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If we use varchar for attributes without considering the actual data, we could have data integrity problems The main reason we use data types is to enforce data integrity If we are storing a date, the datetime data type ensures that the entered information is a valid date Storing a date in a varchar could lead to problems For example, what if the application code has a glitch that omits the first / and writes the date as 35/2007 You might say, So what We can fix that with a script, but can you really fix it Take the date January 13, 2005, as an example If that gets stored as 113/2005, you have a problem It could be converted to 1/13/2005 or 11/3/2005 Had we used the datetime data type from the start, we would have received an error when the application wrote 113/2005, and the problem could have been found and fixed There are literally hundreds of examples, but we think you get the point: Use the correct data type, and you are on your way to having solid data integrity
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Data stored in varchar attributes is, not surprisingly, stored as a string This means that it is stored, referred to, sorted, and grouped as a string Why is this an issue Let s look at the problem you encounter when sorting When you sort strings, they sort alphabetically, and numbers sort numerically This can lead to very different results To illustrate this, we created a table called SortTest using the following code in SQL Server
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CREATE TABLE SortTest( StringDate varchar(50), RealDate datetime)
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As you can see, we have two columns: one varchar and one datetime Now we insert all the dates for January 2007 in the format 1/1/2007 into this table Here are the insert statements we use
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INSERT INSERT INSERT INSERT INSERT INTO INTO INTO INTO SortTest SortTest SortTest SortTest VALUES('1/1/2007' VALUES('1/2/2007' VALUES('1/3/2007' VALUES('1/4/2007' , , , , '1/1/2007') '1/2/2007') '1/3/2007') '1/4/2007')
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INTO SortTest VALUES('1/31/2007' , '1/31/2007')
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Now let s get down to the problem We will select the data from the table and sort the select by each of these columns When we sort by the RealDate column, we get 31 records, all in perfect date order from January 1 to January 31 no big surprise Now let s see what happens when we sort by the StringDate column; the results of the select are shown in Table 81 Table 81 The Results of Sorting by a Date Stored in a String Column
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1/1/2007 1/10/2007 1/11/2007 1/12/2007 1/13/2007 1/14/2007 1/15/2007 1/16/2007 1/17/2007 1/18/2007 1/19/2007 1/2/2007 1/20/2007 1/21/2007 1/22/2007 1/23/2007 1/24/2007 1/25/2007 1/26/2007 1/27/2007 1/28/2007 1/29/2007 1/3/2007 1/30/2007 1/31/2007 1/4/2007 1/5/2007 1/6/2007 1/7/2007 1/8/2007 1/9/2007
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2007-01-01 00:00:00000 2007-01-10 00:00:00000 2007-01-11 00:00:00000 2007-01-12 00:00:00000 2007-01-13 00:00:00000 2007-01-14 00:00:00000 2007-01-15 00:00:00000 2007-01-16 00:00:00000 2007-01-17 00:00:00000 2007-01-18 00:00:00000 2007-01-19 00:00:00000 2007-01-02 00:00:00000 2007-01-20 00:00:00000 2007-01-21 00:00:00000 2007-01-22 00:00:00000 2007-01-23 00:00:00000 2007-01-24 00:00:00000 2007-01-25 00:00:00000 2007-01-26 00:00:00000 2007-01-27 00:00:00000 2007-01-28 00:00:00000 2007-01-29 00:00:00000 2007-01-03 00:00:00000 2007-01-30 00:00:00000 2007-01-31 00:00:00000 2007-01-04 00:00:00000 2007-01-05 00:00:00000 2007-01-06 00:00:00000 2007-01-07 00:00:00000 2007-01-08 00:00:00000 2007-01-09 00:00:00000
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When you sort strings, the first letter is examined and sorted, then the second letter, and then the third, and so on This causes our date data to get out of whack After 1/1/2007, the next number in the alphanumeric sort is 1/10/2007 This makes for an ugly result set To get around this problem now, you must convert the StringDate column during your select operation We won t go into the details of the query engine, but suffice it to say, the conversion of a string into a date adds overhead to the select query Paraphrasing a saying from a wise, uh, man, Overhead leads to higher CPU utilization, higher CPU utilization leads to poor performance, poor performance leads to suffering Again, had we used the correct data type, we could have avoided the problem of needing to convert the string and avoided the eventual suffering
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