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Relational Databases - Lecture 4 - SQL language
- 2. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 SQL • Developed in 1970 by IBM as SEQUEL (Structured English QUEry Language) • Structured Query Language • data definition • data manipulation • data control • Based on relational algebra but not strictly following • Helps managing data held in a relational database management system (RDBMS)
- 3. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Create table CREATE TABLE Student ( stdId int NOT NULL PRIMARY KEY, unvName varchar(50), stdName varchar(30) NOT NULL, stdSurname varchar(30), stdNat varchar(20) NOT NULL, FOREIGN KEY (unvName) REFERENCES University(unvName) ); CREATE TABLE University ( unvName varchar(50) NOT NULL PRIMARY KEY, unvCountry varchar(20) NOT NULL, unvAddress varchar(90) NOT NULL ); STD_ID: INTEGER UNV_NAME: VARCHAR (50) STD_NAME: VARCHAR (30) STD_SURNAME: VARCHAR (30) STD_NAT: VARCHAR (20) STUDENT UNIVERSITY UNV_NAME: VARCHAR (50) UNV_COUNTRY: VARCHAR (20) UNV_ADDRESS: VARCHAR (90)
- 4. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Fill in a table INSERT INTO University VALUES ('UTT', 'France', 'Troyes'), ('PSB', 'France', 'Paris'); INSERT INTO Student VALUES (123, 'UTT', 'Mike', 'Wazowski', 'Polish' ), (345, 'PSB', 'Mary', 'Wazowski', 'Polish' ); STD_ID: INTEGER UNV_NAME: VARCHAR (50) STD_NAME: VARCHAR (30) STD_SURNAME: VARCHAR (30) STD_NAT: VARCHAR (20) STUDENT UNIVERSITY UNV_NAME: VARCHAR (50) UNV_COUNTRY: VARCHAR (20) UNV_ADDRESS: VARCHAR (90)
- 7. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Rules • Sets - collections of objects • Tables -> smallest logical subset of data • Column -> unique category of information • Row -> must be unique • Order of columns or rows doesn’t matter
- 8. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Operations on sets Union A ∪ B Intersection A ∩ B Difference A - B Subset
- 10. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Projection • Choose columns from the full table • The result table have <= attributes than the original
- 11. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Projection SELECT * FROM database.table1 attribute1 attribute2 attribute3 attribute4 1 ab 12.01 # 2 bc 25.06 @ table1 attribute2 attribute4 ab # bc @ table1 SELECT attribute2, attribute4 FROM table1
- 12. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Projection SELECT * FROM Student StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 18.2 95000 Student mean 12.01 18.2 Temp SELECT StdMean AS mean FROM Student
- 13. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Projection SELECT * FROM Student StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 18.2 95000 Student StdMean*1000 1201 1820 Temp SELECT StdMean * 1000 FROM Student
- 14. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Restriction • Choose rows from the full table • The result table have <= tuples than the original • Conditions on attributes: =, >, <, like, in, between, etc. SELECT * FROM database.table1 WHERE condition
- 15. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Restriction = SELECT StdName FROM Student WHERE StdZip = 10000 StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 19.9 95000 3 Mary 18.2 10000 4 Toad 10.8 40000 5 Stan 15.34 10000 6 Franc 13.6 41000 7 Aude 17.9 18000 Student StdName Stan Mary Stan Temp
- 16. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Restriction BETWEEN SELECT StdName FROM Student WHERE StdZip BETWEEN 10000 AND 20000 StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 19.9 95000 3 Mary 18.2 10000 4 Toad 10.8 40000 5 Stan 15.34 10000 6 Franc 13.6 41000 7 Aude 17.9 18000 Student StdName Stan Mary Stan Aude Temp
- 17. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Restriction LIKE SELECT StdName FROM Student WHERE StdName LIKE ‘Ma%’ OR StdName LIKE ‘%e’ StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 19.9 95000 3 Mary 18.2 10000 4 Toad 10.8 40000 5 Stan 15.34 10000 6 Franc 13.6 41000 7 Aude 17.9 18000 Student StdName Marc Mary Aude Temp
- 18. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Duplicates SELECT DISTINCT StdName FROM Student WHERE StdZip = 10000 StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 19.9 95000 3 Mary 18.2 10000 4 Toad 10.8 40000 5 Stan 15.34 10000 6 Franc 13.6 41000 7 Aude 17.9 18000 Student StdName Stan Mary Temp
- 19. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 NULL SELECT StdName FROM Student WHERE StdMean IS null StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc NULL 95000 3 Mary NULL 10000 4 Toad 10.8 40000 5 Stan 15.34 10000 6 Franc 13.6 41000 7 Aude NULL 18000 Student StdName Marc Mary Aude Temp
- 20. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Order • The results are not ordered • ORDER BY attribute is used to order the result • The default order is ASC - ascending (or alphabetical) • Add DESC to make in descending
- 21. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Order by SELECT DISTINCT StdZip FROM Student ORDER BY StdZip ASC StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 19.9 95000 3 Mary 18.2 10000 4 Toad 10.8 40000 5 Stan 15.34 10000 6 Franc 13.6 41000 7 Aude 17.9 18000 Student StdZip 10000 18000 40000 41000 95000 Temp
- 22. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Order by SELECT DISTINCT StdZip FROM Student ORDER BY StdZip DESC StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 19.9 95000 3 Mary 18.2 10000 4 Toad 10.8 40000 5 Stan 15.34 10000 6 Franc 13.6 41000 7 Aude 17.9 18000 Student StdZip 95000 41000 40000 18000 10000 Temp
- 23. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Limit SELECT DISTINCT StdZip FROM Student ORDER BY StdZip DESC LIMIT 3 StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 19.9 95000 3 Mary 18.2 10000 4 Toad 10.8 40000 5 Stan 15.34 10000 6 Franc 13.6 41000 7 Aude 17.9 18000 Student StdZip 95000 41000 40000 Temp
- 24. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Aggregation • MIN, MAX, SUM, AVG, COUNT • SELECT min(attribute) • FROM table • WHERE condition • Group by columns • Having condition
- 25. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Min SELECT MIN(StdMean) FROM Student StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 19.9 95000 3 Mary 18.2 10000 4 Toad 10.8 40000 5 Stan 15.34 10000 6 Franc 13.6 41000 7 Aude 17.9 18000 Student min(Mean) 10.8 Temp
- 26. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Count SELECT COUNT(StudentID) FROM Apply WHERE University = ‘UTT’ StudentID Date University 123 2013 UTT 234 2015 UTT 123 2014 UTT 678 2016 PSB 345 2015 UTT 234 2015 EPF 123 2013 EPF Apply count(StudentID) 4 Temp
- 27. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Count distinct StudentID Date University 123 2013 UTT 234 2015 UTT 123 2014 UTT 678 2016 PSB 345 2015 UTT 234 2015 EPF 123 2013 EPF Apply count(StudentID) 3 Temp SELECT COUNT(distinct StudentID) FROM Apply WHERE University = ‘UTT’
- 28. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Count by StudentID Date University 123 2013 UTT 234 2015 UTT 123 2014 UTT 678 2016 PSB 345 2015 UTT 234 2015 EPF 123 2013 EPF Apply University student UTT 3 PSB 1 EPF 2 Temp SELECT University, COUNT(distinct StudentID) as student FROM Apply GROUP BY University
- 29. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Count by StudentID Date University 123 2013 UTT 234 2015 UTT 123 2014 UTT 678 2016 PSB 345 2015 UTT 234 2015 EPF 123 2013 EPF Apply University student id UTT 3 234 PSB 1 678 EPF 2 123 Temp SELECT University, COUNT(distinct StudentID) as student, StudentID as id FROM Apply GROUP BY University Random
- 30. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Having StudentID Date University 123 2013 UTT 234 2015 UTT 123 2014 UTT 678 2016 PSB 345 2015 UTT 234 2015 EPF 123 2013 EPF Apply University PSB EPF Temp SELECT University FROM Apply GROUP BY University HAVING count(*) < 4
- 32. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Cartesian product • Obtain all combinations of two sets a b c d x y a x a y b x b y c x c y d x d y { { { { } } } } {
- 33. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Cartesian product SELECT StdName, UnvName FROM Student, University StdId StdName StdMean StdZip 1 Stan 12.01 10000 2 Marc 19.9 95000 3 Mary 18.2 10000 Student StdName UnvName Stan PSB Marc UTT Mary PSB Stan UTT Marc PSB Mary UTT Temp UnvId UnvName UnvLocation 1 PSB Paris 2 UTT Troyes University
- 34. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Join • Combine tuples of two tables based on certain condition
- 35. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Simple Join EmpId Name 1 Stan 2 Marc 3 Mary Employee ParentId Name Age 1 Sara 7 1 Amy 10 3 Pole 2Child Employee.Name Child.Name Stan Sara Stan Amy Mary Pole Temp { { SELECT e.Name, c.Name FROM Employee e, Child c WHERE e.EmpId = c.ParentId
- 36. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Inner Join EmpId Name 1 Stan 2 Marc 3 Mary Employee ParentId Name Age 1 Sara 7 1 Amy 10 3 Pole 2Child Employee.Name Child.Name Stan Sara Stan Amy Mary Pole Temp { { SELECT Employee.Name, Child.Name FROM Employee INNER JOIN Child ON EmpId = ParentId
- 37. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Join EmpId Name 1 Stan 2 Marc 3 Mary Employee ParentId Name Age 1 Sara 7 1 Amy 10 3 Pole 2Child Employee.Name Child.Name Stan Sara Stan Amy Mary Pole Temp { { SELECT Employee.Name, Child.Name FROM Employee JOIN Child ON EmpId = ParentId
- 38. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Natural Join ParentId ParentName 1 Stan 2 Marc 3 Mary Employee ParentId ChildName Age 1 Sara 7 1 Amy 10 3 Pole 2Child ParentName ChildName Stan Sara Stan Amy Mary Pole Temp { { SELECT ParentName, ChildName FROM Employee NATURAL JOIN Child key is found automatically
- 39. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Natural Join ParentId ParentName 1 Stan 2 Marc 3 Mary Employee ParentId ChildName Age 1 Sara 7 1 Amy 10 3 Pole 2Child ParentName ChildName Stan Sara Stan Amy Mary Pole Temp { { SELECT ParentName, ChildName FROM Employee JOIN Child USING(ParentId) good practice to define a key
- 40. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Simple Join StdId StdName StdZip 1 Stan 10000 2 Marc 95000 3 Mary 10000Student UnvId UnvName UnvLocation 1 PSB Paris 2 UTT Troyes 3 EPF TroyesUniversity StdId UnvId Date 1 1 01.01.15 2 2 05.05.15 3 1 14.03.15Apply StdName UnvName Stan PSB Mary PSB Temp { { {{ SELECT StdName, UnvName FROM Student, Apply, University WHERE Student.StdId=ApplyStdID AND University.UnvID=Apply.UnvID AND Apply.UnvId=1
- 41. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Simple Join StdId StdName StdZip 1 Stan 10000 2 Marc 95000 3 Mary 10000Student UnvId UnvName UnvLocation 1 PSB Paris 2 UTT Troyes 3 EPF TroyesUniversity StdId UnvId Date 1 1 01.01.15 2 2 05.05.15 3 1 14.03.15Apply StdName UnvName Stan PSB Mary PSB Temp { { {{ SELECT StdName, UnvName FROM Student JOIN Apply ON Student.StdId=Apply.StdID JOIN University ON University.UnvID=Apply.UnvID WHERE Apply.UnvId=1
- 42. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Self Join • Combine tuples of two tables based on certain condition • Used when the same type of information presented as two attributes • Classical example: employee and its manager id name surname manager 1 John Smith 3 2 Mike Wazowski 3 3 Bill Gates NULL 4 Crazy Frog 2 Employee
- 43. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Self Join id name surname manager 1 John Smith 3 2 Mike Wazowski 3 3 Bill Gates NULL 4 Crazy Frog 2 name surname m.name m.surname John Smith Bill Gates Mike Wazowski Bill Gates Crazy Frog Mike Wazowski SELECT e1.name, e1.surname, e2.name as m.name, e2.surname as m.surname FROM Employee e1, Employee e2 WHERE e1.manager = e2.id Employee
- 47. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Intersect • Returns only common tuples of two queries • Removes duplicate rows from the final result set • Two results must: • Have the same number of attributes • Have attributes of the same type
- 48. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Intersect SELECT * FROM sales2015 INTERSECT SELECT * FROM sales2016 Person Amount Alex 1000 Piter 3000 Mary 500 Aude 5600 Person Amount Alex 5000 Piter 3000 John 3500 Aude 5600 Person Amount Piter 3000 Aude 5600 sales2015 sales2016 temp
- 49. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Intersect ALL SELECT person, amount FROM sales2015 INTERSECT ALL SELECT person, amount FROM sales2016 id person amount 1 Alex 1000 2 Piter 3000 3 Mary 500 4 Aude 5600 id person amount 1 Piter 3000 2 Piter 3000 3 John 3500 4 Aude 5600 person amount Piter 3000 Aude 5600 Piter 3000 sales2015 sales2016 temp
- 50. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 IN SELECT * FROM sales2015 WHERE person IN ( SELECT person FROM sales2016 ) Person Amount Alex 1000 Piter 3000 Mary 500 Aude 5600 Person Amount Alex 5000 Piter 3000 John 3500 Aude 5600 Person Amount Piter 3000 Aude 5600 Alex 1000 sales2015 sales2016 temp
- 51. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 EXISTS SELECT person, amount FROM sales2015 s1 WHERE EXISTS (SELECT * FROM sales2016 s2 WHERE s2.person=s1.person) id person amount 1 Alex 1000 2 Piter 3000 3 Mary 500 4 Aude 5600 id person amount 1 Piter 3000 2 Piter 3000 3 John 3500 4 Aude 5600 person amount Piter 3000 Aude 5600 sales2015 sales2016 temp
- 52. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Except • Returns tuples all tuples from the first query that are not present in the second query • EXCEPT ALL does not remove duplicate rows from the final result • Two results must: • Have the same number of attributes • Have attributes of the same type
- 53. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Except SELECT * FROM sales2015 EXCEPT SELECT * FROM sales2016 Person Amount Alex 1000 Piter 3000 Mary 500 Aude 5600 Person Amount Alex 5000 Piter 3000 John 3500 Aude 5600 Person Amount Alex 1000 Mary 500 sales2015 sales2016 temp
- 54. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 NOT IN SELECT * FROM sales2015 WHERE person IN ( SELECT person FROM sales2016 ) Person Amount Alex 1000 Piter 3000 Mary 500 Aude 5600 Person Amount Alex 5000 Piter 3000 John 3500 Aude 5600 Person Amount Mary 500 sales2015 sales2016 temp
- 55. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 NOT EXISTS SELECT person, amount FROM sales2015 s1 WHERE NOT EXISTS (SELECT * FROM sales2016 s2 WHERE s2.person=s1.person) id person amount 1 Alex 1000 2 Piter 3000 3 Mary 500 4 Aude 5600 id person amount 1 Piter 3000 2 Piter 3000 3 John 3500 4 Aude 5600 person amount Alex 1000 Mary 500 sales2015 sales2016 temp
- 56. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Union • Combine the results of two queries into one • Two results must: • Have the same number of attributes • Have attributes of the same type
- 57. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Union SELECT * FROM sales2015 UNION SELECT * FROM sales2016 Person Amount Alex 1000 Piter 3000 Mary 500 Aude 5600 Person Amount Alex 5000 Piter 3000 John 3500 Aude 5600 Person Amount Alex 5000 Piter 3000 John 3500 Aude 5600 Alex 1000 Mary 500 sales2015 sales2016 temp
- 58. 1 0 1 0 0 1 0 1 0 1 0 0 1 0 1 0 0 1 0 0 1 0 ✐ ✐ ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ ✐ ✑ ✐ ✑ ✑ 1 1 1 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 0 0 1 0 1 1 ✑ ✐ ✑ ✐ ✐ ✑ ✑ ✐ ✐ ✑ ✐ 1 1 1 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 0 0 1 ✑ ✑ 1 ✑ ✑ 1 0 1 Union ALL SELECT * FROM sales2015 UNION ALL SELECT * FROM sales2016 Person Amount Alex 1000 Piter 3000 Mary 500 Aude 5600 Person Amount Alex 5000 Piter 3000 John 3500 Aude 5600 Person Amount Alex 5000 Piter 3000 John 3500 Aude 5600 Alex 1000 Piter 3000 Mary 500 Aude 5600sales2015 sales2016 temp