Disk seeks are a huge performance bottleneck. This problem becomes more apparent when the amount of data starts to grow so large that effective caching becomes impossible. For large databases where you access data more or less randomly, you can be sure that you need at least one disk seek to read and a couple of disk seeks to write things. To minimize this problem, use disks with low seek times.
Increase the number of available disk spindles (and thereby reduce the seek overhead) by either symlinking files to different disks or striping the disks:
Using symbolic links
This means that, for
you symlink the index file and data files from their
usual location in the data directory to another disk
(that may also be striped). This makes both the seek and
read times better, assuming that the disk is not used
for other purposes as well. See
Section 8.9.6, “Using Symbolic Links”.
Striping means that you have many disks and put the
first block on the first disk, the second block on the
second disk, and the
block on the (
disk, and so on. This means if your normal data size is
less than the stripe size (or perfectly aligned), you
get much better performance. Striping is very dependent
on the operating system and the stripe size, so
benchmark your application with different stripe sizes.
See Section 8.1.4, “Using Your Own Benchmarks”.
The speed difference for striping is very dependent on the parameters. Depending on how you set the striping parameters and number of disks, you may get differences measured in orders of magnitude. You have to choose to optimize for random or sequential access.
For reliability, you may want to use RAID 0+1 (striping plus
mirroring), but in this case, you need 2 ×
N drives to hold
N drives of data. This is
probably the best option if you have the money for it.
However, you may also have to invest in some
volume-management software to handle it efficiently.
A good option is to vary the RAID level according to how
critical a type of data is. For example, store
semi-important data that can be regenerated on a RAID 0
disk, but store really important data such as host
information and logs on a RAID 0+1 or RAID
N disk. RAID
N can be a problem if you have
many writes, due to the time required to update the parity
On Linux, you can get much better performance by using
hdparm to configure your disk's
interface. (Up to 100% under load is not uncommon.) The
hdparm options should be quite
good for MySQL, and probably for many other applications:
hdparm -m 16 -d 1
Note that performance and reliability when using this
command depend on your hardware, so we strongly suggest that
you test your system thoroughly after using
hdparm. Please consult the
hdparm manual page for more information.
hdparm is not used wisely, file system
corruption may result, so back up everything before
You can also set the parameters for the file system that the database uses:
If you do not need to know when files were last accessed
(which is not really useful on a database server), you can
mount your file systems with the
option. That skips updates to the last access time in inodes
on the file system, which avoids some disk seeks.
On many operating systems, you can set a file system to be
updated asynchronously by mounting it with the
async option. If your computer is reasonably
stable, this should give you better performance without
sacrificing too much reliability. (This flag is on by
default on Linux.)
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