Distributed databases

Definition

A distributed database is a type of database that is spread across multiple locations, allowing data to be stored and processed on different machines within a network. This setup enhances data availability and fault tolerance, as the system can continue to function even if one part fails. By distributing data, organizations can also improve performance by balancing workloads across different nodes.

5 Must Know Facts For Your Next Test

1. Distributed databases enhance resilience by allowing data access even if some nodes are down, ensuring continuity of operations.
2. They can be geographically dispersed, which helps reduce latency for users in different locations by providing local access to data.
3. Data distribution strategies like replication and sharding are crucial for balancing load and improving performance in a distributed environment.
4. Distributed databases often rely on complex algorithms to maintain data consistency across multiple nodes while ensuring high availability.
5. Cloud computing has significantly contributed to the growth of distributed databases, enabling organizations to easily scale their databases without investing in physical infrastructure.

Review Questions

• How do distributed databases enhance network resilience compared to traditional centralized databases?
  • Distributed databases enhance network resilience by spreading data across multiple locations, allowing for continued access even if some nodes fail. In contrast, centralized databases can become points of failure; if the central server goes down, all access to data is lost. By employing techniques like replication and sharding, distributed databases ensure that data remains available and can recover quickly from outages, thus minimizing disruptions.
• What are the key strategies for maintaining data consistency in distributed databases, and why are they important for network resilience?
  • Key strategies for maintaining data consistency in distributed databases include using replication and implementing various consistency models such as strong or eventual consistency. These strategies are crucial because they ensure that all nodes have accurate and synchronized data, which is essential for the reliability of applications relying on that data. When consistency is maintained effectively, it reduces the chances of errors and discrepancies that could arise due to network failures or node outages.
• Evaluate the impact of cloud computing on the development and scalability of distributed databases in modern networks.
  • Cloud computing has revolutionized the development and scalability of distributed databases by providing flexible infrastructure that allows organizations to easily expand their database systems without significant upfront investment. With cloud services offering automated scaling and management features, businesses can quickly adjust their storage needs based on demand. This adaptability not only fosters innovation but also enhances network resilience by enabling more robust disaster recovery solutions through geographic distribution of data across multiple cloud regions.

Related terms

Replication: The process of copying data from one database server to another to ensure consistency and availability across distributed databases.

Sharding: A method of splitting a database into smaller, more manageable pieces, called shards, that can be distributed across multiple servers to improve performance and scalability.

Consistency Models: Rules that define the degree to which the values stored in a distributed database remain the same across different nodes after updates, such as strong consistency or eventual consistency.