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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.
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.
- Disk seeks
It takes time for the disk to find a piece of data. With modern disk in
1999 the mean time for this is usually lower than 10ms. So we can in
theory do about 1000 seeks a second. This time improves slowly with new
disks and is very hard to optimize for a single table. The way to
optimize this is to spread the data on more than one disk.
- Disk reading/writing
When the disk is at the correct position we need to read the data. With
modern disks in 1999 one disks delivers something like 10-20Mb/s. This
is easier to optimize than seeks since you can read in parallel from
multiple disks.
- CPU cycles
When we have got the data into main memory (or if it already where
there) we need to process it to get to our result. When we have small
tables compared to the memory this is the most common limiting
factor. But then with small tables speed is usually not the problem.
- Memory bandwidth
When the CPU needs more data than that fits in the cpu cache the main
memory bandwidth becomes a bottleneck. This is a uncommoon bottleneck
for most systems but one should be aware of it.
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:
-
If you have enough ram, you could remove all swap devices. Some
operating systems will use a SWAP device in some contexts even if you
have free memory.
-
Use the
--skip-locking MySQL option to avoid external
locking. Note that this will not impact MySQL functionality as
long that only run one server. Just remember to take down the server (or
lock relevant parts) before you run myisamchk. On some system
this switch is mandatory since the external locking does not work in any
case.
The --skip-locking option is on by default when compiling with
MIT-pthreads, because flock() isn't fully supported by
MIT-pthreads on all platforms.
The only case when you can't use --skip-locking is if you run
multiple MySQL SERVERS (not clients) on the same data. Or run
myisamchk on the table without first flushing and locking the
mysqld server tables first.
You can still use LOCK TABLES / UNLOCK TABLES even if you
are using --skip-locking
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:
-
If you use
pgcc and compile everything with -O6, the
mysqld server is 11% faster than with gcc versions
older than gcc 2.95.2.
-
If you link dynamically (without
-static), the result is 13%
slower. Note that you still can use a dynamic linked MySQL library. It
is only the server that is critical for performance.
-
If you connect using TCP/IP rather than Unix sockets, the result is 7.5%
slower.
-
On a Sun SPARCstation 10,
gcc 2.7.3 is 13% faster than Sun Pro C++ 4.2.
-
On Solaris 2.5.1, MIT-pthreads is 8-12% slower than Solaris native
threads on a single processor. With more load/cpus the difference should
get bigger.
The MySQL-Linux distribution provided by TcX is compiled with
pgcc and linked statically.
-
As mentioned before disks seeks are a big performance bottleneck. This
problems gets more and more apparent when the data starts to grow so
large that effective caching becomes impossible. For large databases,
where you access data more or less random, you can count on that you
will need at least one disk seek to read and a couple of disk seeks to
write things. To minimize this problem use disks with low seek times.
-
To increase the number of available disk spindles (and thereby reduce
the seek overhead) it is possible to either symlink files to different
disks or stripe the disks.
- Using symbolic links
-
This means that you symlink the index or/and data file(s) from the
normal data directory to another disk (that may also be striped). This
makes both the seek and read times better (if the disk are not used for
other things). See section 10.2.2.1 Using symbolic links for databases and tables.
- Striping
-
Striping means that you have many disks and put the first block on the
first disk, the second block on the second disk, and the Nth on the (Nth
mod number_of_disks) disk, and so on. This means if your normal data
size is less than the stripe size (or perfectly aligned) you will get
much better performance. Note that striping if very dependent on the OS
and stripe-size. So benchmark your application with different
stripe-sizes. See section 10.8 Using your own benchmarks.
Note that the speed difference for striping is very dependent
on the parameters. Depending on how you set the striping parameters and
number of disks you may get difference in orders of magnitude. Note that
you have to choose to optimize for random or sequential access.
-
For reliability you may want to use RAID 0+1 (striping + mirroring), but
in this case you will need 2*N drives to hold N drives of data. This is
probably the best option if you have the money for it! You may however
also have to invest in some volume management software to handle it
efficiently.
-
A good option is to have semi-important data (that can be re-generated)
on RAID 0 disk while store really important data (like host information
and logs) on a RAID 0+1 or RAID N disks. RAID N can be a problem if you
have many writes because of the time to update the parity bits.
-
You may also set the parameters for the file system that the database
uses. One easy change is to mount the file system with the noatime
option. That makes it skip the updating of the last access time in the
inode and by this will avoid some disk seeks.
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))
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,...).
ansi_mode.-
Is
ON if mysqld was started with --ansi.
See section 5.2 Runnning MySQL in ANSI mode.
back_log-
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.
concurrent_inserts-
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.
connect_timeout-
The number of seconds the
mysqld server is waiting for a connect
packet before responding with Bad handshake.
delayed_insert_timeout-
How long a
INSERT DELAYED thread should wait for INSERT
statements before terminating.
delayed_insert_limit-
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.
delay_key_write-
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.
delayed_queue_size-
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.
flush_time-
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).
init_file-
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.
interactive_timeout-
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.
join_buffer_size-
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.)
key_buffer_size-
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.
long_query_time-
If a query takes longer than this (in seconds), the
Slow_queries counter
will be incremented.
max_allowed_packet-
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.
max_connections-
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.
max_connect_errors-
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.
max_delayed_threads-
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.
max_join_size-
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.
max_sort_length-
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).
max_tmp_tables-
(This option doesn't yet do anything).
Maximum number of temporary tables a client can keep open at the same time.
net_buffer_length-
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.)
net_retry_count-
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.
record_buffer-
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.
skip_show_databases-
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.
sort_buffer-
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.
table_cache-
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.
tmp_table_size-
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.
thread_concurrency-
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.
thread_stack-
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.
wait_timeout-
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.
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:
-
Tables that are not currently in use are released, in least-recently-used
order.
-
If the cache is full and no tables can be released, but a new table needs to
be opened, the cache is temporarily extended as necessary.
-
If the cache is in a temporarily-extended state and a table goes from in-use
to not-in-use state, it is closed and released from the cache.
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 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.
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.
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.
-
The key buffer (variable
key_buffer_size) is shared by all
threads; Other buffers used by the server are allocated as
needed. See section 10.2.3 Tuning server parameters.
-
Each connection uses some thread specific space; A stack (default 64K,
variable
thread_stack) a connection buffer (variable
net_buffer_length), and a result buffer (variable
net_buffer_length). The connection buffer and result buffer are
dynamically enlarged up to max_allowed_packet when needed. When
a query is running a copy of the current query string is also allocated.
-
All threads share the same base memory.
-
Nothing is memory-mapped yet (except compressed tables, but that's
another story). This is because the 32-bit memory space of 4GB is not
large enough for most large tables. When systems with a 64-bit
address-space become more common we may add general support for
memory-mapping.
-
Each request doing a sequential scan over a table allocates a read buffer
(variable
record_buffer).
-
All joins are done in one pass and most joins can be done without even
using a temporary table. Most temporary tables are memory-based (HEAP)
tables. Temporary tables with a big record length (calculated as the
sum of all column lengths) or that contain
BLOB columns are
stored on disk.
One problem in MySQL versions before 3.23.2 is that if a HEAP table
exceeds the size of tmp_table_size, you get the error The
table tbl_name is full. In newer versions this is handled by
automatically changing the in-memory (HEAP) table to a disk-based
(MyISAM) table as necessary. To work around this problem, you can
increase the temporary table size by setting the tmp_table_size
option to mysqld, or by setting the SQL option
SQL_BIG_TABLES in the client program. See section 7.25 SET OPTION syntax. In MySQL 3.20, the maximum size of the
temporary table was record_buffer*16, so if you are using this
version, you have to increase the value of record_buffer. You can
also start mysqld with the --big-tables option to always
store temporary tables on disk, however, this will affect the speed of
many complicated queries.
-
Most requests doing a sort allocate a sort buffer and one or two temporary
files. See section 18.5 Where MySQL stores temporary files.
-
Almost all parsing and calculating is done in a local memory store. No
memory overhead is needed for small items and the normal slow memory
allocation and freeing is avoided. Memory is allocated only for
unexpectedly large strings (this is done with
malloc() and
free()).
-
Each index file is opened once and the data file is opened once for each
concurrently-running thread. For each concurrent thread, a table structure,
column structures for each column, and a buffer of size
3 * n is
allocated (where n is the maximum row length, not counting BLOB
columns). A BLOB uses 5 to 8 bytes plus the length of the BLOB
data.
-
For each table having
BLOB columns, a buffer is enlarged dynamically
to read in larger BLOB values. If you scan a table, a buffer as large
as the largest BLOB value is allocated.
-
Table handlers for all in-use tables are saved in a cache and managed as a
FIFO. Normally the cache has 64 entries. If a table has been used by two
running threads at the same time, the cache contains two entries for the
table.
See section 10.2.4 How MySQL opens and closes tables.
-
A
mysqladmin flush-tables command closes all tables that are not in
use and marks all in-use tables to be closed when the currently executing
thread finishes. This will effectively free most in-use memory.
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.
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:
-
If there are no locks on the table, put a write lock on it.
-
Otherwise, put the lock request in the write lock queue.
The locking method MySQL uses for READ locks works as follows:
-
If there are no write locks on the table, put a read lock on it.
-
Otherwise, put the lock request in the read lock queue.
When a lock is released, the lock is made available to the threads
in the write lock queue, 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;
mysql> UNLOCK TABLES;
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.
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:
-
A client issues a
SELECT that takes a long time to run.
-
Another client then issues an
UPDATE on a used table; This client
will wait until the SELECT is finished
-
Another client issues another
SELECT statement on the same table; As
UPDATE has higher priority than SELECT, this SELECT
will wait for the UPDATE to finish. It will also wait for the first
SELECT to finish!
Some possible solutions to this problem are:
-
Try to get the
SELECT statements to run faster; You may have to create
some summary tables to do this.
-
Start
mysqld with --low-priority-updates. This will give
all statements that update (modify) a table lower priority than a SELECT
statement. In this case the last SELECT statement in the previous
scenario would execute before the INSERT statement.
-
You can give a specific
INSERT,UPDATE or DELETE statement
lower priority with the LOW_PRIORITY attribute.
-
Start
mysqld with a low value for max_write_lock_count to give
READ locks after a certain number of WRITE locks.
-
You can specify that all updates from a specific thread should be done with
low priority by using the SQL command:
SET SQL_LOW_PRIORITY_UPDATES=1.
See section 7.25 SET OPTION syntax.
-
You can specify that a specific
SELECT is very important with the
HIGH_PRIORITY attribute. See section 7.12 SELECT syntax.
-
If you have problems with
INSERT combined with SELECT,
switch to use the new MyISAM tables as these supports concurrent
SELECTs and INSERTs.
-
If you mainly mix
INSERT and SELECT statements, the
DELAYED attribute to INSERT will probably solve your problems.
See section 7.14 INSERT syntax.
-
If you have problems with
SELECT and DELETE, the LIMIT
option to DELETE may help. See section 7.11 DELETE syntax.
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:
-
Use the most efficient (smallest) types possible. MySQL has a
many specialized types that save disk space and memory.
-
Use the smaller integer types if possible to get smaller tables. For
example,
MEDIUMINT is often better than INT.
-
Declare columns to be
NOT NULL if possible. It makes everything
faster and you save one bit per column. Note that if you really need
NULL in your application you should definitely use it. Just avoid
haveing it on all columns by default.
-
If you don't have any variable-length columns (
VARCHAR,
TEXT or BLOB columns), a fixed-size record format is
used. This is faster but unfortunately may waste some space.
See section 10.6 Choosing a table type.
-
Each table should have as short as possible primary index. This makes
identification of one row easy and efficient.
-
For each table you have to decide which storage/index method to
use. See section 9.4 MySQL table types. See also See section 10.6 Choosing a table type.
-
Only create the indexes that you really need. Indexes are good for
retrieval but bad when you need to store things fast. If you mostly
access a table by searching on a combination of columns, make an index
on them. The first index part should be the most used column. If you are
ALWAYS using many columns you should use the column with more duplicates
first to get better compression of the index.
-
If its very likely that an index has unique prefix on the first number
of characters, it's better that only index this prefix. MySQL
supports an index on a part of a character column. Shorter indexes is
faster not only because they take less disk space but also because they
will give you more hits in the index cache and thus fewer disk
seeks. See section 10.2.3 Tuning server parameters.
-
In some circumstances it can be beneficial to split a table that is
scanned very often into two. Especially if it is a dynamic format table
and it is possible to a smaller static format table that can be used to
find the relevant rows then scanning.
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:
-
Quickly find the rows that match a
WHERE clause.
-
Retrieve rows from other tables when performing joins.
-
Find the
MAX() or MIN() value for a specific indexed
column.
-
Sort or group a table if the sorting or grouping is done on a leftmost
prefix of a usable key (e.g.,
ORDER BY key_part_1,key_part_2 ). The
key is read in reverse order if all key parts are followed by DESC.
-
In some cases a querey can be optimized to retrieve values without
consulting the data file. If all used columns for some table are numeric
and form a leftmost prefix for some key, the values may be retrieved
from the index tree for greater speed.
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 */
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.
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.
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:
-
To help MySQL optimize queries better, run
myisamchk
--analyze on a table after it has been loaded with relevant data. This
updates a value for each index that indicates the average number of rows
that have the same value. (For unique indexes, this is always 1, of
course.)
-
To sort an index and data according to an index, use
myisamchk
--sort-index --sort-records=1 (if you want to sort on index 1). If you
have a unique index from which you want to read all records in order
according to that index, this is a good way to make that faster. Note
however that this sorting isn't written optimally and will take a long
time for a large table!
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:
-
Removal of unnecessary parentheses:
((a AND b) AND c OR (((a AND b) AND (c AND d))))
-> (a AND b AND c) OR (a AND b AND c AND d)
-
Constant folding:
(a<b AND b=c) AND a=5
-> b>5 AND b=c AND a=5
-
Constant condition removal (needed because of constant folding):
(B>=5 AND B=5) OR (B=6 AND 5=5) OR (B=7 AND 5=6)
-> B=5 OR B=6
-
Constant expressions used by indexes are evaluated only once.
-
COUNT(*) on a single table without a WHERE is retrieved
directly from the table information. This is also done for any NOT NULL
expression when used with only one table.
-
Early detection of invalid constant expressions. MySQL quickly
detects that some
SELECT statements are impossible and returns no rows.
-
HAVING is merged with WHERE if you don't use GROUP
BY or group functions (COUNT(), MIN()...)
-
For each sub join, a simpler
WHERE is constructed to get a fast
WHERE evaluation for each sub join and also to skip records as
soon as possible.
-
All constant tables are read first, before any other tables in the query.
A constant table is:
-
An empty table or a table with 1 row.
-
A table that is used with a
WHERE clause on a UNIQUE
index, or a PRIMARY KEY, where all index parts are used with constant
expressions and the index parts are defined as NOT NULL.
All the following tables are used as constant tables:
mysql> SELECT * FROM t WHERE primary_key=1;
mysql> SELECT * FROM t1,t2
WHERE t1.primary_key=1 AND t2.primary_key=t1.id;
-
The best join combination to join the tables is found by trying all
possibilities :(. If all columns in
ORDER BY and in GROUP
BY come from the same table, then this table is preferred first when
joining.
-
If there is an
ORDER BY clause and a different GROUP BY clause,
or if the ORDER BY or GROUP BY
contains columns from tables other than the first table in the join
queue, a temporary table is created.
-
If you use
SQL_SMALL_RESULT, MySQL will use an in-memory
temporary table.
-
As
DISTINCT is converted to a GROUP BY on all columns,
DISTINCT combined with ORDER BY will in many cases also need
a temporary table.
-
Each table index is queried and the best index that spans fewer than 30% of
the rows is used. If no such index can be found, a quick table scan is used.
-
In some cases, MySQL can read rows from the index without even
consulting the data file. If all columns used from the index are numeric,
then only the index tree is used to resolve the query.
-
Before each record is output, those that do not match the
HAVING clause
are skipped.
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,...
A LEFT JOIN B is in MySQL implemented as follows
-
The table
B is set to be dependent on table A.
-
The table
A is set to be dependent on all tables (except B)
that are used in the LEFT JOIN condition.
-
All
LEFT JOIN conditions are moved to the WHERE clause.
-
All standard join optimizations are done, with the exception that a table is
always read after all tables it is dependent on. If there is a circular
dependence then MySQL will issue an error.
-
All standard
WHERE optimzations are done.
-
If there is a row in
A that matches the WHERE clause, but there
wasn't any row in B that matched the LEFT JOIN condition,
then an extra B row is generated with all columns set to NULL.
-
If you use
LEFT JOIN to find rows that doesn't exist in some
table and you have the following test: column_name IS NULL in the
WHERE part, where column_name is a column that is declared as
NOT NULL, then MySQL will stop searching after more rows
(for a particular key combination) after it has found one row that
matches the LEFT JOIN condition.
In some cases MySQL will handle the query differently when you are
using LIMIT # and not using HAVING:
-
If you are selecting only a few rows with
LIMIT, MySQL
will use indexes in some cases when it normally would prefer to do a
full table scan.
-
If you use
LIMIT # with ORDER BY, MySQL will end the
sorting as soon as it has found the first # lines instead of sorting
the whole table.
-
When combining
LIMIT # with DISTINCT, MySQL will stop
as soon as it finds # unique rows.
-
In some cases a
GROUP BY can be resolved by reading the key in order
(or do a sort on the key) and then calculate summaries until the
key value changes. In this case LIMIT # will not calculate any
unnecessary GROUP's.
-
As soon as MySQL has sent the first
# rows to the client, it
will abort the query.
-
LIMIT 0 will always quickly return an empty set. This is useful
to check the query and to get the column types of the result columns.
-
The size of temporary tables uses the
LIMIT # to calculate how much
space is needed to resolve the query.
The time to insert a record consists approximately of:
-
Connect: (3)
-
Sending query to server: (2)
-
Parsing query: (2)
-
Inserting record: (1 x size of record)
-
Inserting indexes: (1 x number of indexes)
-
Close: (1)
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:
-
If you are inserting many rows from the same client at the same time use
multiple value lists
INSERT statements. This is much faster (many
times in some cases) than using separate INSERT statements.
-
If you are inserting a lot of rows from different clients, you can get
higher speed by using the
INSERT DELAYED statement. See section 7.14 INSERT syntax.
-
Note that with
MyISAM you can insert rows at the same time
SELECTs are running if there are no deleted rows in the tables.
-
When loading a table from a text file, use
LOAD DATA INFILE. This
is usually 20 times faster than using a lot of INSERT statements.
See section 7.16 LOAD DATA INFILE syntax.
-
It is possible with some extra work to make
LOAD DATA INFILE run even
faster when the table has many indexes. Use the following procedure:
-
Optionally create the table with
CREATE TABLE. For example using
mysql or Perl-DBI.
-
Execute a
FLUSH TABLES statement or the shell command mysqladmin
flush-tables.
-
Use
myisamchk --keys-used=0 -rq /path/to/db/tbl_name. This will
remove all usage of all indexes from the table.
-
Insert data into the table with
LOAD DATA INFILE. This will not
update any indexes and will therefore be very fast.
-
If you have
myisampack and want to compress the table, run
myisampack on it. See section 10.6.3 Compressed table characteristics.
-
Recreate the indexes with
myisamchk -r -q
/path/to/db/tbl_name. This will create the index tree in memory before
writing it to disk, which is much faster since it avoid lots of disk
seeks. The resulting index tree is also perfectly balanced.
-
Execute a
FLUSH TABLES statement or the shell command mysqladmin
flush-tables.
This procedure will be built into LOAD DATA INFILE in some future
version of MySQL.
-
You can speed up insertions by locking your tables:
mysql> LOCK TABLES a WRITE;
mysql> INSERT INTO a VALUES (1,23),(2,34),(4,33);
mysql> INSERT INTO a VALUES (8,26),(6,29);
mysql> UNLOCK TABLES;
The main speed difference is that the index buffer is flushed to disk only
once, after all INSERT statements have completed. Normally there would
be as many index buffer flushes as there are different INSERT
statements. Locking is not needed if you can insert all rows with a single
statement.
Locking will also lower the total time of multi-connection tests, but the
maximum wait time for some threads will go up (because they wait for
locks). For example:
thread 1 does 1000 inserts
thread 2, 3, and 4 does 1 insert
thread 5 does 1000 inserts
If you don't use locking, 2, 3 and 4 will finish before 1 and 5. If you
use locking, 2, 3 and 4 probably will not finish before 1 or 5, but the
total time should be about 40% faster.
As INSERT, UPDATE and DELETE operations are very
fast in MySQL, you will obtain better overall performance by
adding locks around everything that does more than about 5 inserts or
updates in a row. If you do very many inserts in a row, you could do a
LOCK TABLES followed by a UNLOCK TABLES once in a while
(about each 1000 rows) to allow other threads access to the table. This
would still result in a nice performance gain.
Of course, LOAD DATA INFILE is much faster still for loading data.
To get some more speed for both LOAD DATA INFILE and
INSERT, enlarge the key buffer. See section 10.2.3 Tuning server parameters.
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.
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.
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.
CREATE TEMPORARY TABLE test TYPE=HEAP
SELECT
tab2.a as a2, tab3.a as a3
FROM
tab2, tab3
WHERE
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;
-
This is the default format. It's used when the table contains no
VARCHAR,
BLOB or TEXT columns.
-
All
CHAR, NUMERIC and DECIMAL columns are space-padded
to the column width.
-
Very quick.
-
Easy to cache.
-
Easy to reconstruct after a crash, because records are located in fixed
positions.
-
Doesn't have to be reorganized (with
myisamchk) unless a huge number of
records are deleted and you want to return free disk space to the operating
system.
-
Usually requires more disk space than dynamic tables.
-
A read-only table made with the
myisampack utility. All customers
with extended MySQL email support are entitled to a copy of
myisampack for their internal usage.
-
The uncompress code exists in all MySQL distributions so that
even customers who don't have
myisampack can read tables that
were compressed with myisampack
-
Takes very little disk space. Minimises disk usage.
-
Each record is compressed separately (very little access overhead). The
header for a record is fixed (1-3 bytes) depending on the biggest record in the
table. Each column is compressed differently. Some of the compression types
are:
-
There is usually a different Huffman table for each column.
-
Suffix space compression.
-
Prefix space compression.
-
Numbers with value
0 are stored using 1 bit.
-
If values in an integer column have a small range, the column is stored using
the smallest possible type. For example, a
BIGINT column (8 bytes) may
be stored as a TINYINT column (1 byte) if all values are in the range
0 to 255.
-
If a column has only a small set of possible values, the column type is
converted to
ENUM.
-
A column may use a combination of the above compressions.
-
Can handle fixed or dynamic length records, but not
BLOB or TEXT
columns.
-
Can be uncompressed with
myisamchk.
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.
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:
-
You need enough extra memory for all HEAP tables that you want to use at
the same time.
-
You can't search on a part of a index.
-
You can't search for the next entry in order (that is to use the index
to do a
ORDER BY).
-
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.
Unsorted tips for faster systems:
-
Use persistent connections to the database to avoid the connection over
head.
-
Always check that all your queries really uses the indexes you have created
in the tables. In MySQL you can do this with the
EXPLAIN
command. See section 7.22 EXPLAIN syntax (Get information about a SELECT).
-
Try to avoid complex
SELECT queries on tables that are updated a
lot. This is to avoid problems with table locking.
-
The new
MyISAM tables can insert rows in a table without deleted
rows at the same time another table is reading from it. If this is important
for you, you should consider methods where you don't have to delete rows
or run OPTIMIZE TABLE after you have deleted a lot of rows.
-
In some cases it may make sense to introduce a column that is 'hashed'
based on information from other columns. If this column is short and
reasonable unique it may be much faster than a big index on many
columns. In MySQL its very easy to use this extra column:
SELECT * from table where hash='calculated hash on col1 and col2'
and col_1='constant' and col_2='constant' and ..
-
For tables that changes a lot you should try to avoid all
VARCHAR
or BLOB columns. You will get dynamic row length as soon as you
are using a single VARCHAR or BLOB columns. See section 9.4 MySQL table types.
-
It's not normally useful to split a table into different tables just
because the rows gets 'big'. To access a row, the biggest performance
hit is the disk seek to find the first byte of the row. After finding
the data most new disks can read the whole row fast enough for most
applications. The only cases it really matters to split up a table is if
its a dynamic row size table (see above) that you can change to a fixed
row size. Or if you very often need to scan the table and don't need
most of the columns. See section 9.4 MySQL table types.
-
If you very often need to calculate things based on information from a
lot of rows (like counts of things) it's probably much better to
introduce a new table and update the counter in real time. An update of
type
UPDATE table set count=count+1 where index_column=constant
is very fast!
This is really important when you use databases like MySQL that
only has table locking (multiple readers / single writers). This will
also give better performance with most databases as the row locking
manager in this case will have less to do.
-
If you need to collect statistics from big log tables use summary tables
instead of scanning the whole table. Maintaing the summarys should be
much faster than trying to do statistics 'live'. It's much faster to
re-generate new summary tables from the logs when things changes
(depending on business decisions) than to have to change the running
application!
-
If possible one should classify reports as 'live' or 'statistical',
where data needed for statistical reports are only generated based on
summary tables that are generated from the actual data.
-
Take advantage of the fact that columns have default values. Insert
values explicitly only when the value to be inserted differs from the
default. This reduces the parsing that MySQL need to do and improves the
insert speed.
-
In some cases its convenient to pack and store data into a blob In this
case you have to add some extra code in your appliction to pack/unpack
things in the blob but this may save a lot of accesses at some stage.
This is practical when you have data that doesn't conform to a static
table structure.
-
Normally you should try to keep all data non-redundant (what
is called 3rd normal form in database theory), but you should not be
afraid of duplicating things or creating summary tables if you need these
to gain more speed.
-
Stored procedures or UDF (user defined functions) may be a good way to
get more performance. In this case you should however always have a way
to do this some other (slower) way if you use some database that doesn't
support this.
-
You can always gain something by caching queries/answers in your
application and try to do many inserts/updates at the same time. If
your database supports lock tables (like MySQL and Oracle),
this should help to ensure that the index cache is only flushed once
after all updates.
-
Use
INSERT /*! DELAYED */ when you do not need to now when your
data is written. This speeds things up since many records can be written
with a single disk write.
-
Use
INSERT /*! LOW_PRIORITY */ when you want your selects are
more important.
-
Use
SELECT /*! HIGH_PRIORITY */ to get selects that jumps the
queue. That is the select is done even if there is somebody waiting to
do a write.
-
Use the multi-line
INSERT statement to store many rows with one
SQL command (many SQL servers supports this)
-
Use
LOAD DATA INFILE to load bigger amounts of data. This if
faster than normal inserts and will be even faster when myisamchk
is integrated in mysqld.
-
Use
AUTO_INCREMENT columns to make unique values.
-
Use
OPTIMIZE TABLE once in a while to avoid fragmentation when
using dynamic table format.See section 7.9 OPTIMIZE TABLE syntax.
-
Use
HEAP tables to get more speed when possibe. See section 9.4 MySQL table types.
-
When using a normal Web server setup, images should be stored as
files. That is store only a file reference in the database. The main
reason for this is that a normal web server is much better at caching
files than database contents. So it it's much easier to get a fast
system if you are using files.
-
Use in memory tables for not critical data that are accessed often (like
information about the last shown banner for users that doesn't have
cookies)
-
Columns with identical information in different tables should be
declared identical and have identical names. Before version 3.23 you
got slow joins otherwise.
Try to keep the names simple (use
name instead of
customer_name in the customer table). To make your names portable
to other SQL servers you should keep them shorter than 18 characters.
-
If you need REALLY high speed you should take a look at the low level
interfaces for data storage that the different SQL servers support! For
example by accessing the MySQL
MyISAM directly you could
get a speed increase of 2-5 times compared to using the SQL interface.
The be able to do this the data must however be on the same server as
the application and usually it should only be accessed by on processes
(because external file locking is real slow). One could eliminate the
above problems by introducing low level MyISAM commands in the
MySQL server (this could be one easy way to get more
performance if needed). By carefully designing the database interface
it should be quite easy to support this types of optimisations.
-
In many cases it's faster to access data from a database (using a live
connection) than accessing a text file, just because the database is
likely to be more compact than the text file (if you are using numerical
data) and this will involve fewer disk accesses. You will also save
code because you don't have to parse your text files to find line and
column boundaries.
-
You can also use replication to speed things up. See section 19.1 Database replication.
-
Declaring a table with
DELAY_KEY_WRITE=1 will make the updating of
indexes faster as these are not logged to disk until the file is closed.
The downside is that you should run myisamchk on these tables before
you start mysqld to ensure that they are ok if something killed
mysqld in the middle. As the key information can always be generated
from the data you should not lose anything by using DELAY_KEY_WRITE.
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!
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.
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.
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 vґcomprehensive 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.
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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