A common source of MySQL performance problems is tables with outdated, redundant, and otherwise-useless data. Slow queries can be fixed by optimizing one or all tables in your database in a way that doesn't lock users out any longer than necessary.

MySQL was originally designed to be the little database that could, yet MySQL installations keep getting bigger and more complicated: larger databases (often running in VMs), and larger and more widely disparate clusters. As database configurations increase in size and complexity, DBAs are more likely to encounter performance slowdowns. Yet the bigger and more complex the installation, the more difficult it is to diagnose and address the speed sappers.

The MySQL Reference Manual includes an overview of factors that affect database performance, as well as sections explaining how to optimize SQL statements, indexes, InnoDB tables, MyISAM tables,MEMORY tables, locking operations, and MySQL Server, among other components.

At the hardware level, the most common sources of performance hits are disk seeks, disk reading and writing, CPU cycles, and memory bandwidth. Of these, memory management generally and disk I/O in particular top the list of performance-robbing suspects. In a June 16, 2014, article, ITworld's Matthew Mombrea focuses on the likelihood of encountering disk thrashing (a.k.a. I/O thrashing) when hosting multiple virtual machines running MySQL Server, each of which contains dozens of databases.

Data is constantly being swapped between RAM and disk, and obviously it's faster to access data in system memory than data on disk. When insufficient RAM is available to MySQL, dozens or hundreds of concurrent queries to disk will result in I/O thrashing. Comparing the server's load value to its CPU utilization will confirm this: high load value and low CPU utilization indicates high disk I/O wait times.

Determining how frequently you need to optimize your tables

The key to a smooth-running database is ensuring your tables are optimized. Striking the right balance between optimizing too often and optimizing too infrequently is a challenge for any DBA working with large MySQL databases. This quandary was presented in a Stack Overflow post from February 2012.

For a statistical database having more than 2,000 tables, each of which has approximately 100 million rows, how often should the tables be optimized when only 60 percent of them are updated every day (the remainder are archives)? You need to run OPTIMIZE on the table in three situations:

• When its datafile is fragmented on disk
• When many of its rows are updated or change size
• When deleting many records and not adding many others

Run CHECK TABLE when you suspect the table's data is corrupted, and then REPAIR TABLE when corruption is reported. Use ANALYZE TABLE to update index cardinality.

In a separate Stack Overflow post from March 2011, the perils of optimizing too frequently are explained. Many databases use InnoDB with a single file rather than separate files per table. Optimizing in such situations can cause more disk space to be used rather than less. (Also, tables are locked during optimization, so large tables may be inaccessible for long periods.)

From the command line, you can use mysqlcheck to optimize one or all databases:

Run "mysqlcheck" from the command line to optimize one or all of your databases quickly. Source:Stack Overflow

Alternatively, you can run this PHP script to optimize all the tables in your database:

This PHP script will optimize all the tables in a database in one fell swoop. Source: Stack Overflow

Other suggestions are to implode the table names into one string so that you need only one optimize table query, and to use MySQL Administrator in the MySQL GUI Tools.

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