有一张桌子

code_1
value_1
code_2
value_2
...
code_10
value_10

而不是有3个表

Code, value和code_value

你永远不知道什么时候你可能需要10对以上的代码，价值。

如果只需要一对，就不会浪费磁盘空间。

对于存储时间值，应该只使用UTC时区。不应使用当地时间。

我最喜欢的SQL反模式:

对非唯一列进行JOIN，并使用SELECT DISTINCT修剪结果。

创建连接多个表的视图，只是为了从一个表中选择少数列。

 CREATE VIEW my_view AS 
     SELECT * FROM table1
     JOIN table2 ON (...)
     JOIN table3 ON (...);

 SELECT col1, col2 FROM my_view WHERE col3 = 123;

使用无意义的表别名:

from employee t1,
department t2,
job t3,
...

使得阅读一个大的SQL语句比它需要的要困难得多

我最不喜欢的是

Using spaces when creating tables, sprocs etc. I'm fine with CamelCase or under_scores and singular or plurals and UPPERCASE or lowercase but having to refer to a table or column [with spaces], especially if [ it is oddly spaced] (yes, I've run into this) really irritates me. Denormalized data. A table doesn't have to be perfectly normalized, but when I run into a table of employees that has information about their current evaluation score or their primary anything, it tells me that I will probably need to make a separate table at some point and then try to keep them synced. I will normalize the data first and then if I see a place where denormalization helps, I'll consider it. Overuse of either views or cursors. Views have a purpose, but when each table is wrapped in a view it's too much. I've had to use cursors a few times, but generally you can use other mechanisms for this. Access. Can a program be an anti-pattern? We have SQL Server at my work, but a number of people use access due to it's availabilty, "ease of use" and "friendliness" to non-technical users. There is too much here to go into, but if you've been in a similar environment, you know.

编写查询的开发人员没有很好地了解SQL应用程序(包括单个查询和多用户系统)的快慢。这包括对以下方面的无知:

physical I/O minimization strategies, given that most queries' bottleneck is I/O not CPU perf impact of different kinds of physical storage access (e.g. lots of sequential I/O will be faster than lots of small random I/O, although less so if your physical storage is an SSD!) how to hand-tune a query if the DBMS produces a poor query plan how to diagnose poor database performance, how to "debug" a slow query, and how to read a query plan (or EXPLAIN, depending on your DBMS of choice) locking strategies to optimize throughput and avoid deadlocks in multi-user applications importance of batching and other tricks to handle processing of data sets table and index design to best balance space and performance (e.g. covering indexes, keeping indexes small where possible, reducing data types to minimum size needed, etc.)