Database Theory Jason Fan.

Database Theory Jason Fan

Outline Basic Concepts Database Design Relational Database
Database and Database Users (Chapter 1) Database System Concepts and Architecture (Chapter 2) Database Design Database Design Process (Chapter 16) Entity-Relationship (ER) Modeling (Chapter3) Functional Dependencies and Normalization for Relational Database (Chapter 14) Relational Design Algorithms (Chapter 15) Relational Data Model Mapping (Chapter 9) Relational Database The Relational Data Model (Chapter 7) Relational Algebra (Chapter 7) SQL – A Relational Database Language (Chapter 8) Relational Calculus (Chapter 9) Database Implementation Transaction Processing (Chapter 19) Concurrency Control (Chapter 20) Database Recovery (Chapter 21) Advanced Topics

Database and Database Users
Basic Concepts Main Characteristics of Database Technology Classes of Database Users Additional Database Characteristics When not to use a DBMS Chapter 1 Database and Database Users

Basic Concepts Database Data Mini-world
A collection of related data. Data Known facts that can be recorded and have implicit meaning. Mini-world Some part of the real world about which data is stored in database. Database Management System (DBMS) A software package to facilitate the creation and maintenance of a computerized database. Database System The DBMS software together with the data itself. Chapter 1 Database and Database Users

Main Characteristics of Database Technology
Self-contained nature of a database system A DBMS catalog stores the description (meta-data) of the database. This allows the DBMS software to work with different databases. Insulations between program and data Data abstractions A data model is used to hide storage details and present the user with a conceptual view of the database. Program-data independence Allows changing data storage structures without having to change the DBMS access programs. Program-operation independence Allows changing operation implementation without having to change the DBMS access programs. Support of multiple views of data Chapter 1 Database and Database Users

Additional Characteristics of Database Technology
Controlling data redundancy Restricting unauthorized access to data. Providing persistent storage for program objects and data structure. Providing multiple interfaces to different classes of users. Representing complex relationships among data. Enforcing integrity constraints on the database. Providing backup and recovery services. Potential for enforcing standards. Flexibility to change data structures. Reduced application development time. Availability of up-to-date information. Economies of scale. Chapter 1 Database and Database Users

Classes of Database Users
Workers on the scene : Persons whose job involves daily use of a large database. Database administrators (DBAs): Responsible for managing the database system. Database designers : Responsible for designing the database. End users : Access the database for querying , updating , generating reports, etc. Casual end users : Occasional users. Parametric (or naive) end users : They use pre-programmed canned transactions to interact continuously with the database. For example, bank tellers or reservation clerks. Sophisticated end users : Use full DBMS capabilities for implementing complex applications. System Analysts/Application programmers : Design and implement canned transactions for Parametric users. Workers behind the scene: Persons whose job involves design , development , operation, and maintenance of the DBMS software and system environment. DBMS designers and implementers : Design and implement the DBMS software package itself. Tool developers : Design and implement tools that facilitate the use of DBMS software. Tools include design tools , performance tools , special interfaces , etc. Operators and maintenance personnel : Work on running and maintaining the hardware and software environment for the database system. Chapter 1 Database and Database Users

When not to Use a DBMS Main costs of using a DBMS
High initial investment and possible need for additional hardware. Overhead for providing generality, security, recovery, integrity, and concurrency control. When DBMS may be unnecessary: If the database and applications are simple, well defined and not expected to change. If there are stringent real-time requirements that may not be met because of the DBMS overhead If access to data by multiple users is not required. Chapter 1 Database and Database Users

Database System Concepts and Architecture
Data Models Three-Schema Architecture Data Independence DBMS Languages DBMS Interfaces DBMS Architecture Database System Utilities Classification of DBMS Chapter 2 Database Concepts and Atchitecture

Data Models Data Model Data Model Operations Categories of data models
A set of concepts to describe the structure of a database, and certain constraints that the database should obey. Data Model Operations Operations for specifying database retrievals and updates by referring to the concepts of data model. Categories of data models Conceptual (high-level, semantic) data models: Provide concepts that are close to the way many users perceive data. (Also called entity-based or object-based data models) Physical (low-level, internal) data models: Provide concepts that describe the details of how data is stored in the computer. Implementation (record-oriented) data models: Provide concepts that fall between above two, balancing user views with some computer storage details. Chapter 2 Database Concepts and Atchitecture

Data Models Database Schema Database catalog Schema Diagram
The description of database. Includes description of database structure and the constraints that should hold on the database. Database catalog Stores database schema. Schema Diagram A diagrammatic display of ( some aspects of) a database schema. Database Instance The actual data stored in a database at a particular moment in time. Also called database state (or occurrence) The database schema changes very infrequently. The database state changes every time the database is updated. Schema is also called intension, whereas the state is called extension. Chapter 2 Database Concepts and Atchitecture

Three Schema Architecture
Internal schema at the internal level to describe data storage structures and access paths. Typically uses a physical data model. Conceptual schema at the conceptual level to describe the structure and constraints for the whole database. Uses a conceptual or an implementation data model . External schemas at the external level to describe the various user views. Usually uses the same data model as the conceptual level. Mappings transform requests and results between levels. Chapter 2 Database Concepts and Atchitecture

Database System Architecture
External Level External View External View external/conceptual mapping Conceptual Level Conceptual Schema conceptual/internal Mapping Internal Level Internal Schema Stored Databases Chapter 2 Database Concepts and Atchitecture

Data Independence Logical Data Independence Physical Data Independence
The capacity to change the conceptual schema without having to change the external schemas and their application programs. Physical Data Independence The capacity to change the internal schema without having to change the conceptual schema. When a schema at a lower level is changed, only the mappings between this schema and higher level schemas need to be changed in a DBMS that fully supports data independence. Mappings create overhead Chapter 2 Database Concepts and Atchitecture

Database System Languages
Data Definition Language (DDL) Used by the DBA and database designers to specify the conceptual schema of a database. In many DBMSs, the DDL is also used to define internal and external schemas(views). In some DBMSs, separate storage definition language (SDL) and view definition language (VDL) are used to define internal and external schemas. Data Manipulation Language (DML) Used to specify database retrievals and updates. High-level (nonprocedural) DML can be used on its own to specify database operations. Low-level (procedural) DML retrieves a record at a time and must be embedded in a general-purpose programming language. When DML is embedded in a general-purpose programming language (host language), it is called data sublanguage. When DML is used in a stand-alone interactive manner, it is called query language Chapter 2 Database Concepts and Atchitecture

DBMS Interfaces Stand-alone query language interfaces.
Programmer interfaces for embedding DML in programming languages: Pre-compiler Approach Procedure (Subroutine) Call Approach Menu-based Graphic-based Forms-based Natural language Combination of above Parametric interfaces using function keys Report generation languages Interfaces for DBA: Creating accounts, granting authorizations Setting system parameters Changing schemas or access path Chapter 2 Database Concepts and Atchitecture

Database System Utilities
Loading data stored in files into a database. Backing up the database periodically on tape. Reorganizing database file structures. Generating Report. Performance monitoring. Sorting files. User monitoring. Data compression. Chapter 2 Database Concepts and Atchitecture

Classification of DBMSs
Based on the data model used: Relational Multidimensional Network Hierarchical. Object-oriented Semantic Entity-Relationship Other Classifications: Single-user vs. multi-user Centralized vs. distributed Homogeneous vs. Heterogeneous OLTP vs. OLAP Chapter 2 Database Concepts and Atchitecture

Database Design Goals of Database Design Database Design Process
Satisfy the information content requirements of the specified users and applications Provide natural and easy-to-understand structuring of information Support processing requirements and any performance objectives Database Design Process Requirement collection and analysis Conceptual database design Choice of DBMS Data model mapping (Logical database design) Physical database design Database system implementation and tuning Chapter 16 Practical Database Design and Tuning

Requirement Collection and Analysis
The major application areas and user groups that will use the database or whose work will be affected by it are identified. Key individuals and committees within each group are chosen to carry out subsequent steps of requirement analysis Existing documentations concerning the applications is studied and analyzed. The current operating environment and planned use of the information is studied. Written responses to sets of questions are sometimes collected from potential users or user groups. Key individuals may be interviewed to help in assessing the worth of information and in setting up of priorities. Chapter 16 Practical Database Design and Tuning

Conceptual Database Design
Conceptual Schema Design Choice of high-level conceptual data model such as ER model and dimensional model Approaches to conceptual schema design centralized schema design approach view integration approach Strategies for conceptual schema design top-down strategy bottom-up strategy inside-out strategy mixed strategy Transaction Design Chapter 16 Practical Database Design and Tuning

Physical Database Design
Criteria for guiding the physical database design Response time Space utilization Transaction throughput Physical database design in relational database Factors that influent the physical database design Analyzing the database queries and transactions Analyzing the expected frequencies of invocation of queries and transactions Analyzing the time constraints for queries and transactions Analyzing the expected frequencies of update operations Analyzing the uniqueness constraints on attributes Physical database design decisions Indexing De-normalization Storage design Chapter 16 Practical Database Design and Tuning

Database Tuning in Relational Database
Goals Make application run fast lower the response time of queries and transactions improve the overall throughput of transactions Tuning indexes Some queries may take too long for lack of an index Some indexes may not get utilized Some indexes may causing excessive overhead Tuning database design De-normalization Table partition Duplicate attributes Tuning queries Chapter 16 Practical Database Design and Tuning

Automated Design Tools
Database Design Tools Erwin Rational Rose Power Designer Schema Diagram Notation UML (Unified Modeling Language) IDEF1X (Integration Definition for Information Modeling) IE (Information Engineering) CHEN's ERD Notation Chapter 16 Practical Database Design and Tuning

Entity-Relationship (ER) Modeling
Example Database Application (COMPANY) ER Model Concepts Entities and Attributes Entity Types, Value Sets, and Key Attributes Relationships and Relationship Types Structural Constraints and Roles Weak Entity Types ER Diagrams Notation Relationships of Higher Degree Enhanced ER Modeling Chapter 3 Data Modeling Using the Entity-Relationship Model

Example of COMPANY Database
Requirements for the COMPANY Database: The company is organized into departments. Each department has a name, number, and a employee who manages the department. We keep track of the start date of the department manager. A department may have several locations. Each department controls a number of projects. Each project has a name, number, and is located at a single location. We store each employee's social security number, address, salary, sex and birth date. Each employee works for one department but may work on several projects. We keep track of the number of hours per week that an employee currently works on each project. We also keep track of the direct supervisor of each employee. each employee may have a number of dependents. For each dependent, we keep their name, sex, birth date, and relationship to the employee. Chapter 3 Data Modeling Using the Entity-Relationship Model

ER Model Concepts: Entities and Attributes
Entities are specific objects or things in the mini-world that are represented in the database; for example, the EMPLOYEE John Smith, the Research DEPARTMENT, the ProductX PROJECT. Attributes Attributes are properties used to describe an entity; for example, an EMPLOYEE entity may have a Name, SSN, Address, Sex, BirthDate. A specific entity will have a value for each of its attributes; for example a specific employee entity may have Name = 'John Smith', SSN = ' ', Address = '731 Fondren , Houston, TX', Sex = 'M', BirthDate = '09-JAN-55'. Attribute Types Simple: each entity has a single atomic value for the attribute; for example SSN or Sex. Composite: Attribute may be composed of several components; for example Name(FirstName, MiddleName, LastName). Composition may form a hierarchy where some components are themselves composite. Multi-Valued: An entity may have multiple values for that attribute; for example Color of a CAR or PreviousDegrees of a STUDENT. Denoted as {Color} or { PreviousDegrees}. Chapter 3 Data Modeling Using the Entity-Relationship Model

ER Model Concept: Entity Types and Key Attributes
Entity type defines a set of entities that have the same attributes. For example, the EMPLOYEE entity type or the PROJECT entity type. Key Attribute An attribute of an entity type for which each entity must have a unique value is called a key attribute of the entity type. For example, SSN of EMPLOYEE. A key attribute may be composite. For example, VehicleRegistrationNumber is a key of the CAR entity type with components(Number, State). An entity type may have more than one key. For example, the CAR entity type may have two keys: VehicleIdentificationNumber and VehicleRegistrationNumber(Number, State). Domains (Value Sets) of Attributes Each simple attribute of an entity type is associates with a domain, which specifies the set of values the may be assigned to that attribute for each individual entity. Chapter 3 Data Modeling Using the Entity-Relationship Model

ER Model Concepts: Relationships and Relationship Types
A relationship relates two or more distinct entities with a specific meaning; for example, EMPLOYEE John Smith works on the ProductX PROJECT or EMPLOYEE Franklin Wong manages the Research DEPARTMENT. Relationship Type Relationship of the same type are grouped or typed into a relationship type. For example, the WORKS_ON relationship type in which EMPLOYEEs and PROJECTs participate, or the MANAGEs relationship type in which EMPLOYEEs and DEPARTMENTs participate. More than one relationship type can exist with the same participating entity types; for example, MANAGES and WORKS_FOR are distinct relationships between EMPLOYEE and DEPARTMENT participate. The degree of a relationship type The degree of a relationship type is the number of participating entity types. binary relationships, ternary relationship, n-ary relationship Chapter 3 Data Modeling Using the Entity-Relationship Model

ER Model Concepts: Structural Constraints and roles
A relationship can relate two entities of the same entity type; for example, a SUPERVISION relationship type relates one EMPLOYEE ( in the role of supervisee) to another EMPLOYEE ( in the role of supervisor). This is called a recursive relationship type. A relationship type can have attributes; for example, HoursPerWeek of WORKS_ON; its value for each relationship instance describes the number of hours per week that an EMPLOYEE works on a PROJECT. Structural constraints on relationships Cardinality ratio ( of a binary relationship): 1:1, 1:N, N:1, or M:N. Participation constraint (on each participating entity type): total (called existence dependency) or partial. Chapter 3 Data Modeling Using the Entity-Relationship Model

ER Model Concepts: Weak Entity Types
An entity type that does not have a key attribute A weak entity type must participate in an identifying relationship type with an owner or identifying entity type Entities are identified by the combination of a partial key of the weak entity type and the key of the identifying entity type. Example Suppose that a DEPENDENT entity is identified by the dependent’s first name and birth date, and the specific EMPLOYEE that the dependent is related to. DEPENDENT is a weak entity type with EMPLOYEE as its identifying entity type via the identifying relationship type DEPENDENT_OF. Chapter 3 Data Modeling Using the Entity-Relationship Model

Conceptual Design of COMPANY Database
Entity types DEPARTMENT PROJECT EMPLOYEE DEPENDENT Relationship types Manage (1:1) Work_for (1:n) Supervision (1:n) Controls (1:n) Works_on (m:n) Has_dependent (1:n) Chapter 3 Data Modeling Using the Entity-Relationship Model

ER Diagram of COMPANY Database
EMPLOYEE Bdate SSN Name Fname Lname Minit Sex Address DEPARTMENT Name Number Location Works_for N 1 Manages 1 StartDate Supervision N 1 Controls 1 N Has_dependents 1 N Works_on Hours M N DEPENDENT Name Sex Bdate Relationship PROJECT Name Number Location Chapter 3 Data Modeling Using the Entity-Relationship Model

Alternative Notation for Relationship Structural Constraints
Specified on each participation of an entity type E in a relationship type R. Specifies that each entity e in E participates in at least min and at most max relationship instances in R. Default(no constraint): min = 0, max = n. Must have min  max, min  0, max  1. Examples A department has exactly one manager and an employee can manage at most one department. Specify (1,1) for participation of DEPARTMENT in MANAGES Specify (0,1) for participation of EMPLOYEE in MANAGES An employee can work for exactly one department but a department can have any number of employees. Specify (1,1) for participation of EMPLOYEE in WORKS_FOR Specify (0,n) for participation of DEPARTMENT in WORKS_FOR Chapter 3 Data Modeling Using the Entity-Relationship Model

ER Diagram of COMPANY Database
EMPLOYEE Bdate SSN Name Fname Lname Minit Sex Address DEPARTMENT Name Number Location Works_for 0:N 1:1 Manages 1:1 0:1 StartDate Supervision N 1 Controls 1 N Has_dependents 1 N Works_on Hours M N DEPENDENT Name Sex Bdate Relationship PROJECT Name Number Location Chapter 3 Data Modeling Using the Entity-Relationship Model

Enhanced Entity-Relationship and Object Modeling
Subclass, Superclass and Inheritance Specialization and Generalization Disjoin/Overlapping Total/Partial Union/Categories Chapter 4 Enhanced Entity-Relationship and Object Modeling

The Relational Data Model
Relational Model Concepts Characteristics of Relations Relational Integrity Constraints Domain Constraints Key Constraints Entity Integrity Constraints Referential Integrity Constraints Update Operations on Relations Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Relational Model Concepts
Relation name Attributes PRODUCT ProductID ProductName UnitPrice UnitInStock 1 Chai 18.00 39 3 Aniseed Syrup 22.00 53 11 Queso Cabrales 21.00 22 Tuples ORDER_ITEM OrderID ProductID Quantity Discount UnitPrice 1 20 0.1 3 15 2 30 0.2 11 10 35 0.15 20.00 25.00 18.00 22.00 21.00 Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Relational Model Concepts
Relation ( informally). A table of values. Each column in the table has a column header called an attribute. Each row is called a tuple. Formal relational concepts. Domain: A set of atomic (indivisible) values. Attribute: A name to suggest the meaning that a domain plays in a particular relation. Each attribute Ai has a domain Dom(Ai). Relation schema: A relation name R and a set of attributes Ai that define the relation. Denoted by R(A1, A2, ... ,an). For example: student(name, SSN, BirthDate, Addr). Relational Database Schema: A set S of relation schemas that belong to the same database. S is the name of the database. S = {R1, R2, ...,Rn}. Degree of a relation: its number of attributes n. Tuple t of R(A1, A2,....,An): a (ordered) set of values t = < v1, v2, ..., vn> where each value vi is an element of Dom(Ai). Also called a n-tuple. Relation instance r(r): A set of tuples r(r) = {t1, t2,...,Tm}, or alternatively r(r) dom(a1)  dom(a2)  ...  dom(an). Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Characteristics of Relations
The tuples are not considered to be ordered, even though they appear to be in the tabular form. We will consider the attributes in R(A1, A2, ...,An) and the values in t = < v1, v2, .., vn> to be orderd.( However, a more general alternative definition of relation does not require this ordering). All values are considered atomic (indivisible). A special null value is used to represent values that are unknown or inapplicable to certain tuples. Notation We refer to component values of a tuple t by t[Ai] = vi (the value of attribute Ai for tuple t) Similarly, t[Au, Av, ..., Aw] refer to the sub-tuple of t containing the values of attributes Au, Av, ..., Aw, respectively. Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Relational Constraints
Relation name Primary key Attributes PRODUCT ProductID ProductName UnitPrice UnitInStock 1 Chai 18.00 39 3 Aniseed Syrup 22.00 53 11 Queso Cabrales 21.00 22 Tuples Primary key Foreign key ORDER_ITEM OrderID ProductID Quantity Discount UnitPrice 1 20 0.1 3 15 2 30 0.2 11 10 0.15 20.00 25.00 18.00 22.00 Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Constraints are conditions that must hold on all valid relation instances. There are three main types of constraints: Domain Constraints Values of each attribute must be atomic. Key Constraints Superkey of R: A set of attributes SK of R such that no two tuples in any valid relation instance r(R) will have the same value for SK. That is, for any distinct tuples t1 and t2 in r(R), t1[SK]  t2[SK] Key (candidate key) of R: A “minimal” superkey; that is, a superkey K such that removal of any attribute form K results in a set of attributes that is not a superkey. Example: The CAR relation schema: CAR(State, Reg#, SerialNo, make, Model, Year) has two keys: Key1 = {State, Reg#}, Key2 {SerialNo}; which are also superkeys. {SerialNo, Make} is a superkey but not a key. If a relation has several candidate keys, one is chosen arbitrarily to be the primary key. The primary key attributes are underlined. Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Entity Integrity The primary key attributes PK of each relation schema R in S can not have null values in any tuple of r(R). This is because primary key values are used to identify the individual tuples. t[PK]  null for any tuple t in r(R). Referential Integrity Referential integrity constraint is used to specify a relationship among tuples in two relations: the referencing relation and referenced relation. It involves two relations. Tuples in the referencing relation R1 have attributes FK (called foreign key attributes) that reference the primary key attributes PK of the referenced relation R2. A tuple t1 in R1 is said to reference a tuple t2 in R2 if t1[FK] = t2 [PK]. A referential integrity constraint can be displayed in a relational database schema as a directed arc from R1.FK to R2. Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Operations Relation name Attributes PRODUCT ProductID ProductName
UnitPrice UnitInStock 1 Chai 18.00 39 Tuples 3 Aniseed Syrup 22.00 53 11 Queso Cabrales 21.00 22 13 Syrup 23.00 20 13 xyz 22.00, 21.00 25, 35 ORDER_ITEM OrderID ProductID Quantity Discount UnitPrice 1 20 0.1 20.00 1 3 15 0.1 25.00 2 1 30 0.2 18.00 2 11 10 0.2 22.00 3 11 35 0.15 21.00 Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Update Operations on Relations
INSERT a tuple DELETE a tuple MODIFY a tuple Integrity constraints should not be violated by the update operations. Insert operation could violate any constraint. Delete operation could violate referential constraints. Modify a primary key or foreign key attribute is equivalent to delete one tuple and insert another. Modify other attributes cause no problems. Several update operations may have to be grouped together. Updates may propagate to cause other updates automatically. This may be necessary to maintain integrity constraints. In case of integrity violation, several actions can be taken: cancel the operation that causes the violation perform the operation but inform the user of violation trigger additional updates so the violation is corrected execute a user-specified error-correction routine Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Data Model Mapping ER-to-Relational Mapping EER-to-Relational Mapping
Capter 9 ER- and EER-to-Relational Mapping, and Other Relational Languages

Relational Model of COMPANY Database
SALARY SEX ADDRESS BDATE SSN LNAME MINIT FNAME EMPLOYEE SUPERSSN DNO DNUMBER DNAME DEPARTMENT MGRSTARTDATE MGRSSN DLOCATION DNUMBER DEPT_LOCATION PLOCATION PNUMBER PNAME PROJECT DNUM HOURS PNO ESSN WORKS_ON SEX BDATE RELATIONSHIP DEPENDENT_NAME ESSN DEPENDENT Capter 9 ER- and EER-to-Relational Mapping, and Other Relational Languages

ER-to-Relational Mapping
STEP 1: For each regular (strong) entity type E in the ER schema, create a relation R that includes all the simple attributes of E. Include only the simple component attributes of a composite attribute. Choose one of the key attributes of E as primary key for R. If the chosen key of E is composite, the set of simple attributes that form it will together form the primary key of R. STEP 2: For each weak entity type W in the ER schema with owner entity type E, create a relation R, and include all simple attributes (or simple components of composite attributes) of W as attributes of R. In addition, include as foreign key attributes of R the primary key attribute(s) of the relation(s) that correspond to the owner entity type(s); this takes care of the identifying relationship type of W. The primary key of R is the combination of the primary key(s) of the owner(s) and the partial key of the weak entity type W, if any. STEP 3: For each binary 1:1 relationship type R in the ER schema, identify the relations S and T that correspond to the entity types participating in R. Choose one of the relations—S, say—and include as foreign key in S the primary key of T. It is better to choose an entity type with total participation in R in the role of S. Include all the simple attributes (or simple components of composite attributes) of the 1:1 relationship type R as attributes of S. STEP 4: For each regular binary 1:N relationship type R, identify the relation S that represents the participating entity type at the N-side of the relationship type. Include as foreign key in S the primary key of the relation T that represents the other entity type participating in R. Include any simple attributes (or simple components of composite attributes) of the 1:N relationship type as attributes of S. Capter 9 ER- and EER-to-Relational Mapping, and Other Relational Languages

STEP 5: For each binary M:N relationship type R, create a new relation S to represent R. Include as foreign key attributes in S the primary keys of the relations that represent the participating entity types; their combination will form the primary key of S. Also include any simple attributes of the M:N relationship type (or simple components of composite attributes) as attributes of S. Notice that we cannot represent an M:N relationship type by a single foreign key attribute in one of the participating relations—as we did for 1:1 or 1:N relationship types—because of the M:N cardinality ratio. STEP 6: For each multivalued attribute A, create a new relation R. This relation R will include an attribute corresponding to A, plus the primary key attribute K—as a foreign key in R—of the relation that represents the entity type or relationship type that has A as an attribute. The primary key of R is the combination of A and K. If the multivalued attribute is composite, we include its simple components. STEP 7: For each n-ary relationship type R, where n > 2, create a new relation S to represent R. Include as foreign key attributes in S the primary keys of the relations that represent the participating entity types. Also include any simple attributes of the n-ary relationship type (or simple components of composite attributes) as attributes of S. The primary key of S is usually a combination of all the foreign keys that reference the relations representing the participating entity types. However, if the cardinality constraints on any of the entity types E participating in R is 1, then the primary key of S should not include the foreign key attribute that references the relation E’ corresponding to E. This concludes the mapping procedure. Capter 9 ER- and EER-to-Relational Mapping, and Other Relational Languages

EER-to-Relational Mapping
STEP 8: Convert each specialization with m subclasses {S1, S2, . . ., Sm} and (generalized) superclass C, where the attributes of C are {k, a1, . . ., an} and k is the (primary) key, into relation schemas using one of the four following options: Option 8A: Create a relation L for C with attributes Attrs(L) = {k, a1, . . ., an} and PK(L) = k. Create a relation Li for each subclass Si, 1 1 i 1 m, with the attributes Attrs(Li) = {k}D {attributes of Si} and PK(Li) = k. Option 8B: Create a relation Li for each subclass Si, 1 1 i 1 m, with the attributes Attrs(Li) = {attributes of Si}D {k, a1, . . ., an} and PK(Li) = k. Option 8C: Create a single relation L with attributes Attrs(L) = {k, a1, . . ., an} D {attributes of S1} D D {attributes of Sm} D {t} and PK(L) = k. This option is for a specialization whose subclasses are disjoint, and t is a type (or discriminating) attribute that indicates the subclass to which each tuple belongs, if any. This option has the potential for generating a large number of null values. Option 8D: Create a single relation schema L with attributes Attrs(L) = {k, a1, . . ., an} D {attributes of S1} D D {attributes of Sm} D {t1, t2, . . ., tm} and PK(L) = k. This option is for a specialization whose subclasses are overlapping (but will also work for a disjoint specialization), and each ti, 1 1 i 1 m, is a Boolean attribute indicating whether a tuple belongs to subclass Si. Capter 9 ER- and EER-to-Relational Mapping, and Other Relational Languages

ER Model Relational Model Entity Entity Relation 1:1 and 1:N relationship type Foreign key M:N relationship type Relationship relation and two foreign keys N-ary relationship type Relationship relation and n foreign keys Simple attribute Attribute Composite attribute Set of component attributes Multi-valued attributes Relation and foreign keys Value set (Domain) Domain Key attribute Primary (or candidate) key Capter 9 ER- and EER-to-Relational Mapping, and Other Relational Languages

The Relational Algebra
Relational algebra is a collection of operations to manipulate relations Query result is in the form of a relation Relational Operations SELECT  PROJECT  operations Sequences of operations and renaming of attributes Set operations UNION  INTERSECTION  DIFFERENCE  CARTESIAN PRODUCT  JOIN operations Other relational operations DIVISION OUTER JOIN AGGREGATE FUNCTIONS. Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Relational Operations
SELECT operation (denoted by ) Selects the tuples (rows) from a relation R that satisfy a certain selection condition c Form of the operation: c(R) The condition c is an arbitrary Boolean expression on the attributes of R Resulting relation has the same attributes as R Resulting relation includes each tuple in r(R) whose attribute values satisfy condition c Examples:  DNO = 4 (EMPLOYEE)  SALARY > 30333(EMPLOYEE)  (( DNO = 4 AND SALARY > ) OR DNO = 5) (EMPLOYEE) Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Examples: DNO = 4 (EMPLOYEE) Jennifer S Wallace Berry, Bellaire, TX F Ahmad V Jabbar Dallas, Huston, TX M Alicia J Zelaya Castle, Spring, TX F SALARY > 30333(EMPLOYEE) Franklin T Wong Voss, Huston, TX M Ramesh K Narayan Fire Oak, Humble, TX M James E Borg Stone, Huston, TX M null Jennifer S Wallace Berry, Bellaire, TX F (( DNO = 4 AND SALARY > ) OR DNO = 5) (EMPLOYEE) Franklin T Wong Voss, Huston, TX M Ramesh K Narayan Fire Oak, Humble, TX M Jennifer S Wallace Berry, Bellaire, TX F Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

PROJECT operation(denoted by ) Keeps only certain attributes (columns) from a relation R specified in an attribute list L Form of operation:  L(R) Resulting relation has only those attributes of R specified in L The PROJECT operation eliminates duplicate tuples in the resulting relation so that it remains a mathematical set ( no duplicate elements) Example  FNAME,LNAME, SALARY(EMPLOYEE)  SEX, SALARY(EMPLOYEE) Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Example FNAME,LNAME, SALARY(EMPLOYEE) John Smith Franklin Wong Joice English Ramesh Narayan James Borg Jennifer Wallace Ahmad Jabbar Alicia Zelaya SEX, SALARY(EMPLOYEE) F F M M M M M Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Sequence of operations: Several operations can be combined to form a relational algebra expression (query) Example Retrieve the names and salaries of employees who work in department 4. FNAME, LNAME, SALARY( DNO=4 (EMPLOYEE)) Jennifer Wallace Ahmad Jabbar Alicia Zelaya Alternatively we specify explicit intermediate relations for each step: DEPT4_EMPS  DNO= 4 (EMPLOYEE) R   FNAME, LNAME, SALLRY(DEPT4_EMPS) Attributes can optionally be renamed in the resulting left-hand side relation(this may be required for some operations that will be presented later): DEPT4_MPS   DNO =4 (EMPLOYEE) R(FIRSTNAME,LASTNAME,SALARY)   FNAME,LNAME,SALARY(DEPT4_EMPS) Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Set Operations UNION: R1  R2, INTERSECTION: R1  R2 SET DIFFERENCE: R1  R2 CARTESIAN PRODUCT: R1  R2 For , , , the operand relations R1(A1, A2, ...,An) and R2(B1, B2, ...,Bn) must have the same number of attributes, and the domains of corresponding attributes must be compatible; that is dom(Ai) = dom(Bi) for i = 1,2,..,n. This condition is called union compatibility. The resulting relation for ,  or  has the same attribute names as the first operand relation R1(by convention). Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Cartesian product R(A1, A2,....,Am,B1,B2,...,Bn)  R1(A1, A2,....,Am)  R2(B1,B2,...,Bn) A tuple t exists in R for each combination of tuples t1 from R1 and t2 from R2 such that t[A1, A2,....,Am ] = t1 and t[B1,B2,...,Bn] = t2 If R1 has n1 tuples and R2 has n2 tuples, then R will have n1*n2 tuples CARTESIAN PRODUCT is a meaningless operation on its own. It can combine related tuples from two relations if followed y the appropriate SELECT operation. Example: Combine each DEPARTMENT tuple with the EMPLOYEE tuple of the manager. DEP_EMP  DEPARTMENT  EMPLOYEE DEPT_MANAGER  MGRSSN = SSN(DEP_EMP) James E Borg … Headquarters Jennifer S Wallace … Administration Franklin T Wong … Research Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

JOIN operation THETA JOIN Similar to CARTESIAN PRODUCT followed by a SELECT. The condition c is called the join condition. R(A1, A2,....,Am,B1,B2,...,Bn)  R1(A1, A2,....,Am) c R2(B1,B2,...,Bn) c is in the form of <condition> AND <condition> AND AND <condition> , where each condition is of the form Ai θ Bj, Ai is an attribute of R, Bj is an attribute of S, Ai and Bj have the same domain, and θ (theta) is one of the comparison operators {=, <, , >, , }. EQUIJOIN The condition c uses only operator '='. The attributes appear in condition c are called join attributes Examples: Retrieve each DEPARTMENT’s name and its manager’s name: T  DEPARTMENT MGRSSN = SSN EMPLOYEE RESULT   DNAME,FNAME,LNAME (T) Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

JOIN operations NATURAL JOIN(*): In an EQUIJOIN R  R1 cR2, the join attributes of R2 appear redundantly in the result relation R. In a NATURAL JOIN, the redundant join attributes of R2 are eliminated from R. The equality condition is implied and need not be specified. R  R1 * ( join attributes of R1), (join attributes of R2) R2 If the join attributes have the same names in both relations, they need not be specified and we can write R R1*R2. Examples: Retrieve each EMPLOYEE’s name and the name of the DEPARTMENT he/she works for: T  EMPLOYEE* (DNO),(DNUMBER)DEPARTMENT RESULT   FNAME, LNAME, DNAME (T) retrieve each EMPLOYEE’s name and the name of his/ser SUPERVISOR: SUPERVISOR (SUPERSSN,SFN,SLN)   SSN,FNAME,LNAME (EMPLOYEE) T  EMPLOYEE*SUPERVISOR RESULT   FNAME,LNAME,SFN,SLN(T) Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Complete Set of Relational Algebra Operations: All the operations discussed so far can be described as a sequence of only the operations SELECT, PROJECT, UNION, SET DIFFERENCE, and CARTESIAN PRODUCT. Hence, the set {, , , -,  } is called a complete set of relational algebra operations. Any query language equivalent to these operations is called relationally complete. For database applications, additional operations are needed that were not part of the original relational algebra. These include: Aggregate functions and grouping OUTER JOIN and OUTER UNION. Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

Aggregate Functions Functions such as SUM, COUNT, AVERAGE, MIN, MAX are often applied to sets of values or sets of tuples in database applications < grouping attributes>  <function list> (R) The grouping attributes are optional Example 1: retrieve the average salary of all employees ( no grouping needed): R(AVGSAL)   AVERAGE SALARY (EMPLOYEE) Example 2: For each department, retrieve the department number, the number of employees , and the average salary ( in the department): R(DNO,NUMEMPS,AVGSAL)  DNO  COUNT SSN,AVERAGE SALARY (EMPLOYEE) Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

OUTER JOIN: In a regular EQUIJOIN or NATURAL JOIN operation, tuples in R1 or R2 that do not have matching tuples in other relation do not appear in the result. Some queries require all tuples in R1 (or R2 or both) to appear in the result. When no matching tuples are found, nulls are placed for the missing attributes LEFT OUTER JOIN: R1 R2 lets every tuple in R1 appear in the result RIGHT OUTER JOIN: R1 R2 lets every tuple in R2 appear in the result FULL OUTER JOIN: R R2 lets every tuple in R1 or R2 appear in the result Chapter 7 The Relational Data Model, Relational Constraints, and the Relational Algebra

SQL - A Relational Database Language
Basic Concepts Data Definition in SQL Retrieval Queries in SQL Specifying Updates in SQL Relational Views in SQL Creating Indexes in SQL Embedding SQL in a Programming Language Recent Advances in SQL Chapter 8 SQL - The Relational Database Standard

Basic Concept Catalog: A collection of schemas
Schema: A collections of tables and other constructs such as constraints. Table: Represents a relation. It includes base tables and views. Column: Represents an attribute. Name Space Hierarchy: catalog -> schema -> table -> column Qualified Name catalog_name[.schema_name[.table_name[.column_name]]] Chapter 8 SQL - The Relational Database Standard

Data Definition in SQL CREATE TABLE : Example :
Specifies a new base relation by giving it a name and specifying each of its attributes and their data types (INTEGER, FLOAT , DECIMAL (i,j), CHAR(n), VARCHAR(n)). A constraint NOT NULL may be specified on an attribute . Example : CREATE TABLE DEPARTMENT ( DNAME VARCHAR(15) NOT NULL , DNUMBER INT NOT NULL ENIQUE, MGRSSN CHAR(9) NOT NULL, MGRSTARTDATE DATETIME, PRIMARY KEY(DNUMBER), FOREIGN KEY (MGRSSN) REFERENCES EMPLOYEE ); Chapter 8 SQL - The Relational Database Standard

Data Definition in SQL DROP TABLE ALTER TABLE
Used to remove a relation (base table) and its definition. The relation can no longer be used in queries , updates or any other commands since its description no longer exists. Example : DROP TABLE DEPENDENT ; ALTER TABLE Used to add an attribute to one of the base relations. The new attribute will have NULLs in all the tuples of the relation right after the command is executed ; hence, the NOT NULL constraint is not allowed for such an attribute. ALTER TABLE EMPLOYEE ADD JOB VARCHAR(12) ; The database users must still enter a value for the new attribute JOB for each EMPLOYEE tuple. This can be done using the UPDATE command. Chapter 8 SQL - The Relational Database Standard

DDL for COMPANY Database
CREATE TABLE EMPLOYEE ( FNAME VARCHAR(15) NOT NULL, MINIT CHAR, LNAME VARCHAR(15) NOT NULL, SSN CHAR(9) NOT NULL, BDATE DATETIME, ADDRESS VARCHAR(30), SEX CHAR, SALARY DECIMAL(19,2), SUPERSSN CHAR(9), DNO INT NOT NULL, PRIMARY KEY(SSN), FOREIGN KEY (SUPERSSN) REFERENCES EMPLOYEE(SSN), ); Chapter 8 SQL - The Relational Database Standard

CREATE TABLE DEPARTMENT ( DNAME VARCHAR(15) NOT NULL , DNUMBER INT NOT NULL UNIQUE, MGRSSN CHAR(9) NOT NULL, MGRSTARTDATE DATETIME, PRIMARY KEY(DNUMBER), FOREIGN KEY (MGRSSN) REFERENCES EMPLOYEE ); ALTER TABLE EMPLOYEE ADD FOREIGN KEY (DNO) REFERENCES DEPARTMENT(DNUMBER); Chapter 8 SQL - The Relational Database Standard

CREATE TABLE DEPT_LOCATIONS (DNUMBER INT NOT NULL, DLOCATION VARCHAR(15) NOT NULL, PRIMARY KEY (DNUMBER, DLOCATION), FOREIGN KEY (DNUMBER) REFERENCES DEPARTMENT(DNUMBER) ); CREATE TABLE PROJECT (PNAME VARCHAR(15) NOT NULL, PNUMBER INT NOT NULL, PLOCATION VARCHAR(15), DNUM INT NOT NULL, PRIMARY KEY (PNUMBER), FOREIGN KEY (DNUM) REFERENCES DEPARTMENT(DNUMBER) Chapter 8 SQL - The Relational Database Standard

CREATE TABLE WORKS_ON (ESSN CHAR(9) NOT NULL, PNO INT NOT NULL, HOURS DECIMAL(3,1) NOT NULL, PRIMARY KEY (ESSN, PNO), FOREIGN KEY (ESSN) REFERENCES EMPLOYEE(SSN), FOREIGN KEY (PNO) REFERENCES PROJECT(PNUMBER) ); CREATE TABLE DEPENDENT (ESSN CHAR(9) NOT NULL, DEPENDENT_NAME VARCHAR(15) NOT NULL, SEX CHAR, BDATE DATETIME, RELATIONSHIP VARCHAR(8), PRIMARY KEY (ESSN,DEPENDENT_NAME), FOREIGN KEY (ESSN) REFERENCES EMPLOYEE(SSN) Chapter 8 SQL - The Relational Database Standard

Basic Queries in SQL SQL has one basic statement for retrieving information from a database; the SELECT statement This is not the same as the SELECT operation of the relational algebra Important distinction between SQL and the formal relational model: SQL allows a table (relation) to have two or more tuples that are identical in all their attribute values. SQL relations can be constrained to be sets by a key constraint, or by using the DISTINCT option in the SELECT statement. Chapter 8 SQL - The Relational Database Standard

The SELECT Statement Basic form of the SQL SELECT statement is called a mapping or a SELECT-FROM-WHERE block SELECT <attribute-list> FROM <table list> WHERE <condition> where <attribute list> is a list of attribute names whose values are to be retrieved by the query. < table list> is al list of the relation names required to process the query. < condition> is a conditional (Boolean) expression that identifies the tuples to be retrieved by the query Basic SQL queries correspond to using the SELECT, PROJECT, and JOIN operations of the relational algebra. Chapter 8 SQL - The Relational Database Standard

Sample Basic Queries Query 0: Retrieve the birth date and address of the employee whose name is 'John B. Smith'. Q0: SELECT BDATE, ADDRESS FROM EMPLOYEE WHERE FNAME = 'John' AND MINIT = 'B' AND LNAME = 'Smith' Query 1: Retrieve the name and address of all employees who work for the 'Research' department. Q1: SELECT FNAME, LNAME, ADDRESS FROM EMPLOYEE, DEPARTMENT WHERE DNAME='Research' AND DNUMBER = DNO Query 2: For every project located in 'Stafford' , list the project number, the controlling department number, and the department manager's last name, address and birth date. Q2: SELECT PNUMBER, DNUM,LNAME, BDATE, ADDRESS FROM PROJECT, DEPARTMENT, EMPLOYEE WHERE DNUM=DNUMBER AND MGRSSN = SSN AND PLOCATION = 'Stafford' Q2x: SELECT PNUMBER, DNUM,LNAME, BDATE, ADDRESS FROM DEPARTMENT JOIN PROJECT ON (DNUM=DNUMBER) JOIN EMPLOYEE ON (MGRSSN = SSN) WHERE PLOCATION = 'Stafford' Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Aliases
Some queries need to refer to the same relation twice. In this case, aliases are given to the relation name Query 8: For each employee, retrieve the employee’s name, and the name of his/her immediate supervisor. Q8: SELECT E.FNAME, E.LNAME, S.FNAME, S.LNAME FROM EMPLOYEE E, EMPLOYEE S WHERE E.SUPERSSN = S.SSN Renaming attributes Q8A: SELECT E.FNAME as "Employee First Name", E.LNAME as "Employee Last Name", S.FNAME as "Supervisor First Name" , S.LNAME as "Supervisor Last Name" Q8B (SQL Server): SELECT E.LNAME + ', ' + E.FNAME as "Employee Name", S.LNAME + ', ' + S.FNAME as "Supervisor Name" FROM EMPLOYEE E, EMPLOYEE S WHERE E.SUPERSSN = S.SSN In Q8, the alternate relation names E and S are called aliases for the EMPLOYEE relation We can think of E and S as two different copies of the EMPLOYEE relation; E represents employee in the role of supervisees and S represents employees in the role of supervisors Aliasing can also be used in any SQL query for convenience Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Unspecified WHERE-clause
A missing WHERE-clause indicates no condition; hence, all tuples of the relations in the FROM-clause are selected. This is equivalent to the condition WHERE TRUE Query 9: Retrieve the ssn values of all employees. Q9: SELECT SSN FROM EMPLOYEE If more than one relation is specified in the FROM-clause and there is no join condition, then the CARTESIAN PRODUCT of tuples is selected Q10: SELECT SSN, DNAME FROM EMPLOYEE, DEPARTNEMT It is extremely important not to overlook specifying any selection and join conditions in the WHERE-clause; otherwise, incorrect and very large relations may result Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: DISTINCT and *
Use of *: To retrieve all the attribute values of the selected tuples, a * is used, which stands for all the attributes. Q1C: SELECT * FROM EMPLOYEE WHERE DNO = 5 Q1D: SELECT * FROM EMPLOYEE , DEPARTMENT WHERE DNAME = 'Research' AND DNO = DNUMBER Tables as Set SQL does not treat a relation as a set; duplicate tuples can appear. To eliminate duplicate tuples, the keyword DISTINCT is used. Q11: SELECT SALARY Q11A: SELECT DISTINCT SALARY Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Set Operations
SQL has directly incorporated some set operations. There is a union operation (UNION), and in some versions of SQL there are set difference (MINUS) and intersection (INTERSECT) operations The resulting relations of these set operations are sets of tuples; duplicate tuples are eliminated from tuples The set operations apply only to union compatible relations; the two relations must have the same attributes and the attributes must appear in the same order Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Set Operations
Example Query 4: Make a list of all project numbers for projects that involve an employee whose last name is 'Smith' as a worker or as a manager of the department that controls the project Q4: (SELECT DISTINCT PNAME FROM PROJECT, DEPARTMENT, EMPLOYEE WHERE DNUM=DNUMBER AND MGRSSN = SSN AND LNAME = 'Smith') UNION (SELECT PNAME FROM PROJECT, WORKS_ON, EMPLOYEE WHERE PNUMBER = PNO AND ESSN = SSN AND LNAME = 'Smith') Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Substring Comparision
Substring Comparison: The LIKE comparison operator is used to compare partial strings Two reserved characters are used : '%' ( or * in some implementations) replaces an arbitrary number of characters, and '_' replaces a single arbitrary character Query 12: Retrieve all employees whose address is in 'Houston, Texas'. Here, the value of the ADDRESS attribute must contain substring 'Houston, TX' Q12: SELECT FNAME, LNAME FROM EMPLOYEE WHERE ADDRESS LIKE '% Houston,TX%' Query 12A: Retrieve all employees who were born during 1950s. Here, '5' must be the 8th character of the string ( according to our format for date), so that the BDATE value is '_______5_', with each underscore as a place holder for a single arbitrary character Q12A: SELECT FNAME, LNAME WHERE BDATE LIKE '_______5_' Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Arithmetic Operators
The standard arithmetic operators '+', '-', '*', and '/' (for addition, subtraction, multiplication, and division, respectively) can be applied to numeric values in an SQL query result Query 13: Show the effect of giving all employees who work on the 'ProductX' project a 10% raise. Q13: SELECT FNAME, LNAME, 1.1*SALARY FROM EMPLOYEE, WORKS_ON, PROJECT WHERE SSN = ESSN AND PNO = PNUMBER AND PNAME = 'ProductX' Query 14: Retrieve all employees in department 5 whose salary is between $30,000 and $40,000. Q14: SELECT * FROM EMPLOYEE WHERE (SALARY BETWEEN AND 40000) AND DNO = 5; Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: ORDER BY
The ORDER BY clause is used to sort the tuples in a query result based on the values of some attributes. The default order is in ascending order of values We can specify the keyword DESC if we want a descending order; the keyword ASC can be used to explicitly specify ascending order, even though it is default Example Query 15: Retrieve a list of employees and the project each works on , ordered by employee's department and within each department ordered alphabetically by employee last name Q15: SELECT DNAME, LNAME, FNAME, PNAME FROM DEPARTMENT, EMPLOYEE, WORKS_ON, PROJECT WHERE DNUMBER = DNO AND SSN = ESSN AND PNO = PNUMBER ORDER BY DNAME, LNAME Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Nesting of Queries
A complete SELECT query, called a nested query , can be specified within the WHERE-clause of another query, called the outer query Query 1: Retrieve the name and the address of all employees who work for the 'Research' department. Q1a: SELECT FNAME, LNAME, ADDRESS FROM EMPLOYEE WHERE DNO IN ( SELECT DNUMBER FROM DEPARTMENT WHERE DNAME='Research') In general, we can have several levels of nested queries A reference to an unqualified attribute refers to the relation declared in the innermost nested query Only the first level select statement can have order by clause Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Correlated Nested Queries
If a condition in the WHERE-clause of a nested query references an attribute of a relation declared in the outer query, the two queries are said to be correlated The result of a correlated nested query is different for each tuple ( or combination of tuples) of the relation(s) the outer query Query 16: Retrieve the name of each employee who has dependent with the same first name as the employee. Q16: SELECT DISTINCT E.FNAME, E.LNAME FROM EMPLOYEE E WHERE E.SSN IN ( SELECT ESSN FROM DEENDENT WHERE ESSN = E.SSN AND E.FNAME = DEPENDENT_NAME) A query written with nested SELECT...FROM ...WHERE ... blocks and using the = or IN comparison operators can always be expressed as a single block query. For example, Q4a may be written as Q16A: SELECT DISTINCT E.FNAME, E.LNAME FORM EMPLOYEE E, DEPENDENT D WHERE E.SSN = D.ESSN AND E.FNAME = D.DEPENDENT_NAME Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: The Exists Function
EXISTS used to check whether the result of a correlated nested query is empty (contains no tuples ) Query 16: Retrieve the name of each employee who has a dependent with the same first name as the employee Q16B: SELECT FNAME, LNAME FROM EMPLOYEE AS e WHERE EXISTS ( SELECT * FROM DEPENDENT WHERE e.SSN = ESSN AND e.FNAME = DEPENDENT_NAME) Query 6: Retrieve the names of employees who have no dependents Q6: SELECT FNAME, LNAME FROM EMPLOYEE WHERE NOT EXISTS (SELECT * WHERE SSN = ESSN ) Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Explicit Sets
It is also possible to use an explicit set of values in the WHERE-clause rather then a nested query. Query 17: Retrieve the social security numbers of all employees who work on project number 1, 2 or 3 Q17: SELECT DISTINCT ESSN FROM WORKS_ON WHERE PNO IN (1, 2, 3) NULLs in SQL Queries SQL allows queries that check if a value is NULL ( missing or undefined or not applicable) SQL uses IS or IS NOT to compare NULLs because it considers each NULL value distinct form other NULL values, so equality comparison is not appropriate Query 18: Retrieve the names of all employees who do not have supervisors Q18: SELECT FNAME, LNAME FROM EMPLOYEE WHERE SUPERSSN IS NULL Note: If a join condition is specified, tuples with NULL values for the join attributes are not included in the result Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Aggregate Functions
Aggregate Functions: COUNT, SUM, MAX, MIN and AVG Query 19: Find the maximum salary, the minimum salary, and the average salaries among all employees. Q19: SELECT MAX (SALARY), MIN(SALARY), AVG(SALARY) FROM EMPLOYEE Query 20: Find the maximum salary, the minimum salary, and the average salaries among employees who work for the 'Research' department. Q20: SELECT MAX(SALARY). MIN(SALARY), AVG(SALARY) FROM EMPLOYEE, DEPARTMENT WHERE DNO = DNUMBER AND DNAME = 'Research' Queries 21 and 22: Retrieve the total number of employees in the company (Q21), and the number of employees in the 'Research' department(Q22) Q21: SELECT COUNT (*) Q22: SELECT COUNT (*) FROM EMPLOYEE , DEPARTMNET Query 5: Retrieve the names of all employees who have two or more dependents Q5: SELECT LNAME, FNAME FROM EMPLOYEE WHERE (SELECT COUNT (*) FROM DEPENDENT WHERE SSN=ESSN) >= 2; Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: Grouping
In many cases, we want to apply the aggregate functions to subgroups of tuples in a relation Each subgroup of tuples consists of the set of tuples that have the same value for grouping attribute(s) The function is applied to each subgroup independently SQL has a GROUP BY-clause for specifying the grouping attributes, which must also appear in the SELECT-clause Query 24: For each department, retrieve the department number, the number of employees in the department and thier average salary Q24: SELECT DNO, COUNT(*), AVG(SALARY) FROM EMPLOYEE GROUP BY DNO A join condition can be used in conjunction with grouping Query 25: For each project, retrieve the project number, project name, and the number of employees who work on that project Q25: SELECT PNUMBER, PNAME, COUNT (*) FROM PROJECT, WORKS_ON WHERE PNUMBER = PNO GROUP BY PNUMBER, PNAME Chapter 8 SQL - The Relational Database Standard

The SELECT Statement: The Having Clause
Sometimes we want to retrieve the values of these functions for only those groups that satisfy certain conditions The HAVING-clause is used for specifying a selection condition on groups (rather than on individual tuples) Example Query 26: For each project on which more than two employees work, retrieve the project number, project name, and the number of employees who work on that project Q26: SELECT PNUMBER, PNAME, COUNT(*) FROM PROJECT, WORKS_ON WHERE PNUMBER = PNO GROUP BY PNUMBER, PNAME HAVING COUNT (*) > 2 Query 28: For each department that has more than five employees, retrieve the department number and the number of its employees who are making more than $40,000. Q28: SELECT DNUMBER, COUNT (*) FROM DEPARTMENT, EMPLOYEE WHERE DNUMBER=DNO AND SALARY>40000 AND DNO IN (SELECT DNO FROM EMPLOYEE GROUP BY DNO HAVING COUNT (*) > 5) GROUP BY DNUMBER; Chapter 8 SQL - The Relational Database Standard

Summary of SQL Queries A query in SQL can consist of up to six clauses, but only the first two, SELECT and FROM , are mandatory. The clauses are specified in the following order: SELECT < attribute list> FROM < table list> [WHERE <condition>] [GROUP BY < grouping attribute(s)>] [ HAVING < group condition>] [ORDER BY < attribute list>] The SELECT-clause lists the attributes or functions to be retrieved The FROM-clause specifies all relations(or aliases) needed in the query but not those needed in the nested queries The WHERE-clause specifies the conditions for selection and join of tuples from the relations specified in the FROM-clause GROUP BY specifies grouping attributes HAVING specifies a condition for selection of groups ORDER BY specifies an order for displaying the result of a query A query is evaluated by first applying the WHERE-clause, then GROUP BY and HAVING, and finally the SELECT-clause Chapter 8 SQL - The Relational Database Standard

Insert Statement In its simples form, it is used to add a single tuple to a relation. Attribute values should be listed in the same order as the attributes were specified in the CREATE TABLE command U1: INSERT INTO EMPLOYEE VALUES ('Richard ' , 'K', 'Marini', ' ', ' ', '98 Oak Forest, Katy, TX', 'M', , ' ', 4) An alternate form of INSERT specifies explicitly the attribute names that correspond to the values in the new tuple. Attributes with NULL values can be left out Insert a tuple for a new EMPLOYEE for whom we only have values for FNAME, LNAME, and the SSN attributes U1A: INSERT INTO EMPLOYEE (FNAME, LNAME, SSN) VALUES ('Richard' , 'Marini', ' ') Chapter 8 SQL - The Relational Database Standard

Insert Statement Another variation of INSERT allows insertion of multiple tuples in a relation in a single command Example: Suppose we want to create a temporary table that has the name, number of employees and total salaries for each department. A table DEPTS_INFO is created by U3A , and is loaded with the summary information retrieved from the database by the query in U3B U3A: CREATE TABLE DEPTS_INFO (DEPT_NAME VARCHAR(10), NO_OF_EMPS INTEGER, TOTAL_SAL INTEGER); U3B: INSERT INTO DEPTS_INFO (DEPT_NAME, NO_OF_EMPS, TOTAL_SAL) SELECT DNAME, COUNT (*), SUM (SALARY) FROM DEPARTMENT , EMPLOYEE WHERE DNUMBER = DNO GROUP BY DNAME; Chapter 8 SQL - The Relational Database Standard

DELETE Statement Removes tuples from a relation
Includes a WHERE - clause to select the tuples to be deleted Examples : U4A: DELETE FROM EMPLOYEE WHERE LNAME='Brown' U4B: DELETE FROM EMPLOYEE WHERE SSN=' ' U4C : DELETE FROM EMPLOYEE WHERE DNO IN ( SELECT DNUMBER FROM DEPARTMENT WHERE DNAME='Research' ) U4D : DELETE FROM EMPLOYEE Chapter 8 SQL - The Relational Database Standard

UPDATE Statement Used to modify attribute values of one or more selected tuples A WHERE-clause selects the tuples to be modified. An additional SET-clause specifies the attributes to be modified and their new values Example : Change the location and controlling department number of project number 10 to 'Bellaire' and 5, respectively. U5 : UPDATE PROJECT SET PLOCATION='Bellaire' , DNUM=5 WHERE PNUMBER=10 Example : Give all employees in the 'Research' department a 10% raise in salary. U6 : UPDATE EMPLOYEE SET SALARY=SALARY*1.1 WHERE DNO IN (SELECT DNUMBER FROM DEPARTMENT WHERE DNAME='Research' ) Chapter 8 SQL - The Relational Database Standard

Views in SQL A view is a single virtual table that is derived from other base tables or views. A view does not necessarily exist in physical form, which limits the possible update operations that can be applied to views. The CREATE VIEW command is used to specify a view by specifying a (virtual) table and a defining query. The view attribute names can be inherited from the tables in the defining query. A view is not realized at the time of view definition, but rather at the time we specify a query on the view. Examples : V1 : CREATE VIEW WORKS_ON1 AS SELECT FNAME,LNAME,PNAME,HOURS FROM EMPLOYEE , PROJECT, WORKS_ON WHERE SSN=ESSN AND PNO=PNUMBER ; V2 : CREATE VIEW DEPT_INFO(DEPT_NAME,NO_OF_EMPS,TOTAL_SAL) AS SELECT DNAME,COUNT(*),SUM(SALARY) FROM DEPARTMENT, EMPLOYEE WHERE DNUMBER=DNO GROUP BY DNAME ; V3: CREATE VIEW EMP_V AS SELECT FNAME,MINIT,LNAME,SSN,BDATE,ADDRESS,SEX,SUPERSSN,DNO FROM EMPLOYEE Chapter 8 SQL - The Relational Database Standard

Views in SQL A view is removed using the DROP VIEW command.
Example : V1A : DROP VIEW WORKS_ON1 ; V2A : DROP VIEW DEPT_INFO ; Views can also be used as a security and authorization mechanism (See chapter 20) Updating The Views : A view update operation may be mapped in multiple ways to update operations on defining base relations The topic of updating views is still an active research area. view update is unambiguous only if one update on the base relations can accomplish the desired update effect on the view If a view update can be mapped to more than one update on the underlying base relations, we must have a certain procedure to choose the desired update We can make the following general observations : A view with a single defining table is updatable if the view attributes contain the primary key Views defined on multiple tables using joins are generally not updatable Views defined aggregate functions are not updatable Chapter 8 SQL - The Relational Database Standard

The Relational Calculus
A formal language based on first-order predicate calculus Many commercial relational languages based on some aspects of relational calculus, including SQL. QBE (Chapter 9) is closer to relational calculus than SQL. Difference from Relational Algebra : One declarative calculus expression specifies a retrieval query A sequence of operation is used in relational algebra Relational Algebra is more procedural Relational calculus is more declarative (less procedural) Expressive power of the two languages is identical. Relational Completeness : A relational query language L is relationally complete if we can express in L any query that can be expressed in the relational calculus (or Algebra). Most relational query languages are relationally complete More expressive power is provided by operations such as aggregate functions, grouping and ordering . Capter 9 ER- and EER-to-Relational Mapping, and Other Relational Languages

Functional Dependencies and Normalization for Relational Database
Informal Design Guidelines for Relational Databases Semantics of the Relational Attributes Redundant Information in Tuples and Update Anomalies Null Values in Tuples Spurious Tuples Functional Dependencies(FDs) Definition of FD Inference Rules for FDs Equivalence of Sets of FDs Minimal Sets of FDs Normal Forms Based on Primary Keys Introduction to Normalization First Normal Form Second Normal Form Third Normal Form General Normal Form Definitions(for Multiple Keys) BCNF(Boyce-Codd Normal Form) Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Informal Design Guidelines for Relational Databases
Guideline 1 (Semantics of the Relation Attributes) Design a relation schema so that it is easy to explain its meaning. Do not combine attributes from multiple entity types and relationship types into a single relation. Intuitively, if a relation schema corresponds to one entity type or one relationship type, the meaning tends to be clear. Otherwise, the relation corresponds to a mixture of multiple entities and relationships and hence becomes semantically unclear. Bad design example DNAME DNO ADDRESS BDATE SSN LNAME MINIT FNAME EMP_DEPT MGRSSN Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Guideline 2 (Redundant information in Tuples And Update Anomalies) Design the base relation schemas so that no insertion, deletion, or modification anomalies are present in the relations. If any anomalies are present, note them clearly and make sure that the programs that update the database will operate correctly. Insertion anomalies To insert a new employee, we must provide values for attributes of department correctly so they are consistent among all employees who work for the same department. It is difficult to insert a new department that has no employee as yet. Deletion anomalies When the last employee of a department is deleted, the information of the department is lost. Modification anomalies To change the value of an attribute of a department, we have to change all employees who work for the department. Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Guideline 3 (Null Values in Tuples) As far as possible, avoid placing attributes in a base relation whose values may frequently be null. If nulls are unavoidable, make sure that they apply in exceptional cases only and do not apply to a majority of tuples in the relation. Null has different interpretations The attribute does not apply to this tuple. The attribute value for this tuple is unknown. The value is known but absent; that is, it has not been recorded yet. Attributes that are NULL frequently could be placed in separate relations (with the primary key) Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Guideline 4 (Spurious Tuples) Design relation schemas so that they can be JOINed with equality conditions on attributes that are either primary keys or foreign keys in a way that guarantees that no spurious tuples are generated. Do not have relations that contain matching attributes other than foreign key-primary key combinations. If such relations are unavoidable, do not join them on such attributes, because the join may produce spurious tuples. Bad designs for a relational database may result in erroneous results for certain JOIN operations The "lossless join" property is used to guarantee meaningful results for join operations Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Guideline 4 (Spurious Tuples) Example DNAME DNO ADDRESS BDATE SSN LNAME MINIT FNAME EMP_DEPT MGRSSN John B Smith 1111 xxx 1 Research Franklin T Wang 2222 WORKS_ON ESSN PNO PNAME DNO HOURS 1111 1 xxx 20 2222 2 30 FNAME MINIT LNAME SSN DNO DNAME PNO HOURS John B Smith 1111 1 Research 20 2222 2 30 Franklin T Wang Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Functional Dependencies
Definition of FD A set of attributes X functionally determines a set of attributes Y if the value of X determines a unique value for Y Written as X Y or can be displayed graphically on a relational schema For any two tuples t1 and t2 in any relation instance r(R): if t1[X] = t2[X] , then t1[Y] = t2[Y] An FD is a property of the attributes in the schema R If K is a key of R , then K functionally determines all attributes in R(since we never have two distinct tuples with t1[K] = t2[K] ) Examples of FD constraints: social security number determines employee name: SSN  ENAME project number determines project name and location: PNUMBER{PNAME , PLOCATION} Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Inference Rules for FDs Given a set of FDs, we can infer additional FDs that hold whenever the FDs in F hold Armstrong's inference rules: IR1. (Reflexive) If X  Y , then XY IR2. (Augmentation) If X Y , then XZYZ IR3. (Transitive) If XY and Y  Z , then X Z IR1 , IR2 ,IR3 form a sound and complete set of inference rules. Some additional inference rules that are useful: (Decomposition) If X YZ ,then XY and X Z (Union)If X  Y and X  Z , then X  YZ (Pseudotransitivity) If X  Y and WY  Z , then WX  Z The last three inference rules, as well as any other inference rule can be deduced from IR1, IR2 , and IR3 (completeness property) Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Inference Rules for FDs Closure of a set F of FDs, denoted as F+, is the set of all FDs that can be inferred from F Armstrong's inference rules are sound and complete Any FD that can infer from F using IR1, IR2, and IR3 holds in every relation. F+ can calculated by repeatedly applying IR1, IR2, and IR3 using the FDs in F Closure of a set of attributes X with respect to F, denoted as X+, is the set of all attributes that are functionally determined by X Algorithm 14.1: calculates X+ with respect to F Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Equivalence of sets of FDs F covers G if every FD in G can be inferred from F (i.e., if G+  F+) Two sets of FDs F and G are equivalent if : every FD in F can be inferred from G, and every FD in G can be inferred from F F and G are equivalent if F covers G and G covers F F and G are equivalent if F+ = G+ Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Minimal Sets of FDs A set of FDs is minimal if it satisfies the following conditions: Every dependency in F has a single attribute for its RHS We can not remove any dependency from F and have a set of dependencies that is equivalent to F. We can not replace any dependency X  A in F with a dependency Y  A , where YX and still have a set of dependencies that is equivalent to F. Every set of FDs has an equivalent minimal set (also called minimal cover) There can be several equivalent minimal sets Algorithm 14.2: Finding a minimal cover of a set of FDs F. Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Normal Forms Based on Primary Keys
Introduction to Normalization Normalization: Process of decomposing unsatisfactory "bad" relations by breaking up their attributes into smaller relations Normal Form: Condition using keys and FDs of a relation to certify whether a relation schema is in a particular normal form 2NF, 3NF, BCNF based on keys and FDs of a relation schema. 4NF based on keys, MVDs; 5NF based on keys, JDs Additional properties may be needed to ensure a good relational design Primary attribute: a member of some candidate key. Chapter 14 Functional Dependencies and Nomalization for Relational Databases

First Normal Form Disallows composite attributes, multi-valued attributes, and nested relations; attributes whose values for an individual tuple are non-atomic Considered to be part of the definition of relation Example: MGRSTARTDATE MGRSSN DNUMBER DNAME DEPARTMENT DLOCATIONS MGRSTARTDATE MGRSSN DNUMBER DNAME DEPARTMENT DLOCATIONS Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Second Normal Form Prime Attribute: attribute that is a member of primary key K Full Functional Dependency: A FD Y  Z , where removal of any attribute from Y means the FD does not hold any more. Examples: { SSN, PNUMBER}  HOURS is a full FD since neither SSN  HOURS nor PNUMBER  HOURS hold A relation schema R is in second normal form (2NF) if every non-prime attribute A in R is fully functionally dependent on the primary key R can be decomposed into 2NF relations via the process of 2NF normalization Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Second Normal Form ENAME PNAME PLOCATION HOURS PNO ESSN EMP_PROJ HOURS PNO ESSN WORKS_ON ENAME SSN EMP PNAME PLOCATION PNO PROJECT Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Third Normal Form Transitive Functional Dependency: a FD YZ that can be derived from two FDs YX and XZ A relation schema R is in third normal form (3NF) if it is in 2NF and no non-prime attribute A in R is transitively dependent on the primary key R can be decomposed into 3NF relations via the process of 3NF normalization ADDRESS DNUM BDATE ENAME SSN EMP_DEPT DNAME DMGRSSN ADDRESS DNUM BDATE ENAME SSN EMP DNAME DMGRSSN DNUM DEPT Chapter 14 Functional Dependencies and Nomalization for Relational Databases

General Normal Form Definitions
General Normal Form Definitions (for Multiple Keys) A relation schema R is in second normal form (2NF) if every non-prime attribute A in R is fully functionally dependent on every key of R Superkey of relation schema R : a set of attributes S of R that contains a key of R A relation schema R is in third normal form (3NF) if whenever a FD X  A holds in R , then either: (a) X is a superkey of R, or (b) A is a prime attribute of R Chapter 14 Functional Dependencies and Nomalization for Relational Databases

BCNF (Boyce-Codd Normal Form)
A relation schema R is in ( Boyce-Codd Normal Form) BCNF if whenever a FD X  A holds in R, then X is a superkey of R Each normal form is strictly stronger than the previous one: Every 2NF relation is in 1NF Every 3NF relation is in 2NF Every BCNF relation is in 3NF There exist relations that are in 3NF but not in BCNF The goal is to have each relation in BCNF (or 3NF) Additional criteria may be needed to ensure that the set of relations in a relational database are satisfactory ( see Chapter 14) Lossless join property Dependency preservation property Additional normal forms are discussed in Chapter 14 4NF ( based on multi-valued dependencies) 5NF (based on join dependencies) Chapter 14 Functional Dependencies and Nomalization for Relational Databases

Relational Database Design Algorithms
Normal Forms are not a sufficient criteria for a good design Example : Any relation with two attributes is always in BCNF - so we can create 2-attribute relations arbitrarily and get BCNF Additional conditions are needed to ensure a good design Relational Decomposition The Dependency Preservation Property The lossless Join Property Null Values and Dangling Tuples Chapter 15 Relational Database Design Algorithms and Further Dependencies

Relational Decomposition
We start with a universal relation schema R containing all the database attributes: R = { A1, A2, ....,An} The design goal is a decomposition D of R into m relation schemas: D = {R1, R2, ...,Rm}, where Each relation Schema Ri contains a subset of the attributes of R. Every attribute in R should appear in at least one Ri. Chapter 15 Relational Database Design Algorithms and Further Dependencies

The Dependency Preservation Property
The database designers define a set F of functional dependencies that should hold on the attributes of R Decomposition should preserve the dependencies; informally, the collection of all dependencies that hold on the individual relations Ri should be equivalent to F. Formally: Define the projection of F on Ri, denoted by F(Ri),to be the set of FDs X  Y in F+ such that (X  Y) in Ri A decomposition D = {R1,R2,...,Rm} is dependency preserving if (F (R1) ...  F(Rm))+ = F+ This property makes it possible to ensure that the FDs in F hold simply by ensuring that the dependencies on each relation Ri hold individually. Relational synthesis algorithm: decompose R into a dependency preserving decomposition D = { R1,R2,...,Rm} with respect to F such that each Ri is in 3NF Find a minimal set of FDs G equivalent to F For each X of an FD X A in G, create a relation schema Ri in D with the attributes {X  A1  A2  ...  Ak} where the Aj's are all the attributes appearing in an FD in G with X as left hand side. If any attributes in R are not placed in any Ri, create another relation in D for these attributes. Problems: must find a minimal cover G for F no efficient algorithm for finding a minimal cover several minimal covers can exist for F; the result of algorithm can be different depending on which is chosen. Chapter 15 Relational Database Design Algorithms and Further Dependencies

The lossless (Non-Additive) Join Property
Informally, this property ensures that no spurious tuples appear when the relations in the decompositions are JOINed. Formally: A decomposition D = { R1,R2,...,Rm} of R has the lossless join property with respect to a set f of FDs if, for every relation instance r(R) whose tuples satisfy all the FDs in F, we have: (R1(r(R))* R2(r(R))*... * Rm(r(R))) = r(R) This condition ensures that whenever a relation instance r(R) satisfies F, no spurious tuples are generated by joining the decomposed relations r(Ri) Since we actually store the decomposed relations as base relations, this condition is necessary to generate meaningful results for queries involving JOINs Chapter 15 Relational Database Design Algorithms and Further Dependencies

Algorithm 15.2 for testing whether a decomposition D satisfies the lossless join property with respect to a set of FDs. Create an initial matrix S with one row i for each relation Ri in D, and one column j for each attribute Aj in R. Set S(i,j)=bij for all matrix entries. (each bij is a distinct symbol associated with indices (i,j)) For each row i representing relation schema Ri For each column j representing attribute Aj if relation Ri includes attribute Aj, then set S(i, j) = aj Repeat the following loop until a complete loop execution results in no changes to S For each functional dependency X Y in F for all rows in S which have the same symbols in the columns corresponding to attributes in X, make the symbols in each column that correspond to an attribute in Y be the same in all these rows as follows: if any of the rows has an “a” symbol for column, set the other rows to that same “a” symbol in the column. If no “a” symbol exists for the attribute in any of the rows, choose one of the “b” symbols that appear in one of the rows for the attribute and set the other rows to that same “b” symbol in the column If a row is made up entirely of “a” symbols, then the decomposition has the loss-less join property, otherwise it does not. Chapter 15 Relational Database Design Algorithms and Further Dependencies

Algorithm 15.3: decomposing R into BCNF relations such that the decompositions have the lossless join property with respect to a set of FDs F on R 1.Set D ¬ {R} 2.while there is a relation schema Q in D that is not in BCNF do begin choose one q in D that is not in BCNF; find a FD X  Y in Q that violates BCNF; replace Q in D by two relation Schemas (Q-Y) and (X  Y) end; This is based on two properties of lossless join decomposition: The decomposition D = {R1, R2} of r has the lossless join property w.r.t. F if and only if either: the FD (R1  R2)  (R1 -R2) is in F+, or the FD (R1  R2)  (R2 -R1) is in F+ If D = {R1, R2,...,Rm} of R has the lossless join property w.r.t. F, and D1 = { Q1, Q2, ..,Qk} of Ri has the lossless join property w.r.t. Ri(F), then D = {R1, R2,..., Ri-1,Q1, Q2, ..,Qk , Rm} has the lossless join property w.r.t. F Chapter 15 Relational Database Design Algorithms and Further Dependencies

There is no algorithm for decomposition into BCNF relations that is dependency preserving. A modification of the synthesis algorithm guarantees both the lossless join and dependency preserving properties but into 3NF relations, not BCNF Fortunately, many 3NF relations are also in BCNF Lossless Join and Dependency Preserving Decomposition into 3NF Relations (Algorithm 15.4): Find a minimal set of FDs G equivalent to F For each left hand side X of an FD X  Y in G create a relation schema Ri in D with the attributes {X  A1  A2  ...  Ak} where Aj's are all the attributes appearing in all FDs in G with X as left hand side. If none of the relations in D contain a key of R, create a relation that contains a key of R and add it to D Chapter 15 Relational Database Design Algorithms and Further Dependencies

Null Values and Dangling Tuples
No fully satisfactory relational design theory that include null values Pay attention to nulls in foreign keys Dangling tuples If a relation schema is decomposed into multiple relations, an inner join between these relations will not reproduce all turples in the original relation. Some tuples are lost and are called dangling turples Chapter 15 Relational Database Design Algorithms and Further Dependencies

Transaction Processing
Basic Concepts Why Concurrent Control Why Recovery is Needed Transaction States The System Log Desirable Properties of Transaction Schedules and Recoverability Serializable Schedules Use of Serializability View Equivalence and View Serializability Chapter 19 Transaction Processing Concepts

Basic Concepts Single user/Multi-user DBMS
A DBMS is single user if at most one user at a time can use the system ; it is multi-user if many users can use the system concurrently. If only a single CPU exists, concurrent execution of the programs is interleaved; If the system has multiple CPU, execution is simultaneous. Transaction A transaction is a logical unit of database processing that includes one or more database operations (insertion, deletion, modification, retrieval). A simplified database model is used for explaining transaction processing concepts. The basic database access operations include : read_item(x): reads a database item named X into the program variable X. write_item(x): write the value of program variable X into the database item named x. Chapter 19 Transaction Processing Concepts

Basic Concepts Executing a read_item(x) includes the following steps:
Find the address of the disk block that contains item x; Copy that disk block into a buffer in main memory; Copy item x from the buffer to the program variable named X. Executing a write_item(x) includes the following steps: Copy item x from the program variable named X into its correct location in the buffer; Store the updated block from the buffer back to disk. Example of concurrent transaction. T1 read_item(X) X=X-N write_item(X) read_item(Y) Y=Y+N write(item(Y) T2 read_item(X) X=X+M write_item(X) Chapter 19 Transaction Processing Concepts

Why Concurrent Control is Needed
Several problems can occur when concurrent transactions execute in an uncontrolled manner. The lost update problem : This occurs when two transactions that access the same database items have their operations interleaved in a way that makes the value of same database item incorrect. The temporary update (or dirty read) problem : This occurs when one transaction updates a database item and then the transaction fails for some reason. The updated item is accessed by another transaction before it is changed back to its original value. The incorrect summary problem : If one transaction is calculating an aggregate function on a number of records while other transaction is updating some of these records, the aggregate function may calculate some values before they are updated and others after they are updated. Whenever a transaction is submitted to a DBMS for execution, the system must make sure that : All the operations in the transaction are completed successfully and their effect is recorded permanently in the database; or the transaction has no effect whatever on the database or on the other transactions in the case of that a transaction fails after executing some of operations but before executing all of them. Chapter 19 Transaction Processing Concepts

Why Concurrent Control is Needed
read_item(X) X=X+M write_item(X) T1 X=X-N read_item(Y) Y=Y+N write(item(Y) T3 rum=0 read_item(A) sum=sum+A read_item(X) sum=sum+X read_item(Y) sum=sum+Y T1 X=X-N write_item(X) Y=Y+N write(item(Y) T2 read_item(X) X=X+M write_item(X) T1 X=X-N read_item(Y) Chapter 19 Transaction Processing Concepts

Why Recovery is Needed Possible reasons for a transaction to fail in the middle of execution System Crash : If the hardware crashes, the contents of main memory may be lost. Transaction or system Error. Local error or exception conditions detected by the transaction Concurrency control enforcement. Disk failure during read or write operation of the transaction. Physical Problems and catastrophes. Whenever a failure occurs, the system must keep sufficient information to recover from the failure. Chapter 19 Transaction Processing Concepts

Transaction Concepts A transaction is an atomic unit of work that either completed in its entirety or not done at all. For recovery purposes, the recovery manager needs to keep track of the following operations: BEGIN_TRANSACTION READ or WRITE END_TRANSACTION COMMIT_TRANSACTION ROLLBACK (or ABORT) States of transaction execution : Active state : transaction starts execution, and issue READ and WRITE operations. Partially committed state : checked by concurrency control and recovery protocol. Committed state : concludes the transaction execution successfully. Fail state : If it is aborted during active state or one of checks fails during partially committed state. Terminated state: indicates the transaction left the system. Chapter 19 Transaction Processing Concepts

The System Log To recover form transaction failures, the system maintains a log, which keeps track of all transaction operations that affect the values of database items. It is kept on disk, and periodically backed up to tape. [ start_transaction , T ] [ write_item, T, X, old_value, new_value ] [ read_item, T,X] [ commit, T] [ abort, T] Chapter 19 Transaction Processing Concepts

Commit Point of Transaction
A transaction T reaches its commit point when all its operations that access the database have been executed successfully and the effect of all operations on the database has been recorded in the log. The transaction writes an entry [ commit , T] into log. Force-writing the log file before committing a transaction. A [checkpoint] record is written into the log periodically at that point when the system writes out to the database on disk the effect of all WRITE operations of committed transactions. Chapter 19 Transaction Processing Concepts

Desirable Properties of Transaction
The desirable properties of atomic transactions (ACID properties) Atomicity : A transaction is an atomic unit of processing. the recovery may need to undo the effects of the transaction if a transaction fails. Consistency preservation : A correct execution of the transaction must keep consistent state (database program or DBMS module) Isolation : A transaction should not make its updates visible to other transactions until it is committed. (Concurrency control). Durability or permanency : Once a transaction is committed, its changes to the database must never be lost because of subsequent failure. (recovery) Chapter 19 Transaction Processing Concepts

Schedules of Recoverability
Schedules of Transactions A schedule S of n transactions T1,T2,...,Tn is an ordering of the operations of the transactions subject to the constraint that, for each transaction Ti, the operations of Ti in S must appear in the same order in which they occur in Ti (Total ordering) The schedule of Figure 19.3(a) Sa : r1(X); r2(X); w1(X);r1(Y);w2(X);c2; w1(Y);c1; The schedule of Figure 19.3(b) : Sb : r1(X); w1(X); r2(X); w2(X); c2; r1(Y); a1 ; Two operations in a schedule are said to conflict if they belong to different transactions, and if they access the same item X, and if one of the two operations is a write_item(X). A schedule S of n transaction T1, T2, ..., Tn is said to be a complete schedule if the following conditions hold : The operations in S are exactly those operations in T1, T2, ..., Tn, including a commit or abort operations as the last operation for each transaction in the schedule. For any pair of operations from the same transaction Ti, their order of appearance in S is the same as their order of appearance in Ti. For any two conflicting operations, one of the two must occur before the other in the schedule Committed projection C(S) of the schedule S includes only the operations in S that belong to committed transactions. Chapter 19 Transaction Processing Concepts

Schedules and Recoverability
Characterizing Schedules Based on recoverability : A schedule S is recoverable if no transaction T in S commits until all transaction T' that have written an item that T reads have committed. A transaction T is said to read from transaction T' in a schedule S if some item X is first written by T' and later read by T. Sc : r1(X); w1(X);r2(X), r1(Y); w2(X); c2 ; a1; is not recoverable. Sd : r1(X); w1(X); r2(X),r1(Y);w2(X); w1(Y) ; c2; c1; is recoverable. Cascading rollback: an uncommitted transaction has to be rolled back because it read an item from a transaction that failed. Se : r1(X); w1(X); r2(X), r1(Y); w2(X); w1(Y); a1, a2; A schedule is said to be cascadeless (avoid cascading rollback) if every transaction in the schedule reads only items that were written by committed transactions. Strict schedule: transactions can neither read nor write an item X until the last transaction that wrote X has committed (or aborted). Sf : w1(X,5) ; w2(X,8) ; a1; Chapter 19 Transaction Processing Concepts

Serialiable Schedules
Serializable Schedules A schedule is serial if, for every transaction T participating the schedule, all the operations of T are executed consecutively in the schedule. Otherwise it is called non-serial schedule. Every serial schedule is considered correct; some nonserial schedules give erroneous results. A schedule S of n transactions is serializable if it is equivalent to some serial schedule of the same n transactions; a nonserial schedule which is not equivalent to any serial schedule is not serializable. The definition of two schedules considered “equivalent”: result equivalent: producing same final state of the database (is not used) conflict equivalent: If the order of any two conflicting operations is the same in both schedules. view equivalent: If each read operation of a transaction reads the result of the same write operation in both schedules and the write operations of each transaction must produce the same results. Conflict serializable: if a schedule S is conflict equivalent to some serial schedule. we can reorder the non-conflicting operations in S until we form the equivalent serial schedule, and S is a serializable schedule. Chapter 19 Transaction Processing Concepts

Testing for Conflict Serializability
Precedence graph (Serialization graph) A precedence graph is a directed graph G = (N,E) that consists of a set of nodes N = { T1, T2, ...,Tn} and a set of directed edges E = {e1, e2,..,em}. There is one node in the graph for each transaction Ti in the schedule. Each edge ei in the graph is of the form (Tj  Tk) , if one of the operations in Tj appears in the schedule before some conflicting operation in Tk. The edges can optionally be labeled by the names of data item that lead to creating the edge. Algorithm If there is a cycle in the precedence graph, schedule S is not serializable. If there is no cycle in the precedence graph, we can create an equivalent serial schedule S' that is equivalent to S by ordering the transactions that participate in S using topological sorting. Chapter 19 Transaction Processing Concepts

Uses of Serializability
A serial schedule represents inefficient processing because no interleaving of operations from different transactions is permitted. A serializable schedule gives us the benefits of concurrent execution without loosing any correctness. It is particularly impossible to determine how the operations of a schedule will be interleaved beforehand to ensure serializability. It is impractical that testing for serializability after transactions are executed, and then cancel the effect of non-serializable schedule. The most practical approach is to ensure serializability without having to test the schedules to themselves for serializability after they are executed. Using the theory of serializability to determine protocols or sets of rules which are followed by every individual transaction or enforced by a DBMS concurrency control system. (Chapter 20) Two-phase locking Timestamp ordering Chapter 19 Transaction Processing Concepts

View Equivalence and View Serializability
Less restrictive definition. Two schedules are said to be view equivalent if the following three conditions hold: The same set of transactions participate in S and S'; and S and S' include the same operations of those transactions. For any operation ri(X) of Ti in S, the value of X read by the operation has been written by an operation Wj(X) of Tj ( or if it is the original value of X before the schedule started), the same condition must hold for the value of X read by operation ri(X) of Ti in S'. If the operation wk(Y) of Tk is the last operation to write item Y in S, then wk(Y) of Tk must also be the last operation to write item Y in S'. A schedule S is said to be view serializable if it is view equivalent to a serial schedule. Constrained Write assumption: any write operation wi(X) in Ti is preceded by a ri(X) in Ti, and that the value written by Wi(X) in Ti depends only on the value of X read by ri(X).(under this assumption, conflict serializability and view serializability are similar) Chapter 19 Transaction Processing Concepts

Transaction Support in SQL
Every transaction has certain characteristics attributed to it Access mode: Read only / Read write Diagnose area size to indicate the number of conditions that can be held simultaneously in the diagnose area. Isolation level Read uncommitted Read committed Repeatable read Serializable Violations Dirty read Non-repeatable read Phantoms Dirty read Nonrepeatable read phantems Read uncommitted y Read committed n Repeatable read serializable Chapter 19 Transaction Processing Concepts

Concurrency Control Locking Techniques
Concurrency Control Based on Timestamp Ordering Multi-version Concurrency Control Techniques Validation Concurrency Control Techniques Granularity of Data Items Some Other Concurrency Control Issues Chapter 20 Concurrency Control Techniques

Locking Techniques Lock Binary lock Shared and exclusive lock
A lock is a variable associated with a data item in the database and describes the status of that item with respect to possible operations that can be applied to the item. Generally, there is one lock for each data item. Binary lock Shared and exclusive lock Two Phase Locking Dead locks Dead Lock Detection and Timeout Chapter 20 Concurrency Control Techniques

Binary Lock A binary lock has two states: locked(=1) , and unlocked (=0). the value of lock associated with data item X is denoted as Lock(X). Two operations, lock_item and unlock_item. They are implemented as critical sections, that is, no interleaving is allowed until the operation terminates. A binary lock enforces mutual exclusion on the data item. A transaction requests access to an item X by issuing a lock_item(X). When the transaction is through using the item, it issues an unlock_item (X). The DBMS has a lock manager subsystem to keep track of and control access to locks. Every transaction must obey the following rules: A transaction T must issue the operation lock_item(X) before any read_item(X) or write_item(X) operations are performed in T A transaction T must issue the operation unlock_item(X) after all read_item(X) and write_item(X) operations are completed in T A transaction T will not issue a lock_item(X) operation if it already holds the lock on item X A transaction T will not issue an unlock_item(X) operation unless it already holds the lock on item X Between the lock_item(X) and unlock_item(X) in the transaction T, T is said to hold the lock on item X. At most, one transaction can hold the lock on a particular item. No two transactions can access the same item concurrently. The system maintains a lock table, which includes a record with two fields: < data item name, LOCK> f Chapter 20 Concurrency Control Techniques

Shared and Exclusive Lock
Multi-mode Lock A multiple-mode lock has three possible states: “read-locked”, “write-locked”, or “unlocked”. A read-locked item is called share-locked, whereas a write-locked item is called exclusive-locked. There are three operations: The system keeps track of the number of transactions the hold a shared lock on an item. Each lock can be a record with three fields: <date item name, LOCK, no_of_reads> The system must enforce the following rules: A transaction T must issue the operation read_lock(X) or write_lock(X) before any read_item(X) is performed in T A transaction T must issue the operation write_lock(X) before any write)item(X) is performed in T. A transaction T must issue the operation unlock(X) after all read_item(X) and write_item(X) operations are completed in T A transaction T will not issue a read_lock(X) operation if it already holds a read(shared) lock or write(exclusive) lock on item X A transaction T will not issue a write_lock(X) operation if it already holds a read(shared) lock or write_exclusive) lock on item X A transaction T will not issue an unlock(X) operation unless it already holds a read(shared) lock or write(exclusive) lock on item X. It is possible to relax conditions 4 and 5 to downgrade and upgrade the lock. (Conversion of locks) Using binary locks or multiple-mode locks does not guarantee serializability of schedules. Chapter 20 Concurrency Control Techniques

Two-Phase Locking Guaranteeing Serializability by two-phase Locking
A transaction is called to follow two-phase locking protocol if all locking operations ( read_lock, write_lock) precede the first unlock operation in the transaction. Transaction can be divided into two phases: Expending Phase, during which new locks on items can be acquired but no locks can be released; Shrinking Phase, during which existing locks can be released but no new locks can b acquired. It can be proved that , if every transaction in a schedule follows the two-phase locking protocol, the schedule is guaranteed to be serializable. Two-phase locking may limit the amount of concurrency that can occur in a schedule. This is the price for guaranteeing serializabilty of all schedules without having to check the schedules themselves. A transaction T may not be able to release an item X after it is through using it if T needs lock other items later. X must remain locked by T until all other items that T needs have been locked. A transaction T must lock an item Y before it needs it so that it can release previous locked item X. Meanwhile, another transaction seeking to access Y has to wait even though T is not using yet. Conservative 2PL: a deadlock-free lock. A transaction locks all the items it accesses before the transaction begins execution, by pre-declaring its read set and write set. Strict 2PL: guarantees strict schedules. A transaction does not release any of its locks until after it commits or aborts. Chapter 20 Concurrency Control Techniques

Dead locks Dead lock Deadlock prevention protocols:
Deadlock occurs when each of two transactions is waiting for the other to release the lock on the item. Meanwhile, neither can proceed to unlock the item that the other is waiting for. Deadlock prevention protocols: Conservative 2PL: Every transaction locks all the items it needs in advance; if any of the items can not be obtained, none of the items are locked. This solution limits concurrency. Timestamp (TS(T)): a unique identifier assigned to each transaction based on the order of in which they are started. wait-die: if TS(Ti)< TS(Tj)(Ti is older than Tj), then Ti is allowed to wait, otherwise abort Ti(Ti dies) and restart it later with the same timestamp wound-wait: if TS(Ti)< TS(Tj)(Ti is older than Tj), then abort Tj(Ti wounds Tj) and restart it later with the same timestamp , otherwise Ti is allowed to wait. no waiting algorithm: If transaction is unable to obtain a lock, it is immediately aborted and then restarted after a certain time delay without checking whether a deadlock will actually occur or not. This may cause transactions to abort and restart needlessly. cautious waiting algorithms: if Tj is not blocked (not waiting for some other locked item), then Ti is blocked and allowed to wait, otherwise abort Ti . The cautious waiting rules reduce the number of needless aborts/restarts. Cautious waiting is deadlock free since the blocking times form a total ordering on all blocked transactions. Chapter 20 Concurrency Control Techniques

Dead Lock Detection and Timeout
Using deadlock detection: periodically check to see if the system is in a state of deadlock. This solution is attractive if there will be little interference among the transactions. Otherwise, it is advantageous to use a deadlock prevention protocol. Construct a wait-for graph to detect the state of deadlock. Each node is created for each transaction , and there is a directed edge(Ti  Tj) , if Ti is waiting to lock the item X that is currently locked by Tj. We have a state of deadlock if and only if the wait-for graph has a cycle. When deadlock occurs, some of transactions causing the deadlock must be aborted. Choosing which transaction to abort is known as victim selection. The algorithm should avoid selecting transactions that have been running for a long time and performed many updates. Starvation occurs if the algorithm for dealing with deadlock selects the same transaction as victim repeatedly, thus causing it to abort and never finish execution. The wit-die and wound-wait can avoid this problem. The standard solution is to have a fair waiting scheme, such as, first-come-first-serve queue. Chapter 20 Concurrency Control Techniques

Concurrency Control Based on Timestamp Ordering
Timestamps A timestamp is a unique identifier by the DBMS to identify a transaction. TS(T) are assigned in the order in which the transactions are submitted to the system. Timestamp can be created using a counter or a system clock. The Timestamp Ordering Algorithm. We order the transaction based on their timestamps so that a schedule in which the transactions participate is serializable, and equivalent serial schedule has the transactions in order of their timestamp values. The TO algorithm associates with each database item X two timestamp values: 1. read_TS(X): the read timestamp of item X; this is the largest timestamp among all the timestamps of transactions that have successfully read item X. 2. write_TS(X): the write timestamp of item X; this is the largest of all the timestamps of transactions that have successfully written item X. Chapter 20 Concurrency Control Techniques

Concurrency Control Based on Timestamp Ordering
Basic TO algorithm: Guarantees serializability Transaction T issues a write_item(X) operation: If read_TS(X) > TS(T) or if write_TS(X) > TS(T) then abort and roll back T and reject the operation. Otherwise, execute the write_item(X) operation of T and set write_TS(X) to TS(T). Transaction T issues a read_item(X) operation: If write_TS(X) > TS(T), then abort and roll back T and reject the operation. If write_TS(X)  TS(T), then execute the read_item(X) operation of T and set read_TS(X) to the larger of TS(T) and the current read_TS(X). Cascading rollback: If T is aborted and rolled back, any transaction T1 that may have used a value written by T must also be rolled back. Similarly, any transaction T2 that may have used a value written by T1 must also be rolled back, and so on. Thomas’s write rule: does not force conflict serializability. If read_TS(X) > TS(T), then abort and rollback T and reject the operation. If write_TS(X) > TS(T) , then do not execute the write operation but continue processing. If neither the condition in part a nor the condition in part b occurs, then execute the write_item(X) operation of T and set write_TS(X) to TS(T) Strict TO: Ensures that schedules are both strict and conflict serializable: A transaction T issues a read_item(X) or write_item(X) such that TS(T) > write_TS(X) has its read or write operation delayed until the transaction T' that wrote the value of X has committed or aborted. Chapter 20 Concurrency Control Techniques

Multiversion Concurrency Control Techniques
Multiversion Cocurrency Control Protocols that keep the several versions of a data item. The algorithms use the concept of view serializability. More storage is needed to maintain multiple versions. Multiversion Technique Based on Timestamp Ordering The system keeps several versions X1, X2, ...,Xk of each data item X. The following two timestamps are kept for each version Xi: read_TS(Xi): The read timestamp of Xi and write_TS(Xi): The write_timestamp of Xi. To ensure serializability, the two rules are used to control the reading and writing of data items: If transaction T issues a write_item(X) operation, and version i of x has the highest write_TS(Xi) of all versions of X that is also less than or equal to TS(T), and read_TS(Xi)>TS(T), then abort and roll back transaction T; otherwise , create a new version Xj of X with read_TS(Xj) = write_TS(Xj) = TS(T). If transaction T issues a read_item(X) operation, find the version i of X that has the highest write_TS(Xi) of all versions of X that is also less than or equal to TS(T); then return the value of Xi to transaction T and set the value of read_TS(Xi) to the larger of TS(T) and the current read_TS(Xi). Chapter 20 Concurrency Control Techniques

Multiversion Concurrency Control Techniques
Multiversion Two-phase Locking Reads can proceed concurrently with a write operation. There are three locking modes for a data item: read, write, and certify. The state of an item X can be one of “read locked”, “write locked”, “certify locked” and “unlocked”. Lock Compatibility Table read write read write certify read y n read y y n write n n write y n n certify n n n Two versions, X old and X new, are kept for each item X. X old has been written by some committed transaction. X new is created when a transaction T acquires a write lock on the item X. Other transactions can continue to read X old while T holds the write lock. T must obtain a certify lock, on all items that it currently holds write locks on before it can commit. The cost is that T may have to delay its commit until it obtains exclusive certify locks on all the items it has updated. X old is set to X new, and X new is discarded and the certify locks are then released. Chapter 20 Concurrency Control Techniques

Validation Concurrency Control Techniques
During transaction execution, all updates are applied to local copies of the data items ( they are not applied directly to the database items). At the end of transaction execution, a validation phase checks whether any of the updates violate serializability. If not, the transaction is committed and the database is updated from the local copies, otherwise the transaction is aborted. Three phases for this protocol: The read phase: A transaction can read values of data items from the database However, updates are applied only to local copies of the data items kept in the transaction workspace. The validation phase: Checking is performed to ensure that serializability will not be violated if the transaction updates are applied to the database. The write phase: If the validation phase is successful, the transaction updates are applied to the database; otherwise, the updates are discarded and the transaction is restarted. Optimistic techniques work well under circumstances of little interference among the transactions. In the validation phase for transaction Ti, if any one of the following conditions holds, Ti does not interfere with any committed transactions, Tj(or with any other transactions currently in their validation phase). Transaction Tj completes its write phase before Ti starts its read phase. Ti starts its write phase after Tj completes its write phase, and the read_set of Ti has no items in common with the write_set of Tj. Both the read_set and the write_set of Ti have no items in common with the write_set of Tj , and Tj completes its read phase before Ti completes its read phase. Chapter 20 Concurrency Control Techniques

Granularity of Data Items
The size of the data items is called the data item granularity. A database item could be one of the following: A database record. A field value of database record. A disk block A whole file. A whole database. Several trade-offs must be considered in choosing the data item size. The larger the data item size is, the lower is the degree of concurrency permitted. The smaller the data item size is, the more items will exist in the database It causes a higher overhead. What is the best size? It depends on the types of transaction involved Chapter 20 Concurrency Control Techniques

Some Other Concurrent Control Issues
Insertion, Deletion, And Phantom records: Phantom problem can occur when a new record that is being inserted by some transaction T satisfies a condition that a set of records accessed by another transaction T' must satisfy. The record that causes the conflict is a phantom record, which may not b recognized by the concurrency control protocol. One solution is index locking, which can be used with a two-phase locking protocol. If the index entry is locked before the record itself can be accessed, then the conflict on the phantom record can be detected since the index locks conflict. Interactive Transactions Problem: A user can input a value of a data item to a transaction T that is based on some value written to the screen by transaction T that is based on some value written to the screen by transaction T', which may not have committed. An approach is to postpone output of transactions to the screen until they have committed. Chapter 20 Concurrency Control Techniques

Database Recovery Recovery Concepts
Recovery Techniques Based on Deferred Updates Recovery Techniques Based on Immediate Updates Shadow Paging Chapter 21 Database Recovery Techniques

Recovery Concepts Recovery Outline
Recovery from transaction failures : The database is restored to some past state so that a correct state (which is close to the time of failure) can be reconstructed from that past state. The system keeps information about the changes to data items in the system log. There are different strategies for the catastrophic and non-catastrophic failures: Catastrophic failure The recovery method restores a past copy of the database that was dumped to archival storage (typically tape) and reconstructs a more current state by reapplying or redoing committed transaction operations from the log up to the time of failure. Non-catastrophic failure of types 1 through 4 (system crash, transaction error, local error, concurrency control enforcement) The strategy is to reverse the changes that caused the inconsistency by undoing some operations. It may also be necessary to redo some operations in order to restore a consistent state of the database, as we shall see. In this case, we do not need a complete archival copy of the database. Rather , the entries kept in the system log are consulted during recovery. Chapter 21 Database Recovery Techniques

Recovery Concepts Recovery Outline
There are two main techniques for recovery from non-catastrophic transaction failures: Deferred Update ( NO-UNDO/REDO algorithm): Updates are not recorded in the database until after a transaction reaches its commit point; before commit, all updates are recorded in the local workspace; during commit, the updates are first recorded in the log and then written to the database. Immediate Update ( UNDO/REDO algorithm): The database may be updated by some operations before a transaction reaches its commit point; these operations are recorded in the log on the disk by force-writing before they are applied to the database. Chapter 21 Database Recovery Techniques

Recovery Concepts System concepts for recovery
Disk pages: Contains one data item DBMS cache: a collection of in-memory buffer. A cache directory (  item name, buffer location ) is used to keep track of which database items are in buffers. Associated with each item in the cache is a dirty bit, which can be included in the directory , to indicate whether or not the item has been modified. Before image: the old value of data item before updating. After image: the new value after updating. Chapter 21 Database Recovery Techniques

Recovery Concepts System concepts for recovery
In-place updating: overwriting the old value of data item on the disk. Write-ahead logging protocol: The before image of an item can not be overwritten by its after image on the disk until all UNDO-type log records for the updating transaction up to this point in time have been force-written to the disk. The commit operation of a transaction can not be completed until all the REDO-type and UNDO-type log records for that transaction have been force written to disk. shadowing: write a new item at a different disk location, so multiple copies of a data item can be maintained. DBMS recovery subsystem maintains a number of lists to make the recovery process more efficient. active transaction list; committed transaction list; aborted transaction list. Chapter 21 Database Recovery Techniques

Recovery Concepts Transaction Rollback
The log entries are used to recover the old values of the data items that must be rolled back. Most recovery mechanisms are designed to avoid cascading rollback. read_item operation entries in the log are needed only for determining cascading rollback. Chapter 21 Database Recovery Techniques

Recovery Techniques Based on Deferred Update
A typical deferred update protocol: A transaction can not change the database until it reaches the commit point. A transaction does not reach its commit point until all its update operations are recorded in the log and the log is force written to the disk. The REDO is needed in case the system fails after the transaction commits but before all its changes are recorded in the database. Recovery Using Deferred Update in a Single User Environment The algorithm of redoing certain write_item operations: PROCEDURE RDU_S Use two lists of transactions: the committed transactions since the last check-point , and the active transactions(at most one transaction will fall in this category, because the system is single user). Apply the REDO operation to all the write_item operations of the committed transactions from the log in the order in which they were written to the log. Restart the active transactions. The REDO procedure is defined as follows: REDO(WRITE_OP) Redoing a write_item operation WRITE_OP consists of examining its log entry [write_item, T, X, new_value] and setting the value of item X in the database to new_value, which is the after image(AFIM). The REDO operation is required to be indempotent so that the result of recovery from a crash during recovery should be the same as the result of recovering when there is no crash during recovery. The transaction in the active list are ignored completely by the recovery process. Chapter 21 Database Recovery Techniques

Deferred Update with Concurrent Execution in a Multi-user Environment The recovery algorithm is depend on the protocol for concurrency control. PROCEDURE (for strict two phase locking) RDU_M Use two lists of transactions maintained by the (WITH CHECKPOINTS) system: the committed transaction T since the last checkpoint, and the active transaction T'. REDO all the WRITE operations of the committed transactions from the log, in the order in which they were written to the log. The transactions that are active and did not commit are effectively canceled and must be resubmitted. We can start from the end of the log to make the algorithm more efficient based on the observation: if a data item X has been updated more than once by the committed transactions, it is only necessary top REDO the last update of X from the log during recovery. Draw back: it limits the concurrent execution of transactions because all items remain locked until the transaction reaches its commit point. Benefit: A transaction does not record its change in the database until it reaches its commit point-that is, until it completes its execution successfully. Hence , a transaction is never rolled back because of failure during transaction execution. A transaction will never read the value of an item that is written by an uncommitted transaction , because items remain locked until a transaction reaches its commit point. Hence, no cascading rollback will occur. Chapter 21 Database Recovery Techniques

Transaction Actions That Do Not Affect the Database. A common method is to issue the commands that generate the reports but keep them as batch jobs, which are executed only after the transaction reaches its commit point. Chapter 21 Database Recovery Techniques

Recovery Techniques Based on Immediate Update
An update operation must be recorded in the log(disk) before it is applied to the database so that we can recover in case of failure (write-ahead logging protocol). Recovery technique includes the capability to rollback a transaction by undoing the effect of its write_item operations. UNDO/REDO Recovery Based on Immediate Update in a Single User environment PROCEDURE RIU_S Use two lists of transactions maintained by the system: the committed transactions since the last checkpoint, and the active transactions(at most one transaction will fall in this category, because the system is single-user). Undo all the write_item operations of the active transaction from the log, using the UNDO procedure described hereafter. Redo all the write_item operations of the committed transaction from the log , in the order in which they were written in the log, using the REDO procedure UNDO (WRITE_OP) Undoing a write_item operation WRITE_OP consists of examining its log entry [ write_item, T, X, old_value, new_value] and setting the value of item X in the database to old_value which is the before image(BFIM). Undoing a number of write_item operations from one or more transactions from the log must proceed in the reverse order from the order in which the operations were written in the log. Chapter 21 Database Recovery Techniques

Recovery Techniques Based on Immediate Update
UNDO/REDO Immediate Update With Concurrent Execution PROCEDURE RUI_M Use two lists of transactions maintained by the system: the committed transactions since the last check point, and the active transactions. Undo all the write_item operations of the active (uncommitted) transactions, using the UNDO procedure. The operations should be undone in the reverse of the order in which they were written in the log. Redo all the write_item operations of the committed transactions from the log, in the order in which they were written into the log. Chapter 21 Database Recovery Techniques

Shadow Paging A page table is kept in the main memory. When a transaction begins executing, the current page table is copied into a shadow page table, which is then saved on the disk. During transaction execution, the shadow page table is never modified. When a write_item operation is performed, a new copy of the modified database page is created, which is written on a new disk block, and then the current page table is modified to point to the new disk block. The two versions are kept: the old version is referenced by the shadow page table, and the new version by the current page table. To recover from a failure during transaction execution: free modified pages; the shadow page table is reinstated so that it becomes the current page table. discard the current page table; Committing a transaction: discard the previous shadow page table; free the old pages. Advantage: no need to undo and redo. This technique can be categorized as a NO-UNDO/NO-REDO recovery technique. Disadvantage: overhead to maintain the page tables in the memory and database pages on the disk. This recovery scheme does not require the log in a single-user environment. In multi-user environment, the log is needed for the concurrency control method. Chapter 21 Database Recovery Techniques

Object Database Object Oriented Database (OODB)
ODMG (Object Data Management Group) Standard Players: Ontos, ObjectStore Object-Relational Database Systems ADTs SQL3

Dimensional Modeling

Dimensional Modeling Approach Data Warehouse
Introduction Dimensional Modeling Approach Seeks user understandability, query performance, and resilience to change Data Warehouse Bill Inmon -- Data warehouse is a subject-oriented, nonvolatile, time variant collection of data in support of management decisions. Ralph Kimball Typical Functionalities of Data Warehouse Roll-up: Data is summarized with increasing generalization (e.g., weekly to quarterly to annually). Drill-down: Increasing levels of detail are revealed (the complement of roll-up). Pivot: Cross tabulation (also referred as rotation) is performed. Slice and dice: Performing projection operations on the dimensions. Sorting: Data is sorted by ordinal value. Selection: Data is available by value or range. Derived (computed) attributes: Attributes are computed by operations on stored and derived values.

Database Requirements
OLTP (or Operational Database) Data Capture Short time frame, rapid change data Entered/updated by DB users High data input performance Data Retrieval Record-level access Predictable usage pattern OLAP (or Data Warehouse) Data Capture Long time frame, static data Entered by IT Data Retrieval Summarizing huge blocks of data Less predictable usage pattern High query performance

Data Modeling Entity-Relationship Modeling Dimensional Modeling
Model the structure of data Use normalization to facilitate data capture and storage Used in operational database design Dimensional Modeling Model the semantics of data Use de-normalization to facilitate data presentation Used in data warehouse design

Multidimensional Database
ROLAP/MOLAP Hypercube Advantage – high query performance Disadvantage Large dataset size Low load performance Suitable for Data Mart Bus Truck Car SUV Q1 Q2 Q3 Q4 N E S W

Dimensional Modeling Overview
Present Data in a Standard Framework Distinguish Roles of Data Attributes – Descriptive data Facts – Measured values Dimensional Framework Categorize Attributes into Dimensions Categorize Facts into Fact Tables

Star Schema Fact Table Dimension Tables Use Surrogate Keys
Foreign keys Facts Dimension Tables Primary key Attributes Use Surrogate Keys Simple integers Improve understandability by avoiding meaningful keys Improve performance

Star Schema and Report

Dimensional Modeling Process
Design Conformed Dimensions Establish Conformed Fact Definitions For Each Subject Area Decide the granularity of the fact table Choosing dimensions Choosing facts

Snowflake Schema Snow Flaking – Move low cardinality attributes in a dimension to separate table (outrigger) Save Space Complicate Data Presentation

Snowflake Schema Example

Approaches to Common Modeling Situations
Slowly Changing Dimensions Role-Playing Dimensions Many-to-Many Dimensions Rapidly Changing Monster Dimensions Hierarchical Dimensions Degenerate Dimensions Junk Dimensions Fact-less Fact Tables Facts of Different Granularity

Slowly Changing Dimension
Modify the dimension record Save old value of an attribute in a separate column Create new record

Role-Playing Dimension
Definition A single dimension appears multiple times in the same star Create multiple logical dimensions (Roles) Based on the same dimension table Each role has different column names

Many-to-Many Dimension
Definition Each fact table record corresponds to multiple dimension records Create role-playing dimensions Create bridge table

Aggregates Contain summarized data from fact tables
Dramatically improve query performance by redirecting user queries to aggregates Used in dimensional modeling Cost More disk space Maintaining aggregate metadata

Redirected query: Original Query: Aggregate Example
SELECT district as District, brand as Brand, SUM(dollars) as ‘Total Dollars’, SUM(cost) ‘Total Cost’, SUM(dollars)-SUM(cost) as ‘Gross Profit’ FROM Sales_Aggregate f, District_Rollup s, Brand_Rollup p WHERE f.brand_key=p.brand_key and f.district_key=s.district_key GROUP BY district, brand Original Query: SELECT district as District, brand as Brand, SUM(dollars) as ‘Total Dollars’, SUM(cost) ‘Total Cost’, SUM(dollars)-SUM(cost) as ‘Gross Profit’ FROM Sales_Fact f, Store_dimension s, Product_dimension p WHERE f.product_key=p.product_key and f.store_key=s.store_key GROUP BY district, brand

Aggregate Overview Decide Aggregation Level Choose Dimensions
Roll Dimensions Up Create new dimension (rollup) by removing high cardinality attributes from a dimension Summarize Base Fact Table Aggregate Navigation Maintain aggregate Metadata Rewrite user query to access aggregate table

Database Theory Jason Fan.

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