Transcript Outline    Introduction Background Distributed DBMS Architecture  Datalogical Architecture  Implementation Alternatives  Component Architecture      Distributed DBMS Architecture Distributed Database Design Semantic Data Control Distributed Query Processing Distributed Transaction Management Parallel Database.

Outline



Introduction
Background
Distributed DBMS Architecture
 Datalogical Architecture
 Implementation Alternatives
 Component Architecture





Distributed DBMS Architecture
Distributed Database Design
Semantic Data Control
Distributed Query Processing
Distributed Transaction Management
Parallel Database Systems
 Distributed Object DBMS
 Database Interoperability
 Current Issues

Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Architecture
Defines the structure of the system

 components identified
 functions of each component defined
 interrelationships and interactions between components
defined
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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ANSI/SPARC Architecture
Users
External
Schema
External
view
External
view
Conceptual
Schema
Conceptual
view
Internal
Schema
Internal view
Distributed DBMS
External
view
© 1998 M. Tamer Özsu & Patrick Valduriez
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Standardization
Reference Model
 A conceptual framework whose purpose is to divide standardization
work into manageable pieces and to show at a general level how these
pieces are related to one another.
Approaches
 Component-based
 Components of the system are defined together with the
interrelationships between components.
 Good for design and implementation of the system.
 Function-based
 Classes of users are identified together with the functionality that
the system will provide for each class.
 The objectives of the system are clearly identified. But how do you
achieve these objectives?
 Data-based
 Identify the different types of describing data and specify the
functional units that will realize and/or use data according to these
views.
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Conceptual Schema Definition
RELATION EMP [
KEY = {ENO}
ATTRIBUTES = {
ENO
: CHARACTER(9)
ENAME : CHARACTER(15)
TITLE
: CHARACTER(10)
}
]
RELATION PAY [
KEY = {TITLE}
ATTRIBUTES = {
TITLE
SAL
: CHARACTER(10)
: NUMERIC(6)
}
]
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Conceptual Schema Definition
RELATION PROJ [
KEY = {PNO}
ATTRIBUTES = {
PNO
: CHARACTER(7)
PNAME : CHARACTER(20)
BUDGET : NUMERIC(7)
}
]
RELATION ASG [
KEY = {ENO,PNO}
ATTRIBUTES = {
ENO
PNO
RESP
DUR
:
:
:
:
CHARACTER(9)
CHARACTER(7)
CHARACTER(10)
NUMERIC(3)
}
]
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Internal Schema Definition
RELATION EMP [
KEY = {ENO}
ATTRIBUTES = {
ENO
ENAME
TITLE
: CHARACTER(9)
: CHARACTER(15)
: CHARACTER(10)
}
]

INTERNAL_REL EMPL [
INDEX ON E# CALL EMINX
FIELD = {
HEADER
E#
ENAME
TIT
: BYTE(1)
: BYTE(9)
: BYTE(15)
: BYTE(10)
}
]
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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External View Definition –
Example 1
Create a BUDGET view from the PROJ relation
CREATE VIEW
BUDGET(PNAME, BUD)
AS
SELECT PNAME, BUDGET
FROM PROJ
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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External View Definition –
Example 2
Create a Payroll view from relations EMP and
TITLE_SALARY
CREATE
AS
Distributed DBMS
VIEW
SELECT
FROM
WHERE
PAYROLL (ENO, ENAME, SAL)
EMP.ENO,EMP.ENAME,PAY.SAL
EMP, PAY
EMP.TITLE = PAY.TITLE
© 1998 M. Tamer Özsu & Patrick Valduriez
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DBMS Implementation
Alternatives
Distribution
Distributed
multi-DBMS
Peer-to-peer
Distributed DBMS
Client/server
Autonomy
Multi-DBMS
Federated DBMS
Heterogeneity
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Dimensions of the Problem

Distribution
 Whether the components of the system are located on the same
machine or not

Heterogeneity
 Various levels (hardware, communications, operating system)
 DBMS important one
 data model, query language,transaction management
algorithms

Autonomy
 Not well understood and most troublesome
 Various versions
 Design autonomy: Ability of a component DBMS to decide on
issues related to its own design.
Communication autonomy: Ability of a component DBMS to
decide whether and how to communicate with other DBMSs.
 Execution autonomy: Ability of a component DBMS to execute
local operations in any manner it wants to.

Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Datalogical Distributed
DBMS Architecture
ES1
ES2
...
ESn
GCS
Distributed DBMS
LCS1
LCS2
LIS1
LIS2
...
...
LCSn
LISn
© 1998 M. Tamer Özsu & Patrick Valduriez
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Datalogical Multi-DBMS
Architecture
LES11
Distributed DBMS
...
GESn
GES1
GES2
LES1n
GCS
LESn1
…
LCS1
LCS2
…
LCSn
LIS1
LIS2
…
LISn
…
© 1998 M. Tamer Özsu & Patrick Valduriez
LESnm
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Timesharing Access to a Central
Database
• No data
storage
Terminals or PC terminal emulators
• Host
running all
software
Batch
requests
Response
Network
Communications
Application Software
DBMS Services
Database
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Multiple Clients/Single Server
Applications
Applications
Applications
Client
Services
Communications
Client
Services
Communications
Client
Services
Communications
LAN
High-level
requests
Filtered
data only
Communications
DBMS Services
Database
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Task Distribution
Application
QL
Interface
…
Programmatic
Interface
Communications Manager
SQL
query
result
table
Communications Manager
Query Optimizer
Lock Manager
Storage Manager
Page & Cache Manager
Database
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Advantages of Client-Server
Architectures

More efficient division of labor

Horizontal and vertical scaling of resources

Better price/performance on client machines

Ability to use familiar tools on client machines

Client access to remote data (via standards)

Full DBMS functionality provided to client
workstations

Overall better system price/performance
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Problems With MultipleClient/Single Server

Server forms bottleneck

Server forms single point of failure

Database scaling difficult
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Multiple Clients/Multiple Servers




directory
caching
query decomposition
commit protocols
Applications
Client
Services
Communications
LAN
Communications
Communications
DBMS Services
DBMS Services
Database
Distributed DBMS
Database
© 1998 M. Tamer Özsu & Patrick Valduriez
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Server-to-Server



SQL interface
programmatic
interface
other application
support
environments
Applications
Client
Services
Communications
LAN
Distributed DBMS
Communications
Communications
DBMS Services
DBMS Services
Database
Database
© 1998 M. Tamer Özsu & Patrick Valduriez
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Peer-to-Peer
Component Architecture
Local
Internal
Schema
Database
Runtime
Support
Processor
System
Local
Conceptual Log
Schema
Local Recovery
Manager
GD/D
Global
Execution
Monitor
Global Query
Optimizer
USER
Global
Conceptual
Schema
Semantic Data
Controller
User
requests
User Interface
Handler
External
Schema
DATA PROCESSOR
Local Query
Processor
USER PROCESSOR
System
responses
Distributed DBMS
© 1998 M. Tamer Özsu & Patrick Valduriez
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Components of a Multi-DBMS
USER
Responses
Local
Requests
GTP
GUI
GQP
GS
GRM
GQO
Component Interface Processor
(CIP)
D
B
M
S
User
Interface
Transaction
Manager
Query
Processor
Scheduler
Query
Optimizer
Recovery
Manager
Runtime Sup.
Processor
Distributed DBMS
Global
Requests
Component Interface Processor Local
(CIP)
Requests
…
D
B
M
S
Transaction
Manager
User
Interface
Scheduler
Query
Processor
Recovery
Manager
Query
Optimizer
Runtime Sup.
Processor
© 1998 M. Tamer Özsu & Patrick Valduriez
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Directory Issues
Type
Global & central
& non-replicated
Local & distributed
& non-replicated
Local & central
& non-replicated (?)
Global & distributed
& non-replicated (?)
Local & central
& replicated (?)
Location
Global & central
& replicated (?)
Local & distributed
& replicated
Replication
Distributed DBMS
Global & distributed
& replicated
© 1998 M. Tamer Özsu & Patrick Valduriez
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