Pmit 6102-14-lec1-intro

PMIT-6103Advanced Database Systems

By-

Jesmin Akhter

Assistant Professor, IIT, Jahangirnagar University

Continue from 16.01.2015-……

Every week

Friday

• From 2:30 PM-4:30 PM

NB: Schedule may change

Attendance =10%

Exercise test =10%

Instant test

Assignment

Presentation

Class Test (Average of three) =20%

Final Examination =60%

================================

=100%

Introduction (Lecture 01)

Overview of Relational DBMS (Lecture 02, 03)

Distributed Database Design (Lecture 04)

Overview of Query Processing (Lecture 05)

Distributed Query Processing (Lecture 06)

Distributed Transaction Management (Lecture 07)

Distributed Concurrency Control (Lecture 08, 09)

Reliability (Lecture 10, 11)

Parallel Database Systems (Lecture 12,13)

Distributed Object DBMS (Lecture14)

Tutorial-1

Tutorial-2

Tutorial-3

Tutorial-4

Tutorial Date and Time

Tutorial-01 06th February 2015

Tutorial-02 27th February 2015

Tutorial-03 20th March 2015

Final Examination 13th June 2013

NB: Schedule may change

Lecture 01Introduction to DDBMS

Introduction

Distributed Database System

Applications

Distributed DBMS Promises

Problem Areas

Architectural Models for Distributed DBMSs

database

DBMS

Applicationprogram 1



Data description

Data manipulation

control

DatabaseTechnology

ComputerNetworks

integration distribution

integration

Distributed

Database

Systems

A number of autonomous processing elements

that are interconnected by a computer network and

that cooperate in performing their assigned tasks.

The “processing element” referred to a computing device that can execute a program on its own.

Processing logic: processing logic or processing elements are

distributed

Functions: Various functions of a computer system could be delegated to various pieces of hardware or software

Data: Data used by a number of applications may be distributed to a number of processing sites

Control: The control of the execution of various tasks might be distributed instead of being performed by one computer system.

“Distributed database system” (DDBS) is used to refer

jointly distributed database and the distributed DBMS.

A distributed database (DDB) is a collection of multiple, logically interrelated databases distributed over a

computer network.

A distributed database management system (D–DBMS) is the software

manages the DDB and

provides an access mechanism

makes this distribution transparent to the users.

Physical distribution does not necessarily imply that the computer systems be geographically far apart;

May be in the same room.

The communication between them is done over a network instead of

through shared memory or shared disk (multiprocessor systems) with the network as the only shared resource.

A timesharing computer system

A loosely or tightly coupled multiprocessor system

Not DDBS, Because in DDBS communication between computer systems is done over a network instead of through shared memory or shared disk with the network as the only shared resource.

A database system

which resides at one of the nodes of a network of computers - this is

a centralized database on a network node

The CPU time is shared by different processes

Time slice is defined by the OS, for sharing CPU time between processes.

P1 Pn M

D

Not a DDBS

P1

M1

D1

Pn

Mn

Dn

Each processor node has its

own primary and secondary memory,

may also have its own peripherals, are quite similar to the distributed environment, but there are differences.

The fundamental difference is the mode of operation.

Database systems that run over multiprocessor systems are called parallel database systems

Not a DDBS

Site 5

Site 1

Site 2

Site 3Site 4

Communication

Network

Not a DDBS

Site 5

Site 1

Site 2

Site 3Site 4

Communication

Network

CommunicationSubsystem

UserQuery

DBMSSoftware

DBMSSoftware

UserApplication

DBMSSoftware

UserApplicationUser

QueryDBMS

Software

UserQuery

DBMSSoftware

Data stored at a number of sites each site logicallyconsists of a single processor.

Processors at different sites are interconnected by a computer network no multiprocessors

parallel database systems

Distributed database is a database, not a collection of files data logically related as exhibited in the users’ access patterns

relational data model

D-DBMS is a full-fledged DBMS

not remote file system.

Manufacturing - especially multi-plant manufacturing

Military command and control

Electronic fund transfers and electronic trading

Corporate MIS

Airline restrictions

Hotel chains

Any organization which has a decentralized organization structure

Transparent management of distributed, fragmented, and replicated data

Improved reliability/availability through distributed transactions

Improved performance

Easier and more economical system expansion

Example: Four relations:

EMP(ENO, ENAME, TITLE)

PROJ(PNO,PNAME, BUDGET)

SAL(TITLE, AMT)

ASG(ENO, PNO, RESP, DUR).

For a centralized DBMS, find out the names of employees with salary who worked on a project for more than 12 months

SELECT ENAME, AMT

FROM EMP, ASG, SAL

WHERE ASG.DUR > 12

AND EMP.ENO = ASG.ENO

AND SAL.TITLE = EMP.TITLE

TITLE AMT

Sal

Elect. Eng. 40000

Syst. Anal. 34000

Mech. Eng. 27000

Programmer 24000

PROJ

PNO PNAME BUDGET

ENO ENAME TITLE

E1 J. Doe Elect. Eng.

E2 M. Smith Syst. Anal.

E3 A. Lee Mech. Eng.

E4 J. Miller Programmer

E5 B. Casey Syst. Anal.

E6 L. Chu Elect. Eng.

E7 R. Davis Mech. Eng.

E8 J. Jones Syst. Anal.

EMP

ENO PNO RESP

E1 P1 Manager 12

DUR

E2 P1 Analyst 24

E2 P2 Analyst 6E3 P3 Consultant 10

E3 P4 Engineer 48

E4 P2 Programmer 18E5 P2 Manager 24

E6 P4 Manager 48

E7 P3 Engineer 36

E8 P3 Manager 40

ASG

P1 Instrumentation 150000

P3 CAD/CAM 250000

P2 Database Develop. 135000

P4 Maintenance 310000

E7 P5 Engineer 23

To localize data such that data about the employees in

Waterloo office are stored in Waterloo,

those in the Boston office are stored in Boston, and so forth.

The same applies to the project and salary information.

That is data is distributed.

We partition each of the relations and store each partition at a

different site. This is known as fragmentation.

Data that are commonly accessed by one user

can be placed on that user’s local machine

as well as on the machine of another user with the same access requirements.

That is data is replicated

SELECT ENAME,AMT

FROM EMP,ASG,SAL

WHERE DUR > 12

AND EMP.ENO = ASG.ENO

AND SAL.TITLE = EMP.TITLE

Paris projects

Paris employees

Paris assignments

Boston employees

Montreal projects

Paris projects

New York projects

with budget > 200000

Montreal employees

Montreal assignments

Boston

Communication

Network

Montreal

Paris

New

York

Boston projects

Boston employees

Boston assignments

Boston projects

New York employees

New York projects

New York assignments

Tokyo

Fully transparent access means that the users can still create the query without paying any attention to the

fragmentation, location, or replication of data.

let the system worry about resolving these issues.

A transparent system “hides” the implementation details from users.

Fundamental issue is to provide Data independence in the distributed environment

Network (distribution) transparency

Replication transparency

Fragmentation transparency

horizontal fragmentation: selection

vertical fragmentation: projection

hybrid

It refers to the immunity of user applications to changes in the definition and organization of data.

Logical data independence

Logical data independence refers to the immunity of user applications to changes in the logical structure (i.e., schema) of the database.

Physical data independence

Deals with hiding the details of the storage structure from

user applications.

In centralized database systems, the only available resource that needs to be shielded from the user is the data.

In a distributed database environment

a second resource that needs to be managed in much the same

manner: the network.

The user should be protected from the operational details of the network; possibly even hiding the existence of the network.

Then there would be no difference between database applications that would run on a centralized database and those that would run on a distributed database.

This type of transparency is referred to as network transparency or distribution transparency.

From a DBMS perspective, distribution transparency requires that users do not have to specify where data are located.

Sometimes two types of distribution transparency are identified:

location transparency

Naming transparency.

Location transparency refers to the fact that the command used to perform a task is independent of

both the location of the data and the system on which an operation

is carried out.

Naming transparency means that a unique name is provided for each object in the database.

In the absence of naming transparency, users are required to embed the location name as part of the object name.

Distribute data in a replicated fashion across the machines on a network.

If one of the machines fails, a copy of the data are still available on another machine on the network

Increase reliability, and availability of data.

Increases the locality of reference.

Data are replicated, the transparency issue is:

The users should not be aware of the existence of copies and the system should handle the management of copies.

The users not to be involved with handling copies and having to specify the fact that a certain action can

and/or should be taken on multiple copies.

Increase performance, availability and reliability.

fragmentation can reduce the negative effects of replication.

Each replica is not the full relation but only a subset of it;

thus less space is required and fewer data items need be managed.

Horizontal fragmentation: A relation is partitioned into a set of sub-relations each of which have a subset of the tuples (rows) of the original relation.

Vertical fragmentation: Where each sub-relation is defined on a subset of the attributes (columns) of the original relation.

Improve reliability since they have replicated components and, thereby eliminate single points of failure.

The failure of a single site, or the failure of a communication link which makes one or more sites unreachable, is not sufficient to bring down the entire system.

Proximity to its points of use (also called data localization).

Requires some support for fragmentation and replication.

This has two potential advantages:

Since each site handles only a portion of the database, contention for CPU and I/O services is not as severe as for centralized databases.

Localization reduces remote access delays that are usually involved in wide area networks.

Issue is database scaling

One aspect of easier system expansion is economics.

It normally costs much less to put together a system of “smaller” computers with the equivalent power of a single big machine.

First, data may be replicated in a distributed environment.

A distributed data base can be designed so that the entire database, or portions of it, reside at different sites of a computer network.

Second, if some sites fail (e.g., by either hardware or software malfunction), or if some communication links fail (making some of the sites unreachable)

While an update is being executed, the effects will not be reflected on the data residing at the failing or unreachable.

The third point is that since each site cannot have instantaneous information on the actions currently being carried out at the other sites,

The synchronization of transactions on multiple sites is considerably harder than for a centralized system.

Possible ways in which a distributed DBMS may be architected:

(1) Autonomy of local systems,

(2) Their distribution, and

(3) Their heterogeneity.

Autonomy

Autonomy, refers to the distribution (or decentralization) of control, not of data.

It indicates the degree to which individual DBMSs can operate independently.

Autonomy is a function of a number of factors such as

whether the component systems (i.e., individual DBMSs)

exchange information,

whether they can independently execute transactions, and whether one is allowed to modify them.

Dimensions of Autonomy

Design autonomy

Individual DBMSs are free to use the data models and transaction management techniques that they prefer.

Communication autonomy

Each of the individual DBMSs is free to make its own decision as to what type of information it wants to provide to the other DBMSs or to the software that controls their global execution.

Execution autonomy

Each DBMS can execute the transactions that are submitted to it in any way that it wants to.

Distribution

The distribution dimension of the taxonomy deals with data.

Physical distribution of data over multiple sites;

The user sees the data as one logical pool.

There are a number of ways DBMSs have been distributed. Two classes:

client/server distribution

peer-to-peer distribution (or full distribution).

Client/server distribution

The client/server distribution concentrates data management duties at servers

while the clients focus on providing the application environment including the user interface.

The communication duties are shared between the client machines and servers.

Peer-to-peer distribution (or full distribution).

In peer-to-peer systems, there is no distinction of client machines versus servers.

Each machine has full DBMS functionality and can communicate with other machines to execute queries and transactions.

Heterogeneity

Hardware heterogeneity

Differences in networking protocols to variations in data managers.

Heterogeneity in query languages

not only involves the use of completely different data access paradigms in different data models.

but also covers differences in languages even when the individual systems use the same data model.

What is the basic difference between Database systems and distributed Database Systems?

What is being distributed?

Define a loosely or tightly coupled multiprocessor system

Draw Distributed Database System –Reality

What do you mean by replicated data?

What are the Promises Distributed DBMS

Pmit 6102-14-lec1-intro

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