临时表滥用。

特别是这类事情:

SELECT personid, firstname, lastname, age
INTO #tmpPeople
FROM People
WHERE lastname like 's%'

DELETE FROM #tmpPeople
WHERE firstname = 'John'

DELETE FROM #tmpPeople
WHERE firstname = 'Jon'

DELETE FROM #tmpPeople
WHERE age > 35

UPDATE People
SET firstname = 'Fred'
WHERE personid IN (SELECT personid from #tmpPeople)

不要从查询中构建临时表，只是为了删除不需要的行。

是的，我在生产db中看到过这种形式的代码页。

SELECT FirstName + ' ' + LastName as "Full Name", case UserRole when 2 then "Admin" when 1 then "Moderator" else "User" end as "User's Role", case SignedIn when 0 then "Logged in" else "Logged out" end as "User signed in?", Convert(varchar(100), LastSignOn, 101) as "Last Sign On", DateDiff('d', LastSignOn, getDate()) as "Days since last sign on", AddrLine1 + ' ' + AddrLine2 + ' ' + AddrLine3 + ' ' + City + ', ' + State + ' ' + Zip as "Address", 'XXX-XX-' + Substring(Convert(varchar(9), SSN), 6, 4) as "Social Security #" FROM Users

或者，把所有内容都塞进一行。

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。

在他们职业生涯的前6个月学习SQL，在接下来的10年里从不学习其他任何东西。特别是没有学习或有效地使用窗口/分析SQL特性。特别是over()和partition by的使用。

窗口函数，比如聚合 函数时，对对象进行聚合 定义的行集(组)，但是 而不是返回一个值 组，窗口函数可以返回 每个组有多个值。

请参阅O'Reilly SQL Cookbook附录A，以获得窗口函数的良好概述。

不必深入浅出:不使用准备好的语句。

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.)