Distributed databases basically provide us the advantages of distributed computing to the database management domain. Basically, we can define a Distributed database as a collection of multiple interrelated databases distributed over a computer network and a distributed database management system as a software system that basically manages a distributed database while making the distribution transparent to the user.
Distributed database management basically proposed for the various reason from organizational decentralization and economical processing to greater autonomy. Some of these advantages are as follows:
1. Management of data with different level of transparency –
Ideally, a database should be distribution transparent in the sense of hiding the details of where each file is physically stored within the system. The following types of transparencies are basically possible in the distributed database system:
- Network transparency:
This basically refers to the freedom for the user from the operational details of the network. These are of two types Location and naming transparency.
- Replication transparencies:
It basically made user unaware of the existence of copies as we know that copies of data may be stored at multiple sites for better availability performance and reliability.
- Fragmentation transparency:
It basically made user unaware about the existence of fragments it may be the vertical fragment or horizontal fragmentation.
2. Increased Reliability and availability –
Reliability is basically defined as the probability that a system is running at a certain time whereas Availability is defined as the probability that the system is continuously available during a time interval. When the data and DBMS software are distributed over several sites one site may fail while other sites continue to operate and we are not able to only access the data that exist at the failed site and this basically leads to improvement in reliability and availability.
3. Easier Expansion –
In a distributed environment expansion of the system in terms of adding more data, increasing database sizes, or adding more data, increasing database sizes or adding more processor is much easier.
4. Improved Performance –
We can achieve interquery and intraquery parallelism by executing multiple queries at different sites by breaking up a query into a number of subqueries that basically executes in parallel which basically leads to improvement in performance.
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- Deadlock detection in Distributed systems
- Comparison - Centralized, Decentralized and Distributed Systems
- Mutual exclusion in distributed system
- Maekawa’s Algorithm for Mutual Exclusion in Distributed System
- Ricart–Agrawala Algorithm in Mutual Exclusion in Distributed System
- Lamport's Algorithm for Mutual Exclusion in Distributed System
- Suzuki–Kasami Algorithm for Mutual Exclusion in Distributed System
- Hierarchical Deadlock Detection in Distributed System
- Algorithm for implementing Distributed Shared Memory
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