MySQL的大小写敏感其实是根据用户的操作系统来的,可以强制以 -O lower_case_table_names=1 参数启动 mysqld(如果使用 --defaults-file=...\\my.cnf 参数来读取指定的配置文件启动 mysqld 的话,你需要在配置文件的 [mysqld] 区段下增加一行 lower_case_tabl
这样MySQL 将在创建与查找时将所有的表名自动转换为小写字符(这个选项缺省地在 Windows 中为 1 ,在 Unix 中为 0。从 MySQL 4.0.2 开始,这个选项同样适用于数据库名)。
当你更改这个选项时,你必须在启动 mysqld 前首先将老的表名转换为小写字母。
换句话说,如果你希望在数据库里面创建表的时候保留大小写字符状态,则应该把这个参数置0: lower_case_table_names=1 。
否则的话你会发现同样的sqldump脚本在不同的操作系统下最终导入的结果不一样(在Windows下所有的大写字符都变成小写了)。
注意:
在Win32上,尽管数据库和表名是忽略Mysql大小写的,你不应该在同一个查询中使用不同的大小写来引用一个给定的数据库和表。下列查询将不工作,因为它作为my_table和作为MY_TABLE引用一个表:
代码如下 | 复制代码 |
1.Mysql> SELECT * FROM my_table WHERE MY_TABLE.col=1; |
2、列名
列名在所有情况下都是忽略大小写的。
3、表的别名
表的别名是区分大小写的。下列查询将不工作,: 因为它用a和A引用别名:
代码如下 | 复制代码 |
1.Mysql> SELECT col_name FROM tbl_name AS a 2.WHERE a.col_name = 1 OR A.col_name = 2; |
4、列的别名
列的别名是忽略大小写的。
5、字符串比较和模式匹配
缺省地,Mysql搜索是大小写不敏感的(尽管有一些字符集从来不是忽略Mysql大小写的,例如捷克语)。这意味着,如果你用col_name LIKE 'a%'搜寻,你将得到所有以A或a开始的列值。如果你想要使这个搜索大小写敏感,使用象INDEX(col_name, "A")=0检查一个前缀。或如果列值必须确切是"A",使用STRCMP(col_name, "A") = 0。
简单的比较操作(>=、>、= 、
LIKE比较在每个字符的大写值上进行(“E”=”e”)。
如果你想要一个列总是被当作Mysql大小写敏感的方式,声明它为BINARY。
例如:
代码如下 | 复制代码 |
|

Stored procedures are precompiled SQL statements in MySQL for improving performance and simplifying complex operations. 1. Improve performance: After the first compilation, subsequent calls do not need to be recompiled. 2. Improve security: Restrict data table access through permission control. 3. Simplify complex operations: combine multiple SQL statements to simplify application layer logic.

The working principle of MySQL query cache is to store the results of SELECT query, and when the same query is executed again, the cached results are directly returned. 1) Query cache improves database reading performance and finds cached results through hash values. 2) Simple configuration, set query_cache_type and query_cache_size in MySQL configuration file. 3) Use the SQL_NO_CACHE keyword to disable the cache of specific queries. 4) In high-frequency update environments, query cache may cause performance bottlenecks and needs to be optimized for use through monitoring and adjustment of parameters.

The reasons why MySQL is widely used in various projects include: 1. High performance and scalability, supporting multiple storage engines; 2. Easy to use and maintain, simple configuration and rich tools; 3. Rich ecosystem, attracting a large number of community and third-party tool support; 4. Cross-platform support, suitable for multiple operating systems.

The steps for upgrading MySQL database include: 1. Backup the database, 2. Stop the current MySQL service, 3. Install the new version of MySQL, 4. Start the new version of MySQL service, 5. Recover the database. Compatibility issues are required during the upgrade process, and advanced tools such as PerconaToolkit can be used for testing and optimization.

MySQL backup policies include logical backup, physical backup, incremental backup, replication-based backup, and cloud backup. 1. Logical backup uses mysqldump to export database structure and data, which is suitable for small databases and version migrations. 2. Physical backups are fast and comprehensive by copying data files, but require database consistency. 3. Incremental backup uses binary logging to record changes, which is suitable for large databases. 4. Replication-based backup reduces the impact on the production system by backing up from the server. 5. Cloud backups such as AmazonRDS provide automation solutions, but costs and control need to be considered. When selecting a policy, database size, downtime tolerance, recovery time, and recovery point goals should be considered.

MySQLclusteringenhancesdatabaserobustnessandscalabilitybydistributingdataacrossmultiplenodes.ItusestheNDBenginefordatareplicationandfaulttolerance,ensuringhighavailability.Setupinvolvesconfiguringmanagement,data,andSQLnodes,withcarefulmonitoringandpe

Optimizing database schema design in MySQL can improve performance through the following steps: 1. Index optimization: Create indexes on common query columns, balancing the overhead of query and inserting updates. 2. Table structure optimization: Reduce data redundancy through normalization or anti-normalization and improve access efficiency. 3. Data type selection: Use appropriate data types, such as INT instead of VARCHAR, to reduce storage space. 4. Partitioning and sub-table: For large data volumes, use partitioning and sub-table to disperse data to improve query and maintenance efficiency.

TooptimizeMySQLperformance,followthesesteps:1)Implementproperindexingtospeedupqueries,2)UseEXPLAINtoanalyzeandoptimizequeryperformance,3)Adjustserverconfigurationsettingslikeinnodb_buffer_pool_sizeandmax_connections,4)Usepartitioningforlargetablestoi


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