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10 Getting maximum performance from MySQL

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

So this chapter will try to explain and give some examples of different ways to optimize MySQL. But remember that there are always some (increasingly harder) ways to make the system even faster left to do.

10.1 Optimization overview

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

The most common bottlenecks are.

10.2 System/Compile time and startup parameter tuning

We start with the system level things sine some of these decisions have to be made very early. In other cases a fast look at this part may suffice since it not that important for the big gains. But it is always nice to have a feeling about how much one gould gain by chancing 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 32bit machines Linux has a 2G file size limit by default. Hopefully this will be fixed soon when new filesystems is released (XFS).

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

Other tips:

10.2.1 How compiling and linking affects the speed of MySQL

Most of the following tests are done on Linux and 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. Using Unix sockets rather than TCP/IP to connect to a database also gives better performance.

On Linux, you will get the fastest code when compiling with pgcc and -O6. 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).

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 yourselves!

On Intel you should for example use pgcc or the Cygnus CodeFusion compiler to get maximum speed. We have tested the new Fujitsu compiler but it is not yet bug free enough to compile MySQL with optimizations on.

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

The MySQL-Linux distribution provided by TcX is compiled with pgcc and linked statically.

10.2.2 Disk issues Using symbolic links for databases and tables

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.

If MySQL notices that a table is a symbolically-linked, it will resolve the symlink and use the table it points to instead. This works on all systems that support the realpath() call (at least Linux and Solaris support realpath())! On systems that don't support realpath(), you should not access the table through the real path and through the symlink at the same time! If you do, the table will be inconsistent after any update.

MySQL doesn't support linking of databases by default. Things 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 (!lstat(to,&stat_buff))  /* Check if it's a symbolic link */
    if (S_ISLNK(stat_buff.st_mode) && realpath(to,buff))

Change the code to this:

if (realpath(to,buff))

10.2.3 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
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_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
net_buffer_length     current value: 16384
query_buffer_size     current value: 0
record_buffer         current value: 131072
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

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

Each option is described below. Values for buffer sizes, lengths and stack sizes are given in bytes. You can specify values with a suffix of `K' or `M' to indicate kilobytes or megabytes. For example, 16M indicates 16 megabytes. Case of suffix letters does not matter; 16M and 16m are equivalent.

You can also see some statistics from a running server by the command SHOW STATUS. See section 7.21 SHOW syntax (Get information about tables, columns,...).

Is ON if mysqld was started with --ansi. See section 5.2 Runnning MySQL in ANSI mode.
The number of outstanding connection requests MySQL can have. This comes into play when the main MySQL thread gets VERY many connection requests in a very short time. It then takes some time (although very little) for the main thread to check the connection and start a new thread. The back_log value indicates how many requests can be stacked during this short time before MySQL momentarily stops answering new requests. You need to increase this only if you expect a large number of connections in a short period of time. In other words, this value is the size of the listen queue for incoming TCP/IP connections. Your operating system has its own limit on the size of this queue. The manual page for the Unix listen(2) system call should have more details. Check your OS documentation for the maximum value for this variable. Attempting to set back_log higher than your operating system limit will be ineffective.
If ON (the default), MySQL will allow you to use INSERT on MyISAM tables at the same time as you run SELECT queries on them. You can turn this option off by starting mysqld with --safe or --skip-new.
The number of seconds the mysqld server is waiting for a connect packet before responding with Bad handshake.
How long a INSERT DELAYED thread should wait for INSERT statements before terminating.
After inserting delayed_insert_limit rows, the INSERT DELAYED handler will check if there are any SELECT statements pending. If so, it allows these to execute before continuing.
If enabled (is on by default), MySQL will honor the delay_key_write option CREATE TABLE. This means that the key buffer for tables with this option will not get flushed on every index update, but only when a table is closed. This will speed up writes on keys a lot but you should add automatic checking of all tables with myisamchk --fast --force if you use this. Note that if you start mysqld with the --delay-key-write-for-all-tables option this means that all tables will be treated as if they where created with the delay_key_write option. You can clear this flag by starting mysqld with --skip-new or --safe-mode.
How big a queue (in rows) should be allocated for handling INSERT DELAYED. If the queue becomes full, any client that does INSERT DELAYED will wait until there is room in the queue again.
If this is set to a non-zero value, then every flush_time seconds all tables will be closed (to free up resources and sync things to disk).
The name of the file specified with the --init-file option when you start the server. This is a file of SQL statements you want the server to execute when it starts.
The number of seconds the server waits for activity on a interactive connection before closing it. An interactive client is defined as a client that uses the CLIENT_INTERACTIVE option to mysql_real_connect(). See also wait_timeout.
The size of the buffer that is used for full joins (joins that do not use indexes). The buffer is allocated one time for each full join between two tables. Increase this value to get a faster full join when adding indexes is not possible. (Normally the best way to get fast joins is to add indexes.)
Index blocks are buffered and are shared by all threads. key_buffer_size is the size of the buffer used for index blocks. Increase this get better index handling (for all reads and multiple writes) to as much as you can afford. If you make this too big the system will starte to page and go REAL slow. Remember that since MySQL does not cache data read that you will have to leave some room for the OS filesystem cache. To get even more speed when writing many rows at the same time use LOCK TABLES. See section 7.24 LOCK TABLES/UNLOCK TABLES syntax.
If a query takes longer than this (in seconds), the Slow_queries counter will be incremented.
The maximum size of one packet. The message buffer is initialized to net_buffer_length bytes, but can grow up to max_allowed_packet bytes when needed. This value by default is small to catch big (possibly wrong) packets. You must increase this value if you are using big BLOB columns. It should be as big as the biggest BLOB you want to use.
The number of simultaneous clients allowed. Increasing this value increases the number of file descriptors that mysqld requires. See below for comments on file descriptor limits. See section 18.2.4 Too many connections error.
If there is more than this number of interrupted connections from a host this host will be blocked for further connections. You can unblock a host with the command FLUSH HOSTS.
Don't start more than this number of threads to handle INSERT DELAYED statements. If you try to insert data in a new table after all INSERT DELAYED threads are in use, the row will be inserted as if the DELAYED attribute wasn't specified.
Joins that are probably going to read more than max_join_size records return an error. Set this value if your users tend to perform joins without a WHERE clause that take a long time and return millions of rows.
The number of bytes to use when sorting BLOB or TEXT values (only the first max_sort_length bytes of each value are used; the rest are ignored).
(This option doesn't yet do anything). Maximum number of temporary tables a client can keep open at the same time.
The communication buffer is reset to this size between queries. This should not normally be changed, but if you have very little memory, you can set it to the expected size of a query. (That is, the expected length of SQL statements sent by clients. If statements exceed this length, the buffer is automatically enlarged, up to max_allowed_packet bytes.)
If a read on a communication port is interrupted, retry this many times before giving up. This value should be quite high on FreeBSD as internal interrupts is sent to all threads.
Each thread that does a sequential scan allocates a buffer of this size for each table it scans. If you do many sequential scans, you may want to increase this value.
This prevents people from doing SHOW DATABASES, if they don't have the PROCESS_PRIV privilege. This can improve security if you're concerned about people being able to see what databases and tables other users have.
Each thread that needs to do a sort allocates a buffer of this size. Increase this value for faster ORDER BY or GROUP BY operations. See section 18.5 Where MySQL stores temporary files.
The number of open tables for all threads. Increasing this value increases the number of file descriptors that mysqld requires. MySQL needs two file descriptors for each unique open table. See below for comments on file descriptor limits. For information about how the table cache works, see section 10.2.4 How MySQL opens and closes tables.
If a temporary table exceeds this size, MySQL generates an error of the form The table tbl_name is full. Increase the value of tmp_table_size if you do many advanced GROUP BY queries.
On Solaris, mysqld will call thr_setconcurrency() with this value. thr_setconcurrency() permits the application to give the threads system a hint, for the desired number of threads that should be run at the same time.
The stack size for each thread. Many of the limits detected by the crash-me test are dependent on this value. The default is large enough for normal operation. See section 10.8 Using your own benchmarks.
The number of seconds the server waits for activity on a connection before closing it. See also interactive_timeout.

MySQL uses algorithms that are very scalable, so you can usually run with very little memory or give MySQL more memory to get better performance.

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

shell> safe_mysqld -O key_buffer=16M -O table_cache=128 \
           -O sort_buffer=4M -O record_buffer=1M &

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

shell> safe_mysqld -O key_buffer=512k -O sort_buffer=100k \
           -O record_buffer=100k &

or even:

shell> safe_mysqld -O key_buffer=512k -O sort_buffer=16k \
           -O table_cache=32 -O record_buffer=8k -O net_buffer=1K &

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.

10.2.4 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 open connections, you should have a table cache of at least 200 * n, where n is the maximum number of tables in a join.

The cache of open tables can grow to a maximum of table_cache (default 64; this can be changed with with the -O table_cache=# option to mysqld). A table is never closed, except when the cache is full and another thread tries to open a table or if you use mysqladmin refresh or mysqladmin flush-tables.

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.

10.2.5 Drawbacks of 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.

10.2.6 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 multithreaded, so it may have many queries on the same table simultaneously. To minimize 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 and one extra file descriptor for the data file. The index file descriptor is shared between all threads.

10.2.7 How MySQL uses memory

The list below 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.

10.2.8 How MySQL locks tables

All locking in MySQL is deadlock-free. 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.

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> delete from insert_table;

You can use the LOW_PRIORITY options with INSERT if you want to prioritize retrieval in some specific cases. See section 7.14 INSERT 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.

10.2.9 Table locking issues

The table locking code in MySQL is deadlock free.

MySQL uses table locking (instead of row locking or column locking) to achieve a very high lock speed. For large tables, table locking is for most applications MUCH better than row locking, but there are of course some pitfalls.

In MySQL 3.23.7 and above, you can insert rows into MyISAM tables at the same time as other threads are reading from the table. Note that currently this only works if there are no deleted rows in the table.

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 others threads that want to access this particular table will wait until the update is ready.

As updates of databases 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.

Starting from MySQL 3.23.7 one can use the max_write_lock_count variable to force MySQL to issue a SELECT after a specific number of inserts on a table.

One main problem with this is the following:

Some possible solutions to this problem are:

10.3 Get your data as small as possible

One of the most basic optimization 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 since disk reads are faster and normally less main memory will also be used. Indexing also takes less resources if done on smaller columns.

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

10.4 MySQL index use

Indexes are used to find find a row with a specific calue on one column fast. Without a index MySQL has to start with the first record and then read through the whole table until it find the relevent rows. The bigger the table the more this costs. If the table has a index for the colums in question MySQL can get fast a possition to seek to in the middle of the data file without having to look at all data. If a table have 1000 rows this is at least 100 times faster than reading sequentially. Note that is you need to access almost all 1000 rows it is faster to read sequentially since we when 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 7.27 CREATE INDEX syntax.

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 optimizer 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 optimizer 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 below:

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.

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

MySQL normally uses the index that finds 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 optimize 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
          /* optimized 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 */

10.5 Speed of queries that access or update data

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 optimize 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 optimized, but there may be some exceptions and the benchmark(loop_count,expression) is a great tool to find if this is a problem with your query.

10.5.1 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, which gives for a 500,000 row table with a 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 you will however 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 N 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 N log N). To avoid this increase the index cache as the data grows. See section 10.2.3 Tuning server parameters.

10.5.2 Speed of SELECT queries

In general, when you want to make a slow SELECT ... WHERE faster, the first thing to check is whether or not you can add an index. See section 10.4 MySQL index use. 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 7.22 EXPLAIN syntax (Get information about a SELECT).

Some general tips:

10.5.3 How MySQL optimizes WHERE clauses

The where optimizes are put in the SELECT part here since they are mostly used there. But the same optimizations are used for there in DELETE and UPDATE statements.

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

Some of the optimizations performed by MySQL are listed below:

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,...

10.5.4 How MySQL optimizes LEFT JOIN

A LEFT JOIN B is in MySQL implemented as follows

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 quickly as there is 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

Then 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

10.5.5 How MySQL optimizes LIMIT

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

10.5.6 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 N 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 10.2.3 Tuning server parameters.

10.5.7 Speed of UPDATE queries

Update queries are optimized 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 are 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.

Not that with dynamic record format updating a record with 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 7.9 OPTIMIZE TABLE syntax.

10.5.8 Speed of DELETE queries

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 10.2.3 Tuning server parameters.

Its also much faster to remove all rows than to remove a big part of the rows from a table.

10.6 Choosing a table type

With MySQL you can currently (version 3.23.5) choose between four usable table formats from a speed point of view.

MyISAM Static
This format is the simplest and most secure format. It is also the fastest of the on disk formats. The speed comes from the easy way data can be found on disk. When looking up something with a index and static format it very simple, just multiply the row number with the row length. Also when scanning a table it is very easy to read a constant number of records with each disk read. The security comes from if your computer crashes when writing to a static MyISAM file, myisamchk can easily figure out where each row starts and ends. So it can usually reclaim all records except the partially written one. Not that in MySQL all indexes can always be reconstructed.
MyISAM Dynamic
This format is a litte more complex since each row has to have a header that says how long it is. One record can also end up at more that one location when it is made longer at a update. You can use OPTIMIZE table or myisamchk to defragment a table. If you have static data that you acess/change a lot in the same table as some VARCHAR or BLOB columns, it might be a good idea to move the dynamic columns to other tables just to avoid fragmentation.
MyISAM compressed
This is a read only type that is generated with the optional myisampack tool.
In memory (HEAP)
This table format is extremely useful for small/medium sized lookup tables. It is possible to copy/create a frequently used lookup table (in joins) to a (maybe temporary) HEAP table to speed up many joins. Suppose we want to do the following join many times with the same data.
SELECT tab1.a, tab3.a FROM tab1, tab2, tab3
        WHERE tab1.a = tab2.a and tab2.a = tab3.a and tab2.c != 0;
To speed this up we could create a temporary table with the join of tab2 and tab3 since that are looked up using the same column (tab1.a). Here is the command to create that table and the resulting select.
                tab2.a as a2, tab3.a as a3
                tab2, tab3
                tab2.a = tab3.a and c = 0;
SELECT tab1.a, test.a3 from tab1, test where tab1.a = test.a2;
SELECT tab1.b, test.a3 from tab1, test where tab1.a = test.a2 and something;

10.6.1 Static (Fixed-length) table characteristics

10.6.2 Dynamic table characteristics

10.6.3 Compressed table characteristics

MySQL can support different index types, but the normal type is ISAM. This is a B-tree index and you can roughly calculate the size for the index file as (key_length+4)*0.67, summed over all keys. (This is for the worst case when all keys are inserted in sorted order.)

String indexes are space compressed. If the first index part is a string, it will also be prefix compressed. Space compression makes the index file smaller if the string column has a lot of trailing space or is a VARCHAR column that is not always used to the full length. Prefix compression helps if there are many strings with an identical prefix.

10.6.4 In memory table characteristics

HEAP tables only exists in memory so they are lost if mysqld is taken down or crashes. But since they are very fast they are usefull as anyway.

The MySQL internal HEAP tables uses 100% dynamic hashing without overflow areas and don't have problems with delete.

You can only access things by equality using a index (usually by the = operator) whith a heap table.

The downside with HEAPS are:

  1. You need enough extra memory for all HEAP tables that you want to use at the same time.
  2. You can't search on a part of a index.
  3. You can't search for the next entry in order (that is to use the index to do a ORDER BY).
  4. MySQL also cannot find out how approximately many rows there are between two values. This is used by the optimizer to chose which index to use. But on the other hand no disk seeks are even needed.

10.7 Other optimization tips

Unsorted tips for faster systems:

10.8 Using your own benchmarks

You should definitely benchmark your application and database to find out where is the bottlenecks. 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 'good enough' you should at least make a 'plan', for each bottleneck, how to solve it if you someday 'really need it fix it'.

For some example portable becnchmark programs look at the MySQL benchmark suite. See section 11 The MySQL benchmark suite. You can take any program 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 then the system is very heavily loaded. And we have had many customer who contacts us then they have a (tested) system in production and have have got load problems. In every on 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 system where not already in production.

To avoid probles like this you should put some effort into benchmarking your whole appliction under the worst possible load!

10.9 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 belive 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 get a performance hit for every query that access more than one column. Since this degenerates so quickly when more that when one columns are accessed we believe that this model is not good for general purpose databases.

The more common case is there the index and data are stored together (like in Oracle/Sybase at all). 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 (depends on how well the index is cached) saves a disk read. The bad things with this layout is:

Table scanning is much slower since you have to read through the indexes to get at the data.
You loose a lot of space as you must duplicate indexes from the nodes (as you can't store the row in the nodes)
Deletes will degenerate the table over times (as indexes in nodes are usually not updated on delete).
You can't use only the index table to retrieve data for a query.
The index data is harder to cache.

10.10 MySQL design limitations/tradeoffs

Since MySQL uses extremely fast table locking (multiple readers / single writers) the biggest remaining problem is a mix of a steady stream of inserts and slow selects on the same table.

We belive that for a huge number of systems the extremely fast performance in other cases make this choice a win. This case is usually also possible to solve by having multiple copies of the table. But it takes more effort and hardware.

We are also working on some extension to solve this problem for some common application niches.

10.11 Portability

Since 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. And if you want a 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 server that it should work with.

When you can use the MySQL crash-me program/web-page http://www.mysql.com/crash-me-choose.htmy to find functions, types and limits you can use with a selection of database servers. Crash-me now test a long way from everything possible but it still is vīcomprehensive with about 450 things tested.

For example, you shouldn't have longer column names 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 of how we have handled this, you can get a feeling of what you have to do to write your application database independent. The benchmark 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/benchmark.html the results from this benchmark.

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

If you strive for database independence you need to get a good feeling of each SQL servers 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 in 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 a 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 REAL high performance is more important than exactness, like in some web applications. A possibility is to create a application layer that caches all results to give you even higher performance. By just letting old results 'expire' after a while you can keep the cache reasonable fresh. This is quite nice in case of extremely high load, in which case you can dynamicly increase the cache to be bigger and set the expire timeout higher until things gets back to normal.

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

10.12 What have we used MySQL for?

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

We get from all stores 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 advertisements 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 generate summary tables grouped by different criterias (product group, customer id, store ...) from the transaction tables. The reports are web pages that are dynamicly generated by a small perl script that parses a web pages, executes the SQL statements in it and inserts the results. Now we would have used PHP or mod_perl instead but they where not available at that time.

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

In most cases a new report can simple by done by copying a 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 simply 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 Mz). We recently upgrade one of our servers to a 2 CPU 400 Mz Ultra sparc and we are now planing to start handling transactions on the product level, which would mean a 10 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 3.32) 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 but Solaris will perform better when you start to get a a high load because of extrema 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 make the machine feel very slow and unresponsive while big batches are going. Hopefully this will be better handled in future Linux Kernels.

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