Fundamentals of Database Systems PDF

Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe  CHAPTER 2 Database System Concepts and Architecture Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 1- 2 Outline Data Models and Their Categories Histor...

Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe  CHAPTER 2 Database System Concepts and Architecture Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 1- 2 Outline Data Models and Their Categories History of Data Models Schemas, Instances, and States Three-Schema Architecture Data Independence DBMS Languages and Interfaces Database System Utilities and Tools Centralized and Client-Server Architectures Classification of DBMSs Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 3 Data Models Data Model: A set of concepts to describe the structure of a database, the operations for manipulating these structures, and certain constraints that the database should obey. Data Model Structure and Constraints: Constructs are used to define the database structure Constructs typically include elements (and their data types) as well as groups of elements (e.g. entity, record, table), and relationships among such groups Constraints specify some restrictions on valid data; these constraints must be enforced at all times Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 4 Data Models (continued) Data Model Operations: These operations are used for specifying database retrievals and updates by referring to the constructs of the data model. Operations on the data model may include basic model operations (e.g. generic insert, delete, update) and user-defined operations (e.g. compute_student_gpa, update_inventory) Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 5 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 details of how data is stored in the computer. These are usually specified in an ad-hoc manner through DBMS design and administration manuals Implementation (representational) data models: Provide concepts that fall between the above two, used by many commercial DBMS implementations (e.g. relational data models used in many commercial systems). Self-Describing Data Models: Combine the description of data with the data values. Examples include XML, key-value stores and some NOSQL systems. Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 6 Schemas versus Instances Database Schema: The description of a database. Includes descriptions of the database structure, data types, and the constraints on the database. Schema Diagram: An illustrative display of (most aspects of) a database schema. Schema Construct: A component of the schema or an object within the schema, e.g., STUDENT, COURSE. Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 7 Schemas versus Instances Database State: The actual data stored in a database at a particular moment in time. This includes the collection of all the data in the database. Also called database instance (or occurrence or snapshot). The term instance is also applied to individual database components, e.g. record instance, table instance, entity instance Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 8 Database Schema vs. Database State Database State: Refers to the content of a database at a moment in time. Initial Database State: Refers to the database state when it is initially loaded into the system. Valid State: A state that satisfies the structure and constraints of the database. Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 9 Database Schema vs. Database State (continued) Distinction The database schema changes very infrequently. The database state changes every time the database is updated. Schema is also called intension. State is also called extension. Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 10 Example of a Database Schema Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 11 Example of a database state Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 12 Three-Schema Architecture Proposed to support DBMS characteristics of: Program-data independence. Support of multiple views of the data. Not explicitly used in commercial DBMS products, but has been useful in explaining database system organization Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 13 Three-Schema Architecture Defines DBMS schemas at three levels: Internal schema at the internal level to describe physical storage structures and access paths (e.g indexes). Typically uses a physical data model. Conceptual schema at the conceptual level to describe the structure and constraints for the whole database for a community of users. 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 schema. Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 14 The three-schema architecture Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 15 DBMS Languages Data Definition Language (DDL) Data Manipulation Language (DML) High-Level or Non-procedural Languages: These include the relational language SQL May be used in a standalone way or may be embedded in a programming language Low Level or Procedural Languages: These must be embedded in a programming language Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 19 Typical DBMS Component Modules Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 30 History of Data Models (Additional Material) Network Model Hierarchical Model Relational Model Object-oriented Data Models Object-Relational Models Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 44 History of Data Models Network Model: The first network DBMS was implemented by Honeywell in 1964-65 (IDS System). Adopted heavily due to the support by CODASYL (Conference on Data Systems Languages) (CODASYL - DBTG report of 1971). Later implemented in a large variety of systems - IDMS (Cullinet - now Computer Associates), DMS 1100 (Unisys), IMAGE (H.P. (Hewlett-Packard)), VAX -DBMS (Digital Equipment Corp., next COMPAQ, now H.P.). Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 45 Network Model Advantages: Network Model is able to model complex relationships and represents semantics of add/delete on the relationships. Can handle most situations for modeling using record types and relationship types. Language is navigational; uses constructs like FIND, FIND member, FIND owner, FIND NEXT within set, GET, etc. Programmers can do optimal navigation through the database. Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 46 Network Model Disadvantages: Navigational and procedural nature of processing Database contains a complex array of pointers that thread through a set of records. Little scope for automated “query optimization” Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 47 History of Data Models Hierarchical Data Model: Initially implemented in a joint effort by IBM and North American Rockwell around 1965. Resulted in the IMS family of systems. IBM’s IMS product had (and still has) a very large customer base worldwide Hierarchical model was formalized based on the IMS system Other systems based on this model: System 2k (SAS inc.) Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 48 Hierarchical Model Advantages: Simple to construct and operate Corresponds to a number of natural hierarchically organized domains, e.g., organization (“org”) chart Language is simple: Uses constructs like GET, GET UNIQUE, GET NEXT, GET NEXT WITHIN PARENT, etc. Disadvantages: Navigational and procedural nature of processing Database is visualized as a linear arrangement of records Little scope for "query optimization" Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 49 History of Data Models Relational Model: Proposed in 1970 by E.F. Codd (IBM), first commercial system in 1981-82. Now in several commercial products (e.g. DB2, ORACLE, MS SQL Server, SYBASE, INFORMIX). Several free open source implementations, e.g. MySQL, PostgreSQL Currently most dominant for developing database applications. SQL relational standards: SQL-89 (SQL1), SQL-92 (SQL2), SQL-99, SQL3, … Chapters 5 through 11 describe this model in detail Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 50 History of Data Models Object-oriented Data Models: Several models have been proposed for implementing in a database system. One set comprises models of persistent O-O Programming Languages such as C++ (e.g., in OBJECTSTORE or VERSANT), and Smalltalk (e.g., in GEMSTONE). Additionally, systems like O2, ORION (at MCC - then ITASCA), IRIS (at H.P.- used in Open OODB). Object Database Standard: ODMG-93, ODMG-version 2.0, ODMG-version 3.0. Chapter 12 describes this model. Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 51 History of Data Models Object-Relational Models: The trend to mix object models with relational was started with Informix Universal Server. Relational systems incorporated concepts from object databases leading to object-relational. Exemplified in the versions of Oracle, DB2, and SQL Server and other DBMSs. Current trend by Relational DBMS vendors is to extend relational DBMSs with capability to process XML, Text and other data types. The term “Object-relational” is receding in the marketplace. Slide 2- 52 Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Chapter Summary Data Models and Their Categories Schemas, Instances, and States Three-Schema Architecture Data Independence DBMS Languages and Interfaces Database System Utilities and Tools Database System Environment Centralized and Client-Server Architectures Classification of DBMSs History of Data Models Copyright © 2017 Ramez Elmasri and Shamkant B. Navathe Slide 2- 53