Some general tips for speeding up queries on
To help MySQL better optimize queries, use
ANALYZE TABLE or run myisamchk --analyze on a table after it has been loaded
with data. This updates a value for each index part that indicates the average number of rows that have
the same value. (For unique indexes, this is always 1.) MySQL uses this to decide which index to choose
when you join two tables based on a nonconstant expression. You can check the result from the table
analysis by using
SHOW INDEX FROM and examining the
--description --verbose shows index distribution information.
To sort an index and data according to an index, use myisamchk --sort-index --sort-records=1 (assuming that you want to sort on index 1). This is a good way to make queries faster if you have a unique index from which you want to read all rows in order according to the index. The first time you sort a large table this way, it may take a long time.
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
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, 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 permit concurrent inserts), even in tables that have deleted rows. See Section
8.10.3, "Concurrent Inserts".
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 14, Storage
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
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 14, Storage
ALTER TABLE ... ORDER BY if you usually retrieve rows in
order. By using this option after extensive
changes to the table, you may be able to get higher performance.
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 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.
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
--myisam-recover-options option, or by running myisamchk before restarting the server. (However,
even in this case, you should not lose anything by using
because the key information can always be generated from the data rows.)
Strings are automatically prefix- and end-space compressed in
MyISAM indexes. See Section 13.1.11,
CREATE INDEX Syntax".
You can increase performance by caching queries or answers in your application and then executing many inserts or updates together. Locking the table during this operation ensures 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".