Apache Spark Certification Practice Test

In the context of running Spark’s distributed applications, the Master component is crucial to start because it is responsible for the initial control of the cluster and manages the resources and the scheduling of tasks. The Master is the point of coordination that keeps track of the status of workers and the resources available in the cluster.

When a Spark application is submitted, the Master allocates resources to various applications by managing which executors are assigned to processes. It oversees the distribution of the application execution between Workers in the cluster and directs the Driver program to dispatch tasks accordingly. Without the Master node actively managing these resources, there would be no effective way to launch and coordinate the execution of distributed tasks, leading to inefficiencies or failure to run the application altogether.

In contrast, while Workers, Executors, and the Driver play essential roles in processing and executing tasks, they rely on the Master’s orchestration to initiate those processes. The Workers execute the tasks assigned to them, Executors run on Workers to carry out the task execution, and the Driver is responsible for maintaining the application and communicating with the Master; hence all these components depend on the Master for the successful distribution and execution of applications across the Spark cluster.