我一直对大多数程序员倾向于在数据访问层混合他们的ui逻辑感到失望:

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

通常，程序员这样做是因为他们想要将数据集直接绑定到一个网格上，而且在服务器端使用SQL Server格式比在客户端使用SQL Server格式更方便。

像上面所示的查询是非常脆弱的，因为它们将数据层与UI层紧密耦合在一起。最重要的是，这种编程风格彻底阻止了存储过程的可重用性。

不必深入浅出:不使用准备好的语句。

像这样将冗余表连接到查询中:

select emp.empno, dept.deptno
from emp
join dept on dept.deptno = emp.deptno;

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.)

反向观点:过度痴迷于正常化。

大多数SQL/ rbdb系统提供了许多非常有用的特性(事务、复制)，即使对于非标准化的数据也是如此。磁盘空间很便宜，有时操作/过滤/搜索获取的数据比编写1NF模式更简单(更容易的代码，更快的开发时间)，并处理其中的所有麻烦(复杂的连接，讨厌的子选择等)。

我发现过度标准化的系统通常是不成熟的优化，特别是在开发的早期阶段。

(再想想……http://writeonly.wordpress.com/2008/12/05/simple-object-db-using-json-and-python-sqlite/)

同一查询中的相同子查询。