Table of Contents
SELECTand Other Statements
MyISAMIndex Statistics Collection
Optimization is a complex task because ultimately it requires understanding of the entire system to be optimized. Although it may be possible to perform some local optimizations with little knowledge of your system or application, the more optimal you want your system to become, the more you must know about it.
This chapter tries to explain and give some examples of different ways to optimize MySQL. Remember, however, that there are always additional ways to make the system even faster, although they may require increasing effort to achieve.
The most important factor in making a system fast is its basic design. You must also know what kinds of processing your system is doing, and what its bottlenecks are. In most cases, system bottlenecks arise from these sources:
Disk seeks. It takes time for the disk to find a piece of data. With modern disks, the mean time for this is usually lower than 10ms, so we can in theory do about 100 seeks a second. This time improves slowly with new disks and is very hard to optimize for a single table. The way to optimize seek time is to distribute the data onto more than one disk.
Disk reading and writing. When the disk is at the correct position, we need to read the data. With modern disks, one disk delivers at least 10–20MB/s throughput. This is easier to optimize than seeks because you can read in parallel from multiple disks.
CPU cycles. When we have the data in main memory, we need to process it to get our result. Having small tables compared to the amount of memory is the most common limiting factor. But with small tables, speed is usually not the problem.
Memory bandwidth. When the CPU needs more data than can fit in the CPU cache, main memory bandwidth becomes a bottleneck. This is an uncommon bottleneck for most systems, but one to be aware of.
When using the
MyISAM storage engine, MySQL
uses extremely fast table locking that allows multiple readers
or a single writer. The biggest problem with this storage engine
occurs when you have a steady stream of mixed updates and slow
selects on a single table. If this is a problem for certain
tables, you can use another storage engine for them. See
Chapter 14, Storage Engines and Table Types.
MySQL can work with both transactional and non-transactional
tables. To make it easier to work smoothly with
non-transactional tables (which cannot roll back if something
goes wrong), MySQL has the following rules. Note that these
rules apply only when you use the
IGNORE specifier for
All columns have default values.
If you insert an inappropriate or out-of-range value into a column, MySQL sets the column to the “best possible value” instead of reporting an error. For numerical values, this is 0, the smallest possible value or the largest possible value. For strings, this is either the empty string or as much of the string as can be stored in the column.
All calculated expressions return a value that can be used
instead of signaling an error condition. For example, 1/0
To change the preceding behaviors, you can enable stricter data
handling by setting the server SQL mode appropriately. For more
information about data handling, see
Section 1.9.6, “How MySQL Deals with Constraints”,
Section 5.2.5, “The Server SQL Mode”, and Section 13.2.4, “
Because all SQL servers implement different parts of standard SQL, it takes work to write portable database applications. It is very easy to achieve portability for very simple selects and inserts, but becomes more difficult the more capabilities you require. If you want an application that is fast with many database systems, it becomes even more difficult.
All database systems have some weak points. That is, they have different design compromises that lead to different behavior.
To make a complex application portable, you need to determine which SQL servers it must work with, and then determine what features those servers support. You can use the MySQL crash-me program to find functions, types, and limits that you can use with a selection of database servers. crash-me does not check for every possible feature, but it is still reasonably comprehensive, performing about 450 tests. An example of the type of information crash-me can provide is that you should not use column names that are longer than 18 characters if you want to be able to use Informix or DB2.
The crash-me program and the MySQL benchmarks
are all very database independent. By taking a look at how they
are written, you can get a feeling for what you must do to make
your own applications database independent. The programs can be
found in the
sql-bench directory of MySQL
source distributions. They are written in Perl and use the DBI
database interface. Use of DBI in itself solves part of the
portability problem because it provides database-independent
access methods. See Section 7.1.4, “The MySQL Benchmark Suite”.
If you strive for database independence, you need to get a good
feeling for each SQL server's bottlenecks. For example, MySQL is
very fast in retrieving and updating rows for
MyISAM tables, but has a problem in mixing
slow readers and writers on the same table. Oracle, on the other
hand, has a big problem when you try to access rows that you
have recently updated (until they are flushed to disk).
Transactional database systems in general are not very good at
generating summary tables from log tables, because in this case
row locking is almost useless.
To make your application really database independent, you should define an easily extendable interface through which you manipulate your data. For example, C++ is available on most systems, so it makes sense to use a C++ class-based interface to the databases.
If you use some feature that is specific to a given database
system (such as the
REPLACE statement, which
is specific to MySQL), you should implement the same feature for
other SQL servers by coding an alternative method. Although the
alternative might be slower, it enables the other servers to
perform the same tasks.
With MySQL, you can use the
/*! */ syntax to
add MySQL-specific keywords to a statement. The code inside
/* */ is treated as a comment (and ignored)
by most other SQL servers. For information about writing
comments, see Section 9.4, “Comment Syntax”.
If high performance is more important than exactness, as for some Web applications, it is possible to create an application layer that caches all results to give you even higher performance. By letting old results expire after a while, you can keep the cache reasonably fresh. This provides a method to handle high load spikes, in which case you can dynamically increase the cache size and set the expiration timeout higher until things get back to normal.
In this case, the table creation information should contain information about the initial cache size and how often the table should normally be refreshed.
An attractive alternative to implementing an application cache is to use the MySQL query cache. By enabling the query cache, the server handles the details of determining whether a query result can be reused. This simplifies your application. See Section 5.13, “The MySQL Query Cache”.
This section describes an early application for MySQL.
During MySQL initial development, the features of MySQL were made to fit our largest customer, which handled data warehousing for a couple of the largest retailers in Sweden.
From all stores, we got weekly summaries of all bonus card transactions, and were expected to provide useful information for the store owners to help them find how their advertising campaigns were affecting their own customers.
The volume of data was quite huge (about seven million summary transactions per month), and we had data for 4–10 years that we needed to present to the users. We got weekly requests from our customers, who wanted instant access to new reports from this data.
We solved this problem by storing all information per month in compressed “transaction tables.” We had a set of simple macros that generated summary tables grouped by different criteria (product group, customer id, store, and so on) from the tables in which the transactions were stored. The reports were Web pages that were dynamically generated by a small Perl script. This script parsed a Web page, executed the SQL statements in it, and inserted the results. We would have used PHP or mod_perl instead, but they were not available at the time.
For graphical data, we wrote a simple tool in C that could process SQL query results and produce GIF images based on those results. This tool also was dynamically executed from the Perl script that parses the Web pages.
In most cases, a new report could be created simply by copying an existing script and modifying the SQL query that it used. In some cases, we needed to add more columns to an existing summary table or generate a new one. This also was quite simple because we kept all transaction-storage tables on disk. (This amounted to about 50GB of transaction tables and 200GB of other customer data.)
We also let our customers access the summary tables directly with ODBC so that the advanced users could experiment with the data themselves.
This system worked well and we had no problems handling the data with quite modest Sun Ultra SPARCstation hardware (2×200MHz). Eventually the system was migrated to Linux.
This benchmark suite is meant to tell any user what operations a
given SQL implementation performs well or poorly. You can get a
good idea for how the benchmarks work by looking at the code and
results in the
sql-bench directory in any
MySQL source distribution.
Note that this benchmark is single-threaded, so it measures the minimum time for the operations performed. We plan to add multi-threaded tests to the benchmark suite in the future.
To use the benchmark suite, the following requirements must be satisfied:
The benchmark suite is provided with MySQL source distributions. You can either download a released distribution from http://dev.mysql.com/downloads/, or use the current development source tree. (See Section 2.9.3, “Installing from the Development Source Tree”.)
The benchmark scripts are written in Perl and use the Perl
DBI module to access database servers, so DBI must be
installed. You also need the server-specific DBD drivers for
each of the servers you want to test. For example, to test
MySQL, PostgreSQL, and DB2, you must have the
DBD::DB2 modules installed. See
Section 2.14, “Perl Installation Notes”.
After you obtain a MySQL source distribution, you can find the
benchmark suite located in its
directory. To run the benchmark tests, build MySQL, and then
change location into the
directory and execute the
perl run-all-tests --server=
server_name should be the name of one
of the supported servers. To get a list of all options and
supported servers, invoke this command:
perl run-all-tests --help
The crash-me script also is located in the
crash-me tries to determine what features a
database system supports and what its capabilities and
limitations are by actually running queries. For example, it
What data types are supported
How many indexes are supported
What functions are supported
How big a query can be
How big a
VARCHAR column can be
You can find the results from crash-me for many different database servers at http://dev.mysql.com/tech-resources/crash-me.php. For more information about benchmark results, visit http://dev.mysql.com/tech-resources/benchmarks/.
You should definitely benchmark your application and database to find out where the bottlenecks are. After fixing one bottleneck (or by replacing it with a “dummy” module), you can proceed to identify the next bottleneck. Even if the overall performance for your application currently is acceptable, you should at least make a plan for each bottleneck and decide how to solve it if someday you really need the extra performance.
For examples of portable benchmark programs, look at those in the MySQL benchmark suite. See Section 7.1.4, “The MySQL Benchmark Suite”. You can take any program from this suite and modify it for your own needs. By doing this, you can try different solutions to your problem and test which really is fastest for you.
Another free benchmark suite is the Open Source Database Benchmark, available at http://osdb.sourceforge.net/.
It is very common for a problem to occur only when the system is very heavily loaded. We have had many customers who contact us when they have a (tested) system in production and have encountered load problems. In most cases, performance problems turn out to be due to issues of basic database design (for example, table scans are not good under high load) or problems with the operating system or libraries. Most of the time, these problems would be much easier to fix if the systems were not already in production.
To avoid problems like this, you should put some effort into benchmarking your whole application under the worst possible load. You can use Super Smack, available at http://jeremy.zawodny.com/mysql/super-smack/. As suggested by its name, it can bring a system to its knees, so make sure to use it only on your development systems.
First, one factor affects all statements: The more complex your
permissions setup, the more overhead you have. Using simpler
permissions when you issue
enables MySQL to reduce permission-checking overhead when clients
execute statements. For example, if you do not grant any
table-level or column-level privileges, the server need not ever
check the contents of the
columns_priv tables. Similarly, if you place no
resource limits on any accounts, the server does not have to
perform resource counting. If you have a very high
statement-processing load, it may be worth the time to use a
simplified grant structure to reduce permission-checking overhead.
If your problem is with a specific MySQL expression or function,
you can perform a timing test by invoking the
BENCHMARK() function using the
mysql client program. Its syntax is
The return value is always zero, but mysql
prints a line displaying approximately how long the statement took
to execute. For example:
SELECT BENCHMARK(1000000,1+1);+------------------------+ | BENCHMARK(1000000,1+1) | +------------------------+ | 0 | +------------------------+ 1 row in set (0.32 sec)
This result was obtained on a Pentium II 400MHz system. It shows that MySQL can execute 1,000,000 simple addition expressions in 0.32 seconds on that system.
All MySQL functions should be highly optimized, but there may be
BENCHMARK() is an excellent
tool for finding out if some function is a problem for your
EXPLAIN [EXTENDED] SELECT
EXPLAIN statement can be used either as a
DESCRIBE or as a way to obtain
information about how MySQL executes a
SHOW COLUMNS FROM
When you precede a
SELECT statement with
EXPLAIN, MySQL displays
information from the optimizer about the query execution
plan. That is, MySQL explains how it would process the
SELECT, including information about how
tables are joined and in which order.
This section describes the second use of
EXPLAIN for obtaining query execution plan
information. For a description of the
statements, see Section 13.3.1, “
DESCRIBE Syntax”, and
Section 18.104.22.168, “
SHOW COLUMNS Syntax”.
With the help of
EXPLAIN, you can see where
you should add indexes to tables to get a faster
SELECT that uses indexes to find rows. You
can also use
EXPLAIN to check whether the
optimizer joins the tables in an optimal order. To force the
optimizer to use a join order corresponding to the order in
which the tables are named in the
statement, begin the statement with
STRAIGHT_JOIN rather than just
If you have a problem with indexes not being used when you
believe that they should be, you should run
TABLE to update table statistics such as cardinality
of keys, that can affect the choices the optimizer makes. See
Section 22.214.171.124, “
ANALYZE TABLE Syntax”.
EXPLAIN returns a row of information for each
table used in the
SELECT statement. The
tables are listed in the output in the order that MySQL would
read them while processing the query. MySQL resolves all joins
using a single-sweep multi-join method.
This means that MySQL reads a row from the first table, and then
finds a matching row in the second table, the third table, and
so on. When all tables are processed, MySQL outputs the selected
columns and backtracks through the table list until a table is
found for which there are more matching rows. The next row is
read from this table and the process continues with the next
In MySQL version 4.1, the
format was changed to work better with constructs such as
UNION statements, subqueries, and derived
tables. Most notable is the addition of two new columns:
select_type. You do
not see these columns when using servers older than MySQL 4.1.
EXPLAIN syntax also was augmented to allow
EXTENDED keyword. When this keyword is
EXPLAIN produces extra information that
can be viewed by issuing a
statement following the
This information displays how the optimizer qualifies table and
column names in the
SELECT statement, what
SELECT looks like after the application
of rewriting and optimization rules, and possibly other notes
about the optimization process.
Each output row from
information about one table, and each row contains the following
SELECT identifier. This is the
sequential number of the
The type of
SELECT, which can be any of
those shown in the following table:
|Second or later |
|Second or later |
|Result of a |
|Derived table |
DEPENDENT typically signifies the use of
a correlated subquery. See
Section 126.96.36.199, “Correlated Subqueries”.
The table to which the row of output refers.
The join type. The different join types are listed here, ordered from the best type to the worst:
The table has only one row (= system table). This is a
special case of the
const join type.
The table has at most one matching row, which is read at
the start of the query. Because there is only one row,
values from the column in this row can be regarded as
constants by the rest of the optimizer.
const tables are very fast because
they are read only once.
const is used when you compare all
parts of a
PRIMARY KEY or
UNIQUE index to constant values. In
the following queries,
tbl_name can be used as a
SELECT * FROM
primary_key=1; SELECT * FROM
One row is read from this table for each combination of
rows from the previous tables. Other than the
types, this is the best possible join type. It is used
when all parts of an index are used by the join and the
index is a
PRIMARY KEY or
eq_ref can be used for indexed
columns that are compared using the
operator. The comparison value can be a constant or an
expression that uses columns from tables that are read
before this table. In the following examples, MySQL can
eq_ref join to process
SELECT * FROM
column; SELECT * FROM
All rows with matching index values are read from this
table for each combination of rows from the previous
ref is used if the join uses
only a leftmost prefix of the key or if the key is not a
PRIMARY KEY or
UNIQUE index (in other words, if the
join cannot select a single row based on the key value).
If the key that is used matches only a few rows, this is
a good join type.
ref can be used for indexed columns
that are compared using the
<=> operator. In the following
examples, MySQL can use a
ref join to
SELECT * FROM
expr; SELECT * FROM
column; SELECT * FROM
This join type is like
ref, but with
the addition that MySQL does an extra search for rows
NULL values. This join
type optimization was added for MySQL 4.1.1 and is used
mostly when resolving subqueries. In the following
examples, MySQL can use a
join to process
SELECT * FROM
This type replaces
ref for some
IN subqueries of the following form:
unique_subquery is just an index
lookup function that replaces the subquery completely
for better efficiency.
This join type is similar to
unique_subquery. It replaces
IN subqueries, but it works for
non-unique indexes in subqueries of the following form:
Only rows that are in a given range are retrieved, using
an index to select the rows. The
column in the output row indicates which index is used.
key_len contains the longest key
part that was used. The
ref column is
NULL for this type.
range can be used when a key column
is compared to a constant using any of the
SELECT * FROM
key_column= 10; SELECT * FROM
key_columnBETWEEN 10 and 20; SELECT * FROM
key_columnIN (10,20,30); SELECT * FROM
key_part1= 10 AND
This join type is the same as
except that only the index tree is scanned. This usually
is faster than
ALL because the index
file usually is smaller than the data file.
MySQL can use this join type when the query uses only columns that are part of a single index.
A full table scan is done for each combination of rows
from the previous tables. This is normally not good if
the table is the first table not marked
const, and usually
very bad in all other cases.
Normally, you can avoid
ALL by adding
indexes that allow row retrieval from the table based on
constant values or column values from earlier tables.
possible_keys column indicates which
indexes MySQL can choose from use to find the rows in this
table. Note that this column is totally independent of the
order of the tables as displayed in the output from
EXPLAIN. That means that some of the keys
possible_keys might not be usable in
practice with the generated table order.
If this column is
NULL, there are no
relevant indexes. In this case, you may be able to improve
the performance of your query by examining the
WHERE clause to check whether it refers
to some column or columns that would be suitable for
indexing. If so, create an appropriate index and check the
EXPLAIN again. See
Section 13.1.2, “
ALTER TABLE Syntax”.
To see what indexes a table has, use
key column indicates the key (index)
that MySQL actually decided to use. The key is
NULL if no index was chosen. To force
MySQL to use or ignore an index listed in the
possible_keys column, use
USE INDEX, or
IGNORE INDEX in your query. See
Section 13.2.7, “
ANALYZE TABLE helps the
optimizer choose better indexes. For
MyISAM tables, myisamchk
--analyze does the same. See
Section 188.8.131.52, “
ANALYZE TABLE Syntax”, and
Section 5.9.4, “Table Maintenance and Crash Recovery”.
key_len column indicates the length
of the key that MySQL decided to use. The length is
NULL if the
NULL. Note that the value of
key_len enables you to determine how many
parts of a multiple-part key MySQL actually uses.
ref column shows which columns or
constants are compared to the index named in the
key column to select rows from the table.
rows column indicates the number of
rows MySQL believes it must examine to execute the query.
This column contains additional information about how MySQL resolves the query. Here is an explanation of the values that can appear in this column:
MySQL is looking for distinct values, so it stops searching for more rows for the current row combination after it has found the first matching row.
MySQL was able to do a
optimization on the query and does not examine more rows
in this table for the previous row combination after it
finds one row that matches the
JOIN criteria. Here is an example of the type
of query that can be optimized this way:
SELECT * FROM t1 LEFT JOIN t2 ON t1.id=t2.id WHERE t2.id IS NULL;
t2.id is defined as
NOT NULL. In this case, MySQL scans
t1 and looks up the rows in
t2 using the values of
t1.id. If MySQL finds a matching row
t2, it knows that
t2.id can never be
NULL, and does not scan through the
rest of the rows in
t2 that have the
id value. In other words, for
each row in
t1, MySQL needs to do
only a single lookup in
regardless of how many rows actually match in
range checked for each record (index map:
MySQL found no good index to use, but found that some of
indexes might be used after column values from preceding
tables are known. For each row combination in the
preceding tables, MySQL checks whether it is possible to
range access method to retrieve
rows. The applicability criteria are as described in
Section 7.2.5, “Range Optimization”, with the exception
that all column values for the preceding table are known
and considered to be constants.
This is not very fast, but is faster than performing a join with no index at all.
MySQL must do an extra pass to find out how to retrieve
the rows in sorted order. The sort is done by going
through all rows according to the join type and storing
the sort key and pointer to the row for all rows that
WHERE clause. The keys then
are sorted and the rows are retrieved in sorted order.
See Section 7.2.9, “
ORDER BY Optimization”.
The column information is retrieved from the table using only information in the index tree without having to do an additional seek to read the actual row. This strategy can be used when the query uses only columns that are part of a single index.
To resolve the query, MySQL needs to create a temporary
table to hold the result. This typically happens if the
GROUP BY and
ORDER BY clauses that list columns
WHERE clause is used to restrict
which rows to match against the next table or send to
the client. Unless you specifically intend to fetch or
examine all rows from the table, you may have something
wrong in your query if the
value is not
Using where and the
table join type is
If you want to make your queries as fast as possible,
you should look out for
Using filesort and
Using index for group-by
Similar to the
Using index way of
accessing a table,
Using index for
group-by indicates that MySQL found an index
that can be used to retrieve all columns of a
GROUP BY or
DISTINCT query without any extra disk
access to the actual table. Additionally, the index is
used in the most efficient way so that for each group,
only a few index entries are read. For details, see
Section 7.2.10, “
GROUP BY Optimization”.
You can get a good indication of how good a join is by taking
the product of the values in the
EXPLAIN output. This should tell you
roughly how many rows MySQL must examine to execute the query.
If you restrict queries with the
max_join_size system variable, this row
product also is used to determine which multiple-table
SELECT statements to execute and which to
abort. See Section 7.5.2, “Tuning Server Parameters”.
The following example shows how a multiple-table join can be
optimized progressively based on the information provided by
Suppose that you have the
shown here and that you plan to examine it using
EXPLAIN SELECT tt.TicketNumber, tt.TimeIn, tt.ProjectReference, tt.EstimatedShipDate, tt.ActualShipDate, tt.ClientID, tt.ServiceCodes, tt.RepetitiveID, tt.CurrentProcess, tt.CurrentDPPerson, tt.RecordVolume, tt.DPPrinted, et.COUNTRY, et_1.COUNTRY, do.CUSTNAME FROM tt, et, et AS et_1, do WHERE tt.SubmitTime IS NULL AND tt.ActualPC = et.EMPLOYID AND tt.AssignedPC = et_1.EMPLOYID AND tt.ClientID = do.CUSTNMBR;
For this example, make the following assumptions:
The columns being compared have been declared as follows:
The tables have the following indexes:
tt.ActualPC values are not evenly
Initially, before any optimizations have been performed, the
EXPLAIN statement produces the following
table type possible_keys key key_len ref rows Extra et ALL PRIMARY NULL NULL NULL 74 do ALL PRIMARY NULL NULL NULL 2135 et_1 ALL PRIMARY NULL NULL NULL 74 tt ALL AssignedPC, NULL NULL NULL 3872 ClientID, ActualPC range checked for each record (key map: 35)
each table, this output indicates that MySQL is generating a
Cartesian product of all the tables; that is, every combination
of rows. This takes quite a long time, because the product of
the number of rows in each table must be examined. For the case
at hand, this product is 74 × 2135 × 74 × 3872
= 45,268,558,720 rows. If the tables were bigger, you can only
imagine how long it would take.
One problem here is that MySQL can use indexes on columns more
efficiently if they are declared as the same type and size. (For
ISAM tables, indexes may not be used at all
unless the columns are declared the same.) In this context,
considered the same if they are declared as the same size.
tt.ActualPC is declared as
CHAR(15), so there is a length mismatch.
To fix this disparity between column lengths, use
TABLE to lengthen
ActualPC from 10
characters to 15 characters:
ALTER TABLE tt MODIFY ActualPC VARCHAR(15);
et.EMPLOYID are both
VARCHAR(15). Executing the
EXPLAIN statement again produces this result:
table type possible_keys key key_len ref rows Extra tt ALL AssignedPC, NULL NULL NULL 3872 Using ClientID, where ActualPC do ALL PRIMARY NULL NULL NULL 2135 range checked for each record (key map: 1) et_1 ALL PRIMARY NULL NULL NULL 74 range checked for each record (key map: 1) et eq_ref PRIMARY PRIMARY 15 tt.ActualPC 1
This is not perfect, but is much better: The product of the
rows values is less by a factor of 74. This
version executes in a couple of seconds.
A second alteration can be made to eliminate the column length
mismatches for the
ALTER TABLE tt MODIFY AssignedPC VARCHAR(15),->
MODIFY ClientID VARCHAR(15);
After that modification,
EXPLAIN produces the
output shown here:
table type possible_keys key key_len ref rows Extra et ALL PRIMARY NULL NULL NULL 74 tt ref AssignedPC, ActualPC 15 et.EMPLOYID 52 Using ClientID, where ActualPC et_1 eq_ref PRIMARY PRIMARY 15 tt.AssignedPC 1 do eq_ref PRIMARY PRIMARY 15 tt.ClientID 1
At this point, the query is optimized almost as well as
possible. The remaining problem is that, by default, MySQL
assumes that values in the
are evenly distributed, and that is not the case for the
tt table. Fortunately, it is easy to tell
MySQL to analyze the key distribution:
ANALYZE TABLE tt;
With the additional index information, the join is perfect and
EXPLAIN produces this result:
table type possible_keys key key_len ref rows Extra tt ALL AssignedPC NULL NULL NULL 3872 Using ClientID, where ActualPC et eq_ref PRIMARY PRIMARY 15 tt.ActualPC 1 et_1 eq_ref PRIMARY PRIMARY 15 tt.AssignedPC 1 do eq_ref PRIMARY PRIMARY 15 tt.ClientID 1
Note that the
rows column in the output from
EXPLAIN is an educated guess from the MySQL
join optimizer. You should check whether the numbers are even
close to the truth by comparing the
product with the actual number of rows that the query returns.
If the numbers are quite different, you might get better
performance by using
STRAIGHT_JOIN in your
SELECT statement and trying to list the
tables in a different order in the
In most cases, you can estimate query performance by counting
disk seeks. For small tables, you can usually find a row in one
disk seek (because the index is probably cached). For bigger
tables, you can estimate that, using B-tree indexes, you need
this many seeks to find a row:
index_block_length / 3 × 2
data_pointer_length)) + 1
In MySQL, an index block is usually 1,024 bytes and the data
pointer is usually four bytes. For a 500,000-row table with an
index length of three bytes (the size of
MEDIUMINT), the formula indicates
log(500,000)/log(1024/3×2/(3+4)) + 1 =
This index would require storage of about 500,000 × 7 × 3/2 = 5.2MB (assuming a typical index buffer fill ratio of 2/3), so you probably have much of the index in memory and so need only one or two calls to read data to find the row.
For writes, however, you need four seek requests to find where to place a new index value and normally two seeks to update the index and write the row.
Note that the preceding discussion does not mean that your
application performance slowly degenerates by log N. As long as
everything is cached by the OS or the MySQL server, things
become only marginally slower as the table gets bigger. After
the data gets too big to be cached, things start to go much
slower until your applications are only bound by disk-seeks
(which increase by log
N). To avoid
this, increase the key cache size as the data grows. For
MyISAM tables, the key cache size is
controlled by the
variable. See Section 7.5.2, “Tuning Server Parameters”.
In general, when you want to make a slow
WHERE query faster, the first thing to check is
whether you can add an index. All references between different
tables should usually be done with indexes. You can use the
EXPLAIN statement to determine which indexes
are used for a
Section 7.2.1, “Optimizing Queries with
EXPLAIN”, and Section 7.4.5, “How MySQL Uses Indexes”.
Some general tips for speeding up queries on
To help MySQL better optimize queries, use
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 non-constant expression. You can check the
result from the table analysis by using
myisamchk --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.
This section discusses optimizations that can be made for
WHERE clauses. The examples use
SELECT statements, but the same optimizations
WHERE clauses in
Work on the MySQL optimizer is ongoing, so this section is incomplete. MySQL performs a great many optimizations, not all of which are documented here.
Some of the optimizations performed by MySQL follow:
Removal of unnecessary parentheses:
((a AND b) AND c OR (((a AND b) AND (c AND d)))) -> (a AND b AND c) OR (a AND b AND c AND d)
(a<b AND b=c) AND a=5 -> b>5 AND b=c AND a=5
Constant condition removal (needed because of constant folding):
(B>=5 AND B=5) OR (B=6 AND 5=5) OR (B=7 AND 5=6) -> B=5 OR B=6
Constant expressions used by indexes are evaluated only once.
COUNT(*) on a single table without a
WHERE is retrieved directly from the
table information for
This is also done for any
expression when used with only one table.
Early detection of invalid constant expressions. MySQL
quickly detects that some
statements are impossible and returns no rows.
HAVING is merged with
WHERE if you do not use
BY or aggregate functions
For each table in a join, a simpler
is constructed to get a fast
evaluation for the table and also to skip rows as soon as
An empty table or a table with one row.
A table that is used with a
clause on a
PRIMARY KEY or a
UNIQUE index, where all index parts
are compared to constant expressions and are defined as
All of the following tables are used as constant tables:
SELECT * FROM t WHERE
primary_key=1; SELECT * FROM t1,t2 WHERE t1.
primary_key=1 AND t2.
The best join combination for joining the tables is found by
trying all possibilities. If all columns in
GROUP BY clauses come
from the same table, that table is preferred first when
If there is an
ORDER BY clause and a
GROUP BY clause, or if the
ORDER BY or
contains columns from tables other than the first table in
the join queue, a temporary table is created.
If you use the
MySQL uses an in-memory temporary table.
Each table index is queried, and the best index is used unless the optimizer believes that it is more efficient to use a table scan. At one time, a scan was used based on whether the best index spanned more than 30% of the table, but a fixed percentage no longer determines the choice between using an index or a scan. The optimizer now is more complex and bases its estimate on additional factors such as table size, number of rows, and I/O block size.
In some cases, MySQL can read rows from the index without even consulting the data file. If all columns used from the index are numeric, only the index tree is used to resolve the query.
Before each row is output, those that do not match the
HAVING clause are skipped.
Some examples of queries that are very fast:
SELECT COUNT(*) FROM
tbl_name; SELECT MIN(
tbl_name; SELECT MAX(
constant; SELECT ... FROM
key_part2,... LIMIT 10; SELECT ... FROM
key_part2DESC, ... LIMIT 10;
MySQL resolves the following queries using only the index tree, assuming that the indexed columns are numeric:
val; SELECT COUNT(*) FROM
The following queries use indexing to retrieve the rows in sorted order without a separate sorting pass:
SELECT ... FROM
key_part2,... ; SELECT ... FROM
key_part2DESC, ... ;
range access method uses a single index
to retrieve a subset of table rows that are contained within one
or several index value intervals. It can be used for a
single-part or multiple-part index. The following sections give
a detailed description of how intervals are extracted from the
For a single-part index, index value intervals can be
conveniently represented by corresponding conditions in the
WHERE clause, so we speak of
range conditions rather than
The definition of a range condition for a single-part index is as follows:
HASH indexes, comparison of a key part
with a constant value is a range condition when using the
IS NULL, or
IS NOT NULL operators.
BTREE indexes, comparison of a key
part with a constant value is a range condition when using
<> operators, or
does not start with a wildcard).
For all types of indexes, multiple range conditions
AND form a range condition.
“Constant value” in the preceding descriptions means one of the following:
A constant from the query string
A column of a
system table from the same join
The result of an uncorrelated subquery
Any expression composed entirely from subexpressions of the preceding types
Here are some examples of queries with range conditions in the
SELECT * FROM t1 WHERE
key_col> 1 AND
key_col< 10; SELECT * FROM t1 WHERE
key_col= 1 OR
key_colIN (15,18,20); SELECT * FROM t1 WHERE
key_colLIKE 'ab%' OR
key_colBETWEEN 'bar' AND 'foo';
Note that some non-constant values may be converted to constants during the constant propagation phase.
MySQL tries to extract range conditions from the
WHERE clause for each of the possible
indexes. During the extraction process, conditions that cannot
be used for constructing the range condition are dropped,
conditions that produce overlapping ranges are combined, and
conditions that produce empty ranges are removed.
Consider the following statement, where
key1 is an indexed column and
nonkey is not indexed:
SELECT * FROM t1 WHERE (key1 < 'abc' AND (key1 LIKE 'abcde%' OR key1 LIKE '%b')) OR (key1 < 'bar' AND nonkey = 4) OR (key1 < 'uux' AND key1 > 'z');
The extraction process for key
key1 is as
Start with original
(key1 < 'abc' AND (key1 LIKE 'abcde%' OR key1 LIKE '%b')) OR (key1 < 'bar' AND nonkey = 4) OR (key1 < 'uux' AND key1 > 'z')
nonkey = 4 and
LIKE '%b' because they cannot be used for a
range scan. The correct way to remove them is to replace
TRUE, so that we do not miss
any matching rows when doing the range scan. Having
replaced them with
TRUE, we get:
(key1 < 'abc' AND (key1 LIKE 'abcde%' OR TRUE)) OR (key1 < 'bar' AND TRUE) OR (key1 < 'uux' AND key1 > 'z')
Collapse conditions that are always true or false:
(key1 LIKE 'abcde%' OR TRUE) is
(key1 < 'uux' AND key1 > 'z')
is always false
Replacing these conditions with constants, we get:
(key1 < 'abc' AND TRUE) OR (key1 < 'bar' AND TRUE) OR (FALSE)
FALSE constants, we obtain:
(key1 < 'abc') OR (key1 < 'bar')
Combining overlapping intervals into one yields the final condition to be used for the range scan:
(key1 < 'bar')
In general (and as demonstrated by the preceding example), the
condition used for a range scan is less restrictive than the
WHERE clause. MySQL performs an additional
check to filter out rows that satisfy the range condition but
not the full
The range condition extraction algorithm can handle nested
OR constructs of
arbitrary depth, and its output does not depend on the order
in which conditions appear in
Range conditions on a multiple-part index are an extension of range conditions for a single-part index. A range condition on a multiple-part index restricts index rows to lie within one or several key tuple intervals. Key tuple intervals are defined over a set of key tuples, using ordering from the index.
For example, consider a multiple-part index defined as
key1(, and the
following set of key tuples listed in key order:
key_part3NULL 1 'abc' NULL 1 'xyz' NULL 2 'foo' 1 1 'abc' 1 1 'xyz' 1 2 'abc' 2 1 'aaa'
defines this interval:
(1,-inf,-inf) <= (
key_part3) < (1,+inf,+inf)
The interval covers the 4th, 5th, and 6th tuples in the preceding data set and can be used by the range access method.
By contrast, the condition
does not define a single interval and cannot
be used by the range access method.
The following descriptions indicate how range conditions work for multiple-part indexes in greater detail.
HASH indexes, each interval
containing identical values can be used. This means that
the interval can be produced only for conditions in the
const2AND ... AND
const2, … are constants,
cmp is one of the
IS NULL comparison operators, and the
conditions cover all index parts. (That is, there are
N conditions, one for each part
N-part index.) For
example, the following is a range condition for a
key_part1= 1 AND
key_part2IS NULL AND
For the definition of what is considered to be a constant, see Section 184.108.40.206, “The Range Access Method for Single-Part Indexes”.
BTREE index, an interval might be
usable for conditions combined with
AND, where each condition compares a
key part with a constant value using
does not start with a wildcard). An interval can be used
as long as it is possible to determine a single key tuple
containing all rows that match the condition (or two
!= is used). For example, for this
key_part1= 'foo' AND
key_part2>= 10 AND
The single interval is:
('foo',10,10) < (
key_part3) < ('foo',+inf,+inf)
It is possible that the created interval contains more
rows than the initial condition. For example, the
preceding interval includes the value
0), which does not satisfy the original
If conditions that cover sets of rows contained within
intervals are combined with
form a condition that covers a set of rows contained
within the union of their intervals. If the conditions are
AND, they form a
condition that covers a set of rows contained within the
intersection of their intervals. For example, for this
condition on a two-part index:
key_part1= 1 AND
key_part2< 2) OR (
The intervals are:
(1,-inf) < (
key_part2) < (1,2) (5,-inf) < (
In this example, the interval on the first line uses one
key part for the left bound and two key parts for the
right bound. The interval on the second line uses only one
key part. The
key_len column in the
EXPLAIN output indicates the maximum
length of the key prefix used.
In some cases,
key_len may indicate
that a key part was used, but that might be not what you
would expect. Suppose that
key_part2 can be
NULL. Then the
key_len column displays two key part
lengths for the following condition:
key_part1>= 1 AND
But, in fact, the condition is converted to this:
key_part1>= 1 AND
key_part2IS NOT NULL
Section 220.127.116.11, “The Range Access Method for Single-Part Indexes”, describes how optimizations are performed to combine or eliminate intervals for range conditions on a single-part index. Analogous steps are performed for range conditions on multiple-part indexes.
MySQL can perform the same optimization on
that it can use for
For example, MySQL can use indexes and ranges to search for
SELECT * FROM
key_colIS NULL; SELECT * FROM
key_col<=> NULL; SELECT * FROM
WHERE clause includes a
condition for a column that is declared as
NULL, that expression is optimized away. This
optimization does not occur in cases when the column might
NULL anyway; for example, if it comes
from a table on the right side of a
MySQL 4.1.1 and up can also optimize the combination
, a form
that is common in resolved subqueries.
col_name IS NULL
when this optimization is used.
This optimization can handle one
IS NULL for
any key part.
Some examples of queries that are optimized, assuming that there
is an index on columns
b of table
SELECT * FROM t1 WHERE t1.a=
exprOR t1.a IS NULL; SELECT * FROM t1, t2 WHERE t1.a=t2.a OR t2.a IS NULL; SELECT * FROM t1, t2 WHERE (t1.a=t2.a OR t2.a IS NULL) AND t2.b=t1.b; SELECT * FROM t1, t2 WHERE t1.a=t2.a AND (t2.b=t1.b OR t2.b IS NULL); SELECT * FROM t1, t2 WHERE (t1.a=t2.a AND t2.a IS NULL AND ...) OR (t1.a=t2.a AND t2.a IS NULL AND ...);
ref_or_null works by first doing a read on
the reference key, and then a separate search for rows with a
NULL key value.
Note that the optimization can handle only one
NULL level. In the following query, MySQL uses key
lookups only on the expression
(t1.a=t2.a AND t2.a IS
NULL) and is not able to use the key part on
SELECT * FROM t1, t2 WHERE (t1.a=t2.a AND t2.a IS NULL) OR (t1.b=t2.b AND t2.b IS NULL);
DISTINCT combined with
BY needs a temporary table in many cases.
DISTINCT may use
BY, you should be aware of how MySQL works with
ORDER BY or
HAVING clauses that are not part of the
selected columns. See Section 12.10.3, “
GROUP BY and
HAVING with Hidden Fields”.
In most cases, a
DISTINCT clause can be
considered as a special case of
GROUP BY. For
example, the following two queries are equivalent:
SELECT DISTINCT c1, c2, c3 FROM t1 WHERE c1 >
const; SELECT c1, c2, c3 FROM t1 WHERE c1 >
constGROUP BY c1, c2, c3;
Due to this equivalence, the optimizations applicable to
GROUP BY queries can be also applied to
queries with a
DISTINCT clause. Thus, for
more details on the optimization possibilities for
DISTINCT queries, see
Section 7.2.10, “
GROUP BY Optimization”.
DISTINCT, MySQL stops as soon as it finds
row_count unique rows.
If you do not use columns from all tables named in a query,
MySQL stops scanning any unused tables as soon as it finds the
first match. In the following case, assuming that
t1 is used before
(which you can check with
stops reading from
t2 (for any particular row
t1) when it finds the first row in
SELECT DISTINCT t1.a FROM t1, t2 where t1.a=t2.a;
MySQL implements a
B is set to depend on table
A and all tables on which
A is set to depend on all
B) that are used
LEFT JOIN condition.
LEFT JOIN condition is used to decide
how to retrieve rows from table
B. (In other words, any condition
WHERE clause is not used.)
All standard join optimizations are performed, with the exception that a table is always read after all tables on which it depends. If there is a circular dependence, MySQL issues an error.
WHERE optimizations are
If there is a row in
WHERE clause, but there is no
B that matches the
ON condition, an extra
B row is generated with all
columns set to
If you use
LEFT JOIN to find rows that do
not exist in some table and you have the following test:
WHERE part, where
col_name is a column that is
NOT NULL, MySQL stops
searching for more rows (for a particular key combination)
after it has found one row that matches the
The implementation of
RIGHT JOIN is analogous
to that of
LEFT JOIN with the roles of the
The join optimizer calculates the order in which tables should
be joined. The table read order forced by
STRAIGHT_JOIN helps the
join optimizer do its work much more quickly, because there are
fewer table permutations to check. Note that this means that if
you do a query of the following type, MySQL does a full scan on
b because the
forces it to be read before
SELECT * FROM a JOIN b LEFT JOIN c ON (c.key=a.key) LEFT JOIN d ON (d.key=a.key) WHERE b.key=d.key;
The fix in this case is reverse the order in which
b are listed in the
SELECT * FROM b JOIN a LEFT JOIN c ON (c.key=a.key) LEFT JOIN d ON (d.key=a.key) WHERE b.key=d.key;
Starting from 4.0.14, for a
LEFT JOIN , if
WHERE condition is always false for the
NULL row, the
JOIN is changed to a normal join. For example, the
WHERE clause would be false in the following
SELECT * FROM t1 LEFT JOIN t2 ON (column1) WHERE t2.column2=5;
Therefore, it is safe to convert the query to a normal join:
SELECT * FROM t1, t2 WHERE t2.column2=5 AND t1.column1=t2.column1;
This can be made faster because MySQL can use table
t2 before table
doing so would result in a better query plan. To force a
specific table order, use
In some cases, MySQL can use an index to satisfy an
ORDER BY clause without doing any extra
The index can also be used even if the
BY does not match the index exactly, as long as all of
the unused portions of the index and all the extra
ORDER BY columns are constants in the
WHERE clause. The following queries use the
index to resolve the
ORDER BY part:
SELECT * FROM t1 ORDER BY
key_part2,... ; SELECT * FROM t1 WHERE
key_part2; SELECT * FROM t1 ORDER BY
key_part2DESC; SELECT * FROM t1 WHERE
key_part1=1 ORDER BY
In some cases, MySQL cannot use indexes to
ORDER BY, although it still uses
indexes to find the rows that match the
clause. These cases include the following:
ORDER BY on different keys:
SELECT * FROM t1 ORDER BY
ORDER BY on non-consecutive parts
of a key:
SELECT * FROM t1 WHERE
SELECT * FROM t1 ORDER BY
The key used to fetch the rows is not the same as the one
used in the
SELECT * FROM t1 WHERE
You are joining many tables, and the columns in the
ORDER BY are not all from the first
non-constant table that is used to retrieve rows. (This is
the first table in the
that does not have a
const join type.)
You have different
ORDER BY and
GROUP BY expressions.
The type of table index used does not store rows in order.
For example, this is true for a
index in a
EXPLAIN SELECT ... ORDER BY, you can
check whether MySQL can use indexes to resolve the query. It
cannot if you see
Using filesort in the
Extra column. See Section 7.2.1, “Optimizing Queries with
Prior to MySQL 4.1, in those cases where MySQL must sort the
result, it uses the following
Read all rows according to key or by table scanning. Rows
that do not match the
WHERE clause are
For each row, store a pair of values in a buffer (the sort
key and the row pointer). The size of the buffer is the
value of the
When the buffer gets full, run a qsort (quicksort) on it and store the result in a temporary file. Save a pointer to the sorted block. (If all pairs fit into the sort buffer, no temporary file is created.)
Repeat the preceding steps until all rows have been read.
Do a multi-merge of up to
regions to one block in another temporary file. Repeat until
all blocks from the first file are in the second file.
Repeat the following until there are fewer than
MERGEBUFF2 (15) blocks left.
On the last multi-merge, only the pointer to the row (the last part of the sort key) is written to a result file.
Read the rows in sorted order by using the row pointers in
the result file. To optimize this, we read in a big block of
row pointers, sort them, and use them to read the rows in
sorted order into a row buffer. The size of the buffer is
the value of the
system variable. The code for this step is in the
sql/records.cc source file.
One problem with this approach is that it reads rows twice: One
time when evaluating the
WHERE clause, and
again after sorting the pair values. And even if the rows were
accessed successively the first time (for example, if a table
scan is done), the second time they are accessed randomly. (The
sort keys are ordered, but the row positions are not.)
In MySQL 4.1 and up, a
is used that records not only the sort key value and row
position, but also the columns required for the query. This
avoids reading the rows twice. The modified
filesort algorithm works like this:
Read the rows that match the
For each row, record a tuple of values consisting of the sort key value and row position, and also the columns required for the query.
Sort the tuples by sort key value
Retrieve the rows in sorted order, but read the required columns directly from the sorted tuples rather than by accessing the table a second time.
Using the modified
filesort algorithm, the
tuples are longer than the pairs used in the original method,
and fewer of them fit in the sort buffer (the size of which is
sort_buffer_size). As a result, it
is possible for the extra I/O to make the modified approach
slower, not faster. To avoid a slowdown, the optimization is
used only if the total size of the extra columns in the sort
tuple does not exceed the value of the
max_length_for_sort_data system variable. (A
symptom of setting the value of this variable too high is that
you should see high disk activity and low CPU activity.)
If you want to increase
ORDER BY speed, check
whether you can get MySQL to use indexes rather than an extra
sorting phase. If this is not possible, you can try the
Increase the size of the
Increase the size of the
tmpdir to point to a dedicated
filesystem with large amounts of empty space. If you use
MySQL 4.1 or later, this option accepts several paths that
are used in round-robin fashion. Paths should be separated
by colon characters (‘
Unix and semicolon characters
;’) on Windows, NetWare,
and OS/2. You can use this feature to spread the load across
several directories. Note: The paths
should be for directories in filesystems that are located on
different physical disks, not different
partitions on the same disk.
By default, MySQL sorts all
queries as if you
ORDER BY in the query as
well. If you include an
ORDER BY clause
explicitly that contains the same column list, MySQL optimizes
it away without any speed penalty, although the sorting still
occurs. If a query includes
GROUP BY but you
want to avoid the overhead of sorting the result, you can
suppress sorting by specifying
ORDER BY NULL.
INSERT INTO foo SELECT a, COUNT(*) FROM bar GROUP BY a ORDER BY NULL;
The most general way to satisfy a
clause is to scan the whole table and create a new temporary
table where all rows from each group are consecutive, and then
use this temporary table to discover groups and apply aggregate
functions (if any). In some cases, MySQL is able to do much
better than that and to avoid creation of temporary tables by
using index access.
The most important preconditions for using indexes for
GROUP BY are that all
BY columns reference attributes from the same index,
and that the index stores its keys in order (for example, this
BTREE index, and not a
HASH index). Whether use of temporary tables
can be replaced by index access also depends on which parts of
an index are used in a query, the conditions specified for these
parts, and the selected aggregate functions.
There are two ways to execute a
query via index access, as detailed in the following sections.
In the first method, the grouping operation is applied together
with all range predicates (if any). The second method first
performs a range scan, and then groups the resulting tuples.
A tight index scan may be either a full index scan or a range index scan, depending on the query conditions.
When the conditions for a loose index scan are not met, it is
still possible to avoid creation of temporary tables for
GROUP BY queries. If there are range
conditions in the
WHERE clause, this method
reads only the keys that satisfy these conditions. Otherwise,
it performs an index scan. Because this method reads all keys
in each range defined by the
or scans the whole index if there are no range conditions, we
term it a tight index scan. Notice that
with a tight index scan, the grouping operation is performed
only after all keys that satisfy the range conditions have
For this method to work, it is sufficient that there is a
constant equality condition for all columns in a query
referring to parts of the key coming before or in between
parts of the
GROUP BY key. The constants
from the equality conditions fill in any “gaps”
in the search keys so that it is possible to form complete
prefixes of the index. These index prefixes then can be used
for index lookups. If we require sorting of the
BY result, and it is possible to form search keys
that are prefixes of the index, MySQL also avoids extra
sorting operations because searching with prefixes in an
ordered index already retrieves all the keys in order.
The following queries do not work with the loose index scan
access method described earlier, but still work with the tight
index scan access method (assuming that there is an index
idx(c1,c2,c3) on table
There is a gap in the
GROUP BY, but it
is covered by the condition
c2 = 'a':
SELECT c1, c2, c3 FROM t1 WHERE c2 = 'a' GROUP BY c1, c3;
GROUP BY does not begin with the
first part of the key, but there is a condition that
provides a constant for that part:
SELECT c1, c2, c3 FROM t1 WHERE c1 = 'a' GROUP BY c2, c3;
In some cases, MySQL handles a query differently when you are
and not using
If you are selecting only a few rows with
LIMIT, MySQL uses indexes in some cases
when normally it would prefer to do a full table scan.
If you use
ORDER BY, MySQL ends the sorting as soon
as it has found the first
row_count rows of the sorted
result, rather than sorting the entire result. If ordering
is done by using an index, this is very fast. If a filesort
must be done, all rows that match the query without the
LIMIT clause must be selected, and most
or all of them must be sorted, before it can be ascertained
that the first
have been found. In either case, after the initial rows have
been found, there is no need to sort any remainder of the
result set, and MySQL does not do so.
DISTINCT, MySQL stops as soon as it finds
row_count unique rows.
In some cases, a
GROUP BY can be resolved
by reading the key in order (or doing a sort on the key) and
then calculating summaries until the key value changes. In
calculate any unnecessary
As soon as MySQL has sent the required number of rows to the
client, it aborts the query unless you are using
LIMIT 0 quickly returns an empty set.
This can be useful for checking the validity of a query.
When using one of the MySQL APIs, it can also be employed
for obtaining the types of the result columns. (This trick
does not work in the MySQL Monitor (the
mysql program), which merely displays
Empty set in such cases; you should
SHOW COLUMNS or
DESCRIBE for this purpose.)
When the server uses temporary tables to resolve the query,
it uses the
calculate how much space is required.
The output from
ALL in the
when MySQL uses a table scan to resolve a query. This usually
happens under the following conditions:
The table is so small that it is faster to perform a table scan than to bother with a key lookup. This is common for tables with fewer than 10 rows and a short row length.
There are no usable restrictions in the
WHERE clause for
You are comparing indexed columns with constant values and
MySQL has calculated (based on the index tree) that the
constants cover too large a part of the table and that a
table scan would be faster. See
Section 7.2.4, “
WHERE Clause Optimization”.
You are using a key with low cardinality (many rows match the key value) through another column. In this case, MySQL assumes that by using the key it probably will do many key lookups and that a table scan would be faster.
For small tables, a table scan often is appropriate and the performance impact is negligible. For large tables, try the following techniques to avoid having the optimizer incorrectly choose a table scan:
to update the
key distributions for the scanned table. See
Section 18.104.22.168, “
ANALYZE TABLE Syntax”.
FORCE INDEX for the scanned table to
tell MySQL that table scans are very expensive compared to
using the given index:
SELECT * FROM t1, t2 FORCE INDEX (
index_for_column) WHERE t1.
Start mysqld with the
--max-seeks-for-key=1000 option or use
SET max_seeks_for_key=1000 to tell the
optimizer to assume that no key scan causes more than 1,000
key seeks. See Section 5.2.2, “Server System Variables”.
The time required for inserting a row is determined by the following factors, where the numbers indicate approximate proportions:
Sending query to server: (2)
Parsing query: (2)
Inserting row: (1 × size of row)
Inserting indexes: (1 × number of indexes)
This does not take into consideration the initial overhead to open tables, which is done once for each concurrently running query.
The size of the table slows down the insertion of indexes by log
N, assuming B-tree indexes.
You can use the following methods to speed up inserts:
If you are inserting many rows from the same client at the
same time, use
INSERT statements with
VALUES lists to insert several
rows at a time. This is considerably faster (many times
faster in some cases) than using separate single-row
INSERT statements. If you are adding data
to a non-empty table, you can tune the
bulk_insert_buffer_size variable to make
data insertion even faster. See
Section 5.2.2, “Server System Variables”.
If you are inserting a lot of rows from different clients,
you can get higher speed by using the
DELAYED statement. See
Section 22.214.171.124, “
INSERT DELAYED Syntax”.
MyISAM table, you can use
concurrent inserts to add rows at the same time that
SELECT statements are running if there
are no deleted rows in middle of the table. See
Section 7.3.3, “Concurrent Inserts”.
When loading a table from a text file, use
DATA INFILE. This is usually 20 times faster than
INSERT statements. See
Section 13.2.5, “
LOAD DATA INFILE Syntax”.
With some extra work, it is possible to make
DATA INFILE run even faster for a
MyISAM table when the table has many
indexes. Use the following procedure:
Optionally create the table with
FLUSH TABLES statement or a
mysqladmin flush-tables command.
Use myisamchk --keys-used=0 -rq
This removes all use of indexes for the table.
Insert data into the table with
INFILE. This does not update any indexes and
therefore is very fast.
If you intend only to read from the table in the future, use myisampack to compress it. See Section 126.96.36.199, “Compressed Table Characteristics”.
Re-create the indexes with myisamchk -rq
This creates the index tree in memory before writing it
to disk, which is much faster that updating the index
LOAD DATA INFILE because it
avoids lots of disk seeks. The resulting index tree is
also perfectly balanced.
FLUSH TABLES statement or a
mysqladmin flush-tables command.
LOAD DATA INFILE performs the
preceding optimization automatically if the
MyISAM table into which you insert data
is empty. The main difference is that you can let
myisamchk allocate much more temporary
memory for the index creation than you might want the server
to allocate for index re-creation when it executes the
LOAD DATA INFILE statement.
As of MySQL 4.0, you can also disable or enable the indexes
MyISAM table by using the following
statements rather than myisamchk. If you
use these statements, you can skip the
tbl_nameDISABLE KEYS; ALTER TABLE
To speed up
INSERT operations that are
performed with multiple statements for non-transactional
tables, lock your tables:
LOCK TABLES a WRITE; INSERT INTO a VALUES (1,23),(2,34),(4,33); INSERT INTO a VALUES (8,26),(6,29); ... UNLOCK TABLES;
This benefits performance because the index buffer is
flushed to disk only once, after all
INSERT statements have completed.
Normally, there would be as many index buffer flushes as
INSERT statements. Explicit
locking statements are not needed if you can insert all rows
with a single
To obtain faster insertions, for transactional tables, you
START TRANSACTION and
COMMIT instead of
Locking also lowers the total time for multiple-connection tests, although the maximum wait time for individual connections might go up because they wait for locks. For example:
Connection 1 does 1000 inserts
Connections 2, 3, and 4 do 1 insert
Connection 5 does 1000 inserts
If you do not use locking, connections 2, 3, and 4 finish before 1 and 5. If you use locking, connections 2, 3, and 4 probably do not finish before 1 or 5, but the total time should be about 40% faster.
DELETE operations are very fast in MySQL,
but you can obtain better overall performance by adding
locks around everything that does more than about five
inserts or updates in a row. If you do very many inserts in
a row, you could do a
followed by an
UNLOCK TABLES once in a
while (each 1,000 rows or so) to allow other threads access
to the table. This would still result in a nice performance
INSERT is still much slower for loading
LOAD DATA INFILE, even when
using the strategies just outlined.
To increase performance for
tables, for both
LOAD DATA INFILE and
INSERT, enlarge the key cache by
variable. See Section 7.5.2, “Tuning Server Parameters”.
An update statement is optimized like a
SELECT query with the additional overhead of
a write. The speed of the write depends on the amount of data
being updated and the number of indexes that are updated.
Indexes that are not changed do not get updated.
Another way to get fast updates is to delay updates and then do many updates in a row later. Performing multiple updates together is much quicker than doing one at a time if you lock the table.
MyISAM table that uses dynamic row
format, updating a row to a longer total length may split the
row. If you do this often, it is very important to use
OPTIMIZE TABLE occasionally. See
Section 188.8.131.52, “
OPTIMIZE TABLE Syntax”.
The time required to delete individual rows is exactly
proportional to the number of indexes. To delete rows more
quickly, you can increase the size of the key cache by
variable. See Section 7.5.2, “Tuning Server Parameters”.
To delete all rows from a table,
if faster than
Section 13.2.9, “
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
thread_cache_size variable. See
Section 7.5.2, “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
EXPLAIN statement. See
Section 7.2.1, “Optimizing Queries with
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 tables that have no deleted
rows in the middle, you can insert rows at the end at the
same time that another query is 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. See Section 14.1, “The
MyISAM Storage Engine”.
To fix any compression issues that may have occurred with
ARCHIVE tables, you can use
OPTIMIZE TABLE. See
Section 14.7, “The
ARCHIVE Storage Engine”.
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 and reasonably unique, 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,
you should 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 Engines and Table Types.
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 14, Storage Engines and Table Types.
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
ISAM that have 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
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 non-redundant (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.
UDFs (user-defined functions) may be a good way to get more performance for some tasks. See Section 19.2, “Adding New Functions to MySQL”, for more information.
You can always gain something by caching queries or answers in your application and then performing many inserts or updates together. If your database system supports table locks (as do MySQL and Oracle), 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 5.13, “The MySQL Query Cache”.
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.
INSERT LOW_PRIORITY when you want to
SELECT statements higher priority
than your inserts.
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.
INSERT statements to
store many rows with one SQL statement. Many SQL servers
support this, including MySQL.
LOAD DATA INFILE to load large
amounts of data. This is faster than using
AUTO_INCREMENT columns to generate
OPTIMIZE TABLE once in a while to
avoid fragmentation with dynamic-format
MyISAM tables. See
Section 14.1.3, “
MyISAM Table Storage Formats”.
tables when possible to get more speed. See
Section 14.4, “The
HEAP) Storage Engine”.
MEMORY tables are useful for non-critical
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. Before MySQL 3.23, you get slow joins otherwise.
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, 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
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 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 6, 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 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
DELAY_KEY_WRITE, because the key
information can always be generated from the data rows.)
MySQL uses table-level locking for
HEAP) tables, page-level locking for
BDB tables, and row-level locking for
In many cases, you can make an educated guess about which locking type is best for an application, but generally it is difficult to say that a given lock type is better than another. Everything depends on the application and different parts of an application may require different lock types.
To decide whether you want to use a storage engine with
row-level locking, you should look at what your application does
and what mix of select and update statements it uses. For
example, most Web applications perform many selects, relatively
few deletes, updates based mainly on key values, and inserts
into a few specific tables. The base MySQL
MyISAM setup is very well tuned for this.
Table locking in MySQL is deadlock-free for storage engines that use table-level locking. Deadlock avoidance is managed by always requesting all needed locks at once at the beginning of a query and always locking the tables in the same order.
The table-locking method MySQL uses for
locks works as follows:
If there are no locks on the table, put a write lock on it.
Otherwise, put the lock request in the write lock queue.
The table-locking method MySQL uses for
locks works as follows:
If there are no write locks on the table, put a read lock on it.
Otherwise, put the lock request in the read lock queue.
When a lock is released, the lock is made available to the
threads in the write lock queue and then to the threads in the
read lock queue. This means that if you have many updates for a
SELECT statements wait until there are
no more updates.
Starting in MySQL 3.23.33, you can analyze the table lock
contention on your system by checking the
Table_locks_immediate status variables:
SHOW STATUS LIKE 'Table%';+-----------------------+---------+ | Variable_name | Value | +-----------------------+---------+ | Table_locks_immediate | 1151552 | | Table_locks_waited | 15324 | +-----------------------+---------+
As of MySQL 3.23.7 (3.23.25 for Windows), if a
MyISAM table contains no free blocks in the
middle, rows always are inserted at the end of the data file. In
this case, you can freely mix concurrent
statements for a
MyISAM table without locks.
That is, you can insert rows into a
table at the same time other clients are reading from it. (Holes
can result from rows having been deleted from or updated in the
middle of the table. If there are holes, concurrent inserts are
disabled but are re-enabled automatically when all holes have
been filled with new data.)
If you want to perform many
SELECT operations on a table when concurrent
inserts are not possible, you can insert rows in a temporary
table and update the real table with the rows from the temporary
table once in a while. This can be done with the following code:
LOCK TABLES real_table WRITE, insert_table WRITE;mysql>
INSERT INTO real_table SELECT * FROM insert_table;mysql>
TRUNCATE TABLE insert_table;mysql>
InnoDB uses row locks and
BDB uses page locks. For these two storage
engines, deadlocks are possible because they automatically
acquire locks during the processing of SQL statements, not at
the start of the transaction.
Advantages of row-level locking:
Fewer lock conflicts when accessing different rows in many threads.
Fewer changes for rollbacks.
Possible to lock a single row for a long time.
Disadvantages of row-level locking:
Requires more memory than page-level or table-level locks.
Slower than page-level or table-level locks when used on a large part of the table because you must acquire many more locks.
Definitely much slower than other locks if you often do
GROUP BY operations on a large part of
the data or if you must scan the entire table frequently.
Table locks are superior to page-level or row-level locks in the following cases:
Most statements for the table are reads.
A mix of reads and writes, where writes are updates or deletes for a single row that can be fetched with one key read:
key_value; DELETE FROM
SELECT combined with concurrent
INSERT statements, and very few
Many scans or
GROUP BY operations on the
entire table without any writers.
With higher-level locks, you can more easily tune applications by supporting locks of different types, because the lock overhead is less than for row-level locks.
Options other than row-level or page-level locking:
Versioning (such as that used in MySQL for concurrent inserts) where it is possible to have one writer at the same time as many readers. This means that the database or table supports different views for the data depending on when access begins. Other common terms for this are “time travel,” “copy on write,” or “copy on demand.”
Copy on demand is in many cases superior to page-level or row-level locking. However, in the worst case, it can use much more memory than using normal locks.
Instead of using row-level locks, you can employ
application-level locks, such as
RELEASE_LOCK() in MySQL. These are
advisory locks, so they work only in well-behaved
Section 12.9.4, “Miscellaneous Functions”.)
To achieve a very high lock speed, MySQL uses table locking
(instead of page, row, or column locking) for all storage
MySQL uses only table locking if you explicitly lock the table
LOCK TABLES. For these storage engines,
we recommend that you not use
LOCK TABLES at
InnoDB uses automatic row-level
BDB uses page-level locking to
ensure transaction isolation.
For large tables, table locking is much better than row locking for most applications, but there are some pitfalls:
Table locking enables many threads to read from a table at the same time, but if a thread wants to write to a table, it must first get exclusive access. During the update, all other threads that want to access this particular table must wait until the update is done.
Table updates normally are considered to be more important
than table retrievals, so they are given higher priority.
This should ensure that updates to a table are not
“starved” even if there is heavy
SELECT activity for the table.
Table locking causes problems in cases such as when a thread is waiting because the disk is full and free space needs to become available before the thread can proceed. In this case, all threads that want to access the problem table are also put in a waiting state until more disk space is made available.
Table locking is also disadvantageous under the following scenario:
A client issues a
SELECT that takes a
long time to run.
Another client then issues an
the same table. This client waits until the
SELECT is finished.
Another client issues another
statement on the same table. Because
UPDATE has higher priority than
waits for the
UPDATE to finish,
and for the first
SELECT to finish.
The following items describe some ways to avoid or reduce contention caused by table locking:
Try to get the
SELECT statements to run
faster so that they lock tables for a shorter time. You
might have to create some summary tables to do this.
Start mysqld with
--low-priority-updates. This gives all
statements that update (modify) a table lower priority than
SELECT statements. In this case, the
SELECT statement in the preceding
scenario would execute before the
statement, and would not need to wait for the first
SELECT to finish.
You can specify that all updates issued in a specific
connection should be done with low priority by using the
SET LOW_PRIORITY_UPDATES=1 statement. See
Section 13.5.3, “
You can give a specific
statement lower priority with the
You can give a specific
higher priority with the
attribute. See Section 13.2.7, “
Starting from MySQL 3.23.7, you can start
mysqld with a low value for the
max_write_lock_count system variable to
force MySQL to temporarily elevate the priority of all
SELECT statements that are waiting for a
table after a specific number of inserts to the table occur.
READ locks after a certain
If you have problems with
SELECT, you might want to consider
MyISAM tables, which support
INSERT statements. (See
Section 7.3.3, “Concurrent Inserts”.)
If you mix inserts and deletes on the same table,
INSERT DELAYED may be of great help. See
Section 184.108.40.206, “
INSERT DELAYED Syntax”.
If you have problems with mixed
DELETE statements, the
LIMIT option to
may help. See Section 13.2.1, “
SELECT statements can help to make the
duration of table locks shorter. See
Section 13.2.7, “
You could change the locking code in
mysys/thr_lock.c to use a single queue.
In this case, write locks and read locks would have the same
priority, which might help some applications.
Here are some tips concerning table locks in MySQL:
Concurrent users are not a problem if you do not mix updates with selects that need to examine many rows in the same table.
You can use
LOCK TABLES to increase
speed, because many updates within a single lock is much
faster than updating without locks. Splitting table contents
into separate tables may also help.
If you encounter speed problems with table locks in MySQL,
you may be able to improve performance by converting some of
your tables to
BDB tables. See Section 14.2, “The
InnoDB Storage Engine”,
and Section 14.5, “The
BerkeleyDB) Storage Engine”.
MyISAM table, you can use concurrent
inserts to add rows at the same time that
SELECT statements are running if there are no
deleted rows in middle of the table.
Under circumstances where concurrent inserts can be used, there
is seldom any need to use the
INSERT statements. See
Section 220.127.116.11, “
INSERT DELAYED Syntax”.
If you are using the update log or binary log, concurrent
inserts are converted to normal inserts for
INSERT ... SELECT
statements. This is done to ensure that you can re-create an
exact copy of your tables by applying the log during a backup
LOAD DATA INFILE, if you specify
CONCURRENT with a
table that satisfies the condition for concurrent inserts (that
is, it contains no free blocks in the middle), other threads can
retrieve data from the table while
is executing. Using this option affects the performance of
LOAD DATA a bit, even if no other thread is
using the table at the same time.
MyISAMIndex Statistics Collection
MySQL keeps row data and index data in separate files. Many (almost all) other database systems mix row and index data in the same file. We believe that the MySQL choice is better for a very wide range of modern systems.
Another way to store the row data is to keep the information for each column in a separate area (examples are SDBM and Focus). This causes a performance hit for every query that accesses more than one column. Because this degenerates so quickly when more than one column is accessed, we believe that this model is not good for general-purpose databases.
The more common case is that the index and data are stored together (as in Oracle/Sybase, et al). In this case, you find the row information at the leaf page of the index. The good thing with this layout is that it, in many cases, depending on how well the index is cached, saves a disk read. The bad things with this layout are:
Table scanning is much slower because you have to read through the indexes to get at the data.
You cannot use only the index table to retrieve data for a query.
You use more space because you must duplicate indexes from the nodes (you cannot store the row in the nodes).
Deletes degenerate the table over time (because indexes in nodes are usually not updated on delete).
It is more difficult to cache only the index data.
One of the most basic optimizations is to design your tables to take as little space on the disk as possible. This can result in huge improvements because disk reads are faster, and smaller tables normally require less main memory while their contents are being actively processed during query execution. Indexing also is a lesser resource burden if done on smaller columns.
MySQL supports many different storage engines (table types) and row formats. For each table, you can decide which storage and indexing method to use. Choosing the proper table format for your application may give you a big performance gain. See Chapter 14, Storage Engines and Table Types.
You can get better performance for a table and minimize storage space by using the techniques listed here:
Use the most efficient (smallest) data types possible. MySQL
has many specialized types that save disk space and memory.
For example, use the smaller integer types if possible to
get smaller tables.
MEDIUMINT is often a
better choice than
INT because a
MEDIUMINT column uses 25% less space.
Declare columns to be
NOT NULL if
possible. It makes everything faster and you save one bit
per column. If you really need
your application, you should definitely use it. Just avoid
having it on all columns by default.
MyISAM tables, if you do not have any
variable-length columns (
columns), a fixed-size row format is used. This is faster
but unfortunately may waste some space. See
Section 14.1.3, “
MyISAM Table Storage Formats”. You can hint that
you want to have fixed length rows even if you have
VARCHAR columns with the
The primary index of a table should be as short as possible. This makes identification of each row easy and efficient.
Create only the indexes that you really need. Indexes are good for retrieval but bad when you need to store data quickly. If you access a table mostly by searching on a combination of columns, create an index on them. The first part of the index should be the column most used. If you always use many columns when selecting from the table, you should use the column with more duplicates first to obtain better compression of the index.
If it is very likely that a string column has a unique
prefix on the first number of characters, it's better to
index only this prefix, using MySQL's support for creating
an index on the leftmost part of the column (see
Section 13.1.4, “
CREATE INDEX Syntax”). Shorter indexes are faster,
not only because they require less disk space, but because
they give also you more hits in the index cache, and thus
fewer disk seeks. See Section 7.5.2, “Tuning Server Parameters”.
In some circumstances, it can be beneficial to split into two a table that is scanned very often. This is especially true if it is a dynamic-format table and it is possible to use a smaller static format table that can be used to find the relevant rows when scanning the table.
All MySQL data types can be indexed. Use of indexes on the
relevant columns is the best way to improve the performance of
The maximum number of indexes per table and the maximum index length is defined per storage engine. See Chapter 14, Storage Engines and Table Types. All storage engines support at least 16 indexes per table and a total index length of at least 256 bytes. Most storage engines have higher limits.
MyISAM and (as of MySQL 4.0.14)
InnoDB storage engines also support indexing
When indexing a
TEXT column, you must
specify a prefix length for the index. For example:
CREATE TABLE test (blob_col BLOB, INDEX(blob_col(10)));
Prefixes can be up to 1000 bytes long (767 bytes for
InnoDB tables). (Before MySQL 4.1.2, the
limit is 255 bytes for all tables.) Note that prefix limits are
measured in bytes, whereas the prefix length in
TABLE statements is interpreted as number of
characters. Be sure to take this into account when
specifying a prefix length for a column that uses a multi-byte
As of MySQL 3.23.23, you can also create
FULLTEXT indexes. They are used for full-text
searches. Only the
MyISAM storage engine
FULLTEXT indexes and only for
TEXT columns. Indexing always takes place
over the entire column and partial (column prefix) indexing is
not supported. For details, see
Section 12.7, “Full-Text Search Functions”.
As of MySQL 4.1.0, you can create indexes on spatial data types.
Spatial indexes use R-trees. Currently, only
MyISAM supports indexes on spatial types.
HASH indexes by default. It also
BTREE indexes as of MySQL 4.1.0.
MySQL can create composite indexes (that is, indexes on multiple columns). An index may consist of up to 15 columns. For certain data types, you can index a prefix of the column (see Section 7.4.3, “Column Indexes”).
A multiple-column index can be considered a sorted array containing values that are created by concatenating the values of the indexed columns.
MySQL uses multiple-column indexes in such a way that queries
are fast when you specify a known quantity for the first column
of the index in a
WHERE clause, even if you
do not specify values for the other columns.
Suppose that a table has the following specification:
CREATE TABLE test ( id INT NOT NULL, last_name CHAR(30) NOT NULL, first_name CHAR(30) NOT NULL, PRIMARY KEY (id), INDEX name (last_name,first_name) );
name index is an index over the
columns. The index can be used for queries that specify values
in a known range for
last_name, or for both
name index is used in the
SELECT * FROM test WHERE last_name='Widenius'; SELECT * FROM test WHERE last_name='Widenius' AND first_name='Michael'; SELECT * FROM test WHERE last_name='Widenius' AND (first_name='Michael' OR first_name='Monty'); SELECT * FROM test WHERE last_name='Widenius' AND first_name >='M' AND first_name < 'N';
name index is
not used in the following queries:
SELECT * FROM test WHERE first_name='Michael'; SELECT * FROM test WHERE last_name='Widenius' OR first_name='Michael';
The manner in which MySQL uses indexes to improve query performance is discussed further in Section 7.4.5, “How MySQL Uses Indexes”.
Indexes are used to find rows with specific column values quickly. Without an index, MySQL must begin with the first row and then read through the entire table to find the relevant rows. The larger the table, the more this costs. If the table has an index for the columns in question, MySQL can quickly determine the position to seek to in the middle of the data file without having to look at all the data. If a table has 1,000 rows, this is at least 100 times faster than reading sequentially. If you need to access most of the rows, it is faster to read sequentially, because this minimizes disk seeks.
Most MySQL indexes (
FULLTEXT) are stored in B-trees. Exceptions
are that indexes on spatial data types use R-trees, and that
support hash indexes.
Strings are automatically prefix- and end-space compressed. See
Section 13.1.4, “
CREATE INDEX Syntax”.
In general, indexes are used as described in the following
discussion. Characteristics specific to hash indexes (as used in
MEMORY tables) are described at the end of
MySQL uses indexes for these operations:
To find the rows matching a
To eliminate rows from consideration. If there is a choice between multiple indexes, MySQL normally uses the index that finds the smallest number of rows.
To retrieve rows from other tables when performing joins.
To find the
MAX() value for a specific indexed column
key_col. This is optimized by a
preprocessor that checks whether you are using
WHERE on all key
parts that occur before
in the index. In this case, MySQL does a single key lookup
MAX() expression and replaces it with a
constant. If all expressions are replaced with constants,
the query returns at once. For example:
To sort or group a table if the sorting or grouping is done
on a leftmost prefix of a usable key (for example,
ORDER BY ). If all key
parts are followed by
DESC, the key is
read in reverse order. See
Section 7.2.9, “
ORDER BY Optimization”.
In some cases, a query can be optimized to retrieve values without consulting the data rows. If a query uses only columns from a table that are numeric and that form a leftmost prefix for some key, the selected values may be retrieved from the index tree for greater speed:
Suppose that you issue the following
SELECT * FROM
If a multiple-column index exists on
col2, the appropriate rows can be fetched
directly. If separate single-column indexes exist on
optimizer tries to find the most restrictive index by deciding
which index finds fewer rows and using that index to fetch the
If the table has a multiple-column index, any leftmost prefix of
the index can be used by the optimizer to find rows. For
example, if you have a three-column index on
col2, col3), you have indexed search capabilities on
(col1, col2), and
(col1, col2, col3).
MySQL cannot use a partial index if the columns do not form a
leftmost prefix of the index. Suppose that you have the
SELECT statements shown here:
SELECT * FROM
val1; SELECT * FROM
val2; SELECT * FROM
val2; SELECT * FROM
If an index exists on
(col1, col2, col3),
only the first two queries use the index. The third and fourth
queries do involve indexed columns, but
are not leftmost prefixes of
A B-tree index can be used for column comparisons in expressions
that use the
operators. The index also can be used for
LIKE comparisons if the argument to
LIKE is a constant string that does not start
with a wildcard character. For example, the following
SELECT statements use indexes:
SELECT * FROM
key_colLIKE 'Patrick%'; SELECT * FROM
In the first statement, only rows with
considered. In the second statement, only rows with
key_col < 'Patricl'
'Pat' <= are considered.
SELECT statements do not use
SELECT * FROM
key_colLIKE '%Patrick%'; SELECT * FROM
In the first statement, the
LIKE value begins
with a wildcard character. In the second statement, the
LIKE value is not a constant.
MySQL 4.0 and later versions perform an additional
LIKE optimization. If you use
LIKE '% and
string is longer than three
characters, MySQL uses the Turbo Boyer-Moore
algorithm to initialize the pattern for the string
and then uses this pattern to perform the search more quickly.
A search using
employs indexes if
col_name is indexed.
Any index that does not span all
WHERE clause is not used to optimize
the query. In other words, to be able to use an index, a prefix
of the index must be used in every
WHERE clauses use indexes:
index= 1 OR
index= 2 */ ... WHERE
index=1 OR A=10 AND
index=2 /* optimized like "
index_part1='hello'" */ ... WHERE
index_part3=5 /* Can use index on
index1but not on
index3*/ ... WHERE
WHERE clauses do
not use indexes:
index_part1is not used */ ... WHERE
index_part3=2 /* Index is not used in both parts of the WHERE clause */ ... WHERE
index=1 OR A=10 /* No index spans all rows */ ... WHERE
Sometimes MySQL does not use an index, even if one is available.
One circumstance under which this occurs is when the optimizer
estimates that using the index would require MySQL to access a
very large percentage of the rows in the table. (In this case, a
table scan is likely to be much faster because it requires fewer
seeks.) However, if such a query uses
to retrieve only some of the rows, MySQL uses an index anyway,
because it can much more quickly find the few rows to return in
Hash indexes have somewhat different characteristics from those just discussed:
They are used only for equality comparisons that use the
operators (but are very fast). They are
not used for comparison operators such as
< that find a range of values.
The optimizer cannot use a hash index to speed up
ORDER BY operations. (This type of index
cannot be used to search for the next entry in order.)
MySQL cannot determine approximately how many rows there are
between two values (this is used by the range optimizer to
decide which index to use). This may affect some queries if
you change a
MyISAM table to a
Only whole keys can be used to search for a row. (With a B-tree index, any leftmost prefix of the key can be used to find rows.)
To minimize disk I/O, the
engine exploits a strategy that is used by many database
management systems. It employs a cache mechanism to keep the
most frequently accessed table blocks in memory:
For index blocks, a special structure called the key cache (or key buffer) is maintained. The structure contains a number of block buffers where the most-used index blocks are placed.
For data blocks, MySQL uses no special cache. Instead it relies on the native operating system filesystem cache.
This section first describes the basic operation of the
MyISAM key cache. Then it discusses changes
made in MySQL 4.1 that improve key cache performance and that
enable you to better control cache operation:
Access to the key cache no longer is serialized among threads. Multiple threads can access the cache concurrently.
You can set up multiple key caches and assign table indexes to specific caches.
The key cache mechanism also is used for
tables. However, the significance of this fact is on the wane.
ISAM table use has been decreasing since
MySQL 3.23 when
MyISAM was introduced. MySQL
4.1 carries this trend further; the
storage engine is disabled by default. (Subsequent MySQL release
series have no support at all for
To control the size of the key cache, use the
key_buffer_size system variable. If this
variable is set equal to zero, no key cache is used. The key
cache also is not used if the
value is too small to allocate the minimal number of block
When the key cache is not operational, index files are accessed using only the native filesystem buffering provided by the operating system. (In other words, table index blocks are accessed using the same strategy as that employed for table data blocks.)
An index block is a contiguous unit of access to the
MyISAM index files. Usually the size of an
index block is equal to the size of nodes of the index B-tree.
(Indexes are represented on disk using a B-tree data structure.
Nodes at the bottom of the tree are leaf nodes. Nodes above the
leaf nodes are non-leaf nodes.)
All block buffers in a key cache structure are the same size. This size can be equal to, greater than, or less than the size of a table index block. Usually one these two values is a multiple of the other.
When data from any table index block must be accessed, the server first checks whether it is available in some block buffer of the key cache. If it is, the server accesses data in the key cache rather than on disk. That is, it reads from the cache or writes into it rather than reading from or writing to disk. Otherwise, the server chooses a cache block buffer containing a different table index block (or blocks) and replaces the data there by a copy of required table index block. As soon as the new index block is in the cache, the index data can be accessed.
If it happens that a block selected for replacement has been modified, the block is considered “dirty.” In this case, prior to being replaced, its contents are flushed to the table index from which it came.
Usually the server follows an LRU (Least Recently Used) strategy: When choosing a block for replacement, it selects the least recently used index block. To make this choice easier, the key cache module maintains a special queue (LRU chain) of all used blocks. When a block is accessed, it is placed at the end of the queue. When blocks need to be replaced, blocks at the beginning of the queue are the least recently used and become the first candidates for eviction.
Prior to MySQL 4.1, access to the key cache is serialized: No two threads can access key cache buffers simultaneously. The server processes a request for an index block only after it has finished processing the previous request. As a result, a request for an index block not present in any key cache buffer blocks access by other threads while a buffer is being updated to contain the requested index block.
Starting from version 4.1.0, the server supports shared access to the key cache:
A buffer that is not being updated can be accessed by multiple threads.
A buffer that is being updated causes threads that need to use it to wait until the update is complete.
Multiple threads can initiate requests that result in cache block replacements, as long as they do not interfere with each other (that is, as long as they need different index blocks, and thus cause different cache blocks to be replaced).
Shared access to the key cache enables the server to improve throughput significantly.
Shared access to the key cache improves performance but does not eliminate contention among threads entirely. They still compete for control structures that manage access to the key cache buffers. To reduce key cache access contention further, MySQL 4.1.1 also provides multiple key caches. This feature enables you to assign different table indexes to different key caches.
Where there are multiple key caches, the server must know
which cache to use when processing queries for a given
MyISAM table. By default, all
MyISAM table indexes are cached in the
default key cache. To assign table indexes to a specific key
cache, use the
CACHE INDEX statement (see
Section 18.104.22.168, “
CACHE INDEX Syntax”). For example, the following
statement assigns indexes from the tables
t3 to the key cache named
CACHE INDEX t1, t2, t3 IN hot_cache;+---------+--------------------+----------+----------+ | Table | Op | Msg_type | Msg_text | +---------+--------------------+----------+----------+ | test.t1 | assign_to_keycache | status | OK | | test.t2 | assign_to_keycache | status | OK | | test.t3 | assign_to_keycache | status | OK | +---------+--------------------+----------+----------+
Note: If the server has been
built with the
ISAM storage engine enabled,
ISAM tables use the key cache mechanism.
ISAM indexes use only the default
key cache and cannot be reassigned to a different cache.
The key cache referred to in a
statement can be created by setting its size with a
SET GLOBAL parameter setting statement or
by using server startup options. For example:
SET GLOBAL keycache1.key_buffer_size=128*1024;
To destroy a key cache, set its size to zero:
SET GLOBAL keycache1.key_buffer_size=0;
Note that you cannot destroy the default key cache. Any attempt to do this will be ignored:
SET GLOBAL key_buffer_size = 0;mysql>
SHOW VARIABLES LIKE 'key_buffer_size';+-----------------+---------+ | Variable_name | Value | +-----------------+---------+ | key_buffer_size | 8384512 | +-----------------+---------+
Key cache variables are structured system variables that have
a name and components. For
keycache1 is the cache variable name and
key_buffer_size is the cache component. See
Section 22.214.171.124, “Structured System Variables”, for a
description of the syntax used for referring to structured key
cache system variables.
By default, table indexes are assigned to the main (default) key cache created at the server startup. When a key cache is destroyed, all indexes assigned to it are reassigned to the default key cache.
For a busy server, we recommend a strategy that uses three key caches:
A “hot” key cache that takes up 20% of the space allocated for all key caches. Use this for tables that are heavily used for searches but that are not updated.
A “cold” key cache that takes up 20% of the space allocated for all key caches. Use this cache for medium-sized, intensively modified tables, such as temporary tables.
A “warm” key cache that takes up 60% of the key cache space. Employ this as the default key cache, to be used by default for all other tables.
One reason the use of three key caches is beneficial is that access to one key cache structure does not block access to the others. Statements that access tables assigned to one cache do not compete with statements that access tables assigned to another cache. Performance gains occur for other reasons as well:
The hot cache is used only for retrieval queries, so its contents are never modified. Consequently, whenever an index block needs to be pulled in from disk, the contents of the cache block chosen for replacement need not be flushed first.
For an index assigned to the hot cache, if there are no queries requiring an index scan, there is a high probability that the index blocks corresponding to non-leaf nodes of the index B-tree remain in the cache.
An update operation most frequently executed for temporary tables is performed much faster when the updated node is in the cache and need not be read in from disk first. If the size of the indexes of the temporary tables are comparable with the size of cold key cache, the probability is very high that the updated node is in the cache.
CACHE INDEX sets up an association between
a table and a key cache, but the association is lost each time
the server restarts. If you want the association to take
effect each time the server starts, one way to accomplish this
is to use an option file: Include variable settings that
configure your key caches, and an
option that names a file containing
INDEX statements to be executed. For example:
key_buffer_size = 4G hot_cache.key_buffer_size = 2G cold_cache.key_buffer_size = 2G init_file=/
The statements in
executed each time the server starts. The file should contain
one SQL statement per line. The following example assigns
several tables each to
CACHE INDEX db1.t1, db1.t2, db2.t3 IN hot_cache CACHE INDEX db1.t4, db2.t5, db2.t6 IN cold_cache
By default, the key cache management system of MySQL 4.1 uses the LRU strategy for choosing key cache blocks to be evicted, but it also supports a more sophisticated method called the midpoint insertion strategy.
When using the midpoint insertion strategy, the LRU chain is
divided into two parts: a hot sub-chain and a warm sub-chain.
The division point between two parts is not fixed, but the key
cache management system takes care that the warm part is not
“too short,” always containing at least
key_cache_division_limit percent of the key
key_cache_division_limit is a
component of structured key cache variables, so its value is a
parameter that can be set per cache.
When an index block is read from a table into the key cache, it is placed at the end of the warm sub-chain. After a certain number of hits (accesses of the block), it is promoted to the hot sub-chain. At present, the number of hits required to promote a block (3) is the same for all index blocks.
A block promoted into the hot sub-chain is placed at the end
of the chain. The block then circulates within this sub-chain.
If the block stays at the beginning of the sub-chain for a
long enough time, it is demoted to the warm chain. This time
is determined by the value of the
key_cache_age_threshold component of the
The threshold value prescribes that, for a key cache
N blocks, the block at
the beginning of the hot sub-chain not accessed within the
hits is to be moved to
the beginning of the warm sub-chain. It then becomes the first
candidate for eviction, because blocks for replacement always
are taken from the beginning of the warm sub-chain.
key_cache_age_threshold / 100
The midpoint insertion strategy allows you to keep more-valued
blocks always in the cache. If you prefer to use the plain LRU
strategy, leave the
key_cache_division_limit value set to its
default of 100.
The midpoint insertion strategy helps to improve performance
when execution of a query that requires an index scan
effectively pushes out of the cache all the index blocks
corresponding to valuable high-level B-tree nodes. To avoid
this, you must use a midpoint insertion strategy with the
key_cache_division_limit set to much less
than 100. Then valuable frequently hit nodes are preserved in
the hot sub-chain during an index scan operation as well.
If there are enough blocks in a key cache to hold blocks of an entire index, or at least the blocks corresponding to its non-leaf nodes, it makes sense to preload the key cache with index blocks before starting to use it. Preloading allows you to put the table index blocks into a key cache buffer in the most efficient way: by reading the index blocks from disk sequentially.
Without preloading, the blocks are still placed into the key cache as needed by queries. Although the blocks will stay in the cache, because there are enough buffers for all of them, they are fetched from disk in random order, and not sequentially.
To preload an index into a cache, use the
INTO CACHE statement. For example, the following
statement preloads nodes (index blocks) of indexes of the
LOAD INDEX INTO CACHE t1, t2 IGNORE LEAVES;+---------+--------------+----------+----------+ | Table | Op | Msg_type | Msg_text | +---------+--------------+----------+----------+ | test.t1 | preload_keys | status | OK | | test.t2 | preload_keys | status | OK | +---------+--------------+----------+----------+
IGNORE LEAVES modifier causes only
blocks for the non-leaf nodes of the index to be preloaded.
Thus, the statement shown preloads all index blocks from
t1, but only blocks for the non-leaf nodes
If an index has been assigned to a key cache using a
CACHE INDEX statement, preloading places
index blocks into that cache. Otherwise, the index is loaded
into the default key cache.
MySQL 4.1 introduces a new
key_cache_block_size variable on a per-key
cache basis. This variable specifies the size of the block
buffers for a key cache. It is intended to allow tuning of the
performance of I/O operations for index files.
The best performance for I/O operations is achieved when the size of read buffers is equal to the size of the native operating system I/O buffers. But setting the size of key nodes equal to the size of the I/O buffer does not always ensure the best overall performance. When reading the big leaf nodes, the server pulls in a lot of unnecessary data, effectively preventing reading other leaf nodes.
Currently, you cannot control the size of the index blocks in
a table. This size is set by the server when the
.MYI index file is created, depending on
the size of the keys in the indexes present in the table
definition. In most cases, it is set equal to the I/O buffer
A key cache can be restructured at any time by updating its parameter values. For example:
SET GLOBAL cold_cache.key_buffer_size=4*1024*1024;
If you assign to either the
key_cache_block_size key cache component
a value that differs from the component's current value, the
server destroys the cache's old structure and creates a new
one based on the new values. If the cache contains any dirty
blocks, the server saves them to disk before destroying and
re-creating the cache. Restructuring does not occur if you
change other key cache parameters.
When restructuring a key cache, the server first flushes the contents of any dirty buffers to disk. After that, the cache contents become unavailable. However, restructuring does not block queries that need to use indexes assigned to the cache. Instead, the server directly accesses the table indexes using native filesystem caching. Filesystem caching is not as efficient as using a key cache, so although queries execute, a slowdown can be anticipated. After the cache has been restructured, it becomes available again for caching indexes assigned to it, and the use of filesystem caching for the indexes ceases.
Storage engines collect statistics about tables for use by the optimizer. Table statistics are based on value groups, where a value group is a set of rows with the same key prefix value. For optimizer purposes, an important statistic is the average value group size.
MySQL uses the average value group size in the following ways:
To estimate how may rows must be read for each
To estimate how many row a partial join will produce; that is, the number of rows that an operation of this form will produce:
As the average value group size for an index increases, the index is less useful for those two purposes because the average number of rows per lookup increases: For the index to be good for optimization purposes, it is best that each index value target a small number of rows in the table. When a given index value yields a large number of rows, the index is less useful and MySQL is less likely to use it.
The average value group size is related to table cardinality,
which is the number of value groups. The
INDEX statement displays a cardinality value based on
N is the number of rows in the table
S is the average value group
size. That ratio yields an approximate number of value groups in
For a join based on the
NULL is not treated differently
from any other value:
NULL <=> NULL,
for any other
However, for a join based on the
NULL is different from
is not true when
expr2 (or both) are
NULL. This affects
accesses for comparisons of the form
: MySQL will not access
the table if the current value of
because the comparison cannot be true.
= comparisons, it does not matter how
NULL values are in the table. For
optimization purposes, the relevant value is the average size of
NULL value groups. However, MySQL
does not currently allow that average size to be collected or
MyISAM tables, you have some control over
collection of table statistics by means of the
myisam_stats_method system variable. This
variable has two possible values, which differ as follows:
values are treated as identical (that is, they all form a
single value group).
NULL value group size is much
higher than the average non-
group size, this method skews the average value group size
upward. This makes index appear to the optimizer to be less
useful than it really is for joins that look for
NULL values. Consequently, the
nulls_equal method may cause the
optimizer not to use the index for
accesses when it should.
values are not considered the same. Instead, each
NULL value forms a separate value group
of size 1.
If you have many
NULL values, this method
skews the average value group size downward. If the average
NULL value group size is large,
NULL values each as a group of
size 1 causes the optimizer to overestimate the value of the
index for joins that look for non-
values. Consequently, the
method may cause the optimizer to use this index for
ref lookups when other methods may be
If you tend to use many joins that use
<=> rather than
NULL values are not special in comparisons
NULL is equal to another. In this
nulls_equal is the appropriate
myisam_stats_method system variable has
global and session values. Setting the global value affects
MyISAM statistics collection for all
MyISAM tables. Setting the session value
affects statistics collection only for the current client
connection. This means that you can force a table's statistics
to be regenerated with a given method without affecting other
clients by setting the session value of
To regenerate table statistics, you can use any of the following methods:
myisam_stats_method, and then issue a
CHECK TABLE statement
Change the table to cause its statistics to go out of date
(for example, insert a row and then delete it), and then set
myisam_stats_method and issue an
ANALYZE TABLE statement
Some caveats regarding the use of
You can force table statistics to be collected explicitly,
as just described. However, MySQL may also collect
statistics automatically. For example, if during the course
of executing statements for a table, some of those
statements modify the table, MySQL may collect statistics.
(This may occur for bulk inserts or deletes, or some
ALTER TABLE statements, for example.) If
this happens, the statistics are collected using whatever
myisam_stats_method has at the
time. Thus, if you collect statistics using one method, but
myisam_stats_method is set to the other
method when a table's statistics are collected automatically
later, the other method will be used.
There is no way to tell which method was used to generate
statistics for a given
myisam_stats_method applies only to
MyISAM tables. Other storage engines have
only one method for collecting table statistics. Usually it
is closer to the
When you execute a mysqladmin status command, you should see something like this:
Uptime: 426 Running threads: 1 Questions: 11082 Reloads: 1 Open tables: 12
Open tables value of 12 can be somewhat
puzzling if you have only six tables.
MySQL is multi-threaded, so there may be many clients issuing
queries for a given table simultaneously. To minimize the
problem with multiple client threads having different states on
the same table, the table is opened independently by each
concurrent thread. This uses additional memory but normally
increases performance. With
one extra file descriptor is required for the data file for each
client that has the table open. (By contrast, the index file
descriptor is shared between all threads.)
max_tmp_tables system variables affect the
maximum number of files the server keeps open. If you increase
one or more of these values, you may run up against a limit
imposed by your operating system on the per-process number of
open file descriptors. Many operating systems allow you to
increase the open-files limit, although the method varies widely
from system to system. Consult your operating system
documentation to determine whether it is possible to increase
the limit and how to do so.
table_cache is related to
max_connections. For example, for 200
concurrent running connections, you should have a table cache
size of at least
N is the maximum number of tables per
join in any of the queries which you execute. You must also
reserve some extra file descriptors for temporary tables and
Make sure that your operating system can handle the number of
open file descriptors implied by the
table_cache setting. If
table_cache is set too high, MySQL may run
out of file descriptors and refuse connections, fail to perform
queries, and be very unreliable. You also have to take into
account that the
MyISAM storage engine needs
two file descriptors for each unique open table. You can
increase the number of file descriptors available to MySQL using
--open-files-limit startup option to
Section A.2.17, “File Not Found”.
The cache of open tables is kept at a level of
table_cache entries. The default value is 64;
this can be changed with the
option to mysqld. Note that MySQL may
temporarily open more tables than this to execute queries.
MySQL closes an unused table and removes it from the table cache under the following circumstances:
When the cache is full and a thread tries to open a table that is not in the cache.
When the cache contains more than
table_cache entries and a table in the
cache is no longer being used by any threads.
When a table flushing operation occurs. This happens when
someone issues a
FLUSH TABLES statement
or executes a mysqladmin flush-tables or
mysqladmin refresh command.
When the table cache fills up, the server uses the following procedure to locate a cache entry to use:
Tables that are not currently in use are released, beginning with the table least recently used.
If a new table needs to be opened, but the cache is full and no tables can be released, the cache is temporarily extended as necessary.
When the cache is in a temporarily extended state and a table goes from a used to unused state, the table is closed and released from the cache.
A table is opened for each concurrent access. This means the
table needs to be opened twice if two threads access the same
table or if a thread accesses the table twice in the same query
(for example, by joining the table to itself). Each concurrent
open requires an entry in the table cache. The first open of any
MyISAM table takes two file descriptors: one
for the data file and one for the index file. Each additional
use of the table takes only one file descriptor for the data
file. The index file descriptor is shared among all threads.
If you are opening a table with the
dedicated table object is allocated for the thread. This table
object is not shared by other threads and is not closed until
the thread calls
thread terminates. When this happens, the table is put back in
the table cache (if the cache is not full). See
Section 13.2.3, “
You can determine whether your table cache is too small by
checking the mysqld status variable
SHOW STATUS LIKE 'Opened_tables';+---------------+-------+ | Variable_name | Value | +---------------+-------+ | Opened_tables | 2741 | +---------------+-------+
If the value is very large, even when you have not issued many
FLUSH TABLES statements, you should increase
the table cache size. See
Section 5.2.2, “Server System Variables”, and
Section 5.2.4, “Server Status Variables”.
If you have many
ISAM tables in a database directory, open,
close, and create operations are slow. If you execute
SELECT statements on many different tables,
there is a little overhead when the table cache is full, because
for every table that has to be opened, another must be closed.
You can reduce this overhead by making the table cache larger.
We start with system-level factors, because some of these decisions must be made very early to achieve large performance gains. In other cases, a quick look at this section may suffice. However, it is always nice to have a sense of how much can be gained by changing factors that apply at this level.
The operating system to use is very important. To get the best use of multiple-CPU machines, you should use Solaris (because its threads implementation works well) or Linux (because the 2.4 and later kernels have good SMP support). Note that older Linux kernels have a 2GB filesize limit by default. If you have such a kernel and a need for files larger than 2GB, you should get the Large File Support (LFS) patch for the ext2 filesystem. Other filesystems such as ReiserFS and XFS do not have this 2GB limitation.
Before using MySQL in production, we advise you to test it on your intended platform.
If you have enough RAM, you could remove all swap devices. Some operating systems use a swap device in some contexts even if you have free memory.
option to avoid external locking. This option is turned on
by default as of MySQL 4.0. Before that, it is on by default
when compiling with MIT-pthreads, because
flock() is not fully supported by
MIT-pthreads on all platforms. It is also on by default for
Linux because Linux file locking is not yet safe.
Note that disabling external locking does not affect MySQL's functionality as long as you run only one server. Just remember to take down the server (or lock and flush the relevant tables) before you run myisamchk. On some systems it is mandatory to disable external locking because it does not work, anyway.
The only case in which you cannot disable external locking is when you run multiple MySQL servers (not clients) on the same data, or if you run myisamchk to check (not repair) a table without telling the server to flush and lock the tables first. Note that using multiple MySQL servers to access the same data concurrently is generally not recommended, except when using MySQL Cluster.
LOCK TABLES and
TABLES statements use internal locking, so you can
use them even if external locking is disabled.
You can determine the default buffer sizes used by the
mysqld server with this command (prior to
MySQL 4.1, omit
mysqld --verbose --help
This command produces a list of all mysqld options and configurable system variables. The output includes the default variable values and looks something like this:
back_log current value: 5 bdb_cache_size current value: 1048540 binlog_cache_size current value: 32768 connect_timeout current value: 5 delayed_insert_limit current value: 100 delayed_insert_timeout current value: 300 delayed_queue_size current value: 1000 flush_time current value: 0 interactive_timeout current value: 28800 join_buffer_size current value: 131072 key_buffer_size current value: 1048540 long_query_time current value: 10 lower_case_table_names current value: 0 max_allowed_packet current value: 1048576 max_binlog_cache_size current value: 4294967295 max_connect_errors current value: 10 max_connections current value: 100 max_delayed_threads current value: 20 max_heap_table_size current value: 16777216 max_join_size current value: 4294967295 max_sort_length current value: 1024 max_tmp_tables current value: 32 max_write_lock_count current value: 4294967295 myisam_sort_buffer_size current value: 8388608 net_buffer_length current value: 16384 net_read_timeout current value: 30 net_retry_count current value: 10 net_write_timeout current value: 60 read_buffer_size current value: 131072 read_rnd_buffer_size current value: 262144 slow_launch_time current value: 2 sort_buffer current value: 2097116 table_cache current value: 64 thread_concurrency current value: 10 thread_stack current value: 131072 tmp_table_size current value: 1048576 wait_timeout current value: 28800
For a mysqld server that is currently running, you can see the current values of its system variables by connecting to it and issuing this statement:
You can also see some statistical and status indicators for a running server by issuing this statement:
System variable and status information also can be obtained using mysqladmin:
MySQL uses algorithms that are very scalable, so you can usually run with very little memory. However, normally you get better performance by giving MySQL more memory.
When tuning a MySQL server, the two most important variables to
table_cache. You should first feel confident
that you have these set appropriately before trying to change
any other variables.
The following examples indicate some typical variable values for
different runtime configurations. The examples use the
mysqld_safe script and use
syntax to set the variable
to the value
value. This syntax is
available as of MySQL 4.0. For older versions of MySQL, take the
following differences into account:
Use safe_mysqld rather than mysqld_safe.
Set variables using
For variable names that end in
may need to specify them without
For example, the old name for
sort_buffer. The old name for
record_buffer. To see which variables
your version of the server recognizes, use mysqld
If you have at least 256MB of memory and many tables and want maximum performance with a moderate number of clients, you should use something like this:
mysqld_safe --key_buffer_size=64M --table_cache=256 \
--sort_buffer_size=4M --read_buffer_size=1M &
If you have only 128MB of memory and only a few tables, but you still do a lot of sorting, you can use something like this:
mysqld_safe --key_buffer_size=16M --sort_buffer_size=1M
If there are very many simultaneous connections, swapping problems may occur unless mysqld has been configured to use very little memory for each connection. mysqld performs better if you have enough memory for all connections.
With little memory and lots of connections, use something like this:
mysqld_safe --key_buffer_size=512K --sort_buffer_size=100K \
Or even this:
mysqld_safe --key_buffer_size=512K --sort_buffer_size=16K \
--table_cache=32 --read_buffer_size=8K \
If you are performing
GROUP BY or
ORDER BY operations on tables that are much
larger than your available memory, you should increase the value
read_rnd_buffer_size to speed up the
reading of rows following sorting operations.
When you have installed MySQL, the
support-files directory contains some
my.cnf sample files:
my-small.cnf. You can use these as a basis
for optimizing your system. (On Windows, look in the MySQL
If you specify an option on the command line for mysqld or mysqld_safe, it remains in effect only for that invocation of the server. To use the option every time the server runs, put it in an option file.
To see the effects of a parameter change, do something like this
(prior to MySQL 4.1, omit
mysqld --key_buffer_size=32M --verbose --help
The variable values are listed near the end of the output. Make
sure that the
--help options are last. Otherwise, the effect
of any options listed after them on the command line are not
reflected in the output.
For information on tuning the
engine, see Section 14.2.12, “
InnoDB Performance Tuning Tips”.
Most of the following tests were performed on Linux with the MySQL benchmarks, but they should give some indication for other operating systems and workloads.
You obtain the fastest executables when you link with
On Linux, it is best to compile the server with
-O3. You need about
200MB memory to compile
these options, because gcc or
pgcc needs a great deal of memory to make all
functions inline. You should also set
when configuring MySQL to avoid inclusion of the
libstdc++ library, which is not needed. Note
that with some versions of pgcc, the
resulting binary runs only on true Pentium processors, even if
you use the compiler option indicating that you want the
resulting code to work on all x586-type processors (such as
By using a better compiler and compilation options, you can obtain a 10–30% speed increase in applications. This is particularly important if you compile the MySQL server yourself.
When we tested both the Cygnus CodeFusion and Fujitsu compilers, neither was sufficiently bug-free to allow MySQL to be compiled with optimizations enabled.
The standard MySQL binary distributions are compiled with
support for all character sets. When you compile MySQL yourself,
you should include support only for the character sets that you
are going to use. This is controlled by the
--with-charset option to
Here is a list of some measurements that we have made:
If you use pgcc and compile everything
-O6, the mysqld
server is 1% faster than with gcc 2.95.2.
If you link dynamically (without
the result is 13% slower on Linux. Note that you still can
use a dynamically linked MySQL library for your client
applications. It is the server that is most critical for
For a connection from a client to a server running on the
same host, if you connect using TCP/IP rather than a Unix
socket file, performance is 7.5% slower. (On Unix, if you
connect to the hostname
uses a socket file by default.)
For TCP/IP connections from a client to a server, connecting to a remote server on another host is 8–11% slower than connecting to a server on the same host, even for connections over 100Mb/s Ethernet.
When running our benchmark tests using secure connections (all data encrypted with internal SSL support) performance was 55% slower than with unencrypted connections.
If you compile with
queries are 20% slower. Some queries may take substantially
longer; for example, the MySQL benchmarks run 35% slower. If
=full), the speed decrease is only 15%.
For a version of mysqld that has been
--with-debug=full, you can
disable memory checking at runtime by starting it with the
--skip-safemalloc option. The execution
speed should then be close to that obtained when configuring
On a Sun UltraSPARC-IIe, a server compiled with Forte 5.0 is 4% faster than one compiled with gcc 3.2.
On a Sun UltraSPARC-IIe, a server compiled with Forte 5.0 is 4% faster in 32-bit mode than in 64-bit mode.
Compiling with gcc 2.95.2 for UltraSPARC
options gives 4% more performance.
On Solaris 2.5.1, MIT-pthreads is 8–12% slower than Solaris native threads on a single processor. With greater loads or more CPUs, the difference should be larger.
Compiling on Linux-x86 using gcc without
frame pointers (
-fomit-frame-pointer -ffixed-ebp) makes
mysqld 1–4% faster.
Binary MySQL distributions for Linux that are provided by MySQL AB used to be compiled with pgcc. We had to go back to regular gcc due to a bug in pgcc that would generate binaries that do not run on AMD. We will continue using gcc until that bug is resolved. In the meantime, if you have a non-AMD machine, you can build a faster binary by compiling with pgcc. The standard MySQL Linux binary is linked statically to make it faster and more portable.
The following list indicates some of the ways that the mysqld server uses memory. Where applicable, the name of the system variable relevant to the memory use is given:
The key buffer (variable
is shared by all threads; other buffers used by the server
are allocated as needed. See
Section 7.5.2, “Tuning Server Parameters”.
Each connection uses some thread-specific space:
A stack (default 64KB before MySQL 4.0.10 and 192KB
A connection buffer (variable
A result buffer (variable
The connection buffer and result buffer are dynamically
enlarged up to
needed. While a query is running, a copy of the current
query string is also allocated.
All threads share the same base memory.
When a thread is no longer needed, the memory allocated to it is released and returned to the system unless the thread goes back into the thread cache. In that case, the memory remains allocated.
MyISAM tables are memory mapped. This is
because the 32-bit memory space of 4GB is not large enough
for most big tables. When systems with a 64-bit address
space become more common, we may add general support for
Each request that performs a sequential scan of a table
allocates a read buffer (variable
When reading rows in an arbitrary sequence (for example,
following a sort), a random-read
read_rnd_buffer_size) may be allocated in
order to avoid disk seeks.
All joins are executed in a single pass, and most joins can
be done without even using a temporary table. Most temporary
tables are memory-based hash tables. Temporary tables with a
large row length (calculated as the sum of all column
lengths) or that contain
BLOB columns are
stored on disk.
One problem before MySQL 3.23.2 is that if an internal
in-memory heap table exceeds the size of
tmp_table_size, the error
occurs. From 3.23.2 on, this is handled automatically by
changing the in-memory heap table to a disk-based
tbl_name is full
MyISAM table as necessary. To work around
this problem for older servers, you can increase the
temporary table size by setting the
tmp_table_size option to
mysqld, or by setting the SQL option
SQL_BIG_TABLES in the client program. See
Section 13.5.3, “
In MySQL 3.20, the maximum size of the temporary table is
record_buffer*16; if you are using this
version, you have to increase the value of
record_buffer. You can also start
mysqld with the
--big-tables option to always store
temporary tables on disk. However, this affects the speed of
many complicated queries.
Most requests that perform a sort allocate a sort buffer and zero to two temporary files depending on the result set size. See Section A.4.4, “Where MySQL Stores Temporary Files”.
Almost all parsing and calculating is done in a local memory
store. No memory overhead is needed for small items, so the
normal slow memory allocation and freeing is avoided. Memory
is allocated only for unexpectedly large strings. This is
ISAM table that is opened, the index file
is opened once and the data file is opened once for each
concurrently running thread. For each concurrent thread, a
table structure, column structures for each column, and a
buffer of size
are allocated (where
N is the maximum row length, not
BLOB columns). A
BLOB column requires five to eight bytes
plus the length of the
BLOB data. The
storage engines maintain one extra row buffer for internal
For each table having
BLOB columns, a
buffer is enlarged dynamically to read in larger
BLOB values. If you scan a table, a
buffer as large as the largest
Handler structures for all in-use tables are saved in a cache and managed as a FIFO. By default, the cache has 64 entries. If a table has been used by two running threads at the same time, the cache contains two entries for the table. See Section 7.4.8, “How MySQL Opens and Closes Tables”.
FLUSH TABLES statement or
mysqladmin flush-tables command closes
all tables that are not in use at once and marks all in-use
tables to be closed when the currently executing thread
finishes. This effectively frees most in-use memory.
FLUSH TABLES does not return until all
tables have been closed.
ps and other system status programs may
report that mysqld uses a lot of memory. This
may be caused by thread stacks on different memory addresses.
For example, the Solaris version of ps counts
the unused memory between stacks as used memory. You can verify
this by checking available swap with
We test mysqld with several memory-leakage
detectors (both commercial and Open Source), so there should be
no memory leaks.
When a new client connects to mysqld, mysqld spawns a new thread to handle the request. This thread first checks whether the hostname is in the hostname cache. If not, the thread attempts to resolve the hostname:
If the operating system supports the thread-safe
gethostbyname_r() calls, the thread uses
them to perform hostname resolution.
If the operating system does not support the thread-safe
calls, the thread locks a mutex and calls
gethostbyname() instead. In this case, no
other thread can resolve hostnames that are not in the
hostname cache until the first thread unlocks the mutex.
You can disable DNS hostname lookups by starting
mysqld with the
--skip-name-resolve option. However, in this
case, you can use only IP numbers in the MySQL grant tables.
If you have a very slow DNS and many hosts, you can get more
performance by either disabling DNS lookups with
--skip-name-resolve or by increasing the
HOST_CACHE_SIZE define (default value: 128)
and recompiling mysqld.
You can disable the hostname cache by starting the server with
--skip-host-cache option. To clear the
hostname cache, issue a
FLUSH HOSTS statement
or execute the mysqladmin flush-hosts
To disallow TCP/IP connections entirely, start
mysqld with the
Disk seeks are a huge performance bottleneck. This problem becomes more apparent when the amount of data starts to grow so large that effective caching becomes impossible. For large databases where you access data more or less randomly, you can be sure that you need at least one disk seek to read and a couple of disk seeks to write things. To minimize this problem, use disks with low seek times.
Increase the number of available disk spindles (and thereby reduce the seek overhead) by either symlinking files to different disks or striping the disks:
Using symbolic links
This means that, for
MyISAM tables, you
symlink the index file and data files from their usual
location in the data directory to another disk (that may
also be striped). This makes both the seek and read times
better, assuming that the disk is not used for other
purposes as well. See Section 7.6.1, “Using Symbolic Links”.
Striping means that you have many disks and put the first
block on the first disk, the second block on the second
disk, and the
N-th block on the
disk, and so on. This means if your normal data size is
less than the stripe size (or perfectly aligned), you get
much better performance. Striping is very dependent on the
operating system and the stripe size, so benchmark your
application with different stripe sizes. See
Section 7.1.5, “Using Your Own Benchmarks”.
The speed difference for striping is very dependent on the parameters. Depending on how you set the striping parameters and number of disks, you may get differences measured in orders of magnitude. You have to choose to optimize for random or sequential access.
For reliability, you may want to use RAID 0+1 (striping plus
mirroring), but in this case, you need 2 ×
N drives to hold
N drives of data. This is probably
the best option if you have the money for it. However, you may
also have to invest in some volume-management software to
handle it efficiently.
A good option is to vary the RAID level according to how
critical a type of data is. For example, store semi-important
data that can be regenerated on a RAID 0 disk, but store
really important data such as host information and logs on a
RAID 0+1 or RAID
N disk. RAID
N can be a problem if you have many
writes, due to the time required to update the parity bits.
On Linux, you can get much more performance by using
hdparm to configure your disk's interface.
(Up to 100% under load is not uncommon.) The following
hdparm options should be quite good for
MySQL, and probably for many other applications:
hdparm -m 16 -d 1
Note that performance and reliability when using this command
depend on your hardware, so we strongly suggest that you test
your system thoroughly after using
Please consult the
hdparm manual page for
more information. If
hdparm is not used
wisely, filesystem corruption may result, so back up
everything before experimenting!
You can also set the parameters for the filesystem that the database uses:
If you do not need to know when files were last accessed
(which is not really useful on a database server), you can
mount your filesystems with the
option. That skips updates to the last access time in inodes
on the filesystem, which avoids some disk seeks.
On many operating systems, you can set a filesystem to be
updated asynchronously by mounting it with the
async option. If your computer is reasonably stable,
this should give you more performance without sacrificing too
much reliability. (This flag is on by default on Linux.)
You can move tables and databases from the database directory to other locations and replace them with symbolic links to the new locations. You might want to do this, for example, to move a database to a file system with more free space or increase the speed of your system by spreading your tables to different disk.
The recommended way to do this is simply to symlink databases to a different disk. Symlink tables only as a last resort.
On Unix, the way to symlink a database is first to create a directory on some disk where you have free space and then to create a symlink to it from the MySQL data directory.
ln -s /dr1/databases/test
MySQL does not support linking one directory to multiple
databases. Replacing a database directory with a symbolic link
works as long as you do not make a symbolic link between
databases. Suppose that you have a database
db1 under the MySQL data directory, and
then make a symlink
db2 that points to
ln -s db1 db2
The result is that, or any table
db1, there also appears to be a table
db2. If one
db1.tbl_a and another client
db2.tbl_a, problems are likely to
If you really need to do this, you can change one of the
source files. The file to modify depends on your version of
MySQL. For MySQL 4.0 and up, look for the following statement
if (!(MyFlags & MY_RESOLVE_LINK) || (!lstat(filename,&stat_buff) && S_ISLNK(stat_buff.st_mode)))
Before MySQL 4.0, look for this statement in the
if (flag & 32 || (!lstat(to,&stat_buff) && S_ISLNK(stat_buff.st_mode)))
Change the statement to this:
Before MySQL 4.0, you should not symlink tables unless you are
very careful with them. The problem is
that if you run
REPAIR TABLE, or
TABLE on a symlinked table, the symlinks are removed
and replaced by the original files. This happens because these
statements work by creating a temporary file in the database
directory and replacing the original file with the temporary
file when the statement operation is complete.
You should not symlink tables on systems that do not have a
realpath() call. (Linux
and Solaris support
realpath()). You can
check whether your system supports symbolic links by issuing a
SHOW VARIABLES LIKE 'have_symlink'
In MySQL 4.0, symlinks are fully supported only for
MyISAM tables. For files used by tables for
other storage engines, you may get strange problems if you try
to use symbolic links.
The handling of symbolic links for
tables in MySQL 4.0 works the following way:
In the data directory, you always have the table format
.frm) file, the data
.MYD) file, and the index
.MYI) file. The data file and index
file can be moved elsewhere and replaced in the data
directory by symlinks. The format file cannot.
You can symlink the data file and the index file independently to different directories.
You can instruct a running MySQL server to perform the
symlinking by using the
INDEX DIRECTORY options to
CREATE TABLE. See
Section 13.1.5, “
CREATE TABLE Syntax”. Alternatively, symlinking
can be accomplished manually from the command line using
ln -s if mysqld is
myisamchk does not replace a symlink with the data file or index file. It works directly on the file to which the symlink points. Any temporary files are created in the directory where the data file or index file is located.
Note: When you drop a
table that is using symlinks, both the symlink
and the file to which the symlink points are
dropped. This is an extremely good reason why
you should not run
mysqld as the system
root or allow system users to have
write access to MySQL database directories.
If you rename a table with
ALTER TABLE ...
RENAME and you do not move the table to another
database, the symlinks in the database directory are
renamed to the new names and the data file and index file
are renamed accordingly.
If you use
ALTER TABLE ... RENAME to
move a table to another database, the table is moved to
the other database directory. The old symlinks and the
files to which they pointed are deleted. In other words,
the new table is not symlinked.
If you are not using symlinks, you should use the
--skip-symbolic-links option to
mysqld to ensure that no one can use
mysqld to drop or rename a file outside
of the data directory.
SHOW CREATE TABLE does not report if a
table has symbolic links prior to MySQL 4.0.15. This is also
true for mysqldump, which uses
SHOW CREATE TABLE to generate
CREATE TABLE statements.
Table symlink operations that are not supported up through MySQL 4.1:
ALTER TABLE ignores the
BACKUP TABLE and
TABLE do not respect symbolic links.
.frm file must
never be a symbolic link (as
indicated previously, only the data and index files can be
symbolic links). Attempting to do this (for example, to
make synonyms) produces incorrect results. Suppose that
you have a database
db1 under the MySQL
data directory, a table
tbl1 in this
database, and in the
db1 directory you
make a symlink
tbl2 that points to
ln -s tbl1.frm tbl2.frmshell>
ln -s tbl1.MYD tbl2.MYDshell>
ln -s tbl1.MYI tbl2.MYI
Problems result if one thread reads
db1.tbl1 and another thread updates
The query cache is “fooled” (it has no
way of knowing that
tbl1 has not
been updated, so it returns outdated results).
ALTER statements on
Beginning with MySQL 3.23.16, the
mysql-max-nt servers for Windows are
compiled with the
-DUSE_SYMDIR option. This
enables you to put a database directory on a different disk by
setting up a symbolic link to it. This is similar to the way
that symbolic links work on Unix, although the procedure for
setting up the link is different.
It is necessary to define
explicitly only before MySQL 4.0; for
mysql-max-nt, you can enable symbolic links
by using the
--symbolic-links option. As of
MySQL 4.0, symbolic links are enabled by default for all
Windows servers. If you do not need them, you can disable them
On Windows, create a symbolic link to a MySQL database by
creating a file in the data directory that contains the path
to the destination directory. The file should be named
db_name is the database name.
Suppose that the MySQL data directory is
C:\mysql\data and you want to have
foo located at
D:\data\foo. Set up a symlink using this
Make sure that the
directory exists by creating it if necessary. If you
already have a database directory named
foo in the data directory, you should
move it to
D:\data. Otherwise, the
symbolic link will be ineffective. To avoid problems, make
sure that the server is not running when you move the
Create a text file
C:\mysql\data\foo.sym that contains
After this, all tables created in the database
foo are created in
D:\data\foo. Note that the
symbolic link is not used if a directory with the same name as
the database exists in the MySQL data directory.