Introduction to Database Systems CSE 544. Lecture #2 January 16, 2007

Transcription

1 Introduction to Database Systems CSE 544 Lecture #2 January 16,

2 Review Questions: NULLS From Lecture 1: What is 3-valued logic? Why do we need it? What is a left outer join? Why do we sometimes need left outer joins in aggregate queries? 2

3 Review Question: Expressive Power of SQL From HW1: Acted together table: A(id1, id2) This is a graph Pairs of actors connected by a path of length 2 How many joins? Pairs of actors connected by a path of length 5 How many joins? Pairs of actors connected by a path of any length How many joins? 3

4 Review Question: ACID From the reading assignment SQL for Web Nerds: What does ACID mean? 4

5 Discussion of Project/Phase 1 Task 1: Schema design Task 2: Import sample data Task 3: Modify starter code 5

6 Task 1: Schema Design Official requirement Read the project description Design a good database schema 6

7 Task 1: Schema Design What you should do: Read description AND look inside the starter code App_code/Provided/ Read the classes, determine the fields 7

8 Task 1: Schema Design Optional: draw an E/R diagram Create a file: Create a second file: (why?) CREATE TABLE Customer ((...)) CREATE TABLE Invoice ((...))... DROP TABLE Customer DROP TABLE Invoice... 8

9 Task 1: Schema Design Things to worry about: Keys/foreign keys: note table order matters! Make sure you represent all the data Null-able or not (don t worry too muchh) Things not to worry about: fname or FirstName or PersonFirstName? varchar(20) or char(200) or varchar(120)? 9

10 Task 2: Import Sample Data Create a file: You may need to run this: INSERT INTO Customer ((...)) VALUES ( John,.) INSERT INTO Customer ((...)) VALUES ( Sue,.)... (why?) DROP TABLE Customer DROP TABLE Invoice... 10

11 Task 3: Modify Starter Code The starter code: C# ASP.NET (you do not need to understand it) It provides a Website for accessing your online store BUT it misses the fragments of code that get the data from the database See Phase1_Example/ 11

12 C# - Crash Course Hello World Properties (getters/setters) Enums Partial classes Dataset: DataTable, DataRow Connecting to a database 12

13 C# - Highlights C# = C++.Sytnax + Java.Semantics It is a safe language (like Java) Can be embedded in Webpages Can access a database Complex, but you should see the predecessors! 13

14 Hello World using System; class Hello { static void Main() { Console.WriteLine("Hello world"); } } 14

15 Properties: Getters and Setters public class Point { private int x; x; private string c; c; public int position { get { return x; x; } set { x = value; c = red ; } } public string color { get { return c; c; } set { c = value; x++; } } Point uvw = new Point(); uvw.position = 55; uvw.color = green ; uvw.position = uvw.position * 2; 2; if if (uvw.color == green ) 15

16 public class Stuff { private int x[]; Indexers Getters and setters with [ ] public int this[int i] i] { get {x[2*i+1]=0; return x[2*i]; } set { x[2*i] = value; x[2*i+1]=1; } } Stuff uvw = new Stuff(); uvw[12] = 55; uvw[99] = uvw[12]*7 + 2; uvw[99] = uvw[12]* ;

17 Enum enum Color: byte { Red = 1, Green = 2, Blue = 4, Black = 0, White = Red Green Blue, } 17

18 Partial Classes Some fields defined in file 1 Other fields defined in file 2 Why? Nguyen creates file 1, you create file 2 18

19 Dataset This is an important class that allows you to interact with a database Dataset = a mini database in main memory DataTable DataRow 19

20 DataSet DataSet mylocaldb = new DataSet(); /* /* create inside a table called books */ */....../* /*(this is is shown on a following slide) */ */ /* /* now use books */ */ DataTable x = mylocaldb.tables[ books ] foreach (DataRow y in in x.rows) { if if (y[ title ] == Harry Potter ) y[ price ]++;; } 20

21 Connecting to a Database Create or edit web.config file Specify iisqlsrv, user, password Give a name Create a SqlConnection refer to name Create a SqlDataAdaptor embed SQL query string Execute the Fill( ) method to run query and store answers in a datarow 21

22 Connecting to a Database /* /* create inside a table called books */ */ SqlConnection c = new SqlConnection(... name...);.); string q = select title, price year from products where price < 100 ; SQL = a string!!! impedance mismatch SqlDataAdapter a = new SqlDataAdapter(q, c); DataSet mylocaldb = new DataSet(); a.fill(mylocaldb, books ); 22

23 Task 3: Modify Starter Code What you have to do: App_Code/Phase1/Billing and Shipping/ Public partial class Customer { /* /* add your own fields, like: */ */ private int id, Procedure List<invoice> GetInvoices() { /* /* your GetInvoices code goes here */ */ } 23

24 Task 3: Modify Starter Code /* /* your GetInvoices code goes here */ */ string s = SELECT FROM. WHERE x.customerid = {0}, id); Substitutes id for {0} Defined in Provided StoreContext store = StoreContext.current; DataSet ds ds = new DataSet( ); ); DataTable invoices = store.getdatatable(s, ds, invoices ); /* /* continued on next slide. */ */ 24

25 Task 3: Modify Starter Code /* /* continued from previous slide */ */ List<Invoice> invoicelist new List<Invoice> ( ); ); foreach(datarow r in in invoices.rows) { invoicelist.add(new Invoice( r )); } return invoicelist; } Now you need to implement this 25

26 Task 3: Modify Starter Code public partial class Invoice { public Invoice(DataRow invoicedata) { /* /* here goes your code, something like that: */ */ init(invoicedata); /* /* may need it it in in several places */ */ }..... private void init(datarow invoicedata) { invoiceid = (int) invoicedata[ invoiceid ]; orderdate = (DateTime) invoicedata[ date ]; In Provided In you SQL 26

27 Time Estimate Task 1: about 9 tables or so, 2 hours or more Task 2: try 2 tuples per table, 1 hour Task 3: finding out what to do, errors, 7-8 hours 27

28 E/R Diagrams E/R diagrams: Chapter 2 Functional Dependencies and Normal Forms: Chapter 19 28

29 Database Design 1. Requirements Analysis: e.g. Phase 1 of the project 2. Conceptual Database Design: E/R diagrams 3. Logical Database Design: from E/R to relations 4. Schema Refinement: Normal Forms 5. Physical Database Design: indexes, etc 6. Application and security: not covered in this course 29

30 Conceptual Design = Entity / Relationship Diagrams Objects entities Classes entity sets Attributes are like in ODL. Product address Relationships: like in ODL except buys - first class citizens (not associated with classes) - not necessarily binary 30

31 name category name price Product makes Company stockprice buys Person employs address name ssn 31

32 Keys in E/R Diagrams Every entity set must have a key name category price Product 32

33 What is a Relation? A mathematical definition: if A, B are sets, then a relation R is a subset of A B 1 A={1,2,3}, B={a,b,c,d}, A B = {(1,a),(1,b),..., (3,d)} 2 A= R = {(1,a), (1,c), (3,b)} - makes is a subset of Product Company: 3 B= a b c d Product makes Company 33

34 Multiplicity of E/R Relations one-one: many-one many-many a b c d a b c d a b c d Note: book places arrow differently 34

35 name category name price Product makes Company buys What does this say? employs stockprice Person address name ssn 35

36 Multi-way Relationships How do we model a purchase relationship between buyers, products and stores? Product Purchase Store Person Can still model as a mathematical set (how?) 36

37 Q: what does the arrow mean? Arrows in Multiway Relationships Product Purchase Store Person A: a given person buys a given product from at most one store 37

38 Q: what does the arrow mean? Arrows in Multiway Relationships Product Purchase Store Person A: a given person buys a given product from at most one store AND every store sells to every person at most one product 38

39 Arrows in Multiway Relationships Q: How do we say that every person shops at at most one store? Product Purchase Store Person A: cannot. This is the best approximation. (Why only approximation?) 39

40 Converting Multi-way Relationships to Binary date ProductOf Product Purchase StoreOf Store BuyerOf Person 40

41 3. Design Principles What s wrong? Product Purchase Person Country President Person Moral: be faithful! 41

42 Design Principles: What s Wrong? Product date Purchase Store personaddr personname Moral: pick the right kind of entities. 42

43 Design Principles: What s Wrong? Dates date Product Purchase Store Moral: don t complicate life more than it already is. Person 43

44 Logical Database Design = E/R Relations Entity set relation Relationship relation 44

45 Entity Set to Relation name category price Product Product(name, category, price) name category price gizmo gadgets $

46 Relationships to Relations price name category Start Year name Product makes Company Stock price Makes(product-name, product-category, company-name, year) Product-name Product-Category Company-name Starting-year gizmo gadgets gizmoworks 1963 (watch out for attribute name conflicts) 46

47 Relationships to Relations price name category Start Year name Product makes Company No need for Makes. Modify Product: Stock price name category price StartYear companyname gizmo gadgets gizmoworks 47

48 Multi-way Relationships to Relations Product name address name price Purchase Store Purchase(prodName,stName,ssn) Person ssn name 48

49 Modeling Subclasses Some objects in a class may be special define a new class better: define a subclass Products Software products Educational products So --- we define subclasses in E/R 49

50 Subclasses name category price Product isa isa Software Product Educational Product platforms 50 Age Group

51 Understanding Subclasses Think in terms of records: Product SoftwareProduct EducationalProduct field1 field2 field1 field2 field3 field1 field2 field4 field5 51

52 Subclasses to Relations Product Name Price Category Gizmo 99 gadget name category Camera 49 photo price Toy 39 gadget Product Sw.Product Name platforms isa isa Ed.Product Gizmo unix Software Product Educational Product Name Age Group platforms Age Group Gizmo todler Toy retired 52

53 Difference between OO and E/R inheritance OO: classes are disjoint (same for Java, C++) Product sp1 SoftwareProduct sp2 p1 p2 p3 ep1 ep3 ep2 EducationalProduct 53

54 Difference between OO and E/R inheritance E/R: entity sets overlap Product SoftwareProduct sp1 sp2 p1 p2 p3 ep1 EducationalProduct ep2 ep3 54

55 No need for multiple inheritance in E/R Product SoftwareProduct sp1 sp2 p1 p2 p3 esp1 esp2 ep1 EducationalProduct ep2 ep3 We have three entity sets, but four different kinds of objects. 55

56 Modeling UnionTypes With Subclasses FurniturePiece Person Company Say: each piece of furniture is owned either by a person, or by a company 56

57 Modeling Union Types with Subclasses Say: each piece of furniture is owned either by a person, or by a company Solution 1. Acceptable, imperfect (What s wrong?) Person FurniturePiece Company ownedbyperson ownedbyperson 57

58 Modeling Union Types with Subclasses Solution 2: better, more laborious Owner isa isa Person ownedby Company FurniturePiece 58

59 Constraints in E/R Diagrams Finding constraints is part of the modeling process. Commonly used constraints: Keys: social security number uniquely identifies a person. Single-value constraints: a person can have only one father. Referential integrity constraints: if you work for a company, it must exist in the database. Other constraints: peoples ages are between 0 and

60 Keys in E/R Diagrams Underline: name category price No formal way to specify multiple keys in E/R diagrams Product Person address name ssn 60

61 Single Value Constraints makes v. s. makes 61

62 Referential Integrity Constraints Product makes Company Each product made by at most one company. Some products made by no company Product makes Company Each product made by exactly one company. 62

63 Other Constraints Product <100 makes Company What does this mean? 63

64 Weak Entity Sets Entity sets are weak when their key comes from other classes to which they are related. Team affiliation University sport number name Notice: we encountered this when converting multiway relationships to binary relationships (last lecture) 64

65 Handling Weak Entity Sets Team affiliation University sport number name Convert to a relational schema (in class) 65

66 Schema Refinements = Normal Forms 1st Normal Form = all tables are flat 2nd Normal Form = obsolete Boyce Codd Normal Form = will study 3rd Normal Form = see book 66

67 First Normal Form (1NF) A database schema is in First Normal Form if all tables are flat Student Student Name Alice Bob Carol GPA Courses Math DB OS DB OS Math OS May need to add keys Name Alice Bob Carol Takes Student Alice Carol Alice Bob Alice Carol GPA DB OS OS Course Math Math DB Course Course Math DB OS 67

68 Relational Schema Design Conceptual Model: name Product buys Person price name ssn Relational Model: plus FD s Normalization: Eliminates anomalies 68

69 Data Anomalies When a database is poorly designed we get anomalies: Redundancy: data is repeated Updated anomalies: need to change in several places Delete anomalies: may lose data when we don t want 69

70 Relational Schema Design Recall set attributes (persons with several phones): Name SSN PhoneNumber City Fred Seattle Fred Seattle Joe Westfield One person may have multiple phones, but lives in only one city Anomalies: Redundancy = repeat data Update anomalies = Fred moves to Bellevue Deletion anomalies = Joe deletes his phone number: what is his city? 70

71 Relation Decomposition Break the relation into two: Name SSN PhoneNumber City Fred Seattle Fred Seattle Joe Westfield Name Fred Joe SSN City Seattle Westfield SSN Anomalies have gone: No more repeated data Easy to move Fred to Bellevue (how?) Easy to delete all Joe s phone number (how?) PhoneNumber

72 Relational Schema Design Main idea: (or Logical Design) Start with some relational schema Find out its functional dependencies Use them to design a better relational schema 72

73 Functional Dependencies A form of constraint hence, part of the schema Finding them is part of the database design Also used in normalizing the relations 73

74 Functional Dependencies Definition: If two tuples agree on the attributes A 11,, A 22,,, A nn then they must also agree on the attributes Formally: B 11,, B 22,,, B m A 1, 1, A 2, 2,, A n n B 1, 1, B 2, 2,, B m 74

75 When Does an FD Hold Definition: A 1,..., A m B 1,..., B n holds in R if: t, t R, (t.a 1 =t.a 1... t.a m =t.a m t.b 1 =t.b 1... t.b n =t.b n ) R A 1... A m B 1... B m t t if t, t agree here then t, t agree here 75

76 Examples An FD holds, or does not hold on an instance: EmpID Name Phone Position E0045 Smith 1234 Clerk E3542 Mike 9876 Salesrep E1111 Smith 9876 Salesrep E9999 Mary 1234 Lawyer EmpID Name, Phone, Position Position Phone but not Phone Position 76

77 Example EmpID Name Phone Position E0045 Smith 1234 Clerk E3542 Mike 9876 Salesrep E1111 Smith 9876 Salesrep E9999 Mary 1234 Lawyer Position Phone 77

78 Example EmpID Name Phone Position E0045 Smith 1234 Clerk E3542 Mike 9876 Salesrep E1111 Smith 9876 Salesrep E9999 Mary 1234 Lawyer but not Phone Position 78

79 Example FD s are constraints: On some instances they hold On others they don t name color category department color, category price name category color department price Gizmo Gadget Green Toys 49 Tweaker Gadget Green Toys 99 Does this instance satisfy all the FDs? 79

80 Example name color category department color, category price name category color department price Gizmo Gadget Green Toys 49 Tweaker Gadget Black Toys 99 Gizmo Stationary Green Office-supp. 59 What about this one? 80

81 An Interesting Observation If all these FDs are true: name color category department color, category price Then this FD also holds: name, category price Why?? 81

82 Goal: Find ALL Functional Dependencies Anomalies occur when certain bad FDs hold We know some of the FDs Need to find all FDs, then look for the bad ones 82

83 Armstrong s Rules (1/3) A 11,, A 22,,, A nn B 11,, B 22,,, B m Is equivalent to Splitting rule and Combing rule A 11,, A 22,,, A nn B 11 A1... Am B1... Bm A 11,, A 22,,, A nn B A 11,, A 22,,, A nn B m 83

84 Armstrong s Rules (1/3) A 11,, A 22,,, A nn A ii Trivial Rule where i = 1, 2,..., n Why? A 1 A m 84

85 Armstrong s Rules (1/3) Transitive Closure Rule If A 11,, A 22,,, A nn B 11,, B 22,,, B m and B 11,, B 22,,, B m C 11,, C 22,,, C pp then A 11,, A 22,,, A nn C 11,, C 22,,, C pp Why? 85

86 A 1 A m B 1 B m C 1... C p 86

87 Example (continued) Start from the following FDs: Infer the following FDs: Inferred FD 4. name, category name 5. name, category color 6. name, category category 7. name, category color, category 8. name, category price name color category department color, category price Which Rule did we apply? 87

88 Example (continued) Answers: name color category department color, category price Inferred FD 4. name, category name 5. name, category color 6. name, category category 7. name, category color, category 8. name, category price Which Rule did we apply? Trivial rule Transitivity on 4, 1 Trivial rule Split/combine on 5, 6 Transitivity on 3, 7 THIS IS TOO HARD! Let s see an easier way. 88

89 Closure of a set of Attributes Given a set of of attributes A 1,,, A n The closure, {A 1,,, A n } + = the set of of attributes B s.t. A 1,,, A n B Example: name color category department color, category price Closures: name + = {name, color} {name, category} + = {name, category, color, department, price} color + = {color} 89

90 Closure Algorithm X={A1,, An}. Repeat until X doesn t change do: if if B 11,,, B nn C is is a FD and B 11,,, B nn are are all all in in X then add C to to X. X. Example: name color category department color, category price {name, category} + = { name, category, color, department, price } Hence: name, category color, department, price 90

91 Example In class: R(A,B,C,D,E,F) A, A, B C A, A, D E B D A, A, F B Compute {A,B} + X = {A, B, } Compute {A, F} + X = {A, F, } 91

92 Why Do We Need Closure With closure we can find all FD s easily To check if X A Compute X + Check if A X + 92

93 Using Closure to Infer ALL FDs Example: A, A, B C A, A, D B B D Step 1: Compute X +, for every X: A+ = A, A, B+ B+ = BD, C+ C+ = C, C, D+ = D AB+ =ABCD, AC+=AC, AD+=ABCD, BC+=BCD, BD+=BD, CD+=CD ABC+ = ABD+ = ACD + = ABCD (no need to to compute why?)?) BCD + = BCD, ABCD+ = ABCD Step 2: Enumerate all FD s X Y, s.t. Y X + and X Y = : 93 AB CD, AD BC, ABC D, D, ABD C, C, ACD B

94 Another Example Enrollment(student, major, course, room, time) student major major, course room course time What else can we infer? [in class, or at home] 94

95 Keys A superkey is a set of attributes A 1,..., A n s.t. for any other attribute B, we have A 1,..., A n B A key is a minimal superkey I.e. set of attributes which is a superkey and for which no subset is a superkey 95

96 Computing (Super)Keys Compute X + for all sets X If X + = all attributes, then X is a key List only the minimal X s 96

97 Example Product(name, price, category, color) name, category price category color What is the key? 97

98 Example Product(name, price, category, color) name, category price category color What is the key? (name, category) + = name, category, price, color Hence (name, category) is a key 98

99 Examples of Keys Enrollment(student, address, course, room, time) student address room, time course student, course room, time (find keys at home) 99

100 Eliminating Anomalies Main idea: X A is OK if X is a (super)key X A is not OK otherwise 100

101 Example Name SSN PhoneNumber City Fred Seattle Fred Seattle Joe Westfield Joe Westfield SSN Name, City What the key? {SSN, PhoneNumber} Hence SSN Name, City is a bad dependency 101

102 Key or Keys? Can we have more than one key? Given R(A,B,C) define FD s s.t. there are two or more keys 102

103 Key or Keys? Can we have more than one key? Given R(A,B,C) define FD s s.t. there are two or more keys AB C BC A or A BC B AC what are the keys here? Can you design FDs such that there are three keys? 103

104 Boyce-Codd Normal Form A simple condition for removing anomalies from relations: A relation R is is in in BCNF if: if: If If A 11,,...,..., A nn B is is a non-trivial dependency in in R,, then {A {A 11,,...,..., A n n } is is a superkey for for R In other words: there are no bad FDs Equivalently: X, either (X + = X) or (X + = all attributes) 104

105 BCNF Decomposition Algorithm repeat choose A 11,,, A m B 11,,, B nn that violates BNCF split R into R 11 (A (A 11,,, A m,, B 11,,, B n n )) and R 22 (A (A 11,,, A m,,[others]) continue with both R 11 and R 22 until no no more violations B s A s Others Is there a 2-attribute relation that is not in BCNF? R 1 R 2 In practice, we have 105 a better algorithm (coming up)

106 Example Name SSN PhoneNumber City Fred Seattle Fred Seattle Joe Westfield Joe Westfield SSN Name, City What the key? {SSN, PhoneNumber} use SSN Name, City to split 106

107 Example Name Fred SSN City Seattle SSN Name, City Joe Westfield SSN PhoneNumber Let s check anomalies: Redundancy? Update? Delete? 107

108 Example Decomposition Person(name, SSN, age, haircolor, phonenumber) SSN name, age age haircolor Decompose in BCNF (in class): 108

109 BCNF Decomposition Algorithm BCNF_Decompose(R) find X s.t.: X X + [all attributes] if if (not found) then R is is in in BCNF let Y = X + - X let Z = [all attributes] - X + decompose R into R1(X Y) and R2(X Z) continue to to decompose recursively R1 and R2 109

110 Find X s.t.: X X + [all attributes] Example BCNF Decomposition Person(name, SSN, age, haircolor, phonenumber) SSN name, age age haircolor Iteration 1: 1: Person SSN+ = SSN, name, age, haircolor Decompose into: P(SSN, name, age, haircolor) Phone(SSN, phonenumber) Iteration 2: 2: P age+ = age, haircolor Decompose: People(SSN, name, age) Hair(age, haircolor) Phone(SSN, phonenumber) What are the keys? 110

111 R(A,B,C,D) Example A B B C R(A,B,C,D) A + = ABC ABCD R 1 (A,B,C) B + = BC ABC R 2 (A,D) R 11 (B,C) R 12 (A,B) What are the keys? What happens if in R we first pick B +? Or AB +? 111

112 Decompositions in General R(A 11,,...,..., A n n,, B 11,,...,..., B m,, C 11,,...,..., C p p )) R 11 (A (A 11,,...,..., A n n,, B 11,,...,..., B m )) R 22 (A (A 11,,...,..., A n n,, C 11,,...,..., C p p )) R 1 = projection of R on A 1,..., A n, B 1,..., B m R 2 = projection of R on A 1,..., A n, C 1,..., C p 112

113 Theory of Decomposition Sometimes it is correct: Name Gizmo OneClick Gizmo Price Category Gadget Camera Camera Name Price Name Category Gizmo Gizmo Gadget OneClick OneClick Camera Gizmo Gizmo Camera Lossless decomposition 113

114 Incorrect Decomposition Sometimes it is not: Name Price Category Gizmo OneClick Gizmo Gadget Camera Camera What s incorrect?? Name Gizmo OneClick Gizmo Category Gadget Camera Camera Lossy decomposition Price Category Gadget Camera Camera 114

115 Decompositions in General R(A 11,,...,..., A n n,, B 11,,...,..., B m,, C 11,,...,..., C p p )) R 11 (A (A 11,,...,..., A n n,, B 11,,...,..., B m )) R 22 (A (A 11,,...,..., A n n,, C 11,,...,..., C p p )) If A 1,..., A n B 1,..., B m Then the decomposition is lossless Note: don t need A 1,..., A n C 1,..., C p BCNF decomposition is always lossless. WHY? 115