Distributed Databases Questions
Data replication in a distributed database refers to the process of creating and maintaining multiple copies of data across different nodes or sites within the database system. The main purpose of data replication is to enhance data availability, improve system performance, and ensure fault tolerance.
In a distributed database, data replication can be achieved through various techniques such as full replication, partial replication, and selective replication.
Full replication involves creating and storing complete copies of the entire database on each node or site within the distributed system. This ensures that every node has access to all the data, thereby increasing data availability and reducing network latency. However, it also requires significant storage space and incurs high overhead in terms of data synchronization and consistency maintenance.
Partial replication, on the other hand, involves replicating only a subset of the database across different nodes. This approach is suitable when certain data items or tables are frequently accessed or updated, and it helps to improve performance by reducing data access and communication overhead. However, it may lead to data inconsistency if updates are not properly synchronized across replicas.
Selective replication involves replicating specific data items or tables based on predefined criteria or policies. This approach allows for more flexibility in choosing which data to replicate and where to replicate it. It helps to optimize performance and resource utilization by replicating only the most relevant or frequently accessed data.
Overall, data replication in a distributed database plays a crucial role in ensuring data availability, improving system performance, and providing fault tolerance by maintaining multiple copies of data across different nodes or sites within the database system.