries It’s very common to  find a slow query in a log file, try re-executing it and  then find that it is fast. …

In this blog post, I want to go over some of the day-to-day MySQL tools and methods DBAs use to analyze queries and visualize “what is going on?” I won’t be going into the nitty-gritty details of each of these tools, I just want to introduce you to them and show you what they look like so you will know what types of information they provide.

This isn’t a comprehensive list by any means but consider it a primer for those starting with MySQL and wanting to know what a query is going to do or is doing.

The two sides of query analysis are examining a query BEFORE you run it, and then analyzing what actually happened AFTER you run it.

Let’s start with the tools you can use to predict a query’s future.

In the beginning, there was EXPLAIN. The venerable EXPLAIN command has been with us a long time as a built-in MySQL utility statement. Its purpose is to explain that what the optimizer …

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 …

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, ' - …

Join Percona’s Senior Architect, Matthew Boehm, as he presents What You Need to Get the Most Out of Indexes – Part 2 webinar on Thursday, October 19, 2017, at 11:00 am PDT / 2:00 pm EDT (UTC-7).

Register Now

Proper indexing is key to database performance. Finely tune your query writing and database performance with tips from the experts. MySQL offers a few different types of indexes and uses them in a variety of ways.

In this session you’ll learn:

• How to use composite indexes
• Other index usages besides lookup
• How to find …

This blog describes how to identify queries that cause a sudden spike in system resources as well as the user and host who executed the culprit query using the Monyog MySQL Monitor and Advisor.

How many times have you seen a system go live and perform much worse than it did in testing? There could be several reasons behind bad performance. For instance, a slow running query in MySQL can be caused by a poor database design or may be due to higher-than-normal latency in network communication. Other issues such as using too few or too many indexes may also be a factor. This blog will identify the types of poorly performing queries and outline some concrete strategies for identifying them using monitoring. Finally, some tips for improving performance will be presented.

The Effects of Misbehaving Queries

Typically, misbehaving queries will result in two possible outcomes: high CPU usage and/or slow execution. The two …

This blog describes how to identify queries that cause a sudden spike in system resources as well as the user and host who executed the culprit query using the Monyog MySQL Monitor and Advisor.

How many times have you seen a system go live and perform much worse than it did in testing? There could be several reasons behind bad performance. For instance, a slow running query in MySQL can be caused by a poor database design or may be due to higher-than-normal latency in network communication. Other issues such as using too few or too many indexes may also be a factor. This blog will identify the types of poorly performing queries and outline some concrete strategies for identifying them using monitoring. Finally, some tips for improving performance will be presented.

The Effects of Misbehaving Queries

Typically, misbehaving queries will result in two possible outcomes: high CPU usage and/or slow execution. The two …