YARN (Yet Another Resource Negotiator) is an essential component of the Hadoop ecosystem that enables distributed processing frameworks to manage resources and schedule tasks across a cluster. The Resource Manager (RM) is the central authority responsible for resource allocation and management in YARN.
The primary roles of the Resource Manager in YARN are as follows:
1. **Resource provisioning**: The RM is responsible for managing and allocating cluster resources (CPU, memory, etc.) to various applications (e.g., MapReduce jobs or Spark applications). It keeps track of the available resources on each node and the resource requirements of the submitted applications. Based on this information, it decides where to allocate resources for a specific application.
2. **Scheduling**: Scheduling is the process of deciding which applications will be run on which node of the cluster. The RM does this by using a pluggable scheduler. YARN provides different scheduling strategies like FIFO Scheduler, Capacity Scheduler, and Fair Scheduler to decide which application should use the resources.
The ResourceManager has two main components:
- **Scheduler**: The Scheduler is responsible for allocating resources to submitted applications based on configured policies, such as queue-based; fair, capacity, or first-in-first-out (FIFO) scheduling; or priority-based scheduling. The Scheduler does not monitor or track the application’s status or progress. It only cares about the resource request of the application.
- **ApplicationManager**: ApplicationManager is responsible for accepting job submissions, negotiating the required resources with the Scheduler, and managing Application Masters (a per-application component responsible for executing individual tasks by requesting resources from the RM, coordinating with the NodeManagers, and monitoring task progress).
In summary, the Resource Manager in YARN is responsible for managing cluster resources, scheduling application execution, and coordinating with Application Masters to run tasks on the nodes. Its primary purpose is to ensure effective utilization and efficient allocation of resources in the Hadoop cluster so that various jobs can be processed concurrently.