1. Session-8 Data Management for Decision Support
DDBMS Architecture
Session-8
Data Management for Decision Support

2. DDBMS Architecture DDBMS and Distribution Transparency
Architecture Alternatives
DDBMS Components

3. Distributed Database Management System
A distributed database
collection of multiple, logically interrelated
stores data on multiple computers (nodes) over the network and
permits access from any node to the joint data
A distributed database management system (DDBMS) is a software system that permits the management of the distributed databases and makes the distribution transparent to the users.

4. Reasons for Data Distribution
Several factors have led to the development of DDBS:
Distributed nature of some database applications
Increased reliability and availability
Allowing data sharing while maintaining some measure of local control
Improved performance

5. Distributed DBMS Environment
Site 1
Site 2
Site 4
Site 3
Site 5
Site 6
Communication Network

6. Additional Functionality of DDBMS
Distribution leads to increased complexity in the system design and implementation
DDBMS must be able to provide additional functions to those of a centralized DBMS Some of these are:
Access remote sites and transmit queries and data among the
Track of the data distribution and replication
Execution strategies for queries
Copy Identification
Consistency of copies of a replicated data item
Global conceptual schema of the distributed database
Recovery from individual site crashes

7. What is not a Distributed Database System?
A DDBS is not a ``collection of files'' that can be individually stored at each node of a computer network
files are not logically related
no access via common interface

8. Centralized DBMS on a Network
data resides only at one node
the database management is no different from centralized DBMS
remote processing, single server­multiple clients
Site 1
Site 2
Site 4
Site 3
Site 5
Site 6
Communication Network

9. Distributed Database System Technology
Distributed database technology attempts to achieve integration without centralization
Database Technology
Computers Networks
Integration
Distributed Computing
Integration Without Centralization
Distributed Database Systems

10. Example Multinational manufacturing company: Data and Information:
head quarters in New York
manufacturing plants in Chicago and Montreal
warehouses in Phoenix and Edmonton
R&D facilities in San Francisco
Data and Information:
employee records (working location)
projects (R&D)
engineering data (manufacturing plants, R&D)
inventory (manufacturing, warehouse)

11. Promises of Distributed DBMS
transparent management of distributed, fragmented, and replicated data
improved reliability and availability through distributed transactions
improved performance
higher system extendibility
motivation and major issues

12. Transparency
Transparency refers to separation of the higher-level semantics of a system from lower-level implementation details.
From data independence in centralized DBMS to fragmentation transparency in DDBMS.
Issues
Who should provide transparency?
What is the state of the art in the industry?

13. Improved Reliability
Distributed DBMS can use replicated components to eliminate single point failure.
The users can still access part of the distributed database with “proper care” even though some of the data is unreachable.
Distributed transactions facilitate maintenance of consistent database state even when failures occur.

14. Improved Performance
Since each site handles only a portion of a database, the contention for CPU and I/O resources is not that severe. Data localization reduces communication overheads.
Inherent parallelism of distributed systems may be exploited
inter-query parallelism
intra-query parallelism
Performance models are not sufficiently developed.
motivation and major issues

15. Easier System Expansion
Ability to add new sites, data, and users over time without major restructuring.
Huge centralized database systems (mainframes) are history (almost!).
PC revolution (Compaq buying Digital, 1998) will make natural distributed processing environments.
New applications (such as, supply chain) are naturally distributed - centralized systems will just not work.
motivation and major issues

16. Disadvantages of DDBMSs
Lack of Experience
No operating true distributed database systems in existence
Complexity
DDBMS problems are inherently more complex than centralized DBMS ones
Cost
More hardware, software and people costs
Distribution of control
Problems of synchronization and coordination to maintain data consistency
Security
Database security + network security
Difficult to convert
No tools to convert centralized DBMSs to DDBMSs

17. Complicating Factors
Data may be replicated in a distributed environment, consequently the DDBMS is responsible for
choosing one of the stored copies of the requested data for access in case of retrievals
making sure that the effect of an update is reflected on each and every copy of that data item
If there is site/link failure while an update is being executed, the DDBMS must make sure that the effects will be reflected on the data residing at the failing or unreachable sites as soon as the system recovers from the failure

18. Complicating Factors
Maintaining consistency of distributed/replicated data.
Since each site cannot have instantaneous information on the actions currently carried out in other sites, the synchronization of transactions at multiple sites is harder than centralized system.
motivation and major issues

19. Distributed DBMS Issues
Distributed Database Design
Distributed Query Processing
Distributed Directory Management
Distributed Concurrency Control
Distributed Deadlock Management
Reliability of Distributed Databases
Operating Systems Support
Heterogeneous Databases
motivation and major issues

20. Distributed Database Design
The problem is how the database and the applications that run against it should be placed across the sites.
The two fundamental design issues are fragmentation (the separation of the database into partitions called fragments), and allocation (distribution), the optimum distribution of fragments. The general problem is NP­hard.

21. Distributed Query Processing
Query processing deals with designing algorithms that analyze queries and convert them into a series of data manipulation operations.
The problem is how to decide on strategy for executing each query over the network in the most cost effective way, however the cost is defined. The objective is to optimize where the inherent parallelism is used to improve the performance of executing the transaction

22. Distributed Directory Management
A directory contains information (such as descriptions and locations) about data items in the database.
A directory may be global to the entire DDBMS, or local to each site, distributed, multiple copies, etc.

23. Distributed Concurrency Control
Concurrency control involves the synchronization of accesses to the distributed database, such that the integrity of the database is maintained.
One not only has to worry about the integrity of a single database, but also about the consistency of multiple copies of the database (mutual consistency)