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The query in Listing 2-7 works just fine, but as our queries get more complicated and involved, we will have a number of different tables. Some of the tables may have the same column names, and we might need to distinguish them. Therefore, we can preface each of the attributes in our query with the name of the table that they come from, as in Listing 2-8.
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Listing 2-8. Prefacing Attribute Names with the Table Name SELECT Member.LastName, Member.FirstName, Member.Phone FROM Member
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Because typing the whole table name can become tiresome and also because in some queries we might need to compare data in more than one row of a table, SQL has the notion of an alias. Have a look at Listing 2-9.
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Listing 2-9. Using an Alias SELECT m.LastName, m.FirstName, m.Phone FROM Member m
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In the FROM clause, we have declared an alias or alternative name for the Member table, in this case m. We can give our alias any name or letter we like; short is good. Then in the rest of the query we can use the alias whenever we want to specify an attribute from that table. Now compare our relational calculus expression in Listing 2-6 and the SQL in Listing 2-9. We can think of the alias in the SQL as serving the same purpose as the row variable in the calculus expression. SQL syntax is based very much on relational calculus. This may all seem unnecessary for a simple query, but as our queries get more complicated, the idea of row variables will simplify things a great deal and make it much easier to get the SQL statements correct. I ll use aliases in all the SQL queries from now on.
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CHAPTER 2 SIMPLE QUERIES ON ONE TABLE
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Combining Subsets of Rows and Columns
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In the previous sections, we saw the algebra operations select (a subset of rows) and project (a subset of columns) acting independently. One of the most powerful features of the algebra is that the result of an operation is another table (or, more formally, another set of unique rows). This means we can apply another operation to the result of the first operation and so build up complex queries. We can use successive operations to create an algebra expression for the query in Figure 2-2c, retrieving the names and handicaps of junior members. First we find the rows for juniors using a select operation, and then we use a project operation to retrieve the required columns from the result. Listing 2-10 shows the full expression.
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Listing 2-10. Combining a Select and Project Operation LastName, FirstName, Handicap ( MemberType = 'Junior' (Member))
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As you can see, the algebra tells us how to get the result we want. First get the appropriate rows, and then get the required columns. The calculus doesn t tell us how to carry out a series of steps; it just describes what the final set of rows will be like. Have a look at Listing 2-11.
Listing 2-11. Calculus Expression to Retrieve Handicaps of Junior Members {m.Lastname, m.FirstName, m.Handicap | Member(m) and m.MemberType = 'Junior'}
The left side of the expression in Listing 2-11 says we are going to retrieve the LastName, FirstName, and Handicap values from some row m. The right side of the expression tells us which rows to include. Picture a finger labeled m, as in Figure 2-3. The expression in Listing 2-11 says that our finger m is going to scan rows in the Member table and include those rows where the value of MemberType is Junior . Now look at Listing 2-12, which shows the SQL for this query.
Listing 2-12. SQL Statement to Retrieve Handicaps of Junior Members SELECT m.Lastname, m.FirstName, m.Handicap FROM Member m WHERE m.MemberType = 'Junior'
Compare the SQL in Listing 2-12 with the calculus expression in Listing 2-11, and you will see that they have all the same parts: an alias or row variable m declared for the Member table, a condition to say which rows to include, and a list of which attributes or columns to retrieve.
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