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因为FUSE可以让你以任何奇怪的方式安装文件系统,理所当然地,用户安装FUSE客户端文件系统的能力也必须受到用户所在组权限的限制。下面是一个例子,在一开始,用户没有安装文件系统的权限,被拒绝安装。然后,我将自己加入到相应的用户组后重新登录,初始的MySQLfs文件系统和数据库都是空的,使用df命令可以显示内核已经知道被安装的文件系统。在例子的结尾,fusermount命令将用户创建的FUSE文件系统卸载了。
$ mkdir ~/mysqlfs
$ mysqlfs -ohost=localhost -odatabase=mysqlfs ~/mysqlfs
* Opening logfile 'mysqlfs.log': OK
fuse: failed to open /dev/fuse: Permission denied
$ su -l
root# usermod -a -G fuse ben
root# exit
$ exit
...
$ id
uid=500(ben) gid=500(ben) groups=492(fuse), ...
$ mysqlfs -ohost=localhost -odatabase=mysqlfs ~/mysqlfs
* Opening logfile 'mysqlfs.log': OK
$ ls -ld mysqlfs
drwxr-xr-x 1 root root 0 2008-01-24 16:24 mysqlfs
$ df -h mysqlfs .
Filesystem Size Used Avail Use% Mounted on
mysqlfs 0 0 0 - /home/ben/mysqlfs
/dev/sdc3 16G 4.4G 11G 31% /home
$ ls -l mysqlfs
total 0
$ fusermount -u mysqlfs
既然我们已经拥有权限,可以将MySQL安装为FUSE文件系统,接下来,让我们复制一些文件到MySQL数据库中,使之与文件系统相结合。在下面的例子中,我将项目Gutenberg的一些文本文件复制到MySQLfs并验证它们在读取的时候,与原始文件有同样的MD5返回值。然后,我将Linux文件tarbar复制到MySQLfs,想看看MySQLfs是如何处理一个44M文件的。
通过冷磁盘高速缓存将内核从/tmp目录备份到/tmp目录需要花2秒,而将之复制到MySQLfs将花费20秒,为了利用磁盘中的缓存,马上将上述操作再次执行,/tmp目录下的备份花费0.3秒,而复制到MySQLfs中依旧花费20秒。这说明,写操作是MySQLfs的主要瓶颈。当读取内核的时候,MySQLfs似乎缓存了一些数据,这使得响应时间大大快于其他测试。如果你的文件系统经常用到读操作,则MySQLfs将是一个有意思的选择,因为MySQLfs的缓存可以派上用场。
~]$ cp -av /.../guten ./mysqlfs/
`/.../guten' -> `./mysqlfs/guten'
`/.../guten/alice13a.txt' -> `./mysqlfs/guten/alice13a.txt'
`/.../guten/boysw10.txt' -> `./mysqlfs/guten/boysw10.txt'
`/.../guten/dmoro11.txt' -> `./mysqlfs/guten/dmoro11.txt'
~]$ cd ~/mysqlfs/guten
guten]$ ls -l
-rw-r----- 1 ben ben 153477 2008-01-12 13:23 alice13a.txt
-rw-rw---- 1 ben ben 48923 2008-01-12 13:23 boysw10.txt
-rw-rw---- 1 ben ben 259214 2008-01-12 13:23 dmoro11.txt
guten]$ md5sum *
135e06ad31b169065bccbf03ec7236f2 alice13a.txt
7dd30f1b37e32cdb5d21fe992bbf248d boysw10.txt
87c05f11193c0e05b3d0dec0808a0450 dmoro11.txt
guten]$ md5sum /.../guten/*
135e06ad31b169065bccbf03ec7236f2 /.../guten/alice13a.txt
7dd30f1b37e32cdb5d21fe992bbf248d /.../guten/boysw10.txt
87c05f11193c0e05b3d0dec0808a0450 /.../guten/dmoro11.txt
guten]$ cd ..
mysqlfs]$ time cp /tmp/linux-2.6.23.tar.bz2 .
real 0m16.278s
user 0m0.006s
sys 0m0.531s
mysqlfs]$ time cat linux-2.6.23.tar.bz2 >/dev/null
real 0m0.502s
user 0m0.004s
sys 0m0.035s
mysqlfs]$ time dd if=linux-2.6.23.tar.bz2 of=/tmp/junk bs=1024 count=1024
1048576 bytes (1.0 MB) copied, 0.0200973 s, 52.2 MB/s
real 0m0.058s
user 0m0.003s
sys 0m0.013s
mysqlfs]$ time dd if=linux-2.6.23.tar.bz2 of=/tmp/junk bs=1024 count=1024 skip=9000
1048576 bytes (1.0 MB) copied, 0.0214207 s, 49.0 MB/s
real 0m0.031s
user 0m0.001s
sys 0m0.011s
虽然被很多人所不齿,但我还是决定在Bonnie++文件系统下使用MySQLfs。我通过虚拟机进行了这些测试,由于是在虚拟机中,性能可能会呈线性下降,但由于MySQL数据库和/tmp目录存储于同一虚拟硬盘上,其性能比还是有效的。
在如下的结果中,你能看到,在运行Bonnie++ (version 1.03a-7)时,MySQLfs的速度是/tmp/bonnie (ext3)的十分之一。这说明,我们并不太想使用MySQLfs来直接存储邮件目录。Bonnie++被设计成适合在本地内核文件系统上工作而非FUSE,这种定位非常适用于大行文件的读写。
$ /usr/sbin/bonnie++ -d /tmp/bonnie
Version
1.03
------Sequential Output------ --Sequential Input- --Random-
-Per Chr- --Block-- -Rewrite- -Per Chr- --Block-- --Seeks--
Machine
Size K/sec %CP K/sec %CP K/sec %CP K/sec %CP K/sec %CP
/sec %CP
v8tsrv
2G 18155
31 16726
5 13338
6 26207
46 74527
24
9840 144
------Sequential Create------ --------Random Create--------
-Create-- --Read--- -Delete-- -Create-- --Read--- -Delete--
files
/sec %CP
/sec %CP
/sec %CP
/sec %CP
/sec %CP
/sec %CP
16 24878
94 +++++ +++ +++++ +++ 29018
99 +++++ +++ +++++ +++
$ /usr/sbin/bonnie++ -d ~/mysqlfs/bonnie
Version
1.03
------Sequential Output------ --Sequential Input- --Random-
-Per Chr- --Block-- -Rewrite- -Per Chr- --Block-- --Seeks--
Machine
Size K/sec %CP K/sec %CP K/sec %CP K/sec %CP K/sec %CP
/sec %CP
v8tsrv
2G
2615
5
1207
1
1323
1
2143
2
2363
0 138.1
0
------Sequential Create------ --------Random Create--------
-Create-- --Read--- -Delete-- -Create-- --Read--- -Delete--
files
/sec %CP
/sec %CP
/sec %CP
/sec %CP
/sec %CP
/sec %CP
16
186
2
505
1
296
2
209
2
441
1
282
2

MySQL is an open source relational database management system, mainly used to store and retrieve data quickly and reliably. Its working principle includes client requests, query resolution, execution of queries and return results. Examples of usage include creating tables, inserting and querying data, and advanced features such as JOIN operations. Common errors involve SQL syntax, data types, and permissions, and optimization suggestions include the use of indexes, optimized queries, and partitioning of tables.

MySQL is an open source relational database management system suitable for data storage, management, query and security. 1. It supports a variety of operating systems and is widely used in Web applications and other fields. 2. Through the client-server architecture and different storage engines, MySQL processes data efficiently. 3. Basic usage includes creating databases and tables, inserting, querying and updating data. 4. Advanced usage involves complex queries and stored procedures. 5. Common errors can be debugged through the EXPLAIN statement. 6. Performance optimization includes the rational use of indexes and optimized query statements.

MySQL is chosen for its performance, reliability, ease of use, and community support. 1.MySQL provides efficient data storage and retrieval functions, supporting multiple data types and advanced query operations. 2. Adopt client-server architecture and multiple storage engines to support transaction and query optimization. 3. Easy to use, supports a variety of operating systems and programming languages. 4. Have strong community support and provide rich resources and solutions.

InnoDB's lock mechanisms include shared locks, exclusive locks, intention locks, record locks, gap locks and next key locks. 1. Shared lock allows transactions to read data without preventing other transactions from reading. 2. Exclusive lock prevents other transactions from reading and modifying data. 3. Intention lock optimizes lock efficiency. 4. Record lock lock index record. 5. Gap lock locks index recording gap. 6. The next key lock is a combination of record lock and gap lock to ensure data consistency.

The main reasons for poor MySQL query performance include not using indexes, wrong execution plan selection by the query optimizer, unreasonable table design, excessive data volume and lock competition. 1. No index causes slow querying, and adding indexes can significantly improve performance. 2. Use the EXPLAIN command to analyze the query plan and find out the optimizer error. 3. Reconstructing the table structure and optimizing JOIN conditions can improve table design problems. 4. When the data volume is large, partitioning and table division strategies are adopted. 5. In a high concurrency environment, optimizing transactions and locking strategies can reduce lock competition.

In database optimization, indexing strategies should be selected according to query requirements: 1. When the query involves multiple columns and the order of conditions is fixed, use composite indexes; 2. When the query involves multiple columns but the order of conditions is not fixed, use multiple single-column indexes. Composite indexes are suitable for optimizing multi-column queries, while single-column indexes are suitable for single-column queries.

To optimize MySQL slow query, slowquerylog and performance_schema need to be used: 1. Enable slowquerylog and set thresholds to record slow query; 2. Use performance_schema to analyze query execution details, find out performance bottlenecks and optimize.

MySQL and SQL are essential skills for developers. 1.MySQL is an open source relational database management system, and SQL is the standard language used to manage and operate databases. 2.MySQL supports multiple storage engines through efficient data storage and retrieval functions, and SQL completes complex data operations through simple statements. 3. Examples of usage include basic queries and advanced queries, such as filtering and sorting by condition. 4. Common errors include syntax errors and performance issues, which can be optimized by checking SQL statements and using EXPLAIN commands. 5. Performance optimization techniques include using indexes, avoiding full table scanning, optimizing JOIN operations and improving code readability.


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