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the entire rows are the same, we can t differentiate them, and so any query that deletes one will delete both. We can, in this situation, create a new table, and then insert just the distinct values with the query in Listing 11-6.
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Listing 11-6. Creating a New Table with Distinct Rows INSERT INTO NewMembership SELECT DISTINCT MemberID,Team FROM Membership
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Another problem is having a Membership table (as in Figure 11-3) with no foreign key constraints. The first row could then have that member 1118 is in TeamA when no member 1118 is listed in the Member table. There are several ways to find such unmatched values in the Membership table. One way is to use a nested query (discussed in 4), as shown in Listing 11-7.
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Listing 11-7. Finding Unmatched MemberIDs in the Membership Table (Using Difference) SELECT ms.MemberID FROM Membership ms WHERE ms.MemberID NOT IN (SELECT m.MemberID FROM Member m)
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Similar Data in Two Tables
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Sometimes a database has extra tables. An example for our club database would be to have a separate table for coaches or managers, as shown in Figure 11-4.
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Coach table
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Some rows and columns from the Member table
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Figure 11-4. An additional table for coaches (poor design)
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For beginners, the extra table in Figure 11-4 makes it easy to create lists of coaches and their phone numbers (which would otherwise require a self join or nested query). However, the additional table soon causes problems. In Figure 11-4, we already see inconsistent data for William Cooper s phone number. The only real cure is to get rid of the extra table.
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Using set operations (discussed in 7) can help us understand what the data contains. We can use the intersection operator to find rows for people who are in both tables, and the difference operator to find those who are in one and not the other. Once the design is correct, creating a view that shows the coach information would be helpful to your novice users. Listing 11-8 does the trick.
Listing 11-8. A View to Retrieve Information About Coaches CREATE VIEW CoachInfo AS SELECT * FROM Member WHERE MemberID IN (SELECT Coach FROM Member )
Wrong Types
Having the fields in a table created with inappropriate types is another problem that can make queries look as though they are not behaving. I ve seen whole databases where every field is a default text field. Having the wrong field type means the data misses out on a whole lot of validity checking. For example, if our Member table had all text fields, we could end up with value like 16a or 1o in the Handicap column or text like Brenda in the Coach column. Incorrectly entered values aside, inappropriate types give rise to other problems. Each type has its own rules for ordering values. Text types order alphabetically, numbers order numerically, and dates order chronologically. Different orderings clearly will be an issue if we add an ORDER BY clause to a query. A text field containing numbers will order alphabetically, giving an order like 1 , 15 , 109 , 20 245 , 33 , as described in 2. Incorrect types also cause a problem with making comparisons. If we ask for values to be compared, the comparison used will depend on how the particular field type involved is sorted. For numbers entered in a text field, we will get comparisons such as 109 < 15 or 33 > 245 . This will cause some odd output if we ask for people with handicaps less than 5, for example. It can be difficult to sort out what is going wrong, because the query syntax is fine and the data appears to be OK. Going behind the scenes to check out the data type is certainly not the first thing that may occur to you. It is possible to change the type of a column in an existing table, but I find it a bit scary. For example, if you change from text to numeric values, 10 will probably be fine, but 1o will cause an error. I prefer a more conservative approach: I make a new table with the appropriate types, and then insert the old values with the aid of a conversion function. Listing 11-9 shows how we could create a new table with a numeric value for the Handicap column.
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