Chapter 1: Introduction

 Database Applications
 Purpose of Database Systems
 View of Data
 Database Languages
 Relational Databases
 Database Design
 Object-based and semi-structured databases
 Data Storage and Querying
 Transaction Management
 Database Architecture
 Database Users and Administrators

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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: all 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 touch all aspects of our lives

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 Purpose of Database Systems
 In the early days, database applications were built directly on top of
file systems
 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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 Purpose of Database Systems (Cont.)
 Drawbacks of using file 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 accessed needed for performance
 Uncontrolled concurrent accesses can lead to inconsistencies
– Example: Two people reading a balance and updating it 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., customer) is stored.
 Logical level: describes data stored in database, and the relationships
among the data.
type customer = record
customer_id : string;
customer_name : string;
customer_street : string;
customer_city : 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
 Schema – the logical structure of the database
 Example: The database consists of information about a set of customers and
accounts and the relationship between them)
 Analogous to type information of a variable in a program
 Physical schema: database design at the physical level
 Logical schema: database design at the logical level
 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
 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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 Data Manipulation Language (DML)
 Language for accessing and manipulating the data organized by the
appropriate data model
 DML also known as query language
 Two classes of languages
 Procedural – user specifies what data is required and how to get
those data
 Declarative (nonprocedural) – user specifies what data is
required without specifying how to get those data
 SQL is the most widely used query language

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 Data Definition Language (DDL)
 Specification notation for defining the database schema
Example: create table account (
account-number char(10),
balance integer)
 DDL compiler generates a set of tables stored in a data dictionary
 Data dictionary contains metadata (i.e., data about data)
 Database schema
 Data storage and definition language
 Specifies the storage structure and access methods used
 Integrity constraints
 Domain constraints
 Referential integrity (references constraint in SQL)
 Assertions
 Authorization

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 Relational Model
Attributes
 Example of tabular data in the relational model

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 A Sample Relational Database

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 SQL
 SQL: widely used non-procedural language
 Example: Find the name of the customer with customer-id 192-83-7465
select customer.customer_name
from customer
where customer.customer_id = ‘192-83-7465’
 Example: Find the balances of all accounts held by the customer with
customer-id 192-83-7465
select account.balance
from depositor, account
where depositor.customer_id = ‘192-83-7465’ and
depositor.account_number = account.account_number
 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
The process of designing the general structure of the database:

 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

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 The Entity-Relationship Model
 Models an enterprise as a collection of entities and relationships
 Entity: a “thing” or “object” in the enterprise that is distinguishable
from other objects
 Described by a set of attributes
 Relationship: an association among several entities
 Represented diagrammatically by an entity-relationship diagram:

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 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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 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 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
 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 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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 Database Users
Users are differentiated by the way they expect to interact with
the system
 Application programmers – interact with system through DML calls
 Sophisticated users – form requests in a database query language
 Specialized users – write specialized database applications that do
not fit into the traditional data processing framework
 Naïve users – invoke one of the permanent application programs that
have been written previously
 Examples, people accessing database over the web, bank tellers,
clerical staff

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 Database Administrator
 Coordinates all the activities of the database system; the
database administrator has a good understanding of the
enterprise’s information resources and needs.
 Database administrator's duties include:
 Schema definition
 Storage structure and access method definition
 Schema and physical organization modification
 Granting user authority to access the database
 Specifying integrity constraints
 Acting as liaison with users
 Monitoring performance and responding to changes in
requirements

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 Overall System Structure

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 History of Database Systems
 1950s and early 1960s:
 Data processing using magnetic tapes for storage
 Tapes provide only sequential access
 Punched cards for input
 Late 1960s and 1970s:
 Hard disks allow 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
 2000s:
 XML and XQuery standards
 Automated database administration

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