Evaluate the role of parallel architectures (e.g., shared-nothing, shared-disk) in improving databaseperformance. Provide examples of parallel execution problems.

Parallel architectures play a significant role in enhancing the performance of databases by enabling them
to handle large volumes of data and high transaction rates. Let’s examine two common parallel
architectures: shared-nothing and shared-disk.

Shared-Nothing Architecture

Description:

i. Decentralization: Each node in the system has its own private memory and disk storage. Nodes
communicate with each other over a network.

ii. Scalability: This architecture scales well since adding more nodes increases both storage
capacity and processing power without contention for shared resources.

Advantages:

i. High Scalability: Easily scales out by adding more nodes.

ii. Fault Tolerance: Failures in one node do not affect others, ensuring high availability.

iii. Performance: Reduces contention for resources, leading to better performance.

Disadvantages:

i. Complexity: Managing data distribution and query processing across multiple nodes can be
complex.

Use Cases:

i. Data Warehousing: Systems like Google Bigtable and Amazon Redshift use shared-nothing
architecture for efficient data storage and querying.

Shared-Disk Architecture

Description:

i. Centralized Storage: All nodes share access to a common disk storage but have their own private
memory. Nodes communicate with the shared disk storage over a high-speed network.

Advantages:

i. Simpler Data Management: Since data is centrally stored, managing and updating it is more
straightforward.

ii. High Availability: If one node fails, others can continue to access the shared storage.

Disadvantages:

i. Scalability Limits: Can face bottlenecks at the shared disk, limiting scalability.

ii. Resource Contention: Potential for contention and latency issues due to shared access to the
disk.

Use Cases:

i. Clustered Databases: Oracle RAC (Real Application Clusters) uses shared-disk architecture to
provide high availability and load balancing.

Examples of Parallel Execution Problems

  1. Skewed Data Distribution:
    o Issue: Uneven distribution of data across nodes can lead to some nodes being
    overloaded while others are underutilized.
    o Impact: Causes performance bottlenecks and inefficient resource utilization.
  2. Network Latency:
    o Issue: High network latency can impact the performance of parallel queries, especially in
    shared-nothing architectures where nodes need to communicate frequently.
    o Impact: Increases query response times and reduces overall system efficiency.
  3. Resource Contention:
    o Issue: In shared-disk architectures, multiple nodes accessing the same disk can lead to
    contention for disk I/O resources.
    o Impact: Causes delays and can become a performance bottleneck.
  4. Synchronization Overhead:
    o Issue: Coordinating and synchronizing parallel tasks can introduce overhead, particularly
    in complex queries that require data from multiple nodes.
    o Impact: Reduces the performance gains from parallelism.

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