Distributed Database Concept

Distributed Database Concept

A distributed database transfers data to be stored at multiple computers or places linked to one another across a network. Another way to explain it is as a database that uses separate computers connected by data communication links to acquire data from several databases. Distributed database systems can provide greater availability and reliability than centrally located database systems. This is done to ensure that the system can still work even if one or more sites are unavailable. By splitting the workload and using the data over several sites, a distributed database system may operate more efficiently.

Accessing Distributed Databases

Different applications can be used by users to access a DDBMS, depending on their data needs. Applications can be divided into two categories: local and global.

Local applications: These apps may run entirely on the data present at the local location and do not require data from other sites. For instance, a local application may consist of producing reports using sales information kept on−site.

Global Applications: To complete their tasks, global applications need data from many places. These programs are made to access and work with dispersed data that is spread across several websites. For instance, a worldwide application would require compiling data from several regional databases and analyzing user behavior.

Operations on Distributed Database

Distributed databases enable a wide range of operations to alter and manage data, just like any other database. Create, Retrieve, Update, and Delete (CRUD) are some of these operations. Let's examine each procedure in further detail:

Create: It involves defining the database structure and providing the data into it. Defining data structures, data types, and constraints for the data to be stored is part of this.

Retrieve: It is used to retrieve information from the database. Conditions are provided in case of specific data retrieval.

Update: It allows us to modify the existing data in the database. Users can update or change values in rows and columns both.

Delete: It is used to remove data from the database. Conditions are provided to delete specific data in a database.

Distributed databases also support advanced operations such as indexing, data replication, and backup and recovery. These operations ensure data integrity, performance, and availability across the distributed environment.

Distributed Processing

A centralized database that is accessible from several locations over a computer network is referred to as distributed processing. In this case, the data is still centralized and accessible to other users via the network. The data is not physically distributed across many sites, hence this is not a distributed database.

Exploring Parallel DBMS Architectures

Parallel Database Management Systems (DBMS) is a specialized type of DBMS that leverage multiple processors to execute operations in parallel. This parallel processing capability enables faster data processing and improved performance. The three main architectures for Parallel DBMS are:

Shared Memory Architecture: In this highly coupled architecture, multiple processors within a single system share system memory. It is also known as symmetric multiprocessing (SMP). Shared memory architecture is commonly found in personal workstations that support a few microprocessors in parallel.

Shared Disk Architecture: For centralized applications that demand good availability and performance, this loosely connected design is utilized. Although each CPU has its private memory, it can access all disks directly. When each CPU is linked to a common disk, it is also known as a cluster.

Shared Nothing Architecture: In this multiple−processor architecture, each processor is a fully functional system with its own memory and disk storage. Each CPU functions independently and has its resources. This architecture, commonly referred to as Massively Parallel Processing (MPP), offers fault tolerance and scalability.

Characteristics of Distributed Databases

Logically Related Shared Data: The data in a distributed database are logically connected. This enables complicated data analysis and processing since many database pieces include connected information.

Data Fragmentation: The process of dividing the database into smaller multiple parts or sub−tables is called fragmentation. The smaller parts or sub−tables are called fragments and are stored at different locations. Data fragmentation should be done in a way that the reconstruction of the original parent database from the fragments is possible.

Duplicate Fragments: The database may contain certain fragments that are repeated on other websites in specific circumstances. Redundancy enables fault tolerance and data availability. The data may still be accessible from another location if one site goes down.

Site Allocation: Within the distributed system, portions of the database are assigned to certain sites. This allocation is dependent on variables including network topology, performance requirements, and data proximity.

Centralized DBMS Control: Although each site in a distributed database can independently handle user requests, the Distributed DBMS has overall control and administration of the data. This guarantees uniformity and coordination amongst the dispersed locations.

Conclusion

This article consists of distributed database concepts. The distributed database consists of data stored at multiple sites and is connected via a network. Accessing distributed databases is dependent on local and global applications. Some common operations are created, retrieve, update, and delete. Advanced operations are indexing, data replication, and backup and recovery which ensures data integrity, performance, and availability. Distributed processing is the data accessed from several locations. Parallel DBMS architecture is shared memory, shared disk, and shared nothing. Characteristics are data fragmentation, duplicate fragmentation, site allocation, logically related shared data, and centralized DBMS control.