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Best Practice for Creating Indexes on your MySQL Tables – Rolling Index Builds

By having appropriate indexes on your MySQL tables, you can greatly enhance the performance of SELECT queries. But, did you know that adding indexes to your tables in itself is an expensive operation, and may take a long time to complete depending on the size of your tables? During this time, you are also likely to experience a degraded performance of queries as your system resources are busy in index-creation work as well. In this blog post, we discuss an approach to optimize the MySQL index creation process in such a way that your regular workload is not impacted.

MySQL Rolling Index Creation

We call this approach a ‘Rolling Index Creation’ - if you have a MySQL master-slave replica set, you can create the index one node at a time in a rolling fashion. You should create the index only on the slave nodes so the master’s …

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Case sensitivity in MySQL and MariaDB queries

Maybe you’re wondering why in MySQL/MariaDB 'string' seems to be the same as 'STRING'. Or maybe that’s not the case for you, but you would like to make a case insensitive search. This article explains how to write a case ...

Database Systems and Indexes – What you should know about Indexes for Performance Optimization ?

Optimal Indexing for Performance – How to plan Index Ops. ? 

An index or database index is a data structure which is used to quickly locate and access the data in a database table. Indexes are created on columns which will be the Search key that contains a copy of the primary key or candidate key of the table. These values are stored in sorted order so that the corresponding data can be accessed quickly (Note that the data may or may not be stored in sorted order). They are also Data Reference Pointers holding the address of the disk block where that particular key value can be found. Indexing in database systems is similar to what we see in books. There are complex design trade-offs involving lookup performance, index size, and index-update performance. Many index designs exhibit logarithmic (O(log(N))) lookup performance and in some applications it is possible to achieve flat (O(1)) performance. Indices can …

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MySQL Functional Indexes

Since MySQL 5.7 one can put indexes on expressions, aka functional indexes, using generated columns. Basically you first need to use the generated column to define the functional expression, then indexed this column.

Quite useful when dealing with JSON functions, you can find an example here and the documentation there.

Starting with MySQL 8.0.13 we have now an easiest way to create functional indexes (or functional key parts as mentioned in the documentation) \o/

Let’s see how with a quick practical example.

Upgrading Spatial Indexes to MySQL 8.0

MySQL has had spatial indexes for many years, but they have all been Cartesian (X and Y coordinates) indexes. MySQL 8.0 adds support for geographic (latitude-longitude) indexes. In an earlier blog post, I described how the feature works. In this post, we’ll go into the details of how to upgrade from 5.7 to 8.0 if you have spatial indexes.…

Geographic Indexes in InnoDB

One of the big features of MySQL 8.0 is geography support. MySQL now has a catalog of spatial reference systems (SRSs), of which almost 500 are geographic. Most functions also support geographic computations. What about indexes?

MySQL 8.0 comes with InnoDB spatial indexes for geographic data.…

Lesson 06: Working with Database Structures

Notes/errata/updates for Chapter 6:
See the official book errata at http://tahaghoghi.com/LearningMySQL/errata.php – Chapter 6 includes pages 179 – 222.

Other notes:
At the end of the “Creating Tables” section (p.183-4), it says “We like using the underscore character to separate words, but that’s just a matter of style and taste; you could use underscores or dashes, or omit the word-separating formatting altogether.” While this is true, beware of using a dash, because MySQL will try to interpret “two-words”, thinking – is a minus sign. I recommend avoiding dashes for this reason (even though the book does this on page 215).

At the end of the “Collation and Character Sets” section (p.186), it says “When you’re creating a database, you can set the default character set and sort order for the database and its …

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Cost/Benefit Analysis of a MySQL Index

We all know that if we add a MySQL index to speed up a read, we end up making writes slower. How often do we do the analysis to look at how much more work is done?

Recently, a developer came to me and wanted to add an index to a very large table (hundreds of gigabytes) to speed up a query. We did some testing on a moderately used server:

Set long_query_time to 0 and turn slow query logging on
Turn slow query logging off after 30 minutes.

Add the index (was on a single field)

Repeat the slow query logging for 30 minutes at a similar time frame (in our case, we did middle of the day usage on a Tuesday and Wednesday, when the database is heavily used).

Then I looked at the write analysis – there were no DELETEs, no UPDATEs that updated the indexed field, and no UPDATEs that used the indexed field in the filtering. There were only INSERTs, and with the help of pt-query-digest, here’s what I found: …

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Why does the MySQL optimizer not do what I think it should?

In May, I presented two talks – one called “Are you getting the best out of your indexes?” and “Optimizing Queries Using EXPLAIN”. I now have slides and video for both of them.

The first talk about indexing should probably be titled “Why is MySQL doing this?!!?!!?” It gives insight into why the MySQL optimizer chooses indexes that you do not expect; especially when it does not use an index you expect it to.

The talk has something for everyone – for beginners it explains B-trees and how they work, and for the more seasoned DBA it explains concepts like average value group size, and how the optimizer uses those concepts applied to metadata to make decisions.

Slides are at http://technocation.org/files/doc/2017_05_MySQLindexes.pdf.
Click the slide image below to go to the video at …

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Correct Index Choices for Equality + LIKE Query Optimization

As part of our support services, we do a lot of query optimization. This is where most performance gains come from. Here’s an example of the work we do.

Some days ago a customer arrived with the following table:

CREATE TABLE `infamous_table` (
  `id` int(11) NOT NULL AUTO_INCREMENT,
  `member_id` int(11) NOT NULL DEFAULT '0',
  `email` varchar(200) NOT NULL DEFAULT '',
  `msg_type` varchar(255) NOT NULL DEFAULT '',
  `t2send` int(11) NOT NULL DEFAULT '0',
  `flag` char(1) NOT NULL DEFAULT '',
  `sent` varchar(100) NOT NULL DEFAULT '',
  PRIMARY KEY (`id`),
  KEY `f` (`flag`),
  KEY `email` (`email`),
  KEY `msg_type` (`msg_type`(5)),
  KEY `t_msg` (`t2send`,`msg_type`(5))
) ENGINE=InnoDB DEFAULT CHARSET=latin1

And a query that looked like this:

SELECT COUNT(*)
  FROM `infamous_table`
 WHERE `t2send` > 1234 …
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