Parallel Databases and Distributed Databases are designed to improve performance and reliability by using multiple processors or computers.
Parallel Databases:
A parallel database uses multiple processors and storage systems to perform database operations more quickly
- Tasks such as query processing, indexing, and data storage are split among multiple processors, which work concurrently. This significantly speeds up data retrieval, especially for large datasets.
- Shared-memory systems: All processors share the same memory space, and tasks are divided into smaller sub-tasks that can be handled simultaneously.
- Shared-disk systems: Multiple processors access shared disk storage and operate on it in parallel.
- Shared-nothing systems: Each processor has its own memory and disk, which allows for independent task processing and scaling.
Distributed Databases:
A distributed database involves storing data across multiple locations (often geographically spread out) rather than in a single centralized database.
- These databases are connected by a network and are designed to appear as one cohesive system to users.
- Data distribution: Data is divided into fragments and stored in different locations, which may improve performance and fault tolerance.
- Replication: Data is replicated across multiple sites to ensure availability and prevent data loss.
- Consistency and concurrency: Distributed databases need mechanisms to maintain consistency and handle concurrent access across the distributed system.
