This section lists a number of miscellaneous tips for improving query processing speed:
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
See Section 8.11.2, “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
statement. See Section 8.8.1, “Optimizing Queries with
Try to avoid complex
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
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, consider
using the table in ways that avoid deleting rows. Another
possibility is to run
TABLE to defragment the table after you have
deleted a lot of rows from it. This behavior is altered by
You can force new rows to be appended (and therefore permit
concurrent inserts), even in tables that have deleted rows.
See Section 8.10.3, “Concurrent Inserts”.
ALTER TABLE ... ORDER BY
usually retrieve rows in
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
MyISAM tables that change frequently,
try to avoid all variable-length columns
TEXT). The table uses dynamic
row format if it includes even a single variable-length
column. See Chapter 15, Alternative 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 15, Alternative 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:
This is very important when you use MySQL storage engines
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, 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
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, 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 20.2, “Using Stored Routines (Procedures and Functions)”, and Section 24.3, “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 (as does MySQL), 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 8.9.3, “The MySQL Query Cache”.
statements to store many rows with one SQL statement. (This
is a relatively portable technique.)
AUTO_INCREMENT columns so that each
row in a table can be identified by a single unique value.
MEMORY tables when possible to get
more speed. See Section 15.3, “The
MEMORY 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
customer, use a column name of
name instead of
customer_name. To make your names
portable to other SQL servers, consider keeping them shorter
than 18 characters.
If you need really high speed, 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
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
If you are using numeric 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 17, Replication.
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 must ensure
that the table is okay by running the server with the
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.)
HIGH_PRIORITY have an effect only for
nontransactional storage engines that use only table-level