1. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-1 David M. Kroenke’s Chapter Two: Introduction to Structured Query Language Part Two Database Processing: Fundamentals, Design, and Implementation

2. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-2 Using MS Access

3. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-3 Using MS Access (Continued)

4. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-4 Using MS Access (Continued)

5. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-5 Using MS Access - Results

6. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-6 Using MS SQL Server [SQL Query Analyzer]

7. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-7 Using Oracle [SQL*Plus]

8. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-8 Using Oracle [Quest Software’s TOAD]Quest Software’s TOAD www.toadsoft.com/toad_oracle.htm

9. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-9 Using MySQL [MySQL Command Line Client]

10. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-10 Using MySQL [MySQL Query Browser]MySQL Query Browser

11. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-11 Sorting the Results: ORDER BY SELECT* FROMORDER_ITEM ORDER BYOrderNumber, Price;

12. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-12 Sort Order: Ascending and Descending SELECT* FROMORDER_ITEM ORDER BYPrice DESC, OrderNumber ASC; NOTE: The default sort order is ASC – does not have to be specified.

13. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-13 WHERE Clause Options: AND SELECT* FROMSKU_DATA WHEREDepartment = 'Water Sports' ANDBuyer = 'Nancy Meyers';

14. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-14 WHERE Clause Options: OR SELECT* FROMSKU_DATA WHEREDepartment = 'Camping' ORDepartment = 'Climbing';

15. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-15 WHERE Clause Options:- IN SELECT* FROMSKU_DATA WHEREBuyer IN ('Nancy Meyers', 'Cindy Lo', 'Jerry Martin');

16. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-16 WHERE Clause Options: NOT IN SELECT* FROMSKU_DATA WHEREBuyer NOT IN ('Nancy Meyers', 'Cindy Lo', 'Jerry Martin');

17. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-17 WHERE Clause Options: Ranges with BETWEEN SELECT* FROMORDER_ITEM WHEREExtendedPrice BETWEEN 100 AND 200;

18. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-18 WHERE Clause Options: Ranges with Math Symbols SELECT* FROMORDER_ITEM WHEREExtendedPrice >= 100 AND ExtendedPrice <= 200;

19. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-19 WHERE Clause Options: LIKE and Wildcards The SQL keyword LIKE can be combined with wildcard symbols: –SQL 92 Standard (SQL Server, Oracle, etc.): _ = Exactly one character % = Any set of one or more characters –MS Access (based on MS DOS) ? = Exactly one character * = Any set of one or more characters

20. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-20 WHERE Clause Options: LIKE and Wildcards (Continued) SELECT* FROMSKU_DATA WHEREBuyer LIKE 'Pete%';

21. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-21 WHERE Clause Options: LIKE and Wildcards (Continued) SELECT* FROMSKU_DATA WHERESKU_Description LIKE '%Tent%';

22. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-22 WHERE Clause Options: LIKE and Wildcards SELECT* FROMSKU_DATA WHERESKU LIKE '%2__';

23. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-23 SQL Built-in Functions There are five SQL Built-in Functions: –COUNT –SUM –AVG –MIN –MAX

24. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-24 SQL Built-in Functions (Continued) SELECTSUM (ExtendedPrice) ASOrder3000Sum FROMORDER_ITEM WHEREOrderNumber = 3000;

25. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-25 SQL Built-in Functions (Continued) SELECTSUM (ExtendedPrice) AS OrderItemSum, AVG (ExtendedPrice) AS OrderItemAvg, MIN (ExtendedPrice) AS OrderItemMin, MAX (ExtendedPrice) AS OrderItemMax FROMORDER_ITEM;

26. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-26 SQL Built-in Functions (Continued) SELECTCOUNT(*) AS NumRows FROMORDER_ITEM;

27. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-27 SQL Built-in Functions (Continued) SELECTCOUNT (DISTINCT Department) AS DeptCount FROMSKU_DATA;

28. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-28 Arithmetic in SELECT Statements SELECTQuantity * Price AS EP, ExtendedPrice FROMORDER_ITEM;

29. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-29 String Functions in SELECT Statements SELECTDISTINCT RTRIM (Buyer) + ' in ' + RTRIM (Department) AS Sponsor FROMSKU_DATA;

30. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-30 The SQL keyword GROUP BY SELECTDepartment, Buyer, COUNT(*) AS Dept_Buyer_SKU_Count FROMSKU_DATA GROUP BY Department, Buyer;

31. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-31 The SQL keyword GROUP BY (Continued) In general, place WHERE before GROUP BY. Some DBMS products do not require that placement, but to be safe, always put WHERE before GROUP BY. The HAVING operator restricts the groups that are presented in the result. There is an ambiguity in statements that include both WHERE and HAVING clauses. The results can vary, so to eliminate this ambiguity SQL always applies WHERE before HAVING.

32. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-32 The SQL keyword GROUP BY (Continued) SELECTDepartment, COUNT(*) AS Dept_SKU_Count FROMSKU_DATA WHERESKU <> 302000 GROUP BY Department ORDER BY Dept_SKU_Count;

33. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-33 The SQL keyword GROUP BY (Continued) SELECTDepartment, COUNT(*) AS Dept_SKU_Count FROMSKU_DATA WHERESKU <> 302000 GROUP BY Department HAVING COUNT (*) > 1 ORDER BYDept_SKU_Count;

34. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-34 Querying Multiple Tables: Subqueries SELECTSUM (ExtendedPrice) AS Revenue FROMORDER_ITEM WHERESKU IN (SELECTSKU FROMSKU_DATA WHERE Department = 'Water Sports'); Note: The second SELECT statement is a subquery.

35. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-35 Querying Multiple Tables: Subqueries (Continued) SELECTBuyer FROMSKU_DATA WHERESKU IN (SELECTSKU FROMORDER_ITEM WHEREOrderNumber IN (SELECTOrderNumber FROMRETAIL_ORDER WHEREOrderMonth = 'January' ANDOrderYear = 2004));

36. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-36 Querying Multiple Tables: Joins SELECTBuyer, ExtendedPrice FROMSKU_DATA, ORDER_ITEM WHERESKU_DATA.SKU = ORDER_ITEM.SKU;

37. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-37 Querying Multiple Tables: Joins (Continued) SELECTBuyer, SUM(ExtendedPrice) AS BuyerRevenue FROMSKU_DATA, ORDER_ITEM WHERESKU_DATA.SKU = ORDER_ITEM.SKU GROUP BYBuyer ORDER BYBuyerRevenue DESC;

38. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-38 Querying Multiple Tables: Joins (Continued) SELECTBuyer, ExtendedPrice, OrderMonth FROMSKU_DATA, ORDER_ITEM, RETAIL_ORDER WHERESKU_DATA.SKU = ORDER_ITEM.SKU ANDORDER_ITEM.OrderNumber = RETAIL_ORDER.OrderNumber;

39. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-39 Subqueries versus Joins Subqueries and joins both process multiple tables. A subquery can only be used to retrieve data from the top table. A join can be used to obtain data from any number of tables, including the “top table” of the subquery. In Chapter 7, we will study the correlated subquery. That kind of subquery can do work that is not possible with joins.

40. DAVID M. KROENKE’S DATABASE PROCESSING, 10th Edition © 2006 Pearson Prentice Hall 2-40 David M. Kroenke’s Database Processing Fundamentals, Design, and Implementation (10 th Edition) End of Presentation: Chapter Two Part Two