Distributed Databases

• Distributed Databases:
  • Definition: Distributed databases store data across multiple nodes in a network, enabling improved scalability, fault tolerance, and performance compared to centralized databases.
  • Fragmentation: Data fragmentation involves dividing the database into smaller subsets or fragments distributed across different nodes. This allows for parallel processing and efficient data retrieval.
  • Replication: Replication involves creating and maintaining copies of data across multiple nodes. It enhances fault tolerance and availability by ensuring data redundancy and allowing for failover mechanisms.
  • Sharding: Sharding, also known as horizontal partitioning, involves dividing data horizontally based on a sharding key and distributing it across multiple nodes. This strategy improves scalability and performance by spreading the workload evenly across nodes.
• Advantages of Distributed Databases:
  • Scalability: Distributed databases can scale horizontally by adding more nodes, allowing them to handle increasing data volumes and user loads.
  • Fault Tolerance: With data distributed across multiple nodes, distributed databases can tolerate node failures without compromising data availability or integrity.
  • Performance: By distributing data closer to where it's needed, distributed databases can improve query response times and overall system performance.
• Challenges of Distributed Databases:
  • Complexity: Designing and managing distributed databases require dealing with additional complexities such as data distribution, replication, and concurrency control.
  • Consistency: Ensuring data consistency across distributed nodes is challenging and often requires implementing sophisticated consistency models or distributed consensus algorithms.
  • Network Overhead: Communication overhead between distributed nodes can impact performance and scalability, requiring optimization strategies to minimize latency and bandwidth usage.