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

FROM TableA, TableB WHERE语法用于连接而不是FROM TableA内部连接TableB上 假设查询将以某种方式返回,而不放入ORDER BY子句,因为这是在查询工具中测试时显示的方式。

其他回答

我发现,在性能方面,有两点是最重要的,并且可能会有很大的成本:

使用游标而不是基于集合 表达式。我想当程序员以过程的方式思考时,这种情况经常发生。 使用相关子查询,当a 连接到派生表可以执行 的工作。

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.

Human readable password fields, egad. Self explanatory. Using LIKE against indexed columns, and I'm almost tempted to just say LIKE in general. Recycling SQL-generated PK values. Surprise nobody mentioned the god-table yet. Nothing says "organic" like 100 columns of bit flags, large strings and integers. Then there's the "I miss .ini files" pattern: storing CSVs, pipe delimited strings or other parse required data in large text fields. And for MS SQL server the use of cursors at all. There's a better way to do any given cursor task.

编辑是因为有太多了!

以下是我的前3名。

1号。指定字段列表失败。(编辑:为了防止混淆:这是一个生产代码规则。它不适用于一次性分析脚本——除非我是作者。)

SELECT *
Insert Into blah SELECT *

应该是

SELECT fieldlist
Insert Into blah (fieldlist) SELECT fieldlist

2号。使用游标和while循环,当while循环和循环变量就可以了。

DECLARE @LoopVar int

SET @LoopVar = (SELECT MIN(TheKey) FROM TheTable)
WHILE @LoopVar is not null
BEGIN
  -- Do Stuff with current value of @LoopVar
  ...
  --Ok, done, now get the next value
  SET @LoopVar = (SELECT MIN(TheKey) FROM TheTable
    WHERE @LoopVar < TheKey)
END

3号。DateLogic通过字符串类型。

--Trim the time
Convert(Convert(theDate, varchar(10), 121), datetime)

应该是

--Trim the time
DateAdd(dd, DateDiff(dd, 0, theDate), 0)

我最近看到了一个高峰“一个问题总比两个好,对吧?”

SELECT *
FROM blah
WHERE (blah.Name = @name OR @name is null)
  AND (blah.Purpose = @Purpose OR @Purpose is null)

这个查询需要两个或三个不同的执行计划,具体取决于参数的值。对于这个SQL文本,只生成一个执行计划并保存在缓存中。无论参数的值是多少,都将使用该计划。这会导致间歇性的性能不佳。最好编写两个查询(每个预期的执行计划一个查询)。

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.)