Database: Introduction

Ranjeet Ranjan Jha

Mathematics Department
 Database Systems

 DBMS contains information about a particular enterprise

• Collection of interrelated data
• Set of programs to access the data
• An environment that is both convenient and efficient to use
 A modern database system is a complex software system whose task
is to manage a large, complex collection of data.
 Databases touch all aspects of our lives
 Database Applications Examples

 Enterprise Information
• Sales: customers, products, purchases
• Accounting: payments, receipts, assets
• Human Resources: Information about employees, salaries, payroll
taxes.

 Manufacturing: management of production, inventory, orders, supply

chain.

 Banking and finance

• customer information, accounts, loans, and banking transactions.
• Credit card transactions
• Finance: sales and purchases of financial instruments (e.g., stocks
and bonds; storing real-time market data

 Universities: registration, grades

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 Database Applications Examples (Cont.)

 Airlines: reservations, schedules

 Telecommunication: records of calls, texts, and data usage, generating

monthly bills, maintaining balances on prepaid calling cards

 Web-based services
• Online retailers: order tracking, customized recommendations
• Online advertisements

 Navigation systems: For maintaining the locations of varies places of

interest along with the exact routes of roads, train systems, buses, etc.

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 Purpose of Database Systems

In the early days, database applications were built directly on top of file
systems, which leads to:
 Data redundancy and inconsistency: data is stored in multiple file
formats resulting in duplication of information in different files

 Difficulty in accessing data

• Need to write a new program to carry out each new task

 Data isolation
• Multiple files and formats

 Integrity problems
• Integrity constraints (e.g., account balance > 0) become “buried”
in program code rather than being stated explicitly
• Hard to add new constraints or change existing ones

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 Purpose of Database Systems (Cont.)

 Atomicity of updates
• Failures may leave database in an inconsistent state with partial
updates carried out
• Example: Transfer of funds from one account to another should either
complete or not happen at all
 Concurrent access by multiple users
• Concurrent access needed for performance
• Uncontrolled concurrent accesses can lead to inconsistencies
 Ex: Two people reading a balance (say 100) and updating it by
withdrawing money (say 50 each) at the same time
 Security problems
• Hard to provide user access to some, but not all, data

Database systems offer solutions to all the above problems

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 University Database Example
 In this text we will be using a university database to illustrate all the
concepts
 Data consists of information about:
• Students
• Instructors
• Classes
 Application program examples:
• Add new students, instructors, and courses
• Register students for courses, and generate class rosters
• Assign grades to students, compute grade point averages (GPA) and
generate transcripts

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 Role of a DBMS

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 Overall Organization (Levels of Data
Abstraction)
 Physical level: describes how a record (e.g., instructor) is stored.
 Logical level: describes data stored in database, and the relationships
among the data. (Design Level)
type instructor = record
ID : string;
name : string;
dept_name : string;
salary : integer;
end;
 View level: application programs hide details of data types. Views can
also hide information (such as an employee’s salary) for security
purposes.
 User’s level: Naive User, Menu Driven

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 View of Data

An architecture for a database system

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 Instances and Schemas

Similar to types (Schemas) and variables (Instances) in programming

languages
 Schema – Definition part or Structure
Student Table:

- StudentID

- Name

- Age

- DepartmentID

Department Table:

- DepartmentID

- DepartmentName

 Instance – the actual content of the database at a particular point in time

Student Table (Instance at time T1):

- StudentID: 101, Name: John, Age: 20, DepartmentID: 1
- StudentID: 102, Name: Mary, Age: 22, DepartmentID: 2
Department Table (Instance at time T1):
- DepartmentID: 1, DepartmentName: Computer Science
- DepartmentID: 2, DepartmentName: Mathematics
 Components of a Database

 Data Definition Language (DDL)

 Data Manipulation Language (DML)


 Query Language

 Database Manager
 Data Definition Language (DDL)

 Specification notation for defining the database schema

Example: create table instructor (
ID char(5),
name varchar(20),
dept_name varchar(20),
salary numeric(8,2))
 DDL compiler generates a set of table templates stored in a data
dictionary
 Data dictionary contains metadata (i.e., data about data)
• Database schema
• Integrity constraints
 Primary key (ID uniquely identifies instructors)

• Authorization
 Who can access what

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 Data Manipulation Language (DML)

 Language for accessing and updating the data organized by the

appropriate data model
• DML also known as query language
 There are basically two types of data-manipulation language
• Procedural DML -- require a user to specify what data are needed
and how to get those data.
• Declarative DML -- require a user to specify what data are needed
without specifying how to get those data.
 Declarative DMLs are usually easier to learn and use than are procedural
DMLs.
 Declarative DMLs are also referred to as non-procedural DMLs
 The portion of a DML that involves information retrieval is called a query
language.

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 SQL Query Language

 Common Procedures: Retrieve, Insert, Delete and Update.

 Query Language is subportion of DML.
 SQL query language is nonprocedural. A query takes as input several
tables (possibly only one) and always returns a single table.
 Example to find all instructors in Comp. Sci. dept
select name
from instructor
where dept_name = 'Mathematics'

 To be able to compute complex functions SQL is usually embedded in

some higher-level language

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 Database Access from Application Program

 Non-procedural query languages such as SQL are not as powerful as a

universal Turing machine.
 SQL does not support actions such as input from users, output to
displays, or communication over the network.
 Such computations and actions must be written in a host language, such
as C/C++, Java or Python, with embedded SQL queries that access the
data in the database.
 Application programs -- are programs that are used to interact with the
database in this fashion.

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Full Diagram for Database Component
 Database Administrator

A person who has central control over the system is called a database
administrator (DBA). Functions of a DBA include:
 Schema definition
 Storage structure and access-method definition
 Schema and physical-organization modification
 Granting of authorization for data access
 Routine maintenance
 Periodically backing up the database
 Ensuring that enough free disk space is available for normal
operations, and upgrading disk space as required
 Monitoring jobs running on the database

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 Data Models
 Conceptually designing of complete database
 Core aspect of database design
 ER-Model
 Conceptually designing of complete database
 Core aspect of database design
 Relational Model

 All the data is stored in various tables.

 Example of tabular data in the relational model

Columns

Rows

Ted Codd
Turing Award 1981

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 A Sample Relational Database

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 Database Architecture

 Centralized databases
• One to a few cores, shared memory
 Client-server,
• One server machine executes work on behalf of multiple client
machines.
 Parallel databases
• Many core shared memory
• Shared disk
• Shared nothing
 Distributed databases
• Geographical distribution
• Schema/data heterogeneity

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 History of Database Systems

 1950s and early 1960s:

• Data processing using magnetic tapes for storage
 Tapes provided only sequential access

• Punched cards for input

 Late 1960s and 1970s:
• Hard disks allowed direct access to data
• Network and hierarchical data models in widespread use
• Ted Codd defines the relational data model
 Would win the ACM Turing Award for this work
 IBM Research begins System R prototype
 UC Berkeley (Michael Stonebraker) begins Ingres prototype
 Oracle releases first commercial relational database

• High-performance (for the era) transaction processing

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 History of Database Systems (Cont.)

 1980s:
• Research relational prototypes evolve into commercial systems
 SQL becomes industrial standard
• Parallel and distributed database systems
 Wisconsin, IBM, Teradata
• Object-oriented database systems
 1990s:
• Large decision support and data-mining applications
• Large multi-terabyte data warehouses
• Emergence of Web commerce

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 History of Database Systems (Cont.)

 2000s
• Big data storage systems
 Google BigTable, Yahoo PNuts, Amazon,
 “NoSQL” systems.
• Big data analysis: beyond SQL
 Map reduce and friends
 2010s
• SQL reloaded
 SQL front end to Map Reduce systems
 Massively parallel database systems
 Multi-core main-memory databases

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