Master-slave architecture

Definition

Master-slave architecture is a design pattern used in computing where one device, the master, controls one or more devices, the slaves, to perform tasks in a coordinated manner. This architecture allows for efficient data processing and management, as the master handles requests and delegates work to the slaves, which execute the tasks and return results. In the context of distributed systems, this architecture is crucial for ensuring that operations are carried out systematically and that data integrity is maintained.

5 Must Know Facts For Your Next Test

1. In master-slave architecture, the master node is responsible for managing and coordinating tasks while slaves execute the assigned jobs.
2. This architecture enhances fault tolerance; if a slave fails, the master can reassign tasks to other available slaves without disrupting overall processing.
3. The master node can consolidate results from multiple slaves, allowing for efficient data aggregation and processing in large-scale computations.
4. Master-slave architecture is commonly used in databases, where a master database handles write operations while slave databases handle read operations for load distribution.
5. It is crucial in systems like Hadoop's MapReduce framework, where the master node schedules tasks across slave nodes to maximize parallel processing.

Review Questions

• How does master-slave architecture improve the efficiency of distributed systems?
  • Master-slave architecture improves the efficiency of distributed systems by centralizing control with a master node that delegates tasks to slave nodes. This allows for parallel processing, as multiple slaves can execute different tasks simultaneously. The master manages workload distribution and consolidates results from slaves, which maximizes resource utilization and minimizes processing time.
• Discuss the impact of master-slave architecture on data integrity and fault tolerance in computing systems.
  • Master-slave architecture significantly impacts data integrity and fault tolerance by allowing the master node to oversee task execution and manage potential failures. If a slave node encounters an issue, the master can redistribute tasks to other slaves without losing data or halting processes. This centralized control not only ensures that operations are performed correctly but also enhances the overall reliability of the system.
• Evaluate how master-slave architecture plays a role in the scalability of big data processing frameworks like Hadoop.
  • Master-slave architecture is essential for the scalability of big data processing frameworks like Hadoop because it allows for the addition of more slave nodes to handle increased data volumes without impacting performance. The master node efficiently coordinates these slaves, enabling parallel processing of large datasets. As data grows, new slaves can be integrated seamlessly into the system, maintaining high levels of throughput and facilitating the rapid analysis needed in big data applications.

Related terms

Distributed Computing: A computing model in which processing power and resources are spread across multiple networked computers to achieve greater efficiency and performance.

Load Balancing: The process of distributing workloads across multiple computing resources to ensure optimal resource use, minimize response time, and avoid overload.

Data Replication: The process of storing copies of data on multiple devices to improve data availability and reliability in distributed systems.