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Parallel database architecture refers to the design and implementation of database systems that leverage multiple processors and disks to perform operations concurrently, enhancing performance and scalability. The three main architectures are: shared memory, shared disk, and shared nothing
1. Shared Memory Architecture:
Multiple processors share a common pool of main memory.
CPUs can directly access and share data stored in the shared memory.
Communication between processors is fast due to the shared memory.
Limited scalability due to bus contention as the number of processors increases.
2. Shared Disk Architecture:
Each processor has its own private memory and can access all disks through a network.
CPUs can access data on disks independently.
Offers better load balancing and fault tolerance than shared memory.
Can experience performance issues with increased CPU count due to potential interference and memory contention.
3. Shared Nothing Architecture:
Each processor has its own local memory and disk, and no shared resources.
Data is partitioned across multiple nodes, and each node manages its partition.
Highly scalable as adding more nodes simply increases processing power and storage.
Communication between nodes is achieved through message passing.
Commonly used in large-scale data warehousing and data processing systems.
Advantages :
Here, we will discuss the advantages of parallel databases. Let's have a look.
Performance Improvement -
By connecting multiple resources like CPU and disks in parallel we can significantly increase the performance of the system.
High availability -
In the parallel database, nodes have less contact with each other, so the failure of one node doesn't cause for failure of the entire system. This amounts to significantly higher database availability.
Proper resource utilization -
Due to parallel execution, the CPU will never be idle. Thus, proper utilization of resources is there.
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