DBMS_Ch1

Database System Concepts, 6th
Ed.
©Silberschatz, Korth and Sudarshan
See www.db-book.com for conditions on re-use
Chapter 1: IntroductionChapter 1: Introduction

©Silberschatz, Korth and Sudarshan1.2Database System Concepts - 6th
Edition
OutlineOutline
The Need for Databases
Data Models
Relational Databases
Database Design
Storage Manager
Query Processing
Transaction Manager

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Database Management System
(DBMS)
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
Database Applications:
Banking: transactions
Airlines: reservations, schedules
Universities: registration, grades
Sales: customers, products, purchases
Online retailers: order tracking, customized recommendations
Manufacturing: production, inventory, orders, supply chain
Human resources: employee records, salaries, tax deductions
Databases can be very large.
Databases touch all aspects of our lives

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University Database Example
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
In the early days, database applications were built directly on
top of file systems

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Drawbacks of using file systems to store
data
Data redundancy and inconsistency
Multiple file formats, 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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Drawbacks of using file systems to store data
(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
 Example: 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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Levels of 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.
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.

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View of Data
An architecture for a database system

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Instances and Schemas
Similar to types and variables in programming languages
Databases change over time as information is inserted and deleted. The
collection of information stored in the database at a particular moment is
called an instance of the database. The overall design of the database is
called the database schema. Schemas are changed infrequently, if at a..
Logical SchemaLogical Schema – the overall logical structure of the database
Example: The database consists of information about a set of
customers and accounts in a bank and the relationship between them
 Analogous to type information of a variable in a program
Physical schemaPhysical schema– the overall physical structure of the database
Instance – the actual content of the database at a particular point in time
Analogous to the value of a variable
Physical Data Independence – the ability to modify the physical
schema without changing the logical schema
Applications depend on the logical schema
In general, the interfaces between the various levels and components
should be well defined so that changes in some parts do not seriously
influence others.

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Data Models
Underlying the structure of a database is the data model.
A collection of tools for describing
Data
Data relationships
Data semantics
Data constraints
Relational model
Entity-Relationship data model (mainly for database design)
Object-based data models (Object-oriented and Object-relational)
Semistructured data model (XML)
Other older models:
Network model
Hierarchical model

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Relational Model
All the data is stored in various tables.
Example of tabular data in the relational model Columns
Rows

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A Sample Relational Database

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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))
Execution of the above DDL statement creates the account table. In addition, it
updates a special set of tables called data dictionary or data directory.
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 manipulating the data organized
by the appropriate data model
DML also known as query language –Retrieval, Insertion,
Deletion, Modification
Two classes of languages
Pure – used for proving properties about computational
power and for optimization
 Relational Algebra
 Tuple relational calculus
 Domain relational calculus
Commercial – used in commercial systems
 SQL is the most widely used commercial language

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SQL
The most widely used commercial language
To be able to compute complex functions SQL is usually
embedded in some higher-level language
Application programs generally access databases through one of
Language extensions to allow embedded SQL
Application program interface (e.g., ODBC/JDBC) which allow
SQL queries to be sent to a database

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Database Design
Logical Design – Deciding on the database schema.
Database design requires that we find a “good” collection of
relation schemas.
Business decision – What attributes should we record in
the database?
Computer Science decision – What relation schemas
should we have and how should the attributes be
distributed among the various relation schemas?
Physical Design – Deciding on the physical layout of the
database
The process of designing the general structure of the database:

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Database Design (Cont.)
Is there any problem with this relation?

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Design Approaches
Need to come up with a methodology to ensure that each of the
relations in the database is “good”
Two ways of doing so:
Entity Relationship Model (Chapter 7)
 Models an enterprise as a collection of entities and
relationships
 Represented diagrammatically by an entity-relationship
diagram:
Normalization Theory (Chapter 8)
 Formalize what designs are bad, and test for them

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Object-Relational Data Models
Relational model: flat, “atomic” values
Object Relational Data Models
Extend the relational data model by including object orientation
and constructs to deal with added data types.
Allow attributes of tuples to have complex types, including non-
atomic values such as nested relations.
Preserve relational foundations, in particular the declarative
access to data, while extending modeling power.
Provide upward compatibility with existing relational languages.

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XML: Extensible Markup Language
Defined by the WWW Consortium (W3C)
Originally intended as a document markup language not a
database language
The ability to specify new tags, and to create nested tag structures
made XML a great way to exchange data, not just documents
XML has become the basis for all new generation data interchange
formats.
A wide variety of tools is available for parsing, browsing and
querying XML documents/data

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Database Engine
Storage manager
Query processing
Transaction manager

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Storage Management
Storage manager is a program module that provides the interface
between the low-level data stored in the database and the application
programs and queries submitted to the system.
The storage manager is responsible to the following tasks:
Interaction with the OS file manager
Efficient storing, retrieving and updating of data
Issues:
Storage access
File organization
Indexing and hashing

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Query Processing
1. Parsing and translation
2. Optimization
3. Evaluation

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Query Processing (Cont.)
Alternative ways of evaluating a given query
Equivalent expressions
Different algorithms for each operation
Cost difference between a good and a bad way of evaluating a
query can be enormous
Need to estimate the cost of operations
Depends critically on statistical information about relations
which the database must maintain
Need to estimate statistics for intermediate results to compute
cost of complex expressions

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Transaction Management
What if the system fails?
What if more than one user is concurrently updating the same
data?
A transaction is a collection of operations that performs a
single logical function in a database application
Transaction-management component ensures that the
database remains in a consistent (correct) state despite system
failures (e.g., power failures and operating system crashes) and
transaction failures.
Concurrency-control manager controls the interaction
among the concurrent transactions, to ensure the consistency of
the database.

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Database Users and Administrators
Database

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Database System Internals

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Database Architecture
The architecture of a database systems is greatly influenced by
the underlying computer system on which the database is running:
Centralized
Client-server
Parallel (multi-processor)
Distributed

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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 begins Ingres prototype
High-performance (for the era) transaction processing

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History (cont.)
1980s:
Research relational prototypes evolve into commercial systems
 SQL becomes industrial standard
Parallel and distributed database systems
Object-oriented database systems
1990s:
Large decision support and data-mining applications
Large multi-terabyte data warehouses
Emergence of Web commerce
Early 2000s:
XML and XQuery standards
Automated database administration
Later 2000s:
Giant data storage systems
 Google BigTable, Yahoo PNuts, Amazon, ..

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End of Chapter 1

DBMS_Ch1

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