Distributed Databases

Definition

Distributed databases are databases that are spread across multiple locations or nodes, allowing data to be stored and accessed from different sites while functioning as a single cohesive unit. This setup enables improved performance, redundancy, and availability, addressing challenges like network latency and data consistency that arise from geographic distribution. They represent a significant evolution in database systems, pushing the boundaries of how data can be managed across varied environments.

5 Must Know Facts For Your Next Test

1. Distributed databases enhance fault tolerance by allowing data to be duplicated across different nodes, ensuring continued access even if one node fails.
2. They can operate in various configurations, including homogeneous (same DBMS across all nodes) or heterogeneous (different DBMSs across nodes), allowing for flexibility in architecture.
3. Network latency is a key concern in distributed databases, as communication between nodes can slow down operations if not properly managed.
4. The CAP theorem applies to distributed databases, highlighting the trade-offs between Consistency, Availability, and Partition tolerance when designing systems.
5. Eventual consistency is a common approach used in distributed databases to ensure that all copies of data converge towards a consistent state over time, rather than requiring immediate consistency.

Review Questions

• How do distributed databases enhance performance compared to traditional centralized databases?
  • Distributed databases improve performance by spreading the workload across multiple nodes or locations, which allows for parallel processing of queries and transactions. This distribution reduces the burden on any single server and helps handle more requests simultaneously. Moreover, by placing data closer to where it is needed geographically, latency can be minimized, leading to quicker response times for users.
• Discuss the implications of the CAP theorem for distributed databases when designing a system.
  • The CAP theorem states that in a distributed database system, it is impossible to simultaneously guarantee Consistency, Availability, and Partition tolerance. When designing a distributed database, one must decide which two of these three properties to prioritize based on the application’s requirements. For instance, a system that prioritizes consistency may sacrifice availability during network partitions, while one that emphasizes availability may allow temporary inconsistencies in the data.
• Evaluate the role of eventual consistency in ensuring reliability within distributed databases and its impact on user experience.
  • Eventual consistency plays a crucial role in distributed databases by allowing systems to remain available even during updates or network failures. Instead of requiring immediate consistency after each transaction, eventual consistency ensures that all nodes will eventually reflect the same data state over time. While this model can enhance reliability and performance by reducing downtime, it can also lead to temporary discrepancies in data that users might encounter during interactions. Understanding this trade-off is essential for designers aiming to optimize user experience while leveraging the benefits of distributed systems.

Related terms

Replication: The process of copying and maintaining database objects, such as tables, in multiple locations to ensure data consistency and availability.

Sharding: A method of distributing data across multiple databases to improve performance by dividing large datasets into smaller, more manageable pieces.

Database Management System (DBMS): Software that interacts with end users, applications, and the database itself to capture and analyze data.