1. PMIT-6103
Advanced Database Systems
By-
Jesmin Akhter
Assistant Professor, IIT, Jahangirnagar University

2. Slide 2
 Continue from 16.01.2015-……
 Every week
Friday
• From 2:30 PM-4:30 PM
NB: Schedule may change

3. Slide 3
 Attendance =10%
 Exercise test =10%
Instant test
Assignment
Presentation
 Class Test (Average of three) =20%
 Final Examination =60%
================================
=100%

4. Slide 4
 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

5. Slide 5

6. Slide 6
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

7. Lecture 01
Introduction to DDBMS

8. Slide 8
 Introduction
Distributed Database System
Applications
Distributed DBMS Promises
Problem Areas
Architectural Models for Distributed DBMSs

9. Slide 9
database
DBMS
Application
program 1
Application
program 2
Application
program 3
Data description
Data manipulation
control

10. Slide 10
Database
Technology
Computer
Networks
integration distribution
integration
Distributed
Database
Systems

11. Slide 11
 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.

12. Slide 12
 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.

13. Slide 13
“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.

14. Slide 14
 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.

15. Slide 15
 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

16. Slide 16
 The CPU time is shared by different processes
 Time slice is defined by the OS, for sharing CPU time
between processes.

17. Slide 17
P1 Pn M
D
Not a DDBS

18. Slide 18
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

19. Slide 19
Site 5
Site 1
Site 2
Site 3Site 4
Communication
Network
Not a DDBS

20. Slide 20
Site 5
Site 1
Site 2
Site 3Site 4
Communication
Network

21. Slide 21
Communication
Subsystem
User
Query
DBMS
Software
DBMS
Software
User
Application
DBMS
Software
User
ApplicationUser
Query
DBMS
Software
User
Query
DBMS
Software

22. Slide 22
 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.

23. Slide 23
 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

24. Slide 24
 Transparent management of distributed, fragmented,
and replicated data
 Improved reliability/availability through distributed
transactions
 Improved performance
 Easier and more economical system expansion

25. Slide 25
 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

26. Slide 26
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 6
E3 P3 Consultant 10
E3 P4 Engineer 48
E4 P2 Programmer 18
E5 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

27. Slide 27
 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

28. Slide 28
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.

29. Slide 29
 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

30. Slide 30
 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.

31. Slide 31
 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.

32. Slide 32
 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.

33. Slide 33
 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.

34. Slide 34
 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.

35. Slide 35
 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.

36. Slide 36
 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.

37. Slide 37
 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.

38. Slide 38
 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.

39. Slide 39
 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.

40. Slide 40
 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.

41. Slide 41
 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.

42. Slide 42
Possible ways in which a distributed DBMS may be architected:
(1) Autonomy of local systems,
(2) Their distribution, and
(3) Their heterogeneity.

43. Slide 43
 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.

44. Slide 44
 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.

45. Slide 45
 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).

46. Slide 46
 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.

47. Slide 47
 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.

48. Slide 48
 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.

49. Slide 49

50. Slide 50
 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