1. Distributed DBMS Architecture
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Distributed DBMS Architecture
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DDBMS Architecture

2. Architecture Defines the structure of the system
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Architecture
Defines the structure of the system
Defines the structure of the system
components identified
functions of each component defined
interrelationships and interactions between components defined
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DDBMS Architecture
DDBMS Architecture

3. Tahir Rashid
Architecture
Goal:
– present the issues that need to be addressed at design
– present a framework within which the design and implementation issues can be discussed
The ISO/OSI 7-layered reference model for computer networks
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DDBMS Architecture
DDBMS Architecture

4. 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.
A reference model can be described according to three different approaches:
Based on components
Based on functions
Based on data
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DDBMS Architecture

5. DBMS STANDARDIZATION Based on components. Based on functions.
The components of the system are defined together with the interrelationships between components. A DBMS consists of a number of components, each of which provides some functionality.
Based on functions.
The different classes of users are identified and the functions that the system will perform for each class are defined. The system specifications within this category typically specify a hierarchical structure for the user classes. The ISO/OSI architecture fall in this category.
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DDBMS Architecture

6. DBMS STANDARDIZATION Based on data.
The different types of data are identified, and an architectural framework is specified which defines the functional units that will realize or use data according to these different views. This approach (also referred as the data logical approach) is claimed to be the preferable choice for standardization activities.
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DDBMS Architecture

7. DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE
The ANSI / SPARC architecture is claimed to be based on the data organization. It recognizes three views of data:
the external view, which is that of the user, who might be a programmer; the internal view, that of the system or machine; and the conceptual view, that of the enterprise.
For each of these views, an appropriate schema definition is required.
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DDBMS Architecture

8. DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE
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DDBMS Architecture

9. DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE
At the lowest level of the architecture is the internal view, which deals with the physical definition and organization of data.
At the other extreme is the external view, which is concerned with how users view the database.
Between these two ends is the conceptual schema, which is an abstract definition of the database. It is the „real world” view of the enterprise being modeled in the database.
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DDBMS Architecture

10. Conceptual Schema Definition
RELATION EMP [
KEY = {ENO}
ATTRIBUTES = {
ENO : CHARACTER(9)
ENAME : CHARACTER(15)
TITLE : CHARACTER(10) } ]
RELATION PAY [
KEY = {TITLE}
SAL : NUMERIC(6)
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DDBMS Architecture

11. Conceptual Schema Definition
RELATION PROJ [
KEY = {PNO}
ATTRIBUTES = {
PNO : CHARACTER(7)
PNAME : CHARACTER(20)
BUDGET : NUMERIC(7) } ]
RELATION ASG [
KEY = {ENO,PNO}
ENO : CHARACTER(9)
RESP : CHARACTER(10)
DUR : NUMERIC(3)
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DDBMS Architecture

12. Internal Schema Definition
RELATION EMP [
KEY = {ENO}
ATTRIBUTES = {
ENO : CHARACTER(9)
ENAME : CHARACTER(15)
TITLE : CHARACTER(10) } ]

INTERNAL_REL EMPL [
INDEX ON E# CALL EMINX
FIELD = {
HEADER : BYTE(1)
E# : BYTE(9)
ENAME : BYTE(15)
TIT : BYTE(10)
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DDBMS Architecture

13. External View Definition – Example 1
Create a BUDGET view from the PROJ relation
CREATE VIEW BUDGET(PNAME, BUD)
AS SELECT PNAME, BUDGET
FROM PROJ
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DDBMS Architecture

14. External View Definition – Example 2
Create a Payroll view from relations EMP and TITLE_SALARY
CREATE VIEW PAYROLL (ENO, ENAME, SAL)
AS SELECT EMP.ENO,EMP.ENAME,PAY.SAL
FROM EMP, PAY
WHERE EMP.TITLE = PAY.TITLE
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DDBMS Architecture

15. DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE
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DDBMS Architecture

16. DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE
The square boxes represent processing functions, whereas the hexagons are administrative roles.
The arrows indicate data, command, program, and description flow, whereas the „I”-shaped bars on them represent interfaces.
The major component that permits mapping between different data organizational views is the data dictionary / directory (depicted as a triangle), which is a meta-database.
The database administrator is responsible for defining the internal schema definition.
The enterprise administrator’s role is to prepare the conceptual schema definition.
The application administrator is responsible for preparing the external schema for applications.
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DDBMS Architecture

17. DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE
Two more users:
Application programmer
System programmer
Two user classes:
Casual user
Retrieve database and possible update
Added in external schema
Novice user
Typically have no knowledge of data base
Example (banking machine)
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DDBMS Architecture

18. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs
The systems are characterized with respect to:
(1) the autonomy of the local systems,
(2) their distribution,
(3) their heterogeneity.
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DDBMS Architecture

19. Architectural models for Distributed DBMSs
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DDBMS Architecture

20. Autonomy • Distribution of control (and not data) - the degree of
independence
– The local operations of the individual DBMSs are not
affected by their participation in the multidatabase system
– The manner in which individual DBMSs process queries and optimize them should not be affected by the execution of global queries
– System consistency should not be compromised when
individual DBMSs join or leave the multidatabase system
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DDBMS Architecture

21. Autonomy On the other hand specifies the dimension of autonomy as:
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.
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DDBMS Architecture

22. Autonomy Possibilities:
Tight integration – a single-image of the entire database is available to any user who wants to share the information, which may reside in multiple databases.
Semiautonomous system – consist of DBMSs that can operate independently, but have decided to participate in a federation to make their local data sharable.
Total isolation – the individual systems are stand-alone DBMSs, which know neither of the existence of other DBMSs nor how to communicate with them.
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DDBMS Architecture

23. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - DISTRIBUTION
Distributions refers to the distributions of data. Of course, we are considering the physical distribution of data over multiple sites; the user sees the data as one logical pool.
Two alternatives:
client / server distribution
peer-to-peer distribution (full distribution)
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DDBMS Architecture

24. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - 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. Client / server DBMSs represent the first attempt at distributing functionality.
Peer-to-peer distribution.
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.
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DDBMS Architecture

25. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - HETEROGENEITY
Heterogeneity may occur in various forms in distributed systems, ranging form hardware heterogeneity and differences in networking protocols to variations in data managers.
Representing data with different modeling tools creates heterogeneity because of the inherent expressive powers and limitations of individual data models. 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.
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DDBMS Architecture

26. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - HETEROGENEITY
Various levels (hardware, communications, operating system)
DBMS important one – data model, query language, transaction management algorithms
Representing data with different modeling tools creates heterogeneity because of the inherent expressive power and limitations of individual data models.
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DDBMS Architecture

27. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - ALTERNATIVES
The dimensions are identified as:
A (autonomy),
D (distribution) and
H (heterogeneity).
The alternatives along each dimension are identified by numbers as: 0, 1 or 2.
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DDBMS Architecture

28. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - ALTERNATIVES
A0 - tight integration
A1 - semiautonomous systems
A2 - total isolation
H0 - homogeneous systems
H1 - heterogeneous systems
D0 - no distribution
D1 - client / server systems
D2 - peer-to-peer systems
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DDBMS Architecture

29. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs
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DDBMS Architecture

30. Alternatives in Distributed Database Systems
Distribution
Client/server
Peer-to-peer
Distributed DBMS
Federated DBMS
Distributed
multi-DBMS
Multi-DBMS
Autonomy
Heterogeneity
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DDBMS Architecture

31. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - ALTERNATIVES
In figure 4.3 , two alternative architectures that are focus of this book:
(A0, D2, H0)
(A2, D2, H1)
Not all the architectures that are identified by this design space are meaningful.
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DDBMS Architecture

32. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - ALTERNATIVES
(A0, D0, H0)
If there is no distribution or heterogeneity, the system is a set of multiple DBMSs that are logically integrated. Such systems can be given generic name composite systems. Not such examples but they may be suitable for shared everything multiprocessor systems.
(A0, D0, H1)
If heterogeneity is introduced, one has multiple data managers that are heterogeneous but provide an integrated view to the user.
(A0, D1, H0)
The more interesting case is where the database is distributed even though an integrated view of the data is provided to users (client / server distribution). Mentioned earlier and will discuss further.
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DDBMS Architecture

33. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - ALTERNATIVES
(A0, D2, H0)
The same type of transparency is provided to the user in a fully distributed environment. There is no distinction among clients and servers, each site providing identical functionality.
(A1, D0, H0)
These are semiautonomous systems, which are commonly termed federated DBMS. The component systems in a federated environment have significant autonomy in their execution, but their participation in the federation indicate that they are willing to cooperate with other in executing user requests that access multiple databases. An example may be multiple installations of an DBMS.
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DDBMS Architecture

34. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - ALTERNATIVES
(A1, D0, H1)
These are systems that introduce heterogeneity as well as autonomy, what we might call a heterogeneous federated DBMS.
(A1, D1, H1)
System of this type introduce distribution by placing component systems on different machines. They may be referred to as distributed, heterogeneous federated DBMS.
(A2, D0, H0)
Now we have full autonomy. These are multidatabase systems (MDBS). The components have no concept of cooperation. Without heterogeneity and distribution, an MDBS is an interconnected collection of autonomous databases.
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DDBMS Architecture

35. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - ALTERNATIVES
(A2, D0, H1)
These case is realistic, maybe even more so than (A1, D0, H1), in that we always want to built applications which access data from multiple storage systems with different characteristics.
(A2, D1, H1) and (A2, D2, H1)
These two cases are together, because of the similarity of the problem. They both represent the case where component databases that make up the MDBS are distributed over a number of sites - we call this the distributed MDBS.
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DDBMS Architecture

36. Data logical Distributed DBMS Architecture
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Data logical Distributed DBMS Architecture
...
ES1
ES2
ESn
ES: External Schema
GCS: Global Conceptual Schema
LCS: Local Conceptual Schema
LIS: Local Internal Schema
GCS
...
LCS1
LCS2
LCSn
...
LIS1
LIS2
LISn
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DDBMS Architecture
DDBMS Architecture

37. Datalogical Multi-DBMS Architecture
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Datalogical Multi-DBMS Architecture
...
GES1
GES2
GESn … …
LES11
LES1n
GCS
LESn1
LESnm …
LCS1
LCS2
LCSn …
LIS1
LIS2
LISn
GES: Global External Schema
LES: Local External Schema
LCS: Local Conceptual Schema
LIS: Local Internal Schema
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DDBMS Architecture
DDBMS Architecture

38. Distributed DBMS Distributed database requires distributed DBMS
Functions of a distributed DBMS:
Locate data with a distributed data dictionary
Determine location from which to retrieve data and process query components
DBMS translation between nodes with different local DBMSs (using middleware)
Data consistency (via multiphase commit protocols)
Global primary key control
Scalability
Security, concurrency, query optimization, failure recovery
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DDBMS Architecture

39. Figure 13-10 – Distributed DBMS architecture
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DDBMS Architecture

40. Local Transaction Steps
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Local Transaction Steps
Application makes request to distributed DBMS
Distributed DBMS checks distributed data repository for location of data. Finds that it is local
Distributed DBMS sends request to local DBMS
Local DBMS processes request
Local DBMS sends results to application
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DDBMS Architecture
DDBMS Architecture

41. Local transaction – all data stored locally
Figure 13-10: Distributed DBMS Architecture (cont.) (showing local transaction steps) 2 1 3 5 4
Local transaction – all data stored locally
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DDBMS Architecture

42. Global Transaction Steps
Application makes request to distributed DBMS
Distributed DBMS checks distributed data repository for location of data. Finds that it is remote
Distributed DBMS routes request to remote site
Distributed DBMS at remote site translates request for its local DBMS if necessary, and sends request to local DBMS
Local DBMS at remote site processes request
Local DBMS at remote site sends results to distributed DBMS at remote site
Remote distributed DBMS sends results back to originating site
Distributed DBMS at originating site sends results to application
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DDBMS Architecture

43. Global transaction – some data is at remote site(s)
Figure 13-10: Distributed DBMS architecture (cont.) (showing global transaction steps) 2 3 1 6 7 4 8 5
Global transaction – some data is at remote site(s)
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DDBMS Architecture

44. DISTRIBUTED DBMS ARCHITECTURE
Client / server systems - (Ax, D1, Hy)
Distributed databases - (A0, D2, H0)
Multidatabase systems - (A2, Dx, Hy)
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DDBMS Architecture

45. The Client/Server Database Environment

46. Client/Server Systems
Networked computing model
Processes distributed between clients and servers
Client – Workstation (usually a PC) that requests and uses a service
Server – Computer (PC/mini/mainframe) that provides a service
For DBMS, server is a database server
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DDBMS Architecture

47. Application Logic in C/S Systems
Presentation Logic
Input – keyboard/mouse
Output – monitor/printer
Processing Logic
I/O processing
Business rules
Data management
Storage Logic
Data storage/retrieval
GUI Interface
Procedures, functions,
programs
DBMS activities
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DDBMS Architecture

48. Client/Server Architectures
File Server Architecture
Database Server Architecture
Three-tier Architecture
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DDBMS Architecture

49. File Server Architecture
All processing is done at the PC that requested the data
Entire files are transferred from the server to the client for processing.
Problems:
Huge amount of data transfer on the network
Each client must contain full DBMS
Heavy resource demand on clients
Client DBMSs must recognize shared locks, integrity checks, etc.
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DDBMS Architecture

50. File Server Architecture
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DDBMS Architecture

51. Database Server Architectures
2-tiered approach
Client is responsible for
I/O processing logic
Some business rules logic
Server performs all data storage and access processing  DBMS is only on server
Advantages
Clients do not have to be as powerful
Greatly reduces data traffic on the network
Improved data integrity since it is all processed centrally
Stored procedures  some business rules done on server
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DDBMS Architecture

52. Advantages of Stored Procedures
Compiled SQL statements
Reduced network traffic
Improved security
Improved data integrity
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DDBMS Architecture

53. Database server architecture
DBMS only on server
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DDBMS Architecture

54. Three-Tier Architectures
Three layers:
Client
Application server
Database server
GUI interface
(I/O processing)
Browser
Business rules
Web Server
Data storage
DBMS
Thin Client
PC just for user interface and a little application processing. Limited or no data storage (sometimes no hard drive)
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DDBMS Architecture

55. Three-tier architecture
Thinnest clients
Business rules on separate server
DBMS only on DB server
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DDBMS Architecture

56. DISTRIBUTED DBMS ARCHITECTURE CLIENT / SERVER SYSTEMS
This provides two-level architecture which make it easier to manage the complexity of modern DBMSs and the complexity of distribution.
The server does most of the data management work (query processing and optimization, transaction management, storage management).
The client is the application and the user interface (management the data that is cached to the client, management the transaction locks).
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DDBMS Architecture

57. DISTRIBUTED DBMS ARCHITECTURE CLIENT / SERVER SYSTEMS
This architecture is quite common in relational systems where the communication between the clients and the server(s) is at the level of SQL statements.
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DDBMS Architecture

58. DISTRIBUTED DBMS ARCHITECTURE CLIENT / SERVER SYSTEMS
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DDBMS Architecture

59. DISTRIBUTED DBMS ARCHITECTURE CLIENT / SERVER SYSTEMS
Multiple client - single server
From a data management perspective, this is not much different from centralized databases since the database is stored on only one machine (the server) which also hosts the software to manage it. However, there are some differences from centralized systems in the way transactions are executed and caches are managed.
Multiple client - multiple server
In this case, two alternative management strategies are possible: either each client manages its own connection to the appropriate server or each client knows of only its “home server” which then communicates with other servers as required.
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DDBMS Architecture

60. Multiple Clients/Single Server
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Multiple Clients/Single Server
Applications
Applications
Applications
Client
Services
Client
Services
Client
Services
Communications
Communications
Communications
LAN
High-level
requests
Filtered
data only
Communications
DBMS Services
Database
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DDBMS Architecture
DDBMS Architecture

61. Communications Manager Communications Manager
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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
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DDBMS Architecture
DDBMS Architecture

62. Advantages of Client-Server Architectures
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Advantages of Client-Server Architectures
More efficient division of labor
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
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DDBMS Architecture
DDBMS Architecture

63. Problems With Multiple-Client/Single Server
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Problems With Multiple-Client/Single Server
Server forms bottleneck
Server forms single point of failure
Database scaling difficult
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DDBMS Architecture
DDBMS Architecture

64. Multiple Clients/Multiple Servers
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Multiple Clients/Multiple Servers
directory
caching
query decomposition
commit protocols
Applications
Client
Services
Communications
LAN
Communications
DBMS Services
Communications
DBMS Services
Database
Database
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DDBMS Architecture
DDBMS Architecture

65. Server-to-Server SQL interface programmatic interface
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Server-to-Server
SQL interface
programmatic interface
other application support environments
Applications
Client
Services
Communications
LAN
Communications
DBMS Services
Communications
DBMS Services
Database
Database
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DDBMS Architecture
DDBMS Architecture

66. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER
DISTRIBUTED SYSTEMS
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DDBMS Architecture

67. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS
Physically data organization on each machine may be, and probably is different.
Then there need to be a internal schema at each site which call LIS and for enterprise view there need to be a external schema which call GCS.
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DDBMS Architecture

68. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS
Data Independence due to model of ANSI/SPARC.
Loc & Rep Transparence by LCS & GCS.
Net Transparence by GCS.
DDBMS translate global queries into a group of local queries, execute by DDBMS components at different sites that communicate one another.
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DDBMS Architecture

69. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS
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DDBMS Architecture

70. What is Schema?
In a relational database, the schema defines the tables, the fields in each table, and the relationships between fields and tables. Schemas are generally stored in a data dictionary. Although a schema is defined in text database language, the term is often used to refer to a graphical depiction of the database structure
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DDBMS Architecture

71. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS
The physical data organization on each machine may be different.
Local internal scheme (LIS) - is an individual internal schema definition at each site.
Global conceptual schema (GCS) - describes the enterprise view of the data.
Local conceptual schema (LCS) - describes the logical organization of data at each site.
External schemas (ESs) - support user applications and user access to the database.
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DDBMS Architecture

72. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS
In these case, the ANSI/SPARC model is extended by the addition of global directory / dictionary (GD/D) to permits the required global mappings. The local mappings are still performed by local directory / dictionary (LD/D). The local database management components are integrated by means of global DBMS functions. Local conceptual schemas are mappings of global schema onto each site.
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DDBMS Architecture

73. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS
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DDBMS Architecture

74. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS
The detailed components of a distributed DBMS.
Two major components:
user processor
data processor
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DDBMS Architecture

75. Tahir Rashid
DDBMS Architecture

76. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS
User processor
user interface handler - is responsible for interpreting user commands as they come in, and formatting the result data as it is sent to the user,
semantic data controller - uses the integrity constraints and authorizations that are defined as part of the global conceptual schema to check if the user query can be processed,
global query optimizer and decomposer - determines an execution strategy to minimize a cost function, and translates the global queries in local ones using the global and local conceptual schemas as well as global directory,
distributed execution monitor - coordinates the distributed execution of the user request.
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DDBMS Architecture

77. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS
Data processor
local query optimizer - is responsible for choosing the best access path to access any data item,
local recovery manager - is responsible for making sure that the local database remains consistent even when failures occur,
run-time support processor - physically accesses the database according to the physical commands in the schedule generated by the query optimizer. This is the interface to the operating system and contains the database buffer (or cache) manager, which is responsible for maintaining the main memory buffers and managing the data accesses.
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DDBMS Architecture

78. Peer-to-Peer Component Architecture
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Peer-to-Peer Component Architecture
USER PROCESSOR
USER
User
requests
System
responses
Database
DATA PROCESSOR
System
Log
Local Recovery
Manager
External
Schema
User Interface
Handler
Global
Conceptual
Schema
Semantic Data
Controller
Global Query
Optimizer
GD/D
Local Query
Processor
Local
Conceptual
Schema
Local
Internal
Schema
Runtime
Support
Processor
Execution
Global
Monitor
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DDBMS Architecture
DDBMS Architecture

79. DISTRIBUTED DBMS ARCHITECTURE MDBS ARCHITECTURE
Models using a Global Conceptual Schema (GCS)
The GCS is defined by integrating either the external schemas of local autonomous databases or parts of their local conceptual schemas If the heterogeneity exists in the system, then two implementation alternatives exists unilingual and multilingual.
Models without a Global Conceptual Schema (GCS)
The existence of a global conceptual schema in a multidatabase system is a controversial issue. There are researchers who even define a multidatabase management system as one that manages “several databases without the global schema”.
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DDBMS Architecture

80. DISTRIBUTED DBMS ARCHITECTURE MDBS ARCHITECTURE - models using a GCS
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DDBMS Architecture

81. DISTRIBUTED DBMS ARCHITECTURE MDBS ARCHITECTURE - models using a GCS
A unilingual multi-DBMS requires the users to utilize possibly different data models and languages when both a local database and the global database are accessed.
Any application that accesses data from multiple databases must do so by means of an external view that is defined on the global conceptual schema.
One application may have a local external schema (LES) defined on the local conceptual schema as well as a global external schema (GES) defined on the global conceptual schema.
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DDBMS Architecture

82. DISTRIBUTED DBMS ARCHITECTURE MDBS ARCHITECTURE - models using a GCS
An alternative is multilingual architecture, where the basic philosophy is to permit each user to access the global database by means of an external schema, defined using the language of the user’s local DBMS.
The multilingual approach obviously makes querying the databases easier from the user’s perspective. However, it is more complicated because we must deal with translation of queries at run time.
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DDBMS Architecture

83. DISTRIBUTED DBMS ARCHITECTURE MDBS ARCHITECTURE - models without a GCS
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DDBMS Architecture

84. DISTRIBUTED DBMS ARCHITECTURE MDBS ARCHITECTURE - models without a GCS
The architecture identifies two layers: the local system layer and the multidatabase layer on top of it.
The local system layer consists of a number of DBMSs, which present to the multidatabase layer the part of their local database they are willing to share with users of the other databases. This shared data is presented either as the actual local conceptual schema or as a local external schema definition.
The multidatabase layer consist of a number of external views, which are constructed where each view may be defined on one local conceptual schema or on multiple conceptual schemas. Thus the responsibility of providing access to multiple databases is delegated to the mapping between the external schemas and the local conceptual schemas.
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DDBMS Architecture

85. DISTRIBUTED DBMS ARCHITECTURE MDBS ARCHITECTURE - models without a GCS
The MDBS provides a layer of software that runs on top of these individual DBMSs and provides users with the facilities of accessing various databases.
Fig. represents a nondistributed multi-DBMS. If the system is distributed, we would need to replicate the multidatabase layer to each site where there is a local DBMS that participates in the system.
Tahir Rashid
DDBMS Architecture