Distributed Database Concept
Introduction , Component of DDBMS and Differentiate b/n Distributed Database & Distributed Processing.
Content
Distributed Database Concept:
A distributed database is a database that is spread across multiple computers or sites, connected by a network. It allows data to be stored and managed on different nodes of the network, providing a higher level of scalability, availability, and fault tolerance compared to a centralized database.
Component of DDBMS (Distributed Database Management System):
1. Data Fragmentation and Allocation: This component deals with dividing the database into smaller fragments or segments and distributing them across different nodes of the network. Each fragment may contain a subset of the data, and the allocation mechanism decides which node stores which fragment.
2. Data Replication: Data replication involves creating and maintaining multiple copies of the same data on different nodes. This improves data availability and reduces the impact of node or network failures. Replication strategies include full replication (all data copies exist on all nodes) or partial replication (only selected data copies exist on specific nodes).
3. Data Integration: Data integration ensures that the distributed database appears as a single logical database to users and applications. It involves handling data consistency, concurrency control, and distributed query processing across multiple nodes.
4. Distributed Transaction Management: Distributed transaction management handles transactions that span multiple nodes in a distributed database. It ensures that all parts of a distributed transaction either commit successfully or rollback as a unit, maintaining data integrity and consistency.
5. Distributed Query Processing: Distributed query processing refers to the execution of database queries that involve data from multiple nodes. The query optimization and execution algorithms are designed to minimize network traffic and maximize query performance.
Differentiating Distributed Database and Distributed Processing:
1. Data Distribution: In a distributed database, the data is distributed and stored on different nodes of the network, providing data partitioning and replication for improved availability and fault tolerance. In distributed processing, the data may be stored in a centralized database, and processing tasks are distributed across multiple nodes for parallel execution.
2. Data Management: Distributed databases provide integrated data management, where a single logical view of the database is presented to users and applications, despite the physical distribution of data. In distributed processing, data management may involve processing tasks independently on each node without the need for a centralized view of the data.
3. Transaction Management: Distributed databases require distributed transaction management to handle transactions that involve data on multiple nodes. In contrast, distributed processing may not necessarily involve transactions, and if transactions are present, they may be managed independently on each node.
4. Query Processing: Distributed databases require distributed query processing algorithms to optimize and execute queries that involve data from multiple nodes. In distributed processing, queries may be processed independently on each node without the need for distributed optimization and execution techniques.
In summary, distributed databases focus on the distribution and management of data across multiple nodes, while distributed processing focuses on distributing processing tasks across multiple nodes for parallel execution, without necessarily requiring data distribution.