MySQL Admin Cookbook
MySQL is the most popular open-source database and is also known for its easy set up
feature. However, proper configuration beyond the default settings is still a challenge,
along with some other day-to-day maintenance tasks such as backup and restoring,
performance tuning, and server monitoring.
This book provides both step-by-step recipes and relevant background information on
these topics and more. It covers everything from basic to advanced aspects of MySQL
administration and configuration. All recipes are based on real-world experience and
were derived from proven solutions used in an enterprise environment.
What This Book Covers
Chapter 1, Replication: In this chapter, you will see how to set up MySQL replication,
useful for load balancing, online backups, and fail-over scenarios. Advanced replication
scenarios using the blackhole engine and streaming slave deployment are discussed
beyond the basic topics.
Chapter 2, Indexing: You will be shown how to create, drop, and modify indexes,
perhaps the most important means of optimizing your MySQL servers’ performance.
Fulltext indexing, clustered and non-clustered indexes are compared and presented with
their respective strengths and typical use cases. Moreover, you will learn how to identify
duplicate indexes, which can hinder your servers’ performance.
Chapter 3, Tools: This chapter will get you acquainted with the MySQL Administrator
and Query Browser GUiTools as well as the MySQL command-line client and how to
use it in concert with external scripts and tools. You will also see how to create custom
diagrams for MySQL Administrator and share connection profiles between
Chapter 4, Backing Up and Restoring MySQL Data: In this chapter, we introduce the
basic approaches to backing up your database and restoring data again. Advanced
techniques like on-the-fly compression, point in time recovery, avoiding extended lock
situations, backup in replication scenarios, and partial backup and restore
are also covered.
Chapter 5, Managing Data: You will learn some tricks beyond the basic SQL
commands, which enable you to delete data in a highly efficient manner and insert data
based on existing database content, and how to import and export data to and
from your database.
Chapter 6, Monitoring and Analyzing a MySQL Installation: We present approaches to
monitoring table space usage, and how to use database metadata to your advantage.
Typical performance bottlenecks and lock contention problems are discussed as well.
Chapter 7, Configuring MySQL: This chapter deals with MySQL configuration and
how to best leverage available settings to their full potential. Table space management,
pool sizing, and logging options are discussed along with platform-specific caveats and
advanced installation scenarios, such as multiple instances on one server.
Chapter 8, MySQL User Management: Management of MySQL user accounts is
discussed in detail throughout this chapter. Typical user roles with appropriate privileges
and approaches to restricting access sensibly are proposed. You will also learn how to
regain access to your database in case the administrative user credentials are lost.
Chapter 9, Managing Schemas: This chapter includes topics such as adding and
removing columns to and from tables and choosing a suitable storage engine and
character set for individual needs. Another recipe covers a technique to add a new
primary key column to a table already filled with data. Ways to manage and automate
database schema evolution, as part of a software life cycle are presented as well. And if
you have always missed “ADD INDEX IF NOT EXISTS”, you will find a
solution to this, too.
Appendix, Good to Know: In this final part of the book you can find several things that
can turn out useful in everyday situations, but did not fit the step-by-step recipe format
naturally. Topics range from choosing character sets to getting the most out of 32 bit
address space limitations.
In this chapter, we will cover:
- Adding indexes to tables
- Adding a fulltext index
- Creating a normalized text search column
- Removing indexes from tables
- Estimating InnoDB index space requirements
- Using prefix primary keys
- Choosing InnoDB primary key columns
- Speeding up searches for (sub)domains
- Finding duplicate indexes
One of the most important features of relational database management systems—MySQL
being no exception—is the use of indexes to allow rapid and efficient access to the enormous
amounts of data they keep safe for us. In this chapter, we will provide some useful recipes for
you to get the most out of your databases.
Infinite storage, infinite expectations
We have got accustomed to nearly infinite storage space at our disposal—storing everything
from music to movies to high resolution medical imagery, detailed geographical information,
or just plain old business data. While we take it for granted that we hardly ever run out of
space, we also expect to be able to locate and retrieve every bit of information we save in an
instant. There are examples everywhere in our lives—business and personal:
- Your pocket music player’s library can easily contain tens of thousands of songs and
yet can be browsed effortlessly by artist name or album title, or show you last week’s
top 10 rock songs.
- Search engines provide thousands of results in milliseconds for any arbitrary search
term or combination.
- A line of business application can render your sales numbers charted and displayed
on a map, grouped by sales district in real-time.
These are a few simple examples, yet for each of them huge amounts of data must be
combed to quickly provide just the right subset to satisfy each request. Even with the
immense speed of modern hardware, this is not a trivial task to do and requires some
Speed by redundancy
Indexes are based on the principle that searching in sorted data sets is way faster than
searching in unsorted collections of records. So when MySQL is told to create an index on one
or more columns, it copies these columns’ contents and stores them in a sorted manner. The
remaining columns are replaced by a reference to the original table with the unsorted data.
This combines two benefits—providing fast retrieval while maintaining reasonably efficient
storage requirements. So, without wasting too much space this approach enables you to
create several of those indexes (or indices, both are correct) at a relatively low cost.
However, there is a drawback to this as well: while reading data, indexes allow for immense
speeds, especially in large databases; however, they do slow down writing operations. In the
course of INSERTs, UPDATEs, and DELETEs, all indexes need to be updated in addition to
the data table itself. This can place significant additional load on the server, slowing down
For this reason, keeping the number of indexes as low as possible is paramount, especially for
the largest tables where they are most important. In this chapter, you’ll find some recipes that
will help you to decide how to define indexes and show you some pitfalls to avoid.
Storage engine differences
We will not go into much detail here about the differences between the MyISAM and the
InnoDB storage engines offered by MySQL. However, regarding indexes there are some
important differences to know between MySQL’s two most important storage engines. They
infl uence some decisions you will have to make.
In the figure below you can see a simplified schema of how indexes work with the MyISAM
storage engine. Their most important property can be summed up as “all indexes are created
equal”. This means that there is no technical difference between the primary and
The diagram shows a single (theoretical) data table called books. It has three columns
named isbn, title, and author. This is a very simple schema, but it is sufficient for explanation
purposes. The exact definition can be found in the Adding indexes to tables recipe in this
chapter. For now, it is not important.
MyISAM tables store information in the order it is inserted. In the example, there are three
records representing a single book each. The ISBN number is declared as the primary key for
this table. As you can see, the records are not ordered in the table itself—the ISBN numbers
are out of what would be their lexical order. Let’s assume they have been inserted by someone
in this order.
Now, have a look at the first index—the PRIMARY KEY. The index is sorted by the isbn column.
Associated with each index entry is a row pointer that leads to the actual data record in the
books table. When looking up a specific ISBN number in the primary key index, the database
server follows the row pointer to retrieve the remaining data fields. The same holds true for
the other two indexes IDX_TITLE and IDX_AUTHOR, which are sorted by the respective fields
and also contain a row pointer each.
Looking up a book’s details by any one of the three possible search criteria is a two-part
operation: first, find the index record, and then follow the row pointer to get the rest of the data.
With this technique you can insert data very quickly because the actual data records are
simply appended to the table. Only the relatively small index records need to be kept in order,
meaning much less data has to be shuffled around on the disk.
There are drawbacks to this approach as well. Even in cases where you only ever want to look
up data by a single search column, there will be two accesses to the storage subsystem—one
for the index, another for the data.
However, InnoDB is different. Its index system is a little more complicated, but it has
Primary (clustered) indexes
Whereas in MyISAM all indexes are structured identically, InnoDB makes a distinction between
the primary key and additional secondary ones.
The primary index in InnoDB is a clustered index. This means that one or more columns
of each record make up a unique key that identifies this exact record. In contrast to other
indexes, a clustered index’s main property is that it itself is part of the data instead of being
stored in a different location. Both data and index are clustered together.
An index is only serving its purpose if it is stored in a sorted fashion. As a result, whenever you
insert data or modify the key column(s), it needs to be put in the correct location according to
the sort order. For a clustered index, the whole record with all its data has to be relocated.
That is why bulk data insertion into InnoDB tables is best performed in correct primary key
order to minimize the amount of disk I/O needed to keep the records in index order. Moreover,
the clustered index should be defined so that it is hardly ever changed for existing rows, as
that too would mean relocating full records to different sectors on the disk.
Of course, there are significant advantages to this approach. One of the most important
aspects of a clustered key is that it actually is a part of the data. This means that when
accessing data through a primary key lookup, there is no need for a two-part operation as
with MyISAM indexes. The location of the index is at the same time the location of the data
itself—there is no need for following a row pointer to get the rest of the column data, saving
an expensive disk access.
Looking up a book by ISBN in our example table simply means locating it quickly, as it is
stored in order, and then reading the complete record in one go.
Consider if you were to search for a book by title to find out the ISBN number. An index on
the name column is required to prevent the database from scanning through the whole
(ISBN-sorted) table. In contrast to MyISAM, the InnoDB storage engine creates secondary
Instead of record pointers, it uses a copy of the whole primary key for each record to establish
the connection to the actual data contents.
In the previous figure, have a look at the IDX_TITLE index. Instead of a simple pointer to the
corresponding record in the data table, you can see the ISBN number duplicated as well. This
is because the isbn column is the primary key of the books table. The same goes for the other
indexes in the figure—they all contain the book ISBN number as well. You do not need to (and
should not) specify this yourself when creating and indexing on InnoDB tables, it all happens
automatically under the covers.
Lookups by secondary index are similar to MyISAM index lookups. In the first step, the index
record that matches your search term is located. Then secondly, the remaining data is
retrieved from the data table by means of another access—this time by primary key.
As you might have figured, the second access is optional, depending on what information you
request in your query. Consider a query looking for the ISBN numbers of all known issues of
SELECT isbn FROM books WHERE title LIKE ‘Moby Dick%';
Issued against a presumably large library database, it will most certainly result in an index
lookup on the IDX_TITLE key. Once the index records are found, there is no need for another
lookup to the actual data pages on disk because the ISBN number is already present in the
index. Even though you cannot see the column in the index definition, MySQL will skip the
second seek saving valuable I/O operations.
But there is a drawback to this as well. MyISAM’s row pointers are comparatively small. The
primary key of an InnoDB table can be much bigger—the longer the key, the more the data
that is stored redundantly.
In the end, it can often be quite difficult to decide on the optimal balance between increased
space requirements and maintenance costs on index updates. But do not worry; we are going
to provide help on that in this chapter as well.
General requirements for the recipes in this chapter
All the recipes in this chapter revolve around changing the database schema. In order to add
indexes or remove them, you will need access to a user account that has an effective INDEX
privilege or the ALTER privilege on the tables you are going to modify.
While the INDEX privilege allows for use of the CREATE INDEX command, ALTER is required
for the ALTER TABLE ADD INDEX syntax. The MySQL manual states that the former is mapped
to the latter automatically. However, an important difference exists: CREATE INDEX can only
be used to add a single index at a time, while ALTER TABLE ADD INDEX can be used to add
more than one index to a table in a single go.
This is especially relevant for InnoDB tables because up to MySQL version 5.1 every change
to the definition of a table internally performs a copy of the whole table. While for small
databases this might not be of any concern, it quickly becomes infeasible for large tables due
to the high load copying may put on the server. With more recent versions this might have
changed, but make sure to consult your version’s manual.
In the recipes throughout this chapter, we will consistently use the ALTER TABLE ADD INDEX
syntax to modify tables, assuming you have the appropriate privileges. If you do not, you will
have to rewrite the statements to use the CREA TE INDEX syntax.