Distributed systems

Definition

Distributed systems are a network of independent computers that appear to the users as a single coherent system. These systems work together to achieve a common goal, often by sharing data and resources across different locations. They are essential for handling large amounts of data, providing fault tolerance, and enabling scalability, which are crucial in managing big data challenges.

5 Must Know Facts For Your Next Test

1. Distributed systems can improve performance by spreading workloads across multiple machines, allowing for faster processing of large datasets.
2. They are designed to be fault-tolerant, meaning if one node fails, others can take over its responsibilities without losing data or disrupting services.
3. Communication between nodes in a distributed system often involves complex protocols that ensure data consistency and synchronization.
4. Common examples of distributed systems include cloud computing platforms, online databases, and peer-to-peer networks.
5. Managing distributed systems can be challenging due to issues like network latency, partial failures, and the need for effective coordination between nodes.

Review Questions

• How do distributed systems enhance performance and scalability in the context of big data?
  • Distributed systems enhance performance and scalability by dividing large datasets across multiple machines, allowing them to process data simultaneously. This parallel processing capability means that tasks can be completed faster than on a single machine. As demand grows, additional nodes can be added to the system to manage increased workloads without significantly affecting performance, making it easier to handle the ever-expanding volume of big data.
• What are some common challenges faced when managing distributed systems, particularly regarding fault tolerance and data consistency?
  • Managing distributed systems comes with challenges such as ensuring fault tolerance and maintaining data consistency across all nodes. When one component fails, it's vital that other components can seamlessly take over to prevent data loss or service disruption. Additionally, achieving data consistency is complex because updates must be synchronized among all nodes, often requiring advanced algorithms and communication protocols to ensure that all copies of the data remain accurate and up-to-date.
• Evaluate the importance of replication in distributed systems and its impact on data availability and reliability.
  • Replication plays a crucial role in distributed systems by creating multiple copies of data across different nodes, which significantly boosts data availability and reliability. If one node goes down or becomes unreachable, other nodes with replicated data can continue serving requests without interruption. This redundancy not only enhances the robustness of the system but also improves access speed for users, as requests can be fulfilled by the nearest available node. Consequently, replication is a fundamental strategy for ensuring that distributed systems can handle failures gracefully while maintaining high performance.

Related terms

Scalability:

The ability of a system to handle a growing amount of work or its potential to accommodate growth.

Fault Tolerance: The capability of a system to continue functioning even when one or more components fail.

Replication: The process of storing copies of data on multiple machines to ensure reliability and availability.