### Set - 6

Question 1 :

When and how much can MySQL replication improve the performance of my system?

MySQL replication is most beneficial for a system with frequent reads and not so frequent writes. In theory, by using a one master/many slaves setup you can scale by adding more slaves until you either run out of network bandwidth, or your update load grows to the point that the master cannot handle it.

In order to determine how many slaves you can get before the added benefits begin to level out, and how much you can improve performance of your site, you need to know your query patterns, and empirically (by benchmarking) determine the relationship between the throughput on reads (reads per second, or max_reads) and on writes max_writes) on a typical master and a typical slave. The example below will show you a rather simplified calculation of what you can get with replication for our imagined system.

Let's say our system load consists of 10% writes and 90% reads, and we have determined that max_reads = 1200 - 2 * max_writes, or in other words, our system can do 1200 reads per second with no writes, our average write is twice as slow as average read, and the relationship is linear. Let us suppose that our master and slave are of the same capacity, and we have N slaves and 1 master. Then we have for each server (master or slave):

reads = 1200 - 2 * writes (from bencmarks)

reads = 9* writes / (N + 1) (reads split, but writes go to all servers)

9*writes/(N+1) + 2 * writes = 1200

writes = 1200/(2 + 9/(N+1)

So if N = 0, which means we have no replication, our system can handle 1200/11, about 109 writes per second (which means we will have 9 times as many reads due to the nature of our application).

If N = 1, we can get up to 184 writes per second.

If N = 8, we get up to 400.

If N = 17, 480 writes.

Eventually as N approaches infinity (and our budget negative infinity), we can get very close to 600 writes per second, increasing system throughput about 5.5 times. However, with only 8 servers, we increased it almost 4 times already.

Note that our computations assumed infinite network bandwidth, and neglected several other factors that could turn out to be signficant on your system. In many cases, you may not be able to make a computation similar to the one above that will accurately predict what will happen on your system if you add N replication slaves. However, answering the following questions should help you decided whether and how much, if at all, the replication will improve the performance of your system:
What is the read/write ratio on your system?
How much more write load can one server handle if you reduce the reads?
How many slaves do you have bandwidth for on your network?

Question 2 :

How can I use replication to provide redundancy/high availability?

With the currently available features, you would have to set up a master and a slave (or several slaves), and write a script that will monitor the master to see if it is up, and instruct your applications and the slaves of the master change in case of failure. Some suggestions:
To tell a slave to change the master use the CHANGE MASTER TO command.
A good way to keep your applications informed where the master is by having a dynamic DNS entry for the master. With bind you can use nsupdate to dynamically update your DNS.
You should run your slaves with the log-bin option and without log-slave-updates. This way the slave will be ready to become a master as soon as you issue STOP SLAVE; RESET MASTER, and CHANGE MASTER TO on the other slaves. It will also help you catch spurious updates that may happen because of misconfiguration of the slave (ideally, you want to configure access rights so that no client can update the slave, except for the slave thread) combined with the bugs in your client programs (they should never update the slave directly).
We are currently working on intergrating an automatic master election system into MySQL, but until it is ready, you will have to create your own monitoring tools.

Question 3 :

MySQL - Troubleshooting Replication

If you have followed the instructions, and your replication setup is not working, first elliminate the user error factor by checking the following:
Is the master logging to the binary log? Check with SHOW MASTER STATUS. If it is, Position will be non-zero. If not, verify that you have given the master log-bin option and have set server-id.
Is the slave running? Check with SHOW SLAVE STATUS. The answer is found in Slave_running column. If not, verify slave options and check the error log for messages.
If the slave is running, did it establish connection with the master? Do SHOW PROCESSLIST, find the thread with system user value in User column and none in the Host column, and check the State column. If it says connecting to master, verify the privileges for the replication user on the master, master host name, your DNS setup, whether the master is actually running, whether it is reachable from the slave, and if all that seems ok, read the error logs.
If the slave was running, but then stopped, look at SHOW SLAVE STATUS output andcheck the error logs. It usually happens when some query that succeeded on the master fails on the slave. This should never happen if you have taken a proper snapshot of the master, and never modify the data on the slave outside of the slave thread. If it does, it is a bug, read below on how to report it.
If a query on that succeeded on the master refuses to run on the slave, and a full database resync ( the proper thing to do ) does not seem feasible, try the following:
First see if there is some stray record in the way. Understand how it got there, then delete it and run SLAVE START If the above does not work or does not apply, try to understand if it would be safe to make the update manually ( if needed) and then ignore the next query from the master.
If you have decided you can skip the next query, do SET SQL_SLAVE_SKIP_COUNTER=1; SLAVE START; to skip a query that does not use auto_increment, last_insert_id or timestamp, or SET SQL_SLAVE_SKIP_COUNTER=2; SLAVE START; otherwise If you are sure the slave started out perfectly in sync with the master, and no one has updated the tables involved outside of slave thread, report the bug, so you will not have to do the above tricks again.
Make sure you are not running into an old bug by upgrading to the most recent version.
If all else fails, read the error logs. If they are big, grep -i slave /path/to/your-log.err on the slave. There is no generic pattern to search for on the master, as the only errors it logs are general system errors - if it can, it will send the error to the slave when things go wrong.
When you have determined that there is no user error involved, and replication still either does not work at all or is unstable, it is time to start working on a bug report. We need to get as much info as possible from you to be able to track down the bug. Please do spend some time and effort preparing a good bug report. Ideally, we would like to have a test case in the format found in mysql-test/t/rpl* directory of the source tree. If you submit a test case like that, you can expect a patch within a day or two in most cases, although, of course, you mileage may vary depending on a number of factors.

Second best option is a just program with easily configurable connection arguments for the master and the slave that will demonstrate the problem on our systems. You can write one in Perl or in C, depending on which language you know better.

If you have one of the above ways to demonstrate the bug, use mysqlbug to prepare a bug report and send it to bugs@lists.mysql.com. If you have a phantom - a problem that does occur but you cannot duplicate "at will":

Verify that there is no user error involved. For example, if you update the slave outside of the slave thread, the data will be out of sync, and you can have unique key violations on updates, in which case the slave thread will stop and wait for you to clean up the tables manually to bring them in sync.
Run slave with log-slave-updates and log-bin - this will keep a log of all updates on the slave.
Save all evidence before reseting the replication. If we have no or only sketchy information, it would take us a while to track down the problem. The evidence you should collect is:
All binary logs on the master
All binary log on the slave
The output of SHOW MASTER STATUS on the master at the time you have discovered the problem
The output of SHOW SLAVE STATUS on the master at the time you have discovered the problem
Error logs on the master and on the slave
Use mysqlbinlog to examine the binary logs. The following should be helpful to find the trouble query, for example:
mysqlbinlog -j pos_from_slave_status /path/to/log_from_slave_status | head

Once you have collected the evidence on the phantom problem, try hard to isolate it into a separate test case first. Then report the problem to bugs@lists.mysql.com with as much info as possible.

Question 4 :

Getting Maximum Performance from MySQL

Optimization is a complicated task because 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) additional ways to make the system even faster.

Question 5 :

MySQL - Optimization Overview

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

The most common bottlenecks are:

Disk seeks. It takes time for the disk to find a piece of data. With modern disks 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 disk delivers something like 10-20Mb/s. This is easier to optimize than seeks because you can read in parallel from multiple disks.
CPU cycles. When we have the data in main memory (or if it already were there) we need to process it to get to our result. Having small tables compared to the memory 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 can fit in the CPU cache the main memory bandwidth becomes a bottleneck. This is an uncommon bottleneck for most systems, but one should be aware of it.

Question 6 :

MySQL - System/Compile Time and Startup Parameter Tuning

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

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

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

Other tips:

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's functionality as long as you 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 because 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. It's also on default for Linux as Linux file locking are not yet safe. 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 12.2.1 How Compiling and Linking Affects the Speed of MySQL
Most of the following tests are done on Linux with the MySQL benchmarks, but they should give some indication for other operating systems and workloads.

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

On Linux, you will get the fastest code when compiling with pgcc and -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). Note that with some versions of pgcc, the resulting code will only run on true Pentium processors, even if you use the compiler option that you want the resulting code to be working on all x586 type processors (like AMD).

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

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

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

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

If you use pgcc and compile everything with -O6, the mysqld server is 1% faster than with gcc 2.95.2. If you link dynamically (without -static), the result is 13% slower on Linux. 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 the same computer. (If you are connection to localhost, MySQL will, by default, use sockets).
If you compile with --with-debug=full, then you will loose 20 % for most queries, but some queries may take substantially longer (The MySQL benchmarks ran 35 % slower) If you use --with-debug, then you will only loose 15 %. On a Sun SPARCstation 20, SunPro C++ 4.2 is 5 % faster than gcc 2.95.2.
Compiling with gcc 2.95.2 for ultrasparc with the option -mcpu=v8 -Wa,-xarch=v8plusa gives 4 % more performance. 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.
Running with --log-bin makes [MySQL 1 % slower.
Compiling without frame pointers -fomit-frame-pointer with gcc makes MySQL 1 % faster.
The MySQL-Linux distribution provided by MySQL AB used to be compiled with pgcc, but we had to go back to regular gcc because of a bug in pgcc that would generate the code that does not run on AMD. We will continue using gcc until that bug is resolved. In the meantime, if you have a non-AMD machine, you can get a faster binary by compiling with pgcc. The standard MySqL Linux binary is linked statically to get it faster and more portable.

Question 7 :

MySQL - Disk Issues

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 randomly, you can be sure 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.
Increase the number of available disk spindles (and thereby reduce the seek overhead) by either symlink files to different disks or striping the disks.
This means that you symlink the index and/or 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 disks are not used for other things).

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 (N 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 is very dependent on the OS and stripe-size. So benchmark your application with different stripe-sizes.

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 a 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 regenerated) on RAID 0 disk while storing really important data (like host information and logs) on a RAID 0+1 or RAID N disk. 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.
On Linux, you can get much more performance (up to 100 % under load is not uncommon) by using hdpram to configure your disk's interface! The following should be quite good hdparm options for MySQL (and probably many other applications): hdparm -m 16 -d 1

Note that the performance/reliability when using the above depends on your hardware, so we strongly suggest that you test your system throughly after using hdparm! Please consult the hdparm man page for more information! If hdparm is not used wisely, filesystem corruption may result. Backup everything before experimenting!
On many operating systems you can mount the disks with the 'async' flag to set the file system to be updated asynchronously. If your computer is reasonable stable, this should give you more performance without sacrificing too much reliability. (This flag is on by default on Linux.)
If you don't need to know when a file was last accessed (which is not really useful on a database server), you can mount your file systems with the noatime flag.

Question 8 :

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

shell> ln -s db1 db2

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

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

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

to

if (1)

On Windows you can use internal symbolic links to directories by compiling MySQL with -DUSE_SYMDIR. This allows you to put different databases on different disks.

Question 9 :

MySQL - Tuning Server Parameters

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

shell> mysqld --help

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

Possible variables for option --set-variable (-O) are:
back_log current value: 5
bdb_cache_size current value: 1048540
binlog_cache_size current_value: 32768
connect_timeout current value: 5
delayed_insert_timeout current value: 300
delayed_insert_limit current value: 100
delayed_queue_size current value: 1000
flush_time current value: 0
interactive_timeout current value: 28800
join_buffer_size current value: 131072
key_buffer_size current value: 1048540
lower_case_table_names current value: 0
long_query_time current value: 10
max_allowed_packet current value: 1048576
max_binlog_cache_size current_value: 4294967295
max_connections current value: 100
max_connect_errors current value: 10
max_delayed_threads current value: 20
max_heap_table_size current value: 16777216
max_join_size current value: 4294967295
max_sort_length current value: 1024
max_tmp_tables current value: 32
max_write_lock_count current value: 4294967295
myisam_sort_buffer_size current value: 8388608
net_buffer_length current value: 16384
net_retry_count current value: 10
net_read_timeout current value: 30
net_write_timeout current value: 60
query_buffer_size current value: 0
record_buffer current value: 131072
slow_launch_time current value: 2
sort_buffer current value: 2097116
table_cache current value: 64
thread_concurrency current value: 10
tmp_table_size current value: 1048576
thread_stack current value: 131072
wait_timeout current value: 28800

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

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

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

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

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

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

shell> safe_mysqld -O key_buffer=64M -O table_cache=256 \
-O sort_buffer=4M -O record_buffer=1M &

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

shell> safe_mysqld -O key_buffer=16M -O sort_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 &

When you have installed MySQL, the `support-files' directory will contain some different my.cnf example files, `my-huge.cnf', `my-large.cnf', `my-medium.cnf', and `my-small.cnf', you can use as a base to optimize your system.
If there are very many connections, ``swapping problems'' may occur unless mysqld has been configured to use very little memory for each connection. mysqld performs better if you have enough memory for all connections, of course.
Note that if you change an option to mysqld, it remains in effect only for that instance of the server.
To see the effects of a parameter change, do something like this:

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

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

Question 10 :

How MySQL Opens and Closes Tables ?

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

table_cache is related to max_connections. For example, for 200 concurrent running connections, you should have a table cache of at least 200 * n, where n is the maximum number of tables in a join.

The cache of open tables can grow to a maximum of table_cache (default 64; this can be changed 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, the table 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.

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

Question 11 :

MySQL - Drawbacks to Creating Large Numbers of Tables in the Same Database

If you have many files in a directory, open, close, and create operations will be slow. If you execute SELECT statements on many different tables, there will be a little overhead when the table cache is full, because for every table that has to be opened, another must be closed. You can reduce this overhead by making the table cache larger.

Question 12 :

MySQL - 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.

Question 13 :

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:

The key buffer (variable key_buffer_size) is shared by all threads; Other buffers used by the server are allocated as needed.

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.
Only the compressed ISAM / MyISAM tables are memory mapped. This is because the 32-bit memory space of 4GB is not large enough for most big tables. When systems with a 64-bit address space become more common we may add general support for 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 Version 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.

In MySQL Version 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 allocates a sort buffer and 0-2 temporary files depending on the result set size.

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. The ISAM/MyISAM table handlers will use one extra row buffer for internal usage.
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.

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.

Question 14 :

How MySQL Locks Tables ?

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

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> TRUNCATE TABLE insert_table;
mysql> UNLOCK TABLES;

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

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.

Question 15 :

MySQL - Table Locking Issues

The table locking code in MySQL is deadlock free.

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

For BDB tables, MySQL only uses table locking if you explicitely lock the table with LOCK TABLES or execute a command that will modify every row in the table, like ALTER TABLE.

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

Table locking enables many threads to read from a table at the same time, but if a thread wants to write to a table, it must first get exclusive access. During the update, all other threads that want to access this particular table will wait until the update is ready.

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

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

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

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!
A thread is waiting for something like full disk, in which case all threads that wants to access the problem table will also be put in a waiting state until more disk space is made available.
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.

You can specify that a specific SELECT is very important with the HIGH_PRIORITY attribute.

If you have problems with INSERT combined with SELECT, switch to use the new MyISAM tables as these support concurrent SELECTs and INSERTs.
If you mainly mix INSERT and SELECT statements, the DELAYED attribute to INSERT will probably solve your problems.

If you have problems with SELECT and DELETE, the LIMIT option to DELETE may help.

Question 16 :

How MySQL uses DNS ?

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

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

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

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

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

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

Question 17 :

MySQL - 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 because disk reads are faster and normally less main memory will be used. Indexing also takes less resources if done on smaller columns.

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

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 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 having 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.

The primary index of a table should be as short as possible. This makes identification of one row easy and efficient. For each table, you have to decide which storage/index method to use.

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 it's very likely that a column has a unique prefix on the first number of characters, it's better to only index this prefix. MySQL supports an index on a part of a character column. Shorter indexes are 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.

In some circumstances it can be beneficial to split into two a table that is scanned very often. This is especially true if it is a dynamic format table and it is possible to use a smaller static format table that can be used to find the relevant rows when scanning the table.

Question 18 :

How MySQL Uses Indexes ?

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

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

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. This is optimized by a preprocessor that checks if you are using WHERE key_part_# = constant on all key parts < N. In this case MySQL will do a single key lookup and replace the MIN() expression with a constant. If all expressions are replaced with constants, the query will return at once:
SELECT MIN(key_part2),MAX(key_part2) FROM table_name where key_part1=10

Sort or group a table if the sorting or grouping is done on a leftmost prefix of a usable key (for example, ORDER BY key_part_1,key_part_2 ). The key is read in reverse order if all key parts are followed by DESC. The index can also be used even if the ORDER BY doesn't match the index exactly, as long as all the unused index parts and all the extra are ORDER BY columns are constants in the WHERE clause. The following queries will use the index to resolve the ORDER BY part:
SELECT * FROM foo ORDER BY key_part1,key_part2,key_part3;
SELECT * FROM foo WHERE column=constant ORDER BY column, key_part1;
SELECT * FROM foo WHERE key_part1=const GROUP BY key_part2;

In some cases a query 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:
SELECT key_part3 FROM table_name WHERE key_part1=1

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 wild-card 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 wild-card 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 an index.

MySQL normally uses the index that finds the least number of rows. An index is used for columns that you compare with the following operators: =, >, >=, >, >=, BETWEEN, and a LIKE with a non-wild-card 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 */

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

If the use of the index would require MySQL to access more than 30 % of the rows in the table. (In this case a table scan is probably much faster, as this will require us to do much fewer seeks). Note that if such a query uses LIMIT to only retrieve part of the rows, MySQL will use an index anyway, as it can much more quickly find the few rows to return in the result.

Question 19 :

MySQL - 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 out if this is a problem with your query.

MySQL - Estimating Query Performance

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

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

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

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

Note that the above doesn't mean that your application will slowly degenerate by 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.

Question 20 :

MySQL - 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.

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.

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 part that indicates the average number of rows that have the same value. (For unique indexes, this is always 1, of course.). MySQL will use this to decide which index to choose when you connect two tables with 'a non-constant expression'. You can check the result from the analyze run by doing SHOW INDEX FROM table_name and examining the Cardinality column.
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!

Question 21 :

How MySQL Optimizes WHERE Clauses ?

The WHERE optimizations are put in the SELECT part here because they are mostly used with SELECT, but the same optimizations apply for WHERE 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>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=50) 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.
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,...

Question 22 :

How MySQL Optimizes DISTINCT ?

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

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

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

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

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

Question 23 :

How MySQL Optimizes LEFT JOIN and RIGHT JOIN ?

A LEFT JOIN B in MySQL is implemented as follows:

The table B is set to be dependent on table A and all tables that A is dependent on.
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 optimizations 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 don'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.
RIGHT JOIN is implemented analogously as LEFT JOIN.

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

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

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

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

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

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

Question 24 :

How MySQL Optimizes LIMIT ?

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 BY'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.

Question 25 :

MySQL - Speed of INSERT Queries ?

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.

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.

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 are going to only read the table in the future, run myisampack on it to make it smaller.

Re-create 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 because it avoids 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 an 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 for loading data.
To get some more speed for both LOAD DATA INFILE and INSERT, enlarge the key buffer.