(Chapter 1) Introduction

( 출처 : 연세대학교 데이터베이스 시스템 수업 (CSI6541) 강의자료 )

Chapter 1. Introduction

(1) DBMS

• provides environment that is both convenient & efficient to use

(2) Architecture of DB system

(3) Data Models

Data Models ?

• collection of tools, for describing data, data relationships, data semantics, data constraints
• ex 1) ER model ( Entity-Relationship model )
• ex 2) Object-oriented model, Semi-structured data model, Network mdoel, Hierarchical model

ER model ( Entity-Relationship model )

(4) DDL, DML

DDL ( Data DEFINITION Language )

• for defining DB structure

• ex) CREATE TABLE
• In addition, it updates a special set of tables called the “data dictionary”

DML ( Data MANIPULATION Language )

• for accessing & manipulating data
• Procedural vs Declarative DML
  • [ Procedural ] user need to specify “WHAT” data are needed & “HOW” to get them
  • [ Declarative ] user need to specify only “WHAT” data are needed
• (term) query language \(\approx\) DML
  • mostly used query language : SQL

(5) DB access from applications

Application programs?

• programs used to interact with DB

• usually written in host language ( ex. Python, Java … )

• access DB via one of…

  • (1) language extensions to allow embedded SQL
  • (2) API ( Application Program Interface )

Application architecture

• 2-tier architecture
  • ex) client programs using ODBC/JDBC to communicate with a database
• 3-tier architecture
  • ex) web-based applications

(6) Overall DB system structure

(7) Query Processing

• step 1) parsing & translation
• step 2) optimization
• step 3) evaluation

(8) Storage Management

• program module that provides the interface between ..
  • (1) low-level data ( stored in DB & application programs )
  • (2) queries ( submitted to the system )
• issues : storage access, file organization, indexing/hashing

(9) History of DB system

1. 1960s~1970s :

   • network & hierarchical data models

   • relational data model

2. 1980s :

   • SQL
   • parallel & distributed DB
   • Objected-oriented DB

3. 1990s :

   • decision support & data mining applications
   • data warehouses

4. 2000s :

   • XML & XQuery

5. 2010s

   • Big data

   • Giant data storage systems

     ( Google BigTable, Hadoop, …)