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5. MySQL Optimisation

5.1 Optimisation Overview  
5.2 Optimising SELECTs and Other Queries  
5.3 Locking Issues  
5.4 Optimising Database Structure  
5.5 Optimising the MySQL Server  
5.6 Disk Issues  

Optimisation is a complicated task because it ultimately requires understanding of the whole system. While it may be possible to do some local optimisations with small knowledge of your system or application, the more optimal you want your system to become the more you will have to know about it.

This chapter will try to explain and give some examples of different ways to optimise MySQL. Remember, however, that there are always some (increasingly harder) additional ways to make the system even faster.

5.1 Optimisation Overview

The most important part for getting a system fast is of course the basic design. You also need to know what kinds of things your system will be doing, and what your bottlenecks are.

The most common bottlenecks are:

5.1.1 MySQL Design Limitations/Tradeoffs  
5.1.2 Portability  
5.1.3 What Have We Used MySQL For?  
5.1.4 The MySQL Benchmark Suite  
5.1.5 Using Your Own Benchmarks  

5.1.1 MySQL Design Limitations/Tradeoffs

When using the MyISAM storage engine, MySQL uses extremely fast table locking (multiple readers / single writers). The biggest problem with this table type is a if you have a mix of a steady stream of updates and slow selects on the same table. If this is a problem with some tables, you can use another table type for these. See section MySQL Table Types.

MySQL can work with both transactional and non-transactional tables. To be able to work smoothly with non-transactional tables (which can't rollback if something goes wrong), MySQL has the following rules:

For more information about this, see See section How MySQL deals with constraints.

The above means that one should not use MySQL to check fields content, but one should do this in the application.

5.1.2 Portability

Because all SQL servers implement different parts of SQL, it takes work to write portable SQL applications. For very simple selects/inserts it is very easy, but the more you need the harder it gets. If you want an application that is fast with many databases it becomes even harder!

To make a complex application portable you need to choose a number of SQL servers that it should work with.

You can use the MySQL crash-me program/web-page http://www.mysql.com/information/crash-me.php to find functions, types, and limits you can use with a selection of database servers. Crash-me now tests far from everything possible, but it is still comprehensive with about 450 things tested.

For example, you shouldn't have column names longer than 18 characters if you want to be able to use Informix or DB2.

Both the MySQL benchmarks and crash-me programs are very database-independent. By taking a look at how we have handled this, you can get a feeling for what you have to do to write your application database-independent. The benchmarks themselves can be found in the `sql-bench' directory in the MySQL source distribution. They are written in Perl with DBI database interface (which solves the access part of the problem).

See http://www.mysql.com/information/benchmarks.html for the results from this benchmark.

As you can see in these results, all databases have some weak points. That is, they have different design compromises that lead to different behaviour.

If you strive for database independence, you need to get a good feeling for each SQL server's bottlenecks. MySQL is very fast in retrieving and updating things, but will have a problem in mixing slow readers/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). Transaction databases in general are not very good at generating summary tables from log tables, as in this case row locking is almost useless.

To get your application really database-independent, you need to define an easy extendable interface through which you manipulate your data. As C++ is available on most systems, it makes sense to use a C++ classes interface to the databases.

If you use some specific feature for some database (like the REPLACE command in MySQL), you should code a method for the other SQL servers to implement the same feature (but slower). With MySQL you can use the /*! */ syntax to add MySQL-specific keywords to a query. The code inside /**/ will be treated as a comment (ignored) by most other SQL servers.

If high performance is more important than exactness, as in some web applications, it is possibile 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 and set the expire timeout higher until things get back to normal.

In this case the table creation information should contain information of the initial size of the cache and how often the table should normally be refreshed.

5.1.3 What Have We Used MySQL For?

During MySQL initial development, the features of MySQL were made to fit our largest customer. They handle data warehousing for a couple of the biggest retailers in Sweden.

From all stores, we get weekly summaries of all bonus card transactions, and we are expected to provide useful information for the store owners to help them find how their advertisement campaigns are affecting their customers.

The data is quite huge (about 7 million summary transactions per month), and we have data for 4-10 years that we need to present to the users. We got weekly requests from the customers that they want to get 'instant' access to new reports from this data.

We solved this by storing all information per month in compressed 'transaction' tables. We have a set of simple macros (script) that generates summary tables grouped by different criteria (product group, customer id, store ...) from the transactional tables. The reports are web pages that are dynamically generated by a small Perl script that parses a web page, executes the SQL statements in it, and inserts the results. We would have used PHP or mod_perl instead but they were not available at that time.

For graphical data we wrote a simple tool in C that can produce GIFs based on the result of an SQL query (with some processing of the result). This is also dynamically executed from the Perl script that parses the HTML files.

In most cases a new report can simply be done by copying an existing script and modifying the SQL query in it. In some cases, we will need to add more fields to an existing summary table or generate a new one, but this is also quite simple, as we keep all transactions tables on disk. (Currently we have at least 50G of transactions tables and 200G of other customer data.)

We also let our customers access the summary tables directly with ODBC so that the advanced users can themselves experiment with the data.

We haven't had any problems handling this with quite modest Sun Ultra SPARCstation (2x200 Mhz). We recently upgraded one of our servers to a 2 CPU 400 Mhz UltraSPARC, and we are now planning to start handling transactions on the product level, which would mean a ten-fold increase of data. We think we can keep up with this by just adding more disk to our systems.

We are also experimenting with Intel-Linux to be able to get more CPU power cheaper. Now that we have the binary portable database format (new in Version 3.23), we will start to use this for some parts of the application.

Our initial feelings are that Linux will perform much better on low-to-medium load and Solaris will perform better when you start to get a high load because of extreme disk IO, but we don't yet have anything conclusive about this. After some discussion with a Linux Kernel developer, this might be a side effect of Linux giving so much resources to the batch job that the interactive performance gets very low. This makes the machine feel very slow and unresponsive while big batches are going. Hopefully this will be better handled in future Linux Kernels.

5.1.4 The MySQL Benchmark Suite

This should contain a technical description of the MySQL benchmark suite (and crash-me), but that description is not written yet. Currently, you can get a good idea of the benchmark by looking at the code and results in the `sql-bench' directory in any MySQL source distributions.

This benchmark suite is meant to be a benchmark that will tell any user what things a given SQL implementation performs well or poorly at.

Note that this benchmark is single threaded, so it measures the minimum time for the operations. We plan to in the future add a lot of multi-threaded tests to the benchmark suite.

For example, (run on the same NT 4.0 machine):

Reading 2000000 rows by index

Seconds

Seconds

mysql

367

249

mysql_odbc

464

db2_odbc

1206

informix_odbc

121126

ms-sql_odbc

1634

oracle_odbc

20800

solid_odbc

877

sybase_odbc

17614

Inserting (350768) rows

Seconds

Seconds

mysql

381

206

mysql_odbc

619

db2_odbc

3460

informix_odbc

2692

ms-sql_odbc

4012

oracle_odbc

11291

solid_odbc

1801

sybase_odbc

4802

In the above test MySQL was run with a 8M index cache.

We have gathered some more benchmark results at http://www.mysql.com/information/benchmarks.html.

Note that Oracle is not included because they asked to be removed. All Oracle benchmarks have to be passed by Oracle! We believe that makes Oracle benchmarks very biased because the above benchmarks are supposed to show what a standard installation can do for a single client.

To run the benchmark suite, you have to download a MySQL source distribution, install the Perl DBI driver, the Perl DBD driver for the database you want to test and then do:

 
cd sql-bench
perl run-all-tests --server=#

where # is one of supported servers. You can get a list of all options and supported servers by doing run-all-tests --help.

crash-me tries to determine what features a database supports and what its capabilities and limitations are by actually running queries. For example, it determines:

We can find the result from crash-me on a lot of different databases at http://www.mysql.com/information/crash-me.php.

5.1.5 Using Your Own Benchmarks

You should definitely benchmark your application and database to find out where the bottlenecks are. By fixing it (or by replacing the bottleneck with a 'dummy module') you can then easily identify the next bottleneck (and so on). Even if the overall performance for your application is sufficient, 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 an example of portable benchmark programs, look at the MySQL benchmark suite. See section MySQL Benchmarks. You can take any program from this suite and modify it for your needs. By doing this, you can try different solutions to your problem and test which is really the fastest solution for you.

It is very common that some problems only occur 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 every one of these cases so far, it has been problems with basic design (table scans are not good at high load) or OS/Library issues. Most of this would be a lot 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 for this, and it is available at: http://www.mysql.com/Downloads/super-smack/super-smack-1.0.tar.gz. As the name suggests, it can bring your system down to its knees if you ask it, so make sure to use it only on your development systems.

5.2 Optimising SELECTs and Other Queries

First, one thing that affects all queries: The more complex permission system setup you have, the more overhead you get.

If you do not have any GRANT statements done, MySQL will optimise the permission checking somewhat. So if you have a very high volume it may be worth the time to avoid grants. Otherwise, more permission check results in a larger overhead.

If your problem is with some explicit MySQL function, you can always time this in the MySQL client:

 
mysql> SELECT BENCHMARK(1000000,1+1);
+------------------------+
| BENCHMARK(1000000,1+1) |
+------------------------+
|                      0 |
+------------------------+
1 row in set (0.32 sec)

The above shows that MySQL can execute 1,000,000 + expressions in 0.32 seconds on a PentiumII 400MHz.

All MySQL functions should be very optimised, but there may be some exceptions, and the BENCHMARK(loop_count,expression) is a great tool to find out if this is a problem with your query.

5.2.1 EXPLAIN Syntax (Get Information About a SELECT)  
5.2.2 Estimating Query Performance  
5.2.3 Speed of SELECT Queries  
5.2.4 How MySQL Optimises WHERE Clauses  
5.2.5 How MySQL Optimises IS NULL  
5.2.6 How MySQL Optimises DISTINCT  
5.2.7 How MySQL Optimises LEFT JOIN and RIGHT JOIN  
5.2.8 How MySQL Optimises ORDER BY  
5.2.9 How MySQL Optimises LIMIT  
5.2.10 Speed of INSERT Queries  
5.2.11 Speed of UPDATE Queries  
5.2.12 Speed of DELETE Queries  
5.2.13 Other Optimisation Tips  

5.2.1 EXPLAIN Syntax (Get Information About a SELECT)

 
    EXPLAIN tbl_name
or  EXPLAIN SELECT select_options

EXPLAIN tbl_name is a synonym for DESCRIBE tbl_name or SHOW COLUMNS FROM tbl_name.

When you precede a SELECT statement with the keyword EXPLAIN, MySQL explains how it would process the SELECT, providing information about how tables are joined and in which order.

With the help of EXPLAIN, you can see when you must add indexes to tables to get a faster SELECT that uses indexes to find the records.

You should frequently run ANALYZE TABLE to update table statistics such as cardinality of keys which can affect the choices the optimiser makes. See section ANALYZE TABLE Syntax.

You can also see if the optimiser joins the tables in an optimal order. To force the optimiser to use a specific join order for a SELECT statement, add a STRAIGHT_JOIN clause.

For non-simple joins, EXPLAIN returns a row of information for each table used in the SELECT statement. The tables are listed in the order they would be read. MySQL resolves all joins using a single-sweep multi-join method. This means that MySQL reads a row from the first table, then finds a matching row in the second table, then in the third table and so on. When all tables are processed, it 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 table.

In MySQL version 4.1 the EXPLAIN output was changed to work better with constructs like UNIONs, subqueries and derived tables. Most notable is the addition of two new columns: id and select_type.

Output from EXPLAIN consists of the following columns:

id

SELECT identifier, the sequential number of this SELECT within the query.

select_type

Type of SELECT clause, which can be any of the following:

SIMPLE

Simple SELECT (without UNIONs or subqueries).

PRIMARY

Outermost SELECT.

UNION

Second and further UNION SELECTs.

DEPENDENT UNION

Second and further UNION SELECTSs, dependent on outer subquery.

SUBQUERY

First SELECT in subquery.

DEPENDENT SUBQUERY

First SELECT, dependent on outer subquery.

DERIVED

Derived table SELECT.

table

The table to which the row of output refers.

type

The join type. The different join types are listed here, ordered from best to worst type:

system

The table has only one row (= system table). This is a special case of the const join type.

const

The table has at most one matching row, which will be 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 optimiser. const tables are very fast as they are read only once!

const is used when you compare all parts of a PRIMARY/UNIQUE key with constants:

 
SELECT * FROM const_table WHERE primary_key=1;

SELECT * FROM const_table WHERE primary_key_part1=1 and primary_key_part2=2;
eq_ref

One row will be read from this table for each combination of rows from the previous tables. This is the best possible join type, other than the const types. It is used when all parts of an index are used by the join and the index is UNIQUE or a PRIMARY KEY.

eq_ref can be used for indexed columns that is compared with =. The compared item may be a constant or an expression that uses columns from tables that are read before this table.

In the following examples, ref_table will be able to use eq_ref

 
SELECT * FROM ref_table,other_table WHERE
ref_table.key_column=other_table.column;

SELECT * FROM ref_table,other_table WHERE
ref_table.key_column_part1=other_table.column AND
ref_table.key_column_part2=1;
ref

All rows with matching index values will be read from this table for each combination of rows from the previous tables. ref is used if the join uses only a leftmost prefix of the key, or if the key is not UNIQUE or a PRIMARY KEY (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 join type is good.

ref can be used for indexed columns that is compared with =.

In the following examples, ref_table will be able to use ref

 
SELECT * FROM ref_table WHERE key_column=expr;

SELECT * FROM ref_table,other_table WHERE
ref_table.key_column=other_table.column;

SELECT * FROM ref_table,other_table WHERE
ref_table.key_column_part1=other_table.column AND
ref_table.key_column_part2=1;
ref_or_null

Like ref, but with the addition that we will do an extra search for rows with NULL. See section How MySQL Optimises IS NULL.

 
SELECT * FROM ref_table WHERE key_column=expr OR key_column IS NULL;

This optimisation is new for MySQL 4.1.1 and is mostly used when resolving sub queries.

range

Only rows that are in a given range will be retrieved, using an index to select the rows. The key column indicates which index is used. The key_len contains the longest key part that was used. The ref column will be NULL for this type.

range can be used for when an key column is compared to a constant with =, <>, >, >=, <, <=, IS NULL, <=>, BETWEEN and IN.

 
SELECT * FROM range_table WHERE key_column = 10;

SELECT * FROM range_table WHERE key_column BETWEEN 10 and 20;

SELECT * FROM range_table WHERE key_column IN (10,20,30);

SELECT * FROM range_table WHERE key_part1= 10 and key_part2 IN (10,20,30);
index

This is the same as ALL, except that only the index tree is scanned. This is usually faster than ALL, as the index file is usually smaller than the datafile.

This can be used when the query only uses columns that are part of one index.

ALL

A full table scan will be 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. You normally can avoid ALL by adding more indexes, so that the row can be retrieved based on constant values or column values from earlier tables.

possible_keys

The possible_keys column indicates which indexes MySQL could use to find the rows in this table. Note that this column is totally independent of the order of the tables. That means that some of the keys in possible_keys may not be usable in practice with the generated table order.

If this column is empty, 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 see if it refers to some column or columns that would be suitable for indexing. If so, create an appropriate index and check the query with EXPLAIN again. See section ALTER TABLE Syntax.

To see what indexes a table has, use SHOW INDEX FROM tbl_name.

key

The 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 an key listed in the possible_keys column, use USE KEY/IGNORE KEY in your query. See section SELECT Syntax.

Also, running myisamchk --analyze (see section myismchk syntax) or ANALYZE TABLE (see section ANALYZE TABLE) on the table will help the optimiser choose better indexes.

key_len

The key_len column indicates the length of the key that MySQL decided to use. The length is NULL if the key is NULL. Note that this tells us how many parts of a multi-part key MySQL will actually use.

ref

The ref column shows which columns or constants are used with the key to select rows from the table.

rows

The rows column indicates the number of rows MySQL believes it must examine to execute the query.

Extra

This column contains additional information of how MySQL will resolve the query. Here is an explanation of the different text strings that can be found in this column:

Distinct

MySQL will not continue searching for more rows for the current row combination after it has found the first matching row.

Not exists

MySQL was able to do a LEFT JOIN optimisation on the query and will not examine more rows in this table for the previous row combination after it finds one row that matches the LEFT JOIN criteria.

Here is an example for this:

 
SELECT * FROM t1 LEFT JOIN t2 ON t1.id=t2.id WHERE t2.id IS NULL;

Assume that t2.id is defined with NOT NULL. In this case MySQL will scan t1 and look up the rows in t2 through t1.id. If MySQL finds a matching row in t2, it knows that t2.id can never be NULL, and will not scan through the rest of the rows in t2 that has the same id. In other words, for each row in t1, MySQL only needs to do a single lookup in t2, independent of how many matching rows there are in t2.

range checked for each record (index map: #)

MySQL didn't find a real good index to use. It will, instead, for each row combination in the preceding tables, do a check on which index to use (if any), and use this index to retrieve the rows from the table. This isn't very fast but is faster than having to do a join without an index.

Using filesort

MySQL will need to 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 + pointer to the row for all rows that match the WHERE. Then the keys are sorted. Finally the rows are retrieved in sorted order.

Using index

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 can be done when all the used columns for the table are part of the same index.

Using temporary

To resolve the query MySQL will need to create a temporary table to hold the result. This typically happens if you do an ORDER BY on a different column set than you did a GROUP BY on.

Using where

A WHERE clause will be used to restrict which rows will be matched against the next table or sent to the client. If you don't have this information and the table is of type ALL or index, you may have something wrong in your query (if you don't intend to fetch/examine all rows from the table).

If you want to get your queries as fast as possible, you should look out for Using filesort and Using temporary.

You can get a good indication of how good a join is by multiplying all values in the rows column of the EXPLAIN output. This should tell you roughly how many rows MySQL must examine to execute the query. This number is also used when you restrict queries with the max_join_size variable. See section Tuning Server Parameters.

The following example shows how a JOIN can be optimised progressively using the information provided by EXPLAIN.

Suppose you have the SELECT statement shown here, that you examine using EXPLAIN:

 
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, assume that:

Initially, before any optimisations have been performed, the EXPLAIN statement produces the following information:

 
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,ClientID,ActualPC NULL NULL    NULL 3872
      range checked for each record (key map: 35)

Because type is ALL for each table, this output indicates that MySQL is generating a Cartesian product of all the tables! This will take quite a long time, as the product of the number of rows in each table must be examined! For the case at hand, this 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't (yet) use indexes on columns efficiently if they are declared differently. In this context, VARCHAR and CHAR are the same unless they are declared as different lengths. Because tt.ActualPC is declared as CHAR(10) and et.EMPLOYID is declared as CHAR(15), there is a length mismatch.

To fix this disparity between column lengths, use ALTER TABLE to lengthen ActualPC from 10 characters to 15 characters:

 
mysql> ALTER TABLE tt MODIFY ActualPC VARCHAR(15);

Now tt.ActualPC and 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,ClientID,ActualPC NULL NULL NULL 3872    Using where
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 now less by a factor of 74). This version is executed in a couple of seconds.

A second alteration can be made to eliminate the column length mismatches for the tt.AssignedPC = et_1.EMPLOYID and tt.ClientID = do.CUSTNMBR comparisons:

 
mysql> ALTER TABLE tt MODIFY AssignedPC VARCHAR(15),
    ->                MODIFY ClientID   VARCHAR(15);

Now 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 where
             ClientID,
             ActualPC
et_1  eq_ref PRIMARY         PRIMARY  15      tt.AssignedPC 1
do    eq_ref PRIMARY         PRIMARY  15      tt.ClientID   1

This is almost as good as it can get.

The remaining problem is that, by default, MySQL assumes that values in the tt.ActualPC column are evenly distributed, and that isn't the case for the tt table. Fortunately, it is easy to tell MySQL about this:

 
shell> myisamchk --analyze PATH_TO_MYSQL_DATABASE/tt
shell> mysqladmin refresh

Now 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 where
             ClientID,
             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 optimiser. To optimise a query, you should check if the numbers are even close to the truth. If not, you may get better performance by using STRAIGHT_JOIN in your SELECT statement and trying to list the tables in a different order in the FROM clause.

5.2.2 Estimating Query Performance

In most cases you can estimate the performance by counting disk seeks. For small tables, you can usually find the row in 1 disk seek (as the index is probably cached). For bigger tables, you can estimate that (using B++ tree indexes) you will need: log(row_count) / log(index_block_length / 3 * 2 / (index_length + data_pointer_length)) + 1 seeks to find a row.

In MySQL an index block is usually 1024 bytes and the data pointer is usually 4 bytes. A 500,000 row table with an index length of 3 (medium integer) gives you: log(500,000)/log(1024/3*2/(3+4)) + 1 = 4 seeks.

As the above index would require about 500,000 * 7 * 3/2 = 5.2M, (assuming that the index buffers are filled to 2/3, which is typical) you will probably have much of the index in memory and you will probably only need 1-2 calls to read data from the OS to find the row.

For writes, however, you will need 4 seek requests (as above) to find where to place the new index and normally 2 seeks to update the index and write the row.

Note that the above doesn't mean that your application will slowly degenerate by log N! As long as everything is cached by the OS or SQL server things will only go marginally slower while the table gets bigger. After the data gets too big to be cached, things will start to go much slower until your applications is only bound by disk-seeks (which increase by log N). To avoid this, increase the index cache as the data grows. See section Tuning Server Parameters.

5.2.3 Speed of SELECT Queries

In general, when you want to make a slow SELECT ... WHERE faster, the first thing to check is whether you can add an index. See section MySQL indexes. All references between different tables should usually be done with indexes. You can use the EXPLAIN command to determine which indexes are used for a SELECT. See section EXPLAIN.

Some general tips:

5.2.4 How MySQL Optimises WHERE Clauses

The WHERE optimisations are put in the SELECT part here because they are mostly used with SELECT, but the same optimisations apply for WHERE in DELETE and UPDATE statements.

Also note that this section is incomplete. MySQL does many optimisations, and we have not had time to document them all.

Some of the optimisations performed by MySQL are listed here:

Some examples of queries that are very fast:

 
mysql> SELECT COUNT(*) FROM tbl_name;
mysql> SELECT MIN(key_part1),MAX(key_part1) FROM tbl_name;
mysql> SELECT MAX(key_part2) FROM tbl_name
    ->        WHERE key_part_1=constant;
mysql> SELECT ... FROM tbl_name
    ->        ORDER BY key_part1,key_part2,... LIMIT 10;
mysql> SELECT ... FROM tbl_name
    ->        ORDER BY key_part1 DESC,key_part2 DESC,... LIMIT 10;

The following queries are resolved using only the index tree (assuming the indexed columns are numeric):

 
mysql> SELECT key_part1,key_part2 FROM tbl_name WHERE key_part1=val;
mysql> SELECT COUNT(*) FROM tbl_name
    ->        WHERE key_part1=val1 AND key_part2=val2;
mysql> SELECT key_part2 FROM tbl_name GROUP BY key_part1;

The following queries use indexing to retrieve the rows in sorted order without a separate sorting pass:

 
mysql> SELECT ... FROM tbl_name
    ->            ORDER BY key_part1,key_part2,... ;
mysql> SELECT ... FROM tbl_name
    ->            ORDER BY key_part1 DESC,key_part2 DESC,... ;

5.2.5 How MySQL Optimises IS NULL

MySQL can do the same optimisation on column IS NULL as it can do with column = constant_value. For example, MySQL can use indexes and ranges to search for NULL with IS NULL.

 
SELECT * FROM table_name WHERE key_col IS NULL;

SELECT * FROM table_name WHERE key_col <=> NULL;

SELECT * FROM table_name WHERE key_col=# OR key_col=# OR key_col IS NULL

If you use column_name IS NULL on a NOT NULL in a WHERE clause on table that is not used OUTER JOIN that expression will be optimised away.

MySQL 4.1.1 can additionally optimise the combination column = expr AND column IS NULL, an form that is common in resolved sub queries. EXPLAIN will show ref_or_null when this optimisation is used.

This optimisation can handle one IS NULL for any key part.

Some examples of queries that are optimised (assuming key on t2 (a,b)):

 
SELECT * FROM t1 WHERE t1.a=expr OR 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 after that a separate search after rows with NULL key.

Note that the optimisation can only handle one IS NULL level.

 
SELECT * FROM t1,t2 where (t1.a=t2.a AND t2.a IS NULL) OR (t1.b=t2.b AND t2.b IS NULL);

Int the above case MySQL will only use key lookups on the part (t1.a=t2.a AND t2.a IS NULL) and not be able to use the key part on b.

5.2.6 How MySQL Optimises DISTINCT

DISTINCT is converted to a GROUP BY on all columns, DISTINCT combined with ORDER BY will in many cases also need a temporary table.

Note that as DISTINCT may use GROUP BY, you should be aware of how MySQL works with in fields in ORDER BY or HAVING that are not part of the selected fields. See section GROUP BY with Hidden Fields.

When combining LIMIT row_count with DISTINCT, MySQL will stop as soon as it finds row_count unique rows.

If you don't use columns from all used tables, MySQL will stop the scanning of the not used tables as soon as it has found the first match.

 
SELECT DISTINCT t1.a FROM t1,t2 where t1.a=t2.a;

In the case, assuming t1 is used before t2 (check with EXPLAIN), then MySQL will stop reading from t2 (for that particular row in t1) when the first row in t2 is found.

5.2.7 How MySQL Optimises LEFT JOIN and RIGHT JOIN

A LEFT JOIN B join_condition in MySQL is implemented as follows:

RIGHT JOIN is implemented analogously as LEFT JOIN.

The table read order forced by LEFT JOIN and STRAIGHT JOIN will help the join optimiser (which calculates in which order tables should be joined) to do its work much more quickly, as there are fewer table permutations to check.

Note that the above means that if you do a query of type:

 
SELECT * FROM a,b LEFT JOIN c ON (c.key=a.key) LEFT JOIN d (d.key=a.key)
         WHERE b.key=d.key

MySQL will do a full scan on b as the LEFT JOIN will force it to be read before d.

The fix in this case is to change the query to:

 
SELECT * FROM b,a LEFT JOIN c ON (c.key=a.key) LEFT JOIN d (d.key=a.key)
         WHERE b.key=d.key

Starting from 4.0.14 MySQL does the following left join optimisation:

If the WHERE condition is always be false for the generated NULL row, the LEFT JOIN is changed to a normal join.

For example, in the following query the WHERE clause would be false if t2.column would be NULL so it's safe to convert to a normal join.

 
SELECT * FROM t1 LEFT t2 ON (column) WHERE t2.column2 =5;
->
SELECT * FROM t1,t2 WHERE t2.column2=5 AND t1.column=t2.column;

this can be made faster as MySQL can now use table t2 before table t1 if this would result in a better query plan. To force a specific table order one should use STRAIGHT JOIN.

5.2.8 How MySQL Optimises ORDER BY

In some cases MySQL can uses index to satisfy an ORDER BY or GROUP BY request without doing any extra sorting.

The index can also be used even if the ORDER BY doesn't match the index exactly, as long as all the unused index parts and all the extra are ORDER BY columns are constants in the WHERE clause. The following queries will use the index to resolve the ORDER BY / GROUP BY part:

 
SELECT * FROM t1 ORDER BY key_part1,key_part2,...
SELECT * FROM t1 WHERE key_part1=constant ORDER BY key_part2
SELECT * FROM t1 WHERE key_part1=constant GROUP BY key_part2
SELECT * FROM t1 ORDER BY key_part1 DESC,key_part2 DESC
SELECT * FROM t1 WHERE key_part1=1 ORDER BY key_part1 DESC,key_part2 DESC

Some cases where MySQL can not use indexes to resolve the ORDER BY: (Note that MySQL will still use indexes to find the rows that matches the WHERE clause):

In the cases where MySQL have to sort the result, it uses the following algorithm:

You can with EXPLAIN SELECT ... ORDER BY check if MySQL can use indexes to resolve the query. If you get Using filesort in the extra column, then MySQL can't use indexes to resolve the ORDER BY. See section EXPLAIN Syntax (Get Information About a SELECT).

If you want to have a higher ORDER BY speed, you should first see if you can get MySQL to use indexes instead of having to do an extra sorting phase. If this is not possible, then you can do:

MySQL by default sorts all GROUP BY x,y[,...] queries as if you would have specified ORDER BY x,y[,...]. MySQL will optimise away any ORDER BY as above without any speed penalty. If you by in some cases don't want to have the result sorted you can specify ORDER BY NULL:

 
INSERT INTO foo SELECT a,COUNT(*) FROM bar GROUP BY a ORDER BY NULL;

5.2.9 How MySQL Optimises LIMIT

In some cases MySQL will handle the query differently when you are using LIMIT row_count and not using HAVING:

5.2.10 Speed of INSERT Queries

The time to insert a record consists approximately of:

where the numbers are somewhat proportional to the overall time. 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 (B-trees).

Some ways to speed up inserts:

To get some more speed for both LOAD DATA INFILE and INSERT, enlarge the key buffer. See section Tuning Server Parameters.

5.2.11 Speed of UPDATE Queries

Update queries are optimised as a SELECT query with the additional overhead of a write. The speed of the write is dependent on the size of the data that is being updated and the number of indexes that are updated. Indexes that are not changed will not be updated.

Also, another way to get fast updates is to delay updates and then do many updates in a row later. Doing many updates in a row is much quicker than doing one at a time if you lock the table.

Note that, with dynamic record format, updating a record to a longer total length may split the record. So if you do this often, it is very important to OPTIMIZE TABLE sometimes. See section OPTIMIZE TABLE.

5.2.12 Speed of DELETE Queries

If you want to delete all rows in the table, you should use TRUNCATE TABLE table_name. See section TRUNCATE Syntax.

The time to delete a record is exactly proportional to the number of indexes. To delete records more quickly, you can increase the size of the index cache. See section Tuning Server Parameters.

5.2.13 Other Optimisation Tips

Unsorted tips for faster systems:

5.3 Locking Issues

5.3.1 How MySQL Locks Tables  
5.3.2 Table Locking Issues  

5.3.1 How MySQL Locks Tables

You can find a discussion about different locking methods in the appendix. See section Locking methods.

All locking in MySQL is deadlock-free, except for InnoDB and BDB type tables. This 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.

InnoDB type tables automatically acquire their row locks and BDB type tables their page locks during the processing of SQL statements, not at the start of the transaction.

The locking method MySQL uses for WRITE locks works as follows:

The locking method MySQL uses for READ locks works as follows:

When a lock is released, the lock is made available to the threads in the write lock queue, then to the threads in the read lock queue.

This means that if you have many updates on a table, SELECT statements will wait until there are no more updates.

To work around this for the case where you want to do many INSERT and SELECT operations on a table, you can insert rows in a temporary table and update the real table with the records from the temporary table once in a while.

This can be done with the following code:

 
mysql> LOCK TABLES real_table WRITE, insert_table WRITE;
mysql> INSERT INTO real_table SELECT * FROM insert_table;
mysql> TRUNCATE TABLE insert_table;
mysql> UNLOCK TABLES;

You can use the LOW_PRIORITY options with INSERT, UPDATE or DELETE or HIGH_PRIORITY with SELECT if you want to prioritise retrieval in some specific cases. You can also start mysqld with --low-priority-updates to get the same behaveour.

Using SQL_BUFFER_RESULT can also help making table locks shorter. See section SELECT Syntax.

You could also 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.

5.3.2 Table Locking Issues

The table locking code in MySQL is deadlock free.

MySQL uses table locking (instead of row locking or column locking) on all table types, except InnoDB and BDB tables, to achieve a very high lock speed. For large tables, table locking is much better than row locking for most applications, but there are, of course, some pitfalls.

For InnoDB and BDB tables, MySQL only uses table locking if you explicitly lock the table with LOCK TABLES. For these table types we recommend you to not use LOCK TABLES at all, because InnoDB uses automatic row level locking and BDB uses page level locking to ensure transaction isolation.

In MySQL Version 3.23.7 and above, you can insert rows into MyISAM tables at the same time other threads are reading from the table. Note that currently this only works if there are no holes after deleted rows in the table at the time the insert is made. When all holes has been filled with new data, concurrent inserts will automatically be enabled again.

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 will wait until the update is ready.

As updates on tables normally are considered to be more important than SELECT, all statements that update a table have higher priority than statements that retrieve information from a table. This should ensure that updates are not 'starved' because one issues a lot of heavy queries against a specific table. (You can change this by using LOW_PRIORITY with the statement that does the update or HIGH_PRIORITY with the SELECT statement.)

Starting from MySQL Version 3.23.7 one can use the max_write_lock_count variable to force MySQL to temporary give all SELECT statements, that wait for a table, a higher priority after a specific number of inserts on a table.

Table locking is, however, not very good under the following senario:

Some possible solutions to this problem are:

5.4 Optimising Database Structure

5.4.1 Design Choices  
5.4.2 Get Your Data as Small as Possible  
5.4.3 How MySQL Uses Indexes  
5.4.4 Column Indexes  
5.4.5 Multiple-Column Indexes  
5.4.6 Why So Many Open tables?  
5.4.7 How MySQL Opens and Closes Tables  
5.4.8 Drawbacks to Creating Large Numbers of Tables in the Same Database  

5.4.1 Design Choices

MySQL keeps row data and index data in separate files. Many (almost all) other databases 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 will cause 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 will 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:

5.4.2 Get Your Data as Small as Possible

One of the most basic optimisation is to get your data (and indexes) to take as little space on the disk (and in memory) as possible. This can give huge improvements because disk reads are faster and normally less main memory will be used. Indexing also takes less resources if done on smaller columns.

MySQL supports a lot of different table types and row formats. Choosing the right table format may give you a big performance gain. See section MySQL Table Types.

You can get better performance on a table and minimise storage space using the techniques listed here:

5.4.3 How MySQL Uses Indexes

Indexes are used to find rows with a specific value of one column fast. Without an index MySQL has to start with the first record and then read through the whole table until it finds the relevant rows. The bigger the table, the more this costs. If the table has an index for the columns in question, MySQL can quickly get a position to seek to in the middle of the datafile without having to look at all the data. If a table has 1000 rows, this is at least 100 times faster than reading sequentially. Note that if you need to access almost all 1000 rows it is faster to read sequentially because we then avoid disk seeks.

All MySQL indexes (PRIMARY, UNIQUE, and INDEX) are stored in B-trees. Strings are automatically prefix- and end-space compressed. See section CREATE INDEX.

Indexes are used to:

Suppose you issue the following SELECT statement:

 
mysql> SELECT * FROM tbl_name WHERE col1=val1 AND col2=val2;

If a multiple-column index exists on col1 and col2, the appropriate rows can be fetched directly. If separate single-column indexes exist on col1 and col2, the optimiser tries to find the most restrictive index by deciding which index will find fewer rows and using that index to fetch the rows.

If the table has a multiple-column index, any leftmost prefix of the index can be used by the optimiser to find rows. For example, if you have a three-column index on (col1,col2,col3), you have indexed search capabilities on (col1), (col1,col2), and (col1,col2,col3).

MySQL can't use a partial index if the columns don't form a leftmost prefix of the index. Suppose you have the SELECT statements shown here:

 
mysql> SELECT * FROM tbl_name WHERE col1=val1;
mysql> SELECT * FROM tbl_name WHERE col2=val2;
mysql> SELECT * FROM tbl_name WHERE col2=val2 AND col3=val3;

If an index exists on (col1,col2,col3), only the first query shown above uses the index. The second and third queries do involve indexed columns, but (col2) and (col2,col3) are not leftmost prefixes of (col1,col2,col3).

MySQL also uses indexes for LIKE comparisons if the argument to LIKE is a constant string that doesn't start with a wildcard character. For example, the following SELECT statements use indexes:

 
mysql> SELECT * FROM tbl_name WHERE key_col LIKE "Patrick%";
mysql> SELECT * FROM tbl_name WHERE key_col LIKE "Pat%_ck%";

In the first statement, only rows with "Patrick" <= key_col < "Patricl" are considered. In the second statement, only rows with "Pat" <= key_col < "Pau" are considered.

The following SELECT statements will not use indexes:

 
mysql> SELECT * FROM tbl_name WHERE key_col LIKE "%Patrick%";
mysql> SELECT * FROM tbl_name WHERE key_col LIKE other_col;

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 does another optimisation on LIKE. If you use ... LIKE "%string%" and string is longer than 3 characters, MySQL will use the Turbo Boyer-Moore algorithm to initialise the pattern for the string and then use this pattern to perform the search quicker.

Searching using column_name IS NULL will use indexes if column_name is an index.

MySQL normally uses the index that finds the least number of rows. An index is used for columns that you compare with the following operators: =, >, >=, <, <=, BETWEEN, and a LIKE with a non-wildcard prefix like 'something%'.

Any index that doesn't span all AND levels in the WHERE clause is not used to optimise the query. In other words: To be able to use an index, a prefix of the index must be used in every AND group.

The following WHERE clauses use indexes:

 
... WHERE index_part1=1 AND index_part2=2 AND other_column=3
... WHERE index=1 OR A=10 AND index=2      /* index = 1 OR index = 2 */
... WHERE index_part1='hello' AND index_part_3=5
          /* optimised like "index_part1='hello'" */
... WHERE index1=1 and index2=2 or index1=3 and index3=3;
          /* Can use index on index1 but not on index2 or index 3 */

These WHERE clauses do NOT use indexes:

 
... WHERE index_part2=1 AND index_part3=2  /* index_part_1 is not used */
... WHERE index=1 OR A=10                  /* Index is not used in
                                                        both AND parts */
... WHERE index_part1=1 OR index_part2=10  /* No index spans all rows  */

Note that in some cases MySQL will not use an index, even if one would be available. Some of the cases where this happens are:

5.4.4 Column Indexes

All MySQL column types can be indexed. Use of indexes on the relevant columns is the best way to improve the performance of SELECT operations.

The maximum number of keys and the maximum index length is defined per storage engine. See section MySQL Table Types. You can with all storage engines have at least 16 keys and a total index length of at least 256 bytes.

For CHAR and VARCHAR columns, you can index a prefix of a column. This is much faster and requires less disk space than indexing the whole column. The syntax to use in the CREATE TABLE statement to index a column prefix looks like this:

 
KEY index_name (col_name(length))

The example here creates an index for the first 10 characters of the name column:

 
mysql> CREATE TABLE test (
    ->        name CHAR(200) NOT NULL,
    ->        KEY index_name (name(10)));

For BLOB and TEXT columns, you must index a prefix of the column. You cannot index the entire column.

In MySQL Version 3.23.23 or later, you can also create special FULLTEXT indexes. They are used for full-text search. Only the MyISAM table type supports FULLTEXT indexes. They can be created only from CHAR, VARCHAR, and TEXT columns. Indexing always happens over the entire column and partial indexing is not supported. See MySQL Full-text Search for details.

5.4.5 Multiple-Column Indexes

MySQL can create indexes on multiple columns. An index may consist of up to 15 columns. (On CHAR and VARCHAR columns you can also use a prefix of the column as a part of an index.)

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 don't specify values for the other columns.

Suppose a table is created using the following specification:

 
mysql> 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));

Then the index name is an index over last_name and first_name. The index will be used for queries that specify values in a known range for last_name, or for both last_name and first_name. Therefore, the name index will be used in the following queries:

 
mysql> SELECT * FROM test WHERE last_name="Widenius";

mysql> SELECT * FROM test WHERE last_name="Widenius"
    ->                    AND first_name="Michael";

mysql> SELECT * FROM test WHERE last_name="Widenius"
    ->                    AND (first_name="Michael" OR first_name="Monty");

mysql> SELECT * FROM test WHERE last_name="Widenius"
    ->                    AND first_name >="M" AND first_name < "N";

However, the name index will NOT be used in the following queries:

 
mysql> SELECT * FROM test WHERE first_name="Michael";

mysql> SELECT * FROM test WHERE last_name="Widenius"
    ->                    OR first_name="Michael";

For more information on the manner in which MySQL uses indexes to improve query performance, see MySQL indexes.

5.4.6 Why So Many Open tables?

When you run mysqladmin status, you'll see something like this:

 
Uptime: 426 Running threads: 1 Questions: 11082 Reloads: 1 Open tables: 12

This can be somewhat perplexing if you only have 6 tables.

MySQL is multi-threaded, so it may have many queries on the same table simultaneously. To minimise the problem with two threads having different states on the same file, the table is opened independently by each concurrent thread. This takes some memory but will normaly increase performance. With ISAM and MyISAM tables this also requires one extra file descriptor for the datafile. With these table types the index file descriptor is shared between all threads.

You can read more about this topic in the next section. See section How MySQL Opens and Closes Tables.

5.4.7 How MySQL Opens and Closes Tables

table_cache, max_connections, and max_tmp_tables affect the maximum number of files the server keeps open. If you increase one or both of these values, you may run up against a limit imposed by your operating system on the per-process number of open file descriptors. However, you can increase the limit on many systems. Consult your OS documentation to find out how to do this, because the method for changing the limit varies widely from system to system.

table_cache is related to max_connections. For example, for 200 concurrent running connections, you should have a table cache of at least 200 * n, where n is the maximum number of tables in a join. You also need to reserve some extra file descriptors for temporary tables and files.

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 in increase the number of file descriptors available for MySQL with the --open-files-limit=# startup option. See section File Not Found.

The cache of open tables will be kept at a level of table_cache entries. The default value is 64; this can be changed with the -O table_cache=# option to mysqld). Note that MySQL may temporarily open even more tables to be able to execute queries.

A not used table is closed and removed from the table cache under the following circumstances:

When the table cache fills up, the server uses the following procedure to locate a cache entry to use:

A table is opened for each concurrent access. This means that if you have two threads accessing the same table or access the table twice in the same query (with AS) the table needs to be opened twice. The first open of any table takes two file descriptors; each additional use of the table takes only one file descriptor. The extra descriptor for the first open is used for the index file; this descriptor is shared among all threads.

If you are opening a table with the HANDLER table_name OPEN statement, a dedicated table object is allocated for the thread. This table object is not shared by other threads an will not be closed until the thread calls HANDLER table_name CLOSE or the thread dies. See section HANDLER. When this happens, the table is put back in the table cache (if it isn't full).

You can check if your table cache is too small by checking the mysqld variable Opened_tables. If this is quite big, even if you haven't done a lot of FLUSH TABLES, you should increase your table cache. See section Opened_tables.

5.4.8 Drawbacks to Creating Large Numbers of Tables in the Same Database

If you have many files in a directory, open, close, and create operations will be slow. If you execute SELECT statements on many different tables, there will be 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.

5.5 Optimising the MySQL Server

5.5.1 System/Compile Time and Startup Parameter Tuning  
5.5.2 Tuning Server Parameters  
5.5.3 How Compiling and Linking Affects the Speed of MySQL  
5.5.4 How MySQL Uses Memory  
5.5.5 How MySQL uses DNS  
5.5.6 SET Syntax  

5.5.1 System/Compile Time and Startup Parameter Tuning

We start with the system level things since some of these decisions have to be made very early. In other cases a fast look at this part may suffice because it not that important for the big gains. However, it is always nice to have a feeling about how much one could gain by changing things at this level.

The default OS to use is really important! To get the most use of multiple-CPU machines one should use Solaris (because the threads works really nice) or Linux (because the 2.2 kernel has really good SMP support). Also on 32-bit machines Linux has a 2G file-size limit by default. Hopefully this will be fixed soon when new filesystems are released (XFS/Reiserfs). If you have a desperate need for files bigger than 2G on Linux-intel 32 bit, you should get the LFS patch for the ext2 filesystem.

Because we have not run MySQL in production on that many platforms, we advice you to test your intended platform before choosing it, if possible.

Other tips:

5.5.2 Tuning Server Parameters

You can get the default buffer sizes used by the mysqld server with this command:

 
shell> mysqld --help

This command produces a list of all mysqld options and configurable variables. The output includes the default values and looks something like this:

 
Possible variables for option --set-variable (-O) are:
back_log                 current value: 5
bdb_cache_size           current value: 1048540
binlog_cache_size        current value: 32768
connect_timeout          current value: 5
delayed_insert_timeout   current value: 300
delayed_insert_limit     current value: 100
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
lower_case_table_names   current value: 0
long_query_time          current value: 10
max_allowed_packet       current value: 1048576
max_binlog_cache_size    current value: 4294967295
max_connections          current value: 100
max_connect_errors       current value: 10
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_retry_count          current value: 10
net_read_timeout         current value: 30
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
tmp_table_size           current value: 1048576
thread_stack             current value: 131072
wait_timeout             current value: 28800

Please note that --set-variable is deprecated since MySQL 4.0, just use --var=option on its own.

If there is a mysqld server currently running, you can see what values it actually is using for the variables by executing this command:

 
shell> mysqladmin variables

You can find a full description for all variables in the SHOW VARIABLES section in this manual. See section SHOW VARIABLES.

You can also see some statistics from a running server by issuing the command SHOW STATUS. See section SHOW STATUS.

MySQL uses algorithms that are very scalable, so you can usually run with very little memory. If you, however, give MySQL more memory, you will normally also get better performance.

When tuning a MySQL server, the two most important variables to use are key_buffer_size and table_cache. You should first feel confident that you have these right before trying to change any of the other variables.

If you have much memory (>=256M) and many tables and want maximum performance with a moderate number of clients, you should use something like this:

 
shell> mysqld_safe -O key_buffer=64M -O table_cache=256 \
           -O sort_buffer=4M -O read_buffer_size=1M &

If you have only 128M and only a few tables, but you still do a lot of sorting, you can use something like:

 
shell> mysqld_safe -O key_buffer=16M -O sort_buffer=1M

If you have little memory and lots of connections, use something like this:

 
shell> mysqld_safe -O key_buffer=512k -O sort_buffer=100k \
           -O read_buffer_size=100k &

or even:

 
shell> mysqld_safe -O key_buffer=512k -O sort_buffer=16k \
           -O table_cache=32 -O read_buffer_size=8k -O net_buffer_length=1K &

If you are doing a GROUP BY or ORDER BY on files that are much bigger than your available memory you should increase the value of read_rnd_buffer_size to speed up the reading of rows after the sorting is done.

When you have installed MySQL, the `support-files' directory will contain some different `my.cnf' example files, `my-huge.cnf', `my-large.cnf', `my-medium.cnf', and `my-small.cnf', you can use as a base to optimise your system.

If there are very many 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, of course.

Note that if you change an option to mysqld, it remains in effect only for that instance of the server.

To see the effects of a parameter change, do something like this:

 
shell> mysqld -O key_buffer=32m --help

Make sure that the --help option is last; otherwise, the effect of any options listed after it on the command-line will not be reflected in the output.

5.5.3 How Compiling and Linking Affects the Speed of MySQL

Most of the following tests are done on Linux with the MySQL benchmarks, but they should give some indication for other operating systems and workloads.

You get the fastest executable when you link with -static.

On Linux, you will get the fastest code when compiling with pgcc and -O3. To compile `sql_yacc.cc' with these options, you need about 200M memory because gcc/pgcc needs a lot of memory to make all functions inline. You should also set CXX=gcc when configuring MySQL to avoid inclusion of the libstdc++ library (it is not needed). Note that with some versions of pgcc, the resulting code will only run on true Pentium processors, even if you use the compiler option that you want the resulting code to be working on all x586 type processors (like AMD).

By just using a better compiler and/or better compiler options you can get a 10-30% speed increase in your application. This is particularly important if you compile the SQL server yourself!

We have tested both the Cygnus CodeFusion and Fujitsu compilers, but when we tested them, neither was sufficiently bug free to allow MySQL to be compiled with optimisations on.

When you compile MySQL you should only include support for the character sets that you are going to use. (Option --with-charset=xxx.) The standard MySQL binary distributions are compiled with support for all character sets.

Here is a list of some measurements that we have done:

The MySQL-Linux distribution provided by MySQL AB used to be compiled with pgcc, but we had to go back to regular gcc because of a bug in pgcc that would generate the code that does 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 get a faster binary by compiling with pgcc. The standard MySQL Linux binary is linked statically to get it faster and more portable.

5.5.4 How MySQL Uses Memory

The following list indicates some of the ways that the mysqld server uses memory. Where applicable, the name of the server variable relevant to the memory use is given:

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 swap -s. We have tested mysqld with commercial memory-leakage detectors, so there should be no memory leaks.

5.5.5 How MySQL uses DNS

When a new thread connects to mysqld, mysqld will spawn a new thread to handle the request. This thread will first check if the hostname is in the hostname cache. If not the thread will call gethostbyaddr_r() and gethostbyname_r() to resolve the hostname.

If the operating system doesn't support the above thread-safe calls, the thread will lock a mutex and call gethostbyaddr() and gethostbyname() instead. Note that in this case no other thread can resolve other hostnames that is not in the hostname cache until the first thread is ready.

You can disable DNS host lookup by starting mysqld with --skip-name-resolve. In this case you can however only use IP names in the MySQL privilege tables.

If you have a very slow DNS and many hosts, you can get more performance by either disabling DNS lookop with --skip-name-resolve or by increasing the HOST_CACHE_SIZE define (default: 128) and recompile mysqld.

You can disable the hostname cache with --skip-host-cache. You can clear the hostname cache with FLUSH HOSTS or mysqladmin flush-hosts.

If you don't want to allow connections over TCP/IP, you can do this by starting mysqld with --skip-networking.

5.5.6 SET Syntax

 
SET [GLOBAL | SESSION] sql_variable=expression, [[GLOBAL | SESSION] sql_variable=expression...]

SET sets various options that affect the operation of the server or your client.

The following examples shows the different syntaxes one can use to set variables:

In old MySQL versions we allowed the use of the SET OPTION syntax, but this syntax is now deprecated.

In MySQL 4.0.3 we added the GLOBAL and SESSION options and access to most important startup variables.

LOCAL can be used as a synonym for SESSION.

If you set several variables on the same command line, the last used GLOBAL | SESSION mode is used.

 
SET sort_buffer_size=10000;
SET @@local.sort_buffer_size=10000;
SET GLOBAL sort_buffer_size=1000000, SESSION sort_buffer_size=1000000;
SET @@sort_buffer_size=1000000;
SET @@global.sort_buffer_size=1000000, @@local.sort_buffer_size=1000000;

The @@variable_name syntax is supported to make MySQL syntax compatible with some other databases.

The different system variables one can set are described in the system variable section of this manual. See section System Variables.

If you are using SESSION (the default) the option you set remains in effect until the current session ends, or until you set the option to a different value. If you use GLOBAL, which require the SUPER privilege, the option is remembered and used for new connections until the server restarts. If you want to make an option permanent, you should set it in one of the MySQL option files. See section `my.cnf' Option Files.

To avoid wrong usage MySQL will give an error if you use SET GLOBAL with a variable that can only be used with SET SESSION or if you are not using SET GLOBAL with a global variable.

If you want to set a SESSION variable to the GLOBAL value or a GLOBAL value to the MySQL default value, you can set it to DEFAULT.

 
SET max_join_size=DEFAULT;

This is identical to:

 
SET @@session.max_join_size=@@global.max_join_size;

If you want to restrict the maximum value a startup option can be set to with the SET command, you can specify this by using the --maximum-variable-name command line option. See section mysqld Command-line Options.

You can get a list of most variables with SHOW VARIABLES. See section SHOW VARIABLES. You can get the value for a specific value with the @@[global.|local.]variable_name syntax:

 
SHOW VARIABLES like "max_join_size";
SHOW GLOBAL VARIABLES like "max_join_size";
SELECT @@max_join_size, @@global.max_join_size;

Here follows a description of the variables that uses a the variables that uses a non-standard SET syntax and some of the other variables. The other variable definitions can be found in the system variable section, among the startup options or in the description of SHOW VARIABLES. See section System Variables. See section mysqld Command-line Options. See section SHOW VARIABLES.

CHARACTER SET character_set_name | DEFAULT

This maps all strings from and to the client with the given mapping. Currently the only option for character_set_name is cp1251_koi8, but you can easily add new mappings by editing the `sql/convert.cc' file in the MySQL source distribution. The default mapping can be restored by using a character_set_name value of DEFAULT.

Note that the syntax for setting the CHARACTER SET option differs from the syntax for setting the other options.

PASSWORD = PASSWORD('some password')

Set the password for the current user. Any non-anonymous user can change his own password!

PASSWORD FOR user = PASSWORD('some password')

Set the password for a specific user on the current server host. Only a user with access to the mysql database can do this. The user should be given in user@hostname format, where user and hostname are exactly as they are listed in the User and Host columns of the mysql.user table entry. For example, if you had an entry with User and Host fields of 'bob' and '%.loc.gov', you would write:

 
mysql> SET PASSWORD FOR bob@"%.loc.gov" = PASSWORD("newpass");

Which is equivalent to:

 
mysql> UPDATE mysql.user SET password=PASSWORD("newpass")
    ->                   WHERE user="bob" AND host="%.loc.gov";
SQL_AUTO_IS_NULL = 0 | 1

If set to 1 (default) then one can find the last inserted row for a table with an AUTO_INCREMENT column with the following construct: WHERE auto_increment_column IS NULL. This is used by some ODBC programs like Access.

AUTOCOMMIT= 0 | 1

If set to 1 all changes to a table will be done at once. To start a multi-command transaction, you have to use the BEGIN statement. See section START TRANSACTION, COMMIT, and ROLLBACK Syntax. If set to 0 you have to use COMMIT / ROLLBACK to accept/revoke that transaction. See section START TRANSACTION, COMMIT, and ROLLBACK Syntax. Note that when you change from not AUTOCOMMIT mode to AUTOCOMMIT mode, MySQL will do an automatic COMMIT on any open transactions.

BIG_TABLES = 0 | 1

If set to 1, all temporary tables are stored on disk rather than in memory. This will be a little slower, but you will not get the error The table tbl_name is full for big SELECT operations that require a large temporary table. The default value for a new connection is 0 (that is, use in-memory temporary tables). This option was before named SQL_BIG_TABLES. In MySQL 4.0 you should normally never need this flag as MySQL will automatically convert in memory tables to disk based ones if need.

SQL_BIG_SELECTS = 0 | 1

If set to 0, MySQL will abort if a SELECT is attempted that probably will take a very long time, which is defined as if the number of examined rows is probably going to be bigger than MAX_JOIN_SIZE. This is useful when an inadvisable WHERE statement has been issued. A big query is defined as a SELECT that probably will have to examine more than max_join_size rows. The default value for a new connection is 1 (which will allow all SELECT statements).

If you set MAX_JOIN_SIZE to another value than DEFAULT SQL_BIG_SELECTS will be set to 0.

SQL_BUFFER_RESULT = 0 | 1

SQL_BUFFER_RESULT will force the result from SELECTs to be put into a temporary table. This will help MySQL free the table locks early and will help in cases where it takes a long time to send the result set to the client.

LOW_PRIORITY_UPDATES = 0 | 1

If set to 1, all INSERT, UPDATE, DELETE, and LOCK TABLE WRITE statements wait until there is no pending SELECT or LOCK TABLE READ on the affected table. This option was before named SQL_LOW_PRIORITY_UPDATES.

MAX_JOIN_SIZE = value | DEFAULT

Don't allow SELECTs that will probably need to examine more than value row combinations or is likely to do more than value disk seeks. By setting this value, you can catch SELECTs where keys are not used properly and that would probably take a long time. Setting this to a value other than DEFAULT will reset the SQL_BIG_SELECTS flag. If you set the SQL_BIG_SELECTS flag again, the SQL_MAX_JOIN_SIZE variable will be ignored. You can set a default value for this variable by starting mysqld with -O max_join_size=#. This option was before named SQL_MAX_JOIN_SIZE.

Note that if the result of the query is already in the query cache, the above check will not be made. Instead, MySQL will send the result to the client. Since the query result is already computed and it will not burden the server to send the result to the client.

QUERY_CACHE_TYPE = OFF | ON | DEMAND
QUERY_CACHE_TYPE = 0 | 1 | 2

Set query cache setting for this thread.

Option

Description

0 or OFF

Don't cache or retrieve results.

1 or ON

Cache all results except SELECT SQL_NO_CACHE ... queries.

2 or DEMAND

Cache only SELECT SQL_CACHE ... queries.

SQL_SAFE_UPDATES = 0 | 1

If set to 1, MySQL will abort if an UPDATE or DELETE is attempted that doesn't use a key or LIMIT in the WHERE clause. This makes it possible to catch wrong updates when creating SQL commands by hand.

SQL_SELECT_LIMIT = value | DEFAULT

The maximum number of records to return from SELECT statements. If a SELECT has a LIMIT clause, the LIMIT takes precedence over the value of SQL_SELECT_LIMIT. The default value for a new connection is "unlimited." If you have changed the limit, the default value can be restored by using a SQL_SELECT_LIMIT value of DEFAULT.

SQL_LOG_OFF = 0 | 1

If set to 1, no logging is done to the standard log for this client, if the client has the SUPER privilege.

SQL_LOG_BIN = 0 | 1

If set to 0, no logging is done to the binary log for the client, if the client has the SUPER privilege.

SQL_LOG_UPDATE = 0 | 1

If set to 0, no logging is done to the update log for the client, if the client has the SUPER privilege. This variable is deprecated starting from version 5.0.

SQL_QUOTE_SHOW_CREATE = 0 | 1

If set to 1, SHOW CREATE TABLE will quote table and column names. This is on by default, for replication of tables with fancy column names to work. SHOW CREATE TABLE.

TIMESTAMP = timestamp_value | DEFAULT

Set the time for this client. This is used to get the original timestamp if you use the binary log to restore rows. timestamp_value should be a Unix epoch timestamp, not a MySQL timestamp.

LAST_INSERT_ID = #

Set the value to be returned from LAST_INSERT_ID(). This is stored in the binary log when you use LAST_INSERT_ID() in a command that updates a table.

INSERT_ID = #

Set the value to be used by the following INSERT or ALTER TABLE command when inserting an AUTO_INCREMENT value. This is mainly used with the binary log.

5.6 Disk Issues

5.6.1 Using Symbolic Links  

5.6.1 Using Symbolic Links

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 to just symlink databases to a different disk and only symlink tables as a last resort.

5.6.1.1 Using Symbolic Links for Databases  
5.6.1.2 Using Symbolic Links for Tables  

5.6.1.1 Using Symbolic Links for Databases

The way to symlink a database is to first create a directory on some disk where you have free space and then create a symlink to it from the MySQL database directory.

 
shell> mkdir /dr1/databases/test
shell> ln -s /dr1/databases/test mysqld-datadir

MySQL doesn't support that you link one directory to multiple databases. Replacing a database directory with a symbolic link will work fine as long as you don't make a symbolic link between databases. Suppose you have a database db1 under the MySQL data directory, and then make a symlink db2 that points to db1:

 
shell> cd /path/to/datadir
shell> ln -s db1 db2

Now, for any table tbl_a in db1, there also appears to be a table tbl_a in db2. If one thread updates db1.tbl_a and another thread updates db2.tbl_a, there will be problems.

If you really need this, you must change the following code in `mysys/mf_format.c':

 
if (flag & 32 || (!lstat(to,&stat_buff) && S_ISLNK(stat_buff.st_mode)))

to

 
if (1)

On Windows you can use internal symbolic links to directories by compiling MySQL with -DUSE_SYMDIR. This allows you to put different databases on different disks. See section Distributing Data Across Different Disks on Windows.

5.6.1.2 Using Symbolic Links for Tables

Before MySQL 4.0 you should not symlink tables, if you are not very careful with them. The problem is that if you run ALTER TABLE, REPAIR TABLE or OPTIMIZE TABLE on a symlinked table, the symlinks will be removed and replaced by the original files. This happens because the above command works by creating a temporary file in the database directory and when the command is complete, replace the original file with the temporary file.

You should not symlink tables on systems that don't have a fully working realpath() call. (At least Linux and Solaris support realpath())

In MySQL 4.0 symlinks are fully supported only for MyISAM tables. For other table types you will probably get strange problems when doing any of the above mentioned commands.

The handling of symbolic links in MySQL 4.0 works the following way (this is mostly relevant only for MyISAM tables).

Things that are not yet supported:

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