Use persistent connections to the database to avoid connection overhead. If you cannot use persistent connections and you are initiating many new connections to the database, you may want to change the value of the thread_cache_size variable. See Section 7.5.3, “Tuning Server Parameters”.

Always check whether all your queries really use the indexes that you have created in the tables. In MySQL, you can do this with the EXPLAIN statement. See Section 7.2.1, “Optimizing Queries with EXPLAIN”.

Try to avoid complex SELECT queries on MyISAM tables that are updated frequently, to avoid problems with table locking that occur due to contention between readers and writers.

MyISAM supports concurrent inserts: If a table has no free blocks in the middle of the data file, you can INSERT new rows into it at the same time that other threads are reading from the table. If it is important to be able to do this, you should consider using the table in ways that avoid deleting rows. Another possibility is to run OPTIMIZE TABLE to defragment the table after you have deleted a lot of rows from it. This behavior is altered by setting the concurrent_insert variable. You can force new rows to be appended (and therefore allow concurrent inserts), even in tables that have deleted rows. See Section 7.3.3, “Concurrent Inserts”.

MySQL Enterprise.  For optimization tips geared to your specific circumstances, subscribe to the MySQL Enterprise Monitor. For more information, see http://www.mysql.com/products/enterprise/advisors.html.

To fix any compression issues that may have occurred with ARCHIVE tables, you can use OPTIMIZE TABLE. See Section 13.12, “The ARCHIVE Storage Engine”.

Use ALTER TABLE ... ORDER BY expr1, expr2, ... if you usually retrieve rows in expr1, expr2, ... order. By using this option after extensive changes to the table, you may be able to get higher performance.

In some cases, it may make sense to introduce a column that is “hashed” based on information from other columns. If this column is short, reasonably unique, and indexed, it may be much faster than a “wide” index on many columns. In MySQL, it is very easy to use this extra column:

SELECT * FROM tbl_name
  WHERE hash_col=MD5(CONCAT(col1,col2))
  AND col1='constant' AND col2='constant';

For MyISAM tables that change frequently, you should try to avoid all variable-length columns (VARCHAR, BLOB, and TEXT). The table uses dynamic row format if it includes even a single variable-length column. See Chapter 13, Storage Engines.

It is normally not useful to split a table into different tables just because the rows become large. In accessing a row, the biggest performance hit is the disk seek needed to find the first byte of the row. After finding the data, most modern disks can read the entire row fast enough for most applications. The only cases where splitting up a table makes an appreciable difference is if it is a MyISAM table using dynamic row format that you can change to a fixed row size, or if you very often need to scan the table but do not need most of the columns. See Chapter 13, Storage Engines.

If you often need to calculate results such as counts based on information from a lot of rows, it may be preferable to introduce a new table and update the counter in real time. An update of the following form is very fast:

UPDATE tbl_name SET count_col=count_col+1 WHERE key_col=constant;

This is very important when you use MySQL storage engines such as MyISAM that has only table-level locking (multiple readers with single writers). This also gives better performance with most database systems, because the row locking manager in this case has less to do.

If you need to collect statistics from large log tables, use summary tables instead of scanning the entire log table. Maintaining the summaries should be much faster than trying to calculate statistics “live.” Regenerating new summary tables from the logs when things change (depending on business decisions) is faster than changing the running application.

If possible, you should classify reports as “live” or as “statistical,” where data needed for statistical reports is created only from summary tables that are generated periodically from the live data.

Take advantage of the fact that columns have default values. Insert values explicitly only when the value to be inserted differs from the default. This reduces the parsing that MySQL must do and improves the insert speed.

In some cases, it is convenient to pack and store data into a BLOB column. In this case, you must provide code in your application to pack and unpack information, but this may save a lot of accesses at some stage. This is practical when you have data that does not conform well to a rows-and-columns table structure.

Normally, you should try to keep all data nonredundant (observing what is referred to in database theory as third normal form). However, there may be situations in which it can be advantageous to duplicate information or create summary tables to gain more speed.

Stored routines or UDFs (user-defined functions) may be a good way to gain performance for some tasks. See Section 19.2, “Using Stored Routines (Procedures and Functions)”, and Section 22.3, “Adding New Functions to MySQL”, for more information.

You can increase performance by caching queries or answers in your application and then executing many inserts or updates together. If your database system supports table locks, this should help to ensure that the index cache is only flushed once after all updates. You can also take advantage of MySQL's query cache to achieve similar results; see Section 7.5.5, “The MySQL Query Cache”.

Use INSERT DELAYED when you do not need to know when your data is written. This reduces the overall insertion impact because many rows can be written with a single disk write.

Use INSERT LOW_PRIORITY when you want to give SELECT statements higher priority than your inserts.

Use SELECT HIGH_PRIORITY to get retrievals that jump the queue. That is, the SELECT is executed even if there is another client waiting to do a write.

LOW_PRIORITY and HIGH_PRIORITY have an effect only for storage engines that use only table-level locking (such as MyISAM, MEMORY, and MERGE).

Use multiple-row INSERT statements to store many rows with one SQL statement. Many SQL servers support this, including MySQL.

Use LOAD DATA INFILE to load large amounts of data. This is faster than using INSERT statements.

Use AUTO_INCREMENT columns so that each row in a table can be identified by a single unique value. unique values.

Use OPTIMIZE TABLE once in a while to avoid fragmentation with dynamic-format MyISAM tables. See Section 13.5.3, “MyISAM Table Storage Formats”.

Use MEMORY tables when possible to get more speed. See Section 13.9, “The MEMORY (HEAP) Storage Engine”. MEMORY tables are useful for noncritical data that is accessed often, such as information about the last displayed banner for users who don't have cookies enabled in their Web browser. User sessions are another alternative available in many Web application environments for handling volatile state data.

With Web servers, images and other binary assets should normally be stored as files. That is, store only a reference to the file rather than the file itself in the database. Most Web servers are better at caching files than database contents, so using files is generally faster.

Columns with identical information in different tables should be declared to have identical data types so that joins based on the corresponding columns will be faster.

Try to keep column names simple. For example, in a table named customer, use a column name of name instead of customer_name. To make your names portable to other SQL servers, you should keep them shorter than 18 characters.

If you need really high speed, you should take a look at the low-level interfaces for data storage that the different SQL servers support. For example, by accessing the MySQL MyISAM storage engine directly, you could get a speed increase of two to five times compared to using the SQL interface. To be able to do this, the data must be on the same server as the application, and usually it should only be accessed by one process (because external file locking is really slow). One could eliminate these problems by introducing low-level MyISAM commands in the MySQL server (this could be one easy way to get more performance if needed). By carefully designing the database interface, it should be quite easy to support this type of optimization.

If you are using numerical data, it is faster in many cases to access information from a database (using a live connection) than to access a text file. Information in the database is likely to be stored in a more compact format than in the text file, so accessing it involves fewer disk accesses. You also save code in your application because you need not parse your text files to find line and column boundaries.

Replication can provide a performance benefit for some operations. You can distribute client retrievals among replication servers to split up the load. To avoid slowing down the master while making backups, you can make backups using a slave server. See Chapter 16, Replication.

Declaring a MyISAM table with the DELAY_KEY_WRITE=1 table option makes index updates faster because they are not flushed to disk until the table is closed. The downside is that if something kills the server while such a table is open, you should ensure that the table is okay by running the server with the --myisam-recover option, or by running myisamchk before restarting the server. (However, even in this case, you should not lose anything by using DELAY_KEY_WRITE, because the key information can always be generated from the data rows.)