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UNIT-3
Distributed DBMS Architecture
Outlines…
• Models- Autonomy, Distribution, Heterogeneity
• DDBMS Architecture – Client/Server, Peer to peer, MDBS
1/11/2017 2Prof. Dhaval R. Chandarana
DBMS STANDARDIZATION
• Based on components.
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.
1/11/2017 3Prof. Dhaval R. Chandarana
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.
1/11/2017 4Prof. Dhaval R. Chandarana
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.
1/11/2017 5Prof. Dhaval R. Chandarana
DBMS STANDARDIZATION
ANSI / SPARC ARCHITECTURE
1/11/2017 6Prof. Dhaval R. Chandarana
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.
1/11/2017 7Prof. Dhaval R. Chandarana
DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE
• The ANSI-SPARC Architecture, where ANSI-SPARC stands for American National Standards
Institute, Standards Planning And Requirements Committee, is an abstract design standard for a
Database Management System (DBMS), first proposed in 1975. The ANSI-SPARC model however
never became a formal standard.
1/11/2017 8Prof. Dhaval R. Chandarana
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.
1/11/2017 9Prof. Dhaval R. Chandarana
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.
1/11/2017 10Prof. Dhaval R. Chandarana
ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - AUTONOMY
• Autonomy refers to the distribution of control, no data. It indicates
the degree to which individual DBMSs can operate independently.
• Three alternatives:
• tight integration (A0)
• semiautonomous systems (A1)
• total isolation (A2)
1/11/2017 11Prof. Dhaval R. Chandarana
ARCHITECTURAL MODELS FOR DISTRIBUTED
DBMSs - AUTONOMY
• 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. From the
users’ perspective, the data is logically centralized in one database.
• Semiautonomous systems.
The DBMSs can operate independently. Each of these DBMSs determine
what parts of their own database they will make accessible to users of
other DBMSs.
• Total isolation.
The individual systems are stand-alone DBMSs, which know neither of the
existence of the other DBMSs nor how to communicate with them.
1/11/2017 12Prof. Dhaval R. Chandarana
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:
• No distribution (D0)
• client / server distribution (D1)
• peer-to-peer distribution (full distribution) (D2)
1/11/2017 13Prof. Dhaval R. Chandarana
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.
1/11/2017 14Prof. Dhaval R. Chandarana
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.
1/11/2017 15Prof. Dhaval R. Chandarana
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.
A0 - tight integration D0 - no distribution
A1 - semiautonomous systems D1 - client / server systems
A2 - total isolation D2 - peer-to-peer systems
H0 - homogeneous systems
H1 - heterogeneous systems
1/11/2017 16Prof. Dhaval R. Chandarana
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.
(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).
1/11/2017 17Prof. Dhaval R. Chandarana
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.
1/11/2017 18Prof. Dhaval R. Chandarana
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 pacing 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 multi database systems
(MDBS). The components have no concept of cooperation. Without
heterogeneity and distribution, an MDBS is an interconnected
collection of autonomous databases.
1/11/2017 19Prof. Dhaval R. Chandarana
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.
1/11/2017 20Prof. Dhaval R. Chandarana
DISTRIBUTED DBMS ARCHITECTURE
• Client / server systems - (Ax, D1, Hy)
• Distributed databases - (A0, D2, H0)
• Multidatabase systems - (A2, Dx, Hy)
1/11/2017 21Prof. Dhaval R. Chandarana
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).
1/11/2017 22Prof. Dhaval R. Chandarana
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.
1/11/2017 23Prof. Dhaval R. Chandarana
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.
1/11/2017 24Prof. Dhaval R. Chandarana
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.
1/11/2017 25Prof. Dhaval R. Chandarana
DISTRIBUTED DBMS ARCHITECTURE
PEER-TO-PEER DISTRIBUTED SYSTEMS
1/11/2017 26Prof. Dhaval R. Chandarana
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.
1/11/2017 27Prof. Dhaval R. Chandarana
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.
1/11/2017 28Prof. Dhaval R. Chandarana
DISTRIBUTED
DBMS
ARCHITECTURE
PEER-TO-PEER
DISTRIBUTED SYSTEMS
• The detailed components
of a distributed DBMS.
• Two major components:
 user processor
 data processor
1/11/2017 29Prof. Dhaval R. Chandarana
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.
1/11/2017 30Prof. Dhaval R. Chandarana
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.
1/11/2017 31Prof. Dhaval R. Chandarana
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”.
1/11/2017 32Prof. Dhaval R. Chandarana
DISTRIBUTED DBMS ARCHITECTURE
MDBS ARCHITECTURE - models using a GCS
1/11/2017 33Prof. Dhaval R. Chandarana
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.
1/11/2017 34Prof. Dhaval R. Chandarana
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.
1/11/2017 35Prof. Dhaval R. Chandarana
DISTRIBUTED DBMS ARCHITECTURE
MDBS ARCHITECTURE - models without a GCS
1/11/2017 36Prof. Dhaval R. Chandarana
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.
1/11/2017 37Prof. Dhaval R. Chandarana
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 no distributed
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.
1/11/2017 38Prof. Dhaval R. Chandarana

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Distributed DBMS - Unit 3 - Distributed DBMS Architecture

  • 2. Outlines… • Models- Autonomy, Distribution, Heterogeneity • DDBMS Architecture – Client/Server, Peer to peer, MDBS 1/11/2017 2Prof. Dhaval R. Chandarana
  • 3. DBMS STANDARDIZATION • Based on components. 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. 1/11/2017 3Prof. Dhaval R. Chandarana
  • 4. 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. 1/11/2017 4Prof. Dhaval R. Chandarana
  • 5. 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. 1/11/2017 5Prof. Dhaval R. Chandarana
  • 6. DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE 1/11/2017 6Prof. Dhaval R. Chandarana
  • 7. 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. 1/11/2017 7Prof. Dhaval R. Chandarana
  • 8. DBMS STANDARDIZATION ANSI / SPARC ARCHITECTURE • The ANSI-SPARC Architecture, where ANSI-SPARC stands for American National Standards Institute, Standards Planning And Requirements Committee, is an abstract design standard for a Database Management System (DBMS), first proposed in 1975. The ANSI-SPARC model however never became a formal standard. 1/11/2017 8Prof. Dhaval R. Chandarana
  • 9. 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. 1/11/2017 9Prof. Dhaval R. Chandarana
  • 10. 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. 1/11/2017 10Prof. Dhaval R. Chandarana
  • 11. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - AUTONOMY • Autonomy refers to the distribution of control, no data. It indicates the degree to which individual DBMSs can operate independently. • Three alternatives: • tight integration (A0) • semiautonomous systems (A1) • total isolation (A2) 1/11/2017 11Prof. Dhaval R. Chandarana
  • 12. ARCHITECTURAL MODELS FOR DISTRIBUTED DBMSs - AUTONOMY • 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. From the users’ perspective, the data is logically centralized in one database. • Semiautonomous systems. The DBMSs can operate independently. Each of these DBMSs determine what parts of their own database they will make accessible to users of other DBMSs. • Total isolation. The individual systems are stand-alone DBMSs, which know neither of the existence of the other DBMSs nor how to communicate with them. 1/11/2017 12Prof. Dhaval R. Chandarana
  • 13. 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: • No distribution (D0) • client / server distribution (D1) • peer-to-peer distribution (full distribution) (D2) 1/11/2017 13Prof. Dhaval R. Chandarana
  • 14. 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. 1/11/2017 14Prof. Dhaval R. Chandarana
  • 15. 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. 1/11/2017 15Prof. Dhaval R. Chandarana
  • 16. 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. A0 - tight integration D0 - no distribution A1 - semiautonomous systems D1 - client / server systems A2 - total isolation D2 - peer-to-peer systems H0 - homogeneous systems H1 - heterogeneous systems 1/11/2017 16Prof. Dhaval R. Chandarana
  • 17. 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. (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). 1/11/2017 17Prof. Dhaval R. Chandarana
  • 18. 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. 1/11/2017 18Prof. Dhaval R. Chandarana
  • 19. 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 pacing 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 multi database systems (MDBS). The components have no concept of cooperation. Without heterogeneity and distribution, an MDBS is an interconnected collection of autonomous databases. 1/11/2017 19Prof. Dhaval R. Chandarana
  • 20. 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. 1/11/2017 20Prof. Dhaval R. Chandarana
  • 21. DISTRIBUTED DBMS ARCHITECTURE • Client / server systems - (Ax, D1, Hy) • Distributed databases - (A0, D2, H0) • Multidatabase systems - (A2, Dx, Hy) 1/11/2017 21Prof. Dhaval R. Chandarana
  • 22. 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). 1/11/2017 22Prof. Dhaval R. Chandarana
  • 23. 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. 1/11/2017 23Prof. Dhaval R. Chandarana
  • 24. 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. 1/11/2017 24Prof. Dhaval R. Chandarana
  • 25. 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. 1/11/2017 25Prof. Dhaval R. Chandarana
  • 26. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS 1/11/2017 26Prof. Dhaval R. Chandarana
  • 27. 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. 1/11/2017 27Prof. Dhaval R. Chandarana
  • 28. 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. 1/11/2017 28Prof. Dhaval R. Chandarana
  • 29. DISTRIBUTED DBMS ARCHITECTURE PEER-TO-PEER DISTRIBUTED SYSTEMS • The detailed components of a distributed DBMS. • Two major components:  user processor  data processor 1/11/2017 29Prof. Dhaval R. Chandarana
  • 30. 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. 1/11/2017 30Prof. Dhaval R. Chandarana
  • 31. 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. 1/11/2017 31Prof. Dhaval R. Chandarana
  • 32. 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”. 1/11/2017 32Prof. Dhaval R. Chandarana
  • 33. DISTRIBUTED DBMS ARCHITECTURE MDBS ARCHITECTURE - models using a GCS 1/11/2017 33Prof. Dhaval R. Chandarana
  • 34. 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. 1/11/2017 34Prof. Dhaval R. Chandarana
  • 35. 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. 1/11/2017 35Prof. Dhaval R. Chandarana
  • 36. DISTRIBUTED DBMS ARCHITECTURE MDBS ARCHITECTURE - models without a GCS 1/11/2017 36Prof. Dhaval R. Chandarana
  • 37. 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. 1/11/2017 37Prof. Dhaval R. Chandarana
  • 38. 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 no distributed 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. 1/11/2017 38Prof. Dhaval R. Chandarana