CSE 530A SQL. Washington University Fall 2013

Transcription

1 CSE 530A SQL Washington University Fall 2013

2 SELECT SELECT * FROM employee; employee_id last_name first_name department salary Bunny Bugs Management Duck Daffy Sales Porky Pig Human Resources Sam Yosemite Sales Fudd Elmer Human Resources Bird Tweety Marketing Coyote Wile Research Runner Road Research (8 rows) Returns all rows in employee

3 WHERE SELECT * FROM employee WHERE department = 'Sales'; employee_id last_name first_name department salary Duck Daffy Sales Sam Yosemite Sales (2 rows) Returns rows where predicate is true

4 WHERE SELECT * FROM employee WHERE department = 'Sales' AND salary > 45000; employee_id last_name first_name department salary Sam Yosemite Sales (1 row) WHERE clause can be a compound boolean expression

5 WHERE SELECT * FROM employee WHERE (department = 'Sales' OR department = 'Research') AND salary > 45000; employee_id last_name first_name department salary Sam Yosemite Sales Runner Road Research (2 rows)

6 Aggregate Functions SELECT count(*) FROM employee WHERE department = 'Sales'; count (1 row) Aggregate functions operate over the matching rows

7 Aggregate Functions SELECT avg(salary) FROM employee WHERE department = 'Sales'; avg (1 row) Common aggregate functions include avg, count, min, max, sum

8 GROUP BY SELECT department, avg(salary) FROM employee GROUP BY department; department avg Research Marketing Management Human Resources Sales (5 rows) GROUP BY allows aggregate functions to be applied to groups sharing a property

9 GROUP BY SELECT department, avg(salary), count(*) FROM employee GROUP BY department; department avg count Research Marketing Management Human Resources Sales (5 rows)

10 HAVING SELECT department, avg(salary), count(*) FROM employee GROUP BY department HAVING count(*) > 1; department avg count Research Human Resources Sales (3 rows) HAVING filters grouped results

11 WHERE GROUP BY SELECT department, avg(salary), count(*) FROM employee WHERE salary > GROUP BY department; department avg count Research Management Sales (3 rows) WHERE is applied before the grouping, HAVING is applied after

12 WHERE GROUP BY -HAVING SELECT department, avg(salary), count(*) FROM employee WHERE salary > GROUP BY department HAVING count(*) > 1; department avg count Research (1 row)

13 WHERE GROUP BY -HAVING SELECT department, avg(salary), count(*) FROM employee WHERE salary > GROUP BY department HAVING count(*) > 1; department avg count (0 rows)

14 ORDER BY Results are returned in arbitrary order unless explicitly specified SELECT * FROM employee ORDER BY last_name, first_name; employee_id last_name first_name department salary Bird Tweety Marketing Bunny Bugs Management Coyote Wile Research Duck Daffy Sales Fudd Elmer Human Resources Porky Pig Human Resources Runner Road Research Sam Yosemite Sales (8 rows)

15 SELECT Summary SELECT FROM WHERE GROUP BY HAVING ORDER BY ; SELECT and FROM clauses are required Other clauses are optional but must appear in this order (and are applied in this order)

16 Aliases Fields in the SELECT clause and tables in the FROM clause can be given aliases SELECT department, avg(salary) AS average_salary, count(*) AS number FROM employee AS emp WHERE emp.salary > GROUP BY department HAVING count(*) > 1; department average_salary number Research (1 row)

17 Aliases The AS keyword is optional SELECT department, avg(salary) average_salary, count(*) number FROM employee emp WHERE emp.salary > GROUP BY department HAVING count(*) > 1; department average_salary number Research (1 row)

18 Joining A key feature of relational databases is the ability to combine fields from multiple tables using common values

19 Example Consider our employee example Table "public.employee" Column Type Modifiers employee_id integer last_name text first_name text department text salary integer Suppose we want to add data about the departments, such as budget?

20 Example We could add it to the employee table Table "public.employee" Column Type Modifiers employee_id integer last_name text first_name text department text salary integer budget integer but that leads to duplicate data (and isn't really about the employees)

21 Example employee_id last_name first_name department salary budget Coyote Wile Research Runner Road Research There's a danger that the budget data can become inconsistent Solution: Create a separate table for department

22 Example CREATE TABLE department ( name text, budget integer ); INSERT INTO department VALUES ('Management', ); INSERT INTO department VALUES ('Sales', ); INSERT INTO department VALUES ('Human Resources', ); INSERT INTO department VALUES ('Marketing', ); INSERT INTO department VALUES ('Research', );

23 Example SELECT * FROM department; name budget Management Sales Human Resources Marketing Research (5 rows)

24 Cross Join SELECT * FROM employee, department; SELECT * FROM employee CROSS JOIN department; CROSS JOIN returns the Cartesian product of the tables, combining every row of the first table with every row of the second table employee_id last_name first_name department salary name budget Bunny Bugs Management Management Duck Daffy Sales Management Porky Pig Human Resources Management Sam Yosemite Sales Management Fudd Elmer Human Resources Management Bird Tweety Marketing Management Coyote Wile Research Management Runner Road Research Management Bunny Bugs Management Sales Duck Daffy Sales Sales Porky Pig Human Resources Sales Sam Yosemite Sales Sales Fudd Elmer Human Resources Sales Bird Tweety Marketing Sales

25 Cross Join Coyote Wile Research Sales Runner Road Research Sales Bunny Bugs Management Human Resources Duck Daffy Sales Human Resources Porky Pig Human Resources Human Resources Sam Yosemite Sales Human Resources Fudd Elmer Human Resources Human Resources Bird Tweety Marketing Human Resources Coyote Wile Research Human Resources Runner Road Research Human Resources Bunny Bugs Management Marketing Duck Daffy Sales Marketing Porky Pig Human Resources Marketing Sam Yosemite Sales Marketing Fudd Elmer Human Resources Marketing Bird Tweety Marketing Marketing Coyote Wile Research Marketing Runner Road Research Marketing Bunny Bugs Management Research Duck Daffy Sales Research Porky Pig Human Resources Research Sam Yosemite Sales Research Fudd Elmer Human Resources Research Bird Tweety Marketing Research Coyote Wile Research Research Runner Road Research Research (40 rows)

26 Cross Join Cross joins are generally not very useful unless combined with a WHERE clause SELECT * FROM employee, department WHERE employee.department = department.name; employee_id last_name first_name department salary name budget Bunny Bugs Management Management Sam Yosemite Sales Sales Duck Daffy Sales Sales Fudd Elmer Human Resources Human Resources Porky Pig Human Resources Human Resources Bird Tweety Marketing Marketing Runner Road Research Research Coyote Wile Research Research (8 rows)

27 Inner Join A cross join with a WHERE clause is the same as an inner join SELECT * FROM employee INNER JOIN department ON (employee.department = department.name); employee_id last_name first_name department salary name budget Bunny Bugs Management Management Sam Yosemite Sales Sales Duck Daffy Sales Sales Fudd Elmer Human Resources Human Resources Porky Pig Human Resources Human Resources Bird Tweety Marketing Marketing Runner Road Research Research Coyote Wile Research Research (8 rows) The inner join syntax is now preferred

28 Aliases in Joins SELECT * FROM employee AS emp INNER JOIN department AS dep ON (emp.department = dep.name); Table aliases can sometime simplify complex statements

29 NULLs in Joins What about NULLs? Lets add an employee without a department INSERT INTO employee (employee_id, last_name, first_name) VALUES (90123, 'Martian', 'Marvin'); Could also do Or INSERT INTO employee VALUES (90123, 'Martian', 'Marvin', NULL, NULL); INSERT INTO employee VALUES (90123, 'Martian', 'Marvin'); Note that NULL values at the end can be left off

30 NULLs in Joins SELECT * FROM employee ORDER BY employee_id; employee_id last_name first_name department salary Bunny Bugs Management Duck Daffy Sales Porky Pig Human Resources Sam Yosemite Sales Fudd Elmer Human Resources Bird Tweety Marketing Coyote Wile Research Runner Road Research Martian Marvin (9 rows) Note empty values in last row

31 NULLs in Joins What happens when we join employee with department? Remember that NULL doesn't match anything, even itself

32 NULLs in Joins SELECT * FROM employee AS emp INNER JOIN department AS dep ON (emp.department = dep.name) ORDER BY employee_id; employee_id last_name first_name department salary name budget Bunny Bugs Management Management Duck Daffy Sales Sales Porky Pig Human Resources Human Resources Sam Yosemite Sales Sales Fudd Elmer Human Resources Human Resources Bird Tweety Marketing Marketing Coyote Wile Research Research Runner Road Research Research (8 rows) Note that Marvin is missing!

33 Outer Joins Outer joins include rows without matches LEFT OUTER JOIN: includes all rows from the first (left) table whether or not there are matching rows in the second (right) table RIGHT OUTER JOIN: includes all rows from the second (right) table FULL OUTER JOIN: includes all rows from both tables

34 Example INSERT INTO department VALUES ('IT', ); SELECT * FROM department; name budget Management Sales Human Resources Marketing Research IT (6 rows)

35 LEFT OUTER JOIN SELECT * FROM employee AS emp LEFT OUTER JOIN department AS dep ON (emp.department = dep.name) ORDER BY employee_id; employee_id last_name first_name department salary name budget Bunny Bugs Management Management Duck Daffy Sales Sales Porky Pig Human Resources Human Resources Sam Yosemite Sales Sales Fudd Elmer Human Resources Human Resources Bird Tweety Marketing Marketing Coyote Wile Research Research Runner Road Research Research Martian Marvin (9 rows) Note that Marvin is included but the IT department is not

36 RIGHT OUTER JOIN SELECT * FROM employee AS emp RIGHT OUTER JOIN department AS dep ON (emp.department = dep.name) ORDER BY employee_id; employee_id last_name first_name department salary name budget Bunny Bugs Management Management Duck Daffy Sales Sales Porky Pig Human Resources Human Resources Sam Yosemite Sales Sales Fudd Elmer Human Resources Human Resources Bird Tweety Marketing Marketing Coyote Wile Research Research Runner Road Research Research IT (9 rows) Note that the IT department is included but Marvin is not

37 FULL OUTER JOIN SELECT * FROM employee AS emp FULL OUTER JOIN department AS dep ON (emp.department = dep.name) ORDER BY employee_id; employee_id last_name first_name department salary name budget Bunny Bugs Management Management Duck Daffy Sales Sales Porky Pig Human Resources Human Resources Sam Yosemite Sales Sales Fudd Elmer Human Resources Human Resources Bird Tweety Marketing Marketing Coyote Wile Research Research Runner Road Research Research Martian Marvin IT (10 rows) Note that both Marvin and the IT department are included

38 Multiple Joins Multiple tables can be joined together Example: SELECT * FROM address; employee_id address Acme Lane Acme Lake Acme Ave (3 rows)

39 Multiple Joins SELECT * FROM employee AS emp LEFT OUTER JOIN department AS dep ON (emp.department = dep.name) LEFT OUTER JOIN address AS adr ON (emp.employee_id = adr.employee_id) ORDER BY emp.employee_id; employee_id last_name first_name department salary name budget employee_id address Bunny Bugs Management Management Acme Lane Duck Daffy Sales Sales Acme Lake Porky Pig Human Resources Human Resources Acme Ave Sam Yosemite Sales Sales Fudd Elmer Human Resources Human Resources Bird Tweety Marketing Marketing Coyote Wile Research Research Runner Road Research Research Martian Marvin (9 rows) Note that employee_id shows up twice, once for each table it is in, since we used * in the SELECT

40 Disambiguation We can eliminate the duplicate fields by explicitly listing the fields we want, but we must use the table name or alias to disambiguate which employee_id we want Note that we also needed to disambiguate the ORDER BY field SELECT emp.employee_id, first_name, last_name, address FROM employee AS emp LEFT OUTER JOIN address AS adr ON (emp.employee_id = adr.employee_id) ORDER BY emp.employee_id; employee_id first_name last_name address Bugs Bunny 1 Acme Lane Daffy Duck 2 Acme Lake Pig Porky 3 Acme Ave Yosemite Sam Elmer Fudd Tweety Bird Wile Coyote Road Runner Marvin Martian (9 rows)

41 USING If we're joining on columns with the same name then the USING construct can be used SELECT * FROM employee AS emp LEFT OUTER JOIN address AS adr USING (employee_id) ORDER BY employee_id; employee_id last_name first_name department salary address Bunny Bugs Management Acme Lane Duck Daffy Sales Acme Lake Porky Pig Human Resources Acme Ave Sam Yosemite Sales Fudd Elmer Human Resources Bird Tweety Marketing Coyote Wile Research Runner Road Research Martian Marvin (9 rows) Note there is only one employee_id column in the results And therefore we don't need to disambiguate the ORDER BY employee_id

42 IN SELECT * FROM employee WHERE department IN ('Sales', 'Research') ORDER BY employee_id; employee_id last_name first_name department salary Duck Daffy Sales Sam Yosemite Sales Coyote Wile Research Runner Road Research (4 rows)

43 Subselect SELECT * FROM employee AS emp WHERE department IN ( SELECT name FROM department WHERE budget > ) ORDER BY employee_id; employee_id last_name first_name department salary Bird Tweety Marketing (1 row)

44 Join SELECT * FROM employee emp INNER JOIN department dep ON (emp.department = dep.name) WHERE dep.budget > ORDER BY employee_id; employee_id last_name first_name department salary name budget Bird Tweety Marketing Marketing (1 row)