PMIT-6102 Advanced Database Systems

PMIT-6102Advanced Database

SystemsBy-

Jesmin AkhterAssistant Professor, IIT, Jahangirnagar University

Schedule

Continue from 25.01.2013-14.06.2013Every week

Friday• From 2:00 PM-5:00 PM

NB: Schedule may change

Grading Policy Attendance =10% Exercise test =5%

Instant testAssignmentPresentation

Class Test (Average of three) =15% Mid-Term Examination =30% Final Examination =40%=======================

=========

=100%

Course Plan 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)

Mid-Term

Tutorial-1

Tutorial-2

Tutorial-3

Total : 14 lectures+ 4Tutorials + Midterm + Final Exam =20 weeks.

Tutorial-4

Text Book Principles of Distributed Database Systems

M. Tamer Özsu & Patrick Valduriez

Exam scheduleTutorial Date and Time

Tutorial-01 22nd February 2013Tutorial-02 22th March 2013Mid term Examination 29th March 2013Tutorial-03 3rd May 2013Tutorial-04 31st May 2013Final Examination 14th June 2013

NB: Schedule may change

Lecture 01Introduction to DDBMS

Outline

IntroductionDistributed Database SystemApplicationsDistributed DBMS PromisesProblem AreasArchitectural Models for Distributed

DBMSs

Database Management

database

DBMS

Applicationprogram 1



Data descriptionData manipulation

control

Integrate Databases and Commuinication

DatabaseTechnology

ComputerNetworks

integration distribution

integration

DistributedDatabaseSystems

Distributed Processing (Distributed Computing 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.

What is being distributed?

What is a Distributed Database 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.

What is a Distributed Database System?

A timesharing computer system A loosely or tightly coupled multiprocessor

systemNot 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

What is not a DDBS?

Timesharing computer system

The CPU time is shared by different processes

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

Shared-Memory Architecture (Tightly

coupled)

P1 Pn MD

Not a DDBS

Shared-Nothing Architecture (Loosely

coupled)

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

Centralized DBMS on a Network

Site 5

Site 1Site 2

Site 3Site 4

CommunicationNetwork

Not a DDBS

Distributed DBMS Environment

Site 5

Site 1Site 2

Site 3Site 4


Distributed DBMS - Reality

CommunicationSubsystem

UserQuery

DBMSSoftware

DBMSSoftware User

Application

DBMSSoftware

UserApplicationUser

QueryDBMS

Software

UserQuery

DBMSSoftware

Implicit Assumptions Data stored at a number of sites each

site logically consists 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

Applications

Transparent management of distributed, fragmented, and replicated data

Improved reliability/availability through distributed transactions

Improved performance Easier and more economical system

expansion

Distributed DBMS Promises

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, AMTFROM EMP, ASG, SALWHERE ASG.DUR > 12AND EMP.ENO = ASG.ENOAND SAL.TITLE = EMP.TITLE

Fully transparent access means that the users can still pose the query as specified above, without paying any attention to

the fragmentation, location, or replication of data, and let the system worry about

resolving these issues.


Example

TITLE AMT

Sal

Elect. Eng. 40000Syst. Anal. 34000Mech. Eng. 27000Programmer 24000

PROJPNO PNAME BUDGET

ENO ENAME TITLEE1 J. Doe Elect. Eng.E2 M. Smith Syst. Anal.E3 A. Lee Mech. Eng.E4 J. Miller ProgrammerE5 B. Casey Syst. Anal.E6 L. Chu Elect. Eng.E7 R. Davis Mech. Eng.E8 J. Jones Syst. Anal.

EMPENO PNO RESP

E1 P1 Manager 12

DUR

E2 P1 Analyst 24E2 P2 Analyst 6E3 P3 Consultant 10E3 P4 Engineer 48E4 P2 Programmer 18E5 P2 Manager 24E6 P4 Manager 48E7 P3 Engineer 36

E8 P3 Manager 40

ASG

P1 Instrumentation 150000

P3 CAD/CAM 250000P2Database 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


Transparent Access

SELECT ENAME,AMTFROM EMP,ASG,SALWHERE DUR > 12AND EMP.ENO = ASG.ENOAND SAL.TITLE =

EMP.TITLE

Paris projectsParis employeesParis assignmentsBoston employees

Montreal projectsParis projectsNew York projects with budget > 200000Montreal employeesMontreal assignments

Boston


Montreal

Paris

NewYork

Boston projectsBoston employeesBoston assignments

Boston projectsNew York employeesNew York projectsNew 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) transparencyReplication transparencyFragmentation 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 independenceDeals with hiding the details of the storage

structure from user applications.

Data independence

Network Transparency In centralized database systems, the only available

resource that needs to be shielded from the user is the data.

In a distributed database environmenta 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 transparencyNaming transparency.

Network 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.

Network Transparency

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 networkIncrease reliability, and availability of

data.Increases the locality of reference.

Replication Transparency

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.

Replication Transparency

Fragmentation Transparency 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.

Fragmentation Transparency

Reliability Through Distributed Transactions

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.

Improved Performance 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.

System Expansion

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.

Problem Areas 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.

Architectural Models for Distributed DBMSsPossible 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.

Architectural Models for Distributed DBMSs

Dimensions of AutonomyDesign 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.


Thank You

Exercise 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 Advanced Database Systems

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