Distributed DBMSs – Concepts and Design

Distributed DBMSs – Concepts and Design

Chapter 22 in Elmasry bookCopy is available at Miss Fatima Shameem the RA

Overview

2

Concepts. What is a distributed DBMS? Distributed Processing. Homogeneous vs. Heterogeneous.

Functions of a DDBMS. Components of a DDBMS. Advantages and Disadvantages. DDBMS Design.

Fragmentation. Replication. Allocation.

DDBMS Transparencies. Date’s 12 Rules for a DDBMS.

Concepts

3

Centralized DBMS systems with a single logical database

located at one site under the control of a single DBMS.

Distributed DBs logically interrelated collection of shared

data physically distributed over a computer network.

Applications can be classified into: Local applications.

Global applications.

Distributed DBMS

4

Distributed DBMS The software system that: manages the distributed DBs.

makes distribution transparent to users.

allows users to access data on their own site as well

as remote sites.

Transparent distribution is the fundamental

principle of DDBMS.

Characteristics of DDBMS

5

• A collection of logically related shared data.

• The data is split into a number of fragments.

• Fragments may be replicated.

• Fragments/replicas are allocated to sites.

• The sites are linked by a communications networks.

• The data at each site is under the control of a DBMS.

• The DBMS at each site can handle local applications.

• Each DBMS participates in at least one global application.

Distributed DBMS Topology

6

Site 1

Site 2

Site 3

Site 4

Computer Network

Data itself is distributed and access to it can be local or remote.

Distributed Processing

7

Site 1

Site 2

Site 3

Site 4

Computer Network

Data itself is centralized but access to it can be local or remote.

Homogeneous vs. Heterogeneous DDBMS

8

Homogenous system: all sites use the same DBMS product.

Heterogeneous system: sites may run different DBMS

products & data model.

Possible differences between data in different DBS:

• Data type difference.

• Value difference.

• Semantic difference.

Functions of a DDBMS

9

• Provide access to remote sites and allow transfer of

queries & data among the network’s site.

• Store data distribution details.

• Distributed data processing.

• Security control.

• Concurrency control.

• Recovery services.

Components of a DDBMS

10

Site 1

Site 3

Computer Network

DDBMS

DC LDBMS

DDBMS

DC

GSC

GSC

DB

Global system catalog

Data communication component

Advantages of DDBMS

11

• Reflects organizational structure.

• Improve sharability & local autonomy.

• Improved availability.

• Improved reliability.

• Improved performance.

Disadvantages of DDBMS

12

• Complexity.

• Cost.

• Security.

• Integrity control more difficult.

• Lack of standards.

• Lack of experience.

• DB design more complex.

Distributed Relational DB Design

13

We have a group of tables and we want to distribute them between a group of sites.

Consists of 3 major steps:1. Fragmentation divide a relation into a number of sub-relations (fragments).

(Horizontal & vertical).

2. Replication make a copy of a fragment.

3. Allocation decide where (which site) each of the fragments and replicas are

to be stored.

Distributed Relational DB Design

14

When we fragment, replicate and allocate, we try

to achieve:

• Locality of reference.

• Improved reliability and availability.

• Good performance.

• Balanced storage capacities and costs.

• Minimal communication costs.

Rules of Fragmentation

15

Completeness: Nothing (rows or columns) gets lost while we fragment.

Reconstruction: We can get back the original table after we fragmented it.

Dis-jointness: No row or column appears in 2 fragments (there is 1 exception).

Types of Fragmentation

16

Horizontal fragmentation

Vertical fragmentation

Mixedfragmentation

Original PropertyForRent Table

17

PA14 16 Holhead Aberdeen AB7 5SU House 6 650 CO46 SA9 B007PG4 6 Lawrence Glasgow G11 9QX Flat 3 350 CO40 SG14 B003PG16 5 Novar Dr Glasgow G12 9AX Flat 4 450 CO93 SG14 B003PG21 18 Dale Rd Glasgow G12 House 5 600 CO87 SG37 B003PG36 2 Manor Rd Glasgow G32 4QX Flat 3 375 CO93 SG37 B003PL94 6 Argy11 St London NW2 Flat 4 400 CO87 SL41 B005

PropertyNo CityStreet PostCode Type Rooms Rent OwnerNo StaffNo BranchNo

18

BranchNo

PropertyNo CityStreet

Fragment P1

PostCode Type Rooms Rent OwnerNo StaffNo BranchNo

PA14 16 Holhead Aberdeen AB7 5SU House 6 650 CO46 SA9 B007PG21 18 Dale Rd Glasgow G12 House 5 600 CO87 SG37 B003


Fragment P2

PostCode Type Rooms Rent OwnerNo StaffNo

PL94 6 Argy11 St London NW2 Flat 4 400 CO87 SL41 B005PG4 6 Lawrence Glasgow G11 9QX Flat 3 350 CO40 SG14 B003PG36 2 Manor Rd Glasgow G32 4QX Flat 3 375 CO93 SG37 B003PG16 5 Novar Dr Glasgow G12 9AX Flat 4 450 CO93 SG14 B003

Based on type of property.

P1: Type=‘House’ (PropertyForRent)

P2: Type=‘Flat’ (PropertyForRent)

Horizontal Fragmentation

Original Staff Table

19

John

Ann

David

Susan

FName

White

Beech

Ford

Brand

LName BranchNo

B005

B003

B003

B007

SL21

SG37

SG14

SG5

StaffNo

Manager

Assistant

Supervisor

Assistant

Position

M

F

M

F

sex Salary

30000

12000

18000

24000

DOB

1 Oct 93

10 Nov 60

24 Mar 58

3 Jun 40

20

SL21

SG37

SG14

SG5

StaffNo

John

Ann

David

Susan

FName

White

Beech

Ford

Brand

LName BranchNo

B005

B003

B003

B007

SL21

SG37

SG14

SG5

StaffNo

Manager

Assistant

Supervisor

Assistant

Position

M

F

M

F

sex Salary

30000

12000

18000

24000

DOB

1 Oct 93

10 Nov 60

24 Mar 58

3 Jun 40

Fragment S1 Fragment S2

S1: staffno,Position,sex,DOB, Salary(STAFF)

S2: staffno,fname,lname,BranchNo(STAFF)

Vertical Fragmentation

21

StaffNo FName LName BranchNo

SG5 Susan Brand B007

SL21

SG37

SG14

SG5

StaffNo

Manager

Assistant

Supervisor

Assistant

Position

M

F

M

F

sex Salary

30000

12000

18000

24000

DOB

1 Oct 93

10 Nov 60

24 Mar 58

3 Jun 40

Fragment S2.3

SL21

StaffNo

John

FName

White

LName BranchNo

B005

Fragment S2.1

StaffNo FName LName BranchNo

Fragment S2.2

SG14 David Ford B003

SG37 Ann Beech B003

S2.1: BranchNo=‘B005’ (S2)



S1: staffno,Position,sex,DOB, Salary(STAFF)

S2: staffoo,fname,lname,BranchNo(STAFF)

Fragment S1

Mixed Fragmentation – Vertical then Horizontal

Derived Horizontal Fragmentation

22

Derived Horizontal Fragmentation is the horizontal fragmentation of a table (child), T1, because we horizontally fragmented another related table (parent), T2.

It is not explicitly specified in design but implied from fragmentation of T2.

T1 (child) has a foreign key that belongs to T2 (parent).

Relationship between T1 and T2 either 1-to-1 or Many-to-1.

Use Semi-join operation:


23

You were required by the design to horizontally fragment Staff table. S1: BranchNo=‘B003’ (Staff) S2: BranchNo=‘B005’ (Staff) S3: BranchNo=‘B007’ (Staff)

John

Ann

David

Susan

FName

White

Beech

Ford

Brand

LName BranchNo

B005

B003

B003

B007

SL21

SG37

SG14

SG5

StaffNo

Manager

Assistant

Supervisor

Assistant

Position

M

F

M

F

sex Salary

30000

12000

18000

24000

DOB

1 Oct 93

10 Nov 60

24 Mar 58

3 Jun 40


24

Ann

David

FName

Beech

Ford

LName BranchNo

B003

B003

SG37

SG14

StaffNo

Assistant

Supervisor

Position

F

M

sex Salary

12000

18000

DOB

10 Nov 60

24 Mar 58

FName LName BranchNoStaffNo Position sex SalaryDOB

John White B005SL21 Manager M 300001 Oct 93

FName LName BranchNoStaffNo Position sex SalaryDOB

Susan Brand B007SG5 Assistant F 240003 Jun 40

Fragment S1

Fragment S2

Fragment S3


25

After we fragmented Staff, we found out that there is a table related to it, PropertyForRent.

Because Staff is now fragmented, it makes sense to fragment PropertyForRent too.

PropertyForRent

Staffhandle

s1 N

S1: BranchNo=‘B003’ (Staff)

S2: BranchNo=‘B005’ (Staff) Pi: PropertyForRent staffNo Si

S3: BranchNo=‘B007’ (Staff)

Original PropertyForRent Table

26

PA14 16 Holhead Aberdeen AB7 5SU House 6 650 CO46 SA9 B007PG4 6 Lawrence Glasgow G11 9QX Flat 3 350 CO40 SG14 B003PG16 5 Novar Dr Glasgow G12 9AX Flat 4 450 CO93 SG14 B003PG21 18 Dale Rd Glasgow G12 House 5 600 CO87 SG37 B003PG36 2 Manor Rd Glasgow G32 4QX Flat 3 375 CO93 SG37 B003PL94 6 Argy11 St London NW2 Flat 4 400 CO87 SL41 B005

PropertyNo CityStreet PostCode Type Rooms Rent OwnerNo StaffNo BranchNo

27


Fragment P1


PG4 6 Lawrence Glasgow G11 9QX Flat 3 350 CO40 SG14 B003PG21 18 Dale Rd Glasgow G12 House 5 600 CO87 SG37 B003PG36 2 Manor Rd Glasgow G32 4QX Flat 3 375 CO93 SG37 B003PG16 5 Novar Dr Glasgow G12 9AX Flat 4 450 CO93 SG14 B003


Fragment P2


PL94 6 Argy11 St London NW2 Flat 4 400 CO87 SL41 B005


Fragment P3


PA14 16 Holhead Aberdeen AB7 5SU House 6 650 CO46 SA9 B007


Transparencies in a DDBMS

28

4 main transparencies:1. Distribution Transparency.

a. Fragmnetation.b. Location. c. Replication.d. Local Mapping.e. Naming.

2. Transaction Transparency.3. Performance Transparency.4. DBMS Transparency.

1. Distribution Transparency

29

Allows the user to perceive the DB as a single, logical entity. Types:

a. Fragmentation: the user does not need to know the data is fragmented.

b. Location: the user does not need to know the location of fragments.

c. Replication: the user does not need to know the fragments are replicated.

d. Local Mapping: the user specifies the fragment and its location.

e. Naming: DDBMS makes sure every item name is unique.

Consider the distribution of the STAFF relation: S1: staffno,Position,sex,DOB, Salary(STAFF) S2: staffno,fname,lname,BranchNo(STAFF) S21: BranchNo=‘B003’ (S2) S22: BranchNo=‘B005’ (S2) S22: BranchNo=‘B007’ (S2)

a. Fragmentation Transparency

30

Highest level of distribution transparency. The user does not need to know that the data is

fragmented. User treats DDB like a centralized DB. The database access are based on the global schema. Fragmentation of the data can be changed without

impacting the user.

Example:SELECT Fname, Lname FROM Staff WHERE position = ‘Manager’;

b. Location Transparency

31

The middle level of distribution transparency.

The user must know that the data is fragmented but still does not need

to know the location of the data.

Data location can be changed without impact on the user.

Example:

SELECT Fname, Lname FROM S21

WHERE staffNo IN (SELECT staffNo FROM S1 WHERE position=‘Manager’)

UNION



UNION



c. Replication Transparency

32

User unaware of replication and location but knows that data is fragmented.

On the same level with location transparency.

d. Local Mapping Transparency

33

The lowest level of distribution transparency.

The user knows that the data is fragmented and the location of the data.

Example:

SELECT Fname, Lname FROM S21 AT SITE 3

WHERE staffNo IN

(SELECT staffNo FROM S1 AT SITE 5 WHERE position=‘Manager’)

UNION


WHERE staffNo IN


UNION


WHERE staffNo IN


e. Naming Transparency

34

Each item in distributed database must have a unique name.

DDBMS must ensure that no two sites violate that.

Solutions Create a central name server.

Bottleneck. against local autonomy.

Prefix an object with the identifier of the site. loss of distribution transparency.

2. Transaction Transparency

35

All transactions must ensure the consistency and integrity of the DDB.

Each transaction that needs to access data in multiple sites is divided into multiple sub-transactions.

Even if transaction is split, atomicity has to be maintained.

3. Performance Transparency

36

DDBMS performs as if it were a centralized DBMS.

Should not suffer because it is distributed (network communication cost).

When a site issues a query, the system must figure out the fastest way of executing it.

Distributed Query Processor (DQP) must figure out: Which fragment to access. Which copy of fragment to access (if replication is

used). Where are the fragments.


37

Consider the following distributed DB: Property(PropertyNo, city) 10,000 records in London Client(ClientNo, maxPrice) 100,000 records in Glasgow Viewing(PropertNo, ClientNo) 1,000,000 records in London

London site wants to list properties in Aberdeen that have been viewed by clients who have a maximum price limit greater than 200,000.

SELECT p.propertyNo

FROM Property P INNER JOIN

(Client c INNER JOIN Viewing v ON c.clientNo = v.clientNo)

ON p.propertyNo = v.propertyNo

WHERE p.city = ‘Aberdeen’ AND

c.maxprice > 200000;


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After the query is issued, DDBMS must determine the most cost-effective strategy to execute the query.

Strategies:

1. Move Client table to London and process query there.

2. Move Property and Viewing relation to Glasgow and process query there then return result.

3. Join Property and Viewing at London, project only property number and client number and move result to Glasgow to join with clients with salary > 200,000 then return results.

4. Select clients at Glasgow with salary > 200000, move them to London and join with viewing and Aberdeen property.

4. DBMS Transparency

39

Hides the fact that different sites have different local DBMSs.

Heterogeneous DDBMSs.

Date’s 12 Rules for a DDBMS

40

1. Local autonomy.

2. No reliance on a central site.

3. Continuous operation.

4. Location independence.

5. Fragmentation independence.

6. Replication independence.

7. Distributed query processing.

8. Distributed transaction processing.

9. Hardware independence.

10. Operating system independence.

11. Network independence.

12. Database independence.

Documents

Distributed DBMSs – Concepts and Design