Review of Database Systems Concepts Chapters 1,2,3,4,5,8,9 in [R]

1. Review of Database Systems ConceptsChapters 1,2,3,4,5,8,9 in [R] [R] = Ramakrishnan & Gehrke: Database Management Systems, Third Edition

2. What Is a DBMS? • A very large, integrated collection of data. • Models real-world enterprise. • Entities (e.g., students, courses) • Relationships (e.g., Madonna is taking CS564) • A Database Management System (DBMS)is a software package designed to store and manage databases.

3. Files vs. DBMS • Application must stage large datasets between main memory and secondary storage (e.g., buffering, page-oriented access, 32-bit addressing, etc.) • Special code for different queries • Must protect data from inconsistency due to multiple concurrent users • No Crash recovery • No Security and access control

4. Why Use a DBMS? • Data independence and efficient access. • Reduced application development time. • Data integrity and security. • Uniform data administration. • Concurrent access, recovery from crashes.

5. Why Study Databases?? ? • Shift from computation to information • at the “low end”: scramble to webspace (a mess!) • at the “high end”: scientific applications • Datasets increasing in diversity and volume. • Digital libraries, interactive video, Human Genome project, EOS project • ... need for DBMS exploding • DBMS encompasses most of CS • OS, languages, theory, AI, multimedia, logic

6. Data Models • A data modelis a collection of concepts for describing data. • Aschemais a description of a particular collection of data, using the a given data model. • The relational model of datais the most widely used model today. • Main concept: relation, basically a table with rows and columns. • Every relation has a schema, which describes the columns, or fields.

7. Levels of Abstraction • Many views, single conceptual (logical) schemaand physical schema. • Views describe how users see the data. • Conceptual schema defines logical structure • Physical schema describes the files and indexes used. View 1 View 2 View 3 Conceptual Schema Physical Schema • Schemas are defined using DDL; data is modified/queried using DML.

8. Example: University Database • Logical schema: • Students(sid: string, name: string, login: string, age: integer, gpa:real) • Courses(cid: string, cname:string, credits:integer) • Enrolled(sid:string, cid:string, grade:string) • Physical schema: • Relations stored as unordered files. • Index on first column of Students. • External Schema (View): • Course_info(cid:string,enrollment:integer)

9. Data Independence * • Applications insulated from how data is structured and stored. • Logical data independence: Protection from changes in logical structure of data. • Physical data independence: Protection from changes in physical structure of data. • One of the most important benefits of using a DBMS!

12. Why Study the Relational Model? • Most widely used model. • Vendors: IBM, Informix, Microsoft, Oracle, Sybase, etc. • “Legacy systems” in older models • E.G., IBM’s IMS • Recent competitor: object-oriented model • ObjectStore, Versant, Ontos • A synthesis emerging: object-relational model • Informix Universal Server, UniSQL, O2, Oracle, DB2

13. Relational Database: Definitions • Relational database:a set of relations • Relation: made up of 2 parts: • Instance : a table, with rows and columns. #Rows = cardinality, #fields = degree / arity. • Schema :specifiesname of relation, plus name and type of each column. • E.G. Students(sid: string, name: string, login: string, age: integer, gpa: real). • Can think of a relation as a setof rows or tuples (i.e., all rows are distinct).

14. Primary Key Constraints • A set of fields is a keyfor a relation if : 1. No two distinct tuples can have same values in all key fields, and 2. This is not true for any subset of the key. • Part 2 false? A superkey. • If there’s >1 key for a relation, one of the keys is chosen (by DBA) to be the primary key. • E.g., sid is a key for Students. (What about name?) The set {sid, gpa} is a superkey. • Every relation must have a key

15. Example Instance of Students Relation • Cardinality = 3, degree = 5, all rows distinct • Do all columns in a relation instance have to • be distinct?

16. Relational Query Languages • A major strength of the relational model: supports simple, powerful querying of data. • Queries can be written intuitively, and the DBMS is responsible for efficient evaluation. • The key: precise semantics for relational queries. • Allows the optimizer to extensively re-order operations, and still ensure that the answer does not change.

17. The SQL Query Language • Developed by IBM (system R) in the 1970s • Need for a standard since it is used by many vendors • Standards: • SQL-86 • SQL-89 (minor revision) • SQL-92 (major revision) • SQL-99 (major extensions, current standard)

18. The SQL Query Language • To find all 18 year old students, we can write: SELECT * FROM Students S WHERE S.age=18 • To find just names and logins, replace the first line: SELECT S.name, S.login

19. Querying Multiple Relations SELECT S.name, E.cid FROM Students S, Enrolled E WHERE S.sid=E.sid AND E.grade=“A” • What does the following query compute? Given the following instances of Enrolled and Students: we get:

20. Creating Relations in SQL • Creates the Students relation. Observe that the type (domain) of each field is specified, and enforced by the DBMS whenever tuples are added or modified. • As another example, the Enrolled table holds information about courses that students take. CREATE TABLE Students (sid: CHAR(20), name: CHAR(20), login: CHAR(10), age: INTEGER, gpa: REAL) CREATE TABLE Enrolled (sid: CHAR(20), cid: CHAR(20), grade: CHAR(2))

21. Adding and Deleting Tuples • Can insert a single tuple using: INSERT INTO Students (sid, name, login, age, gpa) VALUES (53688, ‘Smith’, ‘smith@ee’, 18, 3.2) • Can delete all tuples satisfying some condition (e.g., name = Smith): DELETE FROM Students S WHERE S.name = ‘Smith’ • Powerful variants of these commands are available; more later!

22. Integrity Constraints (ICs) • IC: condition that must be true for any instance of the database; e.g., domain constraints. • ICs are specified when schema is defined. • ICs are checked when relations are modified. • A legalinstance of a relation is one that satisfies all specified ICs. • DBMS should not allow illegal instances. • If the DBMS checks ICs, stored data is more faithful to real-world meaning. • Avoids data entry errors, too!

23. Primary and Candidate Keys in SQL • Possibly many candidate keys(specified using UNIQUE), one of which is chosen as the primary key. CREATE TABLE Enrolled (sid CHAR(20) cid CHAR(20), grade CHAR(2), PRIMARY KEY (sid,cid) ) • “For a given student and course, there is a single grade.” vs. “Students can take only one course, and receive a single grade for that course; further, no two students in a course receive the same grade.” • Used carelessly, an IC can prevent the storage of database instances that arise in practice! CREATE TABLE Enrolled (sid CHAR(20) cid CHAR(20), grade CHAR(2), PRIMARY KEY (sid), UNIQUE (cid, grade) )

24. Foreign Keys, Referential Integrity • Foreign key : Set of fields in one relation that is used to `refer’ to a tuple in another relation. (Must correspond to primary key of the second relation.) Like a `logical pointer’. • E.g. sid is a foreign key referring to Students: • Enrolled(sid: string, cid: string, grade: string) • If all foreign key constraints are enforced, referential integrity is achieved, i.e., no dangling references. • Can you name a data model w/o referential integrity? • Links in HTML! (XML)

25. Foreign Keys in SQL • Only students listed in the Students relation should be allowed to enroll for courses. CREATE TABLE Enrolled (sid CHAR(20), cid CHAR(20), grade CHAR(2), PRIMARY KEY (sid,cid), FOREIGN KEY (sid) REFERENCESStudents ) Enrolled Students

26. Enforcing Referential Integrity • Consider Students and Enrolled; sid in Enrolled is a foreign key that references Students. • What should be done if an Enrolled tuple with a non-existent student id is inserted? (Reject it!) • What should be done if a Students tuple is deleted? • Also delete all Enrolled tuples that refer to it. • Disallow deletion of a Students tuple that is referred to. • Set sid in Enrolled tuples that refer to it to a default sid. • (In SQL, also: Set sid in Enrolled tuples that refer to it to a special value null, denoting `unknown’ or `inapplicable’.) • Similar if primary key of Students tuple is updated.

27. Referential Integrity in SQL • SQL/92 and SQL:1999 support all 4 options on deletes and updates. • Default is NO ACTION (delete/update is rejected) • CASCADE (also delete all tuples that refer to deleted tuple) • SET NULL / SET DEFAULT (sets foreign key value of referencing tuple) CREATE TABLE Enrolled (sid CHAR(20), cid CHAR(20), grade CHAR(2), PRIMARY KEY (sid,cid), FOREIGN KEY (sid) REFERENCESStudents ON DELETE CASCADE ON UPDATE SET DEFAULT )

28. Where do ICs Come From? • ICs are based upon the semantics of the real-world enterprise that is being described in the database relations. • We can check a database instance to see if an IC is violated, but we can NEVER infer that an IC is true by looking at an instance. • An IC is a statement about all possible instances! • From example, we know name is not a key, but the assertion that sid is a key is given to us. • Key and foreign key ICs are the most common; more general ICs supported too.

29. The Entity-Relationship Model Chapter 2

30. Overview of Database Design • Conceptual design: (ER Model is used at this stage.) • What are the entities and relationships in the enterprise? • What information about these entities and relationships should we store in the database? • What are the integrity constraints or business rules that hold? • A database `schema’ in the ER Model can be represented pictorially (ER diagrams). • Can map an ER diagram into a relational schema.

31. name ssn lot Employees ER Model Basics • Entity: Real-world object distinguishable from other objects. An entity is described (in DB) using a set of attributes. • Entity Set: A collection of similar entities. E.g., all employees. • All entities in an entity set have the same set of attributes. (Until we consider ISA hierarchies, anyway!) • Each entity set has a key. • Each attribute has a domain.

32. name ssn lot Employees super-visor Reports_To ER Model Basics (Contd.) • Relationship: Association among two or more entities. E.g., Attishoo works in Pharmacy department. • Relationship Set: Collection of similar relationships. • An n-ary relationship set R relates n entity sets E1 ... En; each relationship in R involves entities e1 E1, ..., en En • Same entity set could participate in different relationship sets, or in different “roles” in same set. since name dname subor-dinate ssn budget lot did Works_In Employees Departments

33. since name dname ssn lot Employees Manages Review: Key Constraints • Each dept has at most one manager, according to the key constrainton Manages. budget did Departments Translation to relational model? 1-to-1 1-to Many Many-to-1 Many-to-Many

34. name ssn lot Employees Logical DB Design: ER to Relational • Entity sets to tables: CREATE TABLE Employees (ssn CHAR(11), name CHAR(20), lot INTEGER, PRIMARY KEY (ssn))

35. Relationship Sets to Tables • In translating a relationship set to a relation, attributes of the relation must include: • Keys for each participating entity set (as foreign keys). • This set of attributes forms a superkey for the relation. • All descriptive attributes. CREATE TABLE Works_In( ssn CHAR(11), did INTEGER, since DATE, PRIMARY KEY (ssn, did), FOREIGN KEY (ssn) REFERENCES Employees, FOREIGN KEY (did) REFERENCES Departments)

36. Logical DB Design: ER to Relational (Cont.) • Not so simple! • Some attributes are mapped to a separate relation • Some relationships (1:1, !:many) are mapped to an extra attribute in the ‘1’ relation • See any standard DB book for details

37. Summary of Conceptual Design • Conceptual design follows requirements analysis, • Yields a high-level description of data to be stored • ER model popular for conceptual design • Constructs are expressive, close to the way people think about their applications. • Basic constructs: entities, relationships, and attributes (of entities and relationships). • Some additional constructs: weak entities, ISA hierarchies, and aggregation. • Note: There are many variations on ER model.

38. Formal Relational Query Languages • Two mathematical Query Languages form the basis for “real” languages (e.g. SQL), and for implementation: • Relational Algebra: More operational, very useful for representing execution plans. • Relational Calculus: Lets users describe what they want, rather than how to compute it. (Non-operational, declarative.)

39. Example Instances R1 • “Sailors” and “Reserves” relations for our examples. • We’ll use positional or named field notation, assume that names of fields in query results are `inherited’ from names of fields in query input relations. S1 S2

40. Relational Algebra • Basic operations: • Selection ( ) Selects a subset of rows from relation. • Projection ( ) Deletes unwanted columns from relation. • Cross-product( ) Allows us to combine two relations. • Set-difference ( ) Tuples in reln. 1, but not in reln. 2. • Union( ) Tuples in reln. 1 and in reln. 2. • Additional operations: • Intersection, join, division, renaming: Not essential, but (very!) useful. • Since each operation returns a relation, operationscan be composed! (Algebra is “closed”.)

41. Projection • Deletes attributes that are not in projection list. • Schema of result contains exactly the fields in the projection list, with the same names that they had in the (only) input relation. • Projection operator has to eliminate duplicates! (Why??) • Note: real systems typically don’t do duplicate elimination unless the user explicitly asks for it. (Why not?)

42. Selection • Selects rows that satisfy selection condition. • No duplicates in result! (Why?) • Schema of result identical to schema of (only) input relation. • Result relation can be the input for another relational algebra operation! (Operatorcomposition.)

43. Union, Intersection, Set-Difference • All of these operations take two input relations, which must be union-compatible: • Same number of fields. • `Corresponding’ fields have the same type. • What is the schema of result?

44. Cross-Product • Each row of S1 is paired with each row of R1. • Result schema has one field per field of S1 and R1, with field names `inherited’ if possible. • Conflict: Both S1 and R1 have a field called sid. • Renaming operator:

45. Joins • Condition Join: • Result schema same as that of cross-product. • Fewer tuples than cross-product, might be able to compute more efficiently • Sometimes called a theta-join.

46. Joins • Equi-Join: A special case of condition join where the condition c contains only equalities. • Result schema similar to cross-product, but only one copy of fields for which equality is specified. • Natural Join: Equijoin on all common fields.

47. Division • Not supported as a primitive operator, but useful for expressing queries like: Find sailors who have reserved allboats. • Let A have 2 fields, x and y; B have only field y: • A/B = • i.e., A/B contains all x tuples (sailors) such that for everyy tuple (boat) in B, there is an xy tuple in A. • Or: If the set of y values (boats) associated with an x value (sailor) in A contains all y values in B, the x value is in A/B. • In general, x and y can be any lists of fields; y is the list of fields in B, and x y is the list of fields of A.

48. Examples of Division A/B B1 B2 B3 A/B1 A/B2 A/B3 A

49. Solution 2: • Solution 3: Find names of sailors who’ve reserved boat #103 • Solution 1:

50. A more efficient solution: Find names of sailors who’ve reserved a red boat • Information about boat color only available in Boats; so need an extra join: A query optimizer can find this, given the first solution!