我们所有使用关系数据库的人都知道(或正在学习)SQL是不同的。获得期望的结果,并有效地这样做,涉及到一个乏味的过程,其部分特征是学习不熟悉的范例,并发现一些我们最熟悉的编程模式在这里不起作用。常见的反模式是什么?


当前回答

select some_column, ...
from some_table
group by some_column

假设结果将按some_column排序。我在Sybase上看到过这种情况,其中假设成立(目前)。

其他回答

过度使用临时表和游标。

也许这不是一个反模式,但它惹恼了我,当某些数据库的DBA(好吧,我在这里说的是Oracle)用Oracle风格和代码约定编写SQL Server代码,当它运行如此糟糕时抱怨。受够了游标Oracle的人!SQL是基于设置的。

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.

同一查询中的相同子查询。

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.)