A distributed database is a database in which data is stored across different physical locations. It may be stored in multiple computers located in the same physical location (e.g. a data centre); or maybe dispersed over a network of interconnected computers. Unlike parallel systems, in which the processors are tightly coupled and constitute a single database system, a distributed database system consists of loosely coupled sites that share no physical components.
System administrators can distribute collections of data (e.g. in a database) across multiple physical locations. A distributed database can reside on organised network servers or decentralised independent computers on the Internet, on corporate intranets or extranets, or on other organisation networks. Because distributed databases store data across multiple computers, distributed databases may improve performance at end-user worksites by allowing transactions to be processed on many machines, instead of being limited to one.
Two processes ensure that the distributed databases remain up-to-date and current: replication and duplication.
Replication involves using specialized software that looks for changes in the distributive database. Once the changes have been identified, the replication process makes all the databases look the same. The replication process can be complex and time-consuming, depending on the size and number of the distributed databases. This process can also require much time and computer resources.
Duplication, on the other hand, has less complexity. It identifies one database as a master and then duplicates that database. The duplication process is normally done at a set time after hours. This is to ensure that each distributed location has the same data. In the duplication process, users may change only the master database. This ensures that local data will not be overwritten.
Both replication and duplication can keep the data current in all distributive locations.
Besides distributed database replication and fragmentation, there are many other distributed database design technologies.
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Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each of which is a data center. Cloud computing relies on sharing of resources to achieve coherence and typically uses a pay-as-you-go model, which can help in reducing capital expenses but may also lead to unexpected operating expenses for users.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility. Replication in computing can refer to: Data replication, where the same data is stored on multiple storage devices Computation replication, where the same computing task is executed many times.
In concurrency control of databases, transaction processing (transaction management), and various transactional applications (e.g., transactional memory and software transactional memory), both centralized and distributed, a transaction schedule is serializable if its outcome (e.g., the resulting database state) is equal to the outcome of its transactions executed serially, i.e. without overlapping in time. Transactions are normally executed concurrently (they overlap), since this is the most efficient way.
This course is intended for students who want to understand modern large-scale data analysis systems and database systems. It covers a wide range of topics and technologies, and will prepare students
The use of transactions in distributed systems dates back to the 70's. The last decade has also seen the proliferation of transactional systems. In the existing transactional systems, many protocols employ a centralized approach in executing a distributed ...
EPFL2018
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The atomic commit problem lies at the heart of distributed database systems. The problem consists for a set of processes (database nodes) to agree on whether to commit or abort a transaction (agreement property). The commit decision can only be taken if al ...
2017
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The atomic commit problem lies at the heart of distributed database systems. The problem consists for a set of processes (database nodes) to agree on whether to commit or abort a transaction (agreement property). The commit decision can only be taken if al ...