HDFS RM

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                                 HDFS RM

In Hadoop, “RM” typically stands for “ResourceManager,” which is a key component of the Hadoop YARN (Yet Another Resource Negotiator) architecture. YARN is responsible for managing and allocating resources in a Hadoop cluster to run various applications, including MapReduce jobs and other distributed data processing workloads. The ResourceManager plays a central role in this process.

Here’s an overview of the HDFS ResourceManager (RM):

  1. Resource Management:

    • The ResourceManager is responsible for managing and allocating cluster resources to different applications. It keeps track of available resources (CPU, memory, and other cluster resources) and allocates them to running applications based on their resource requirements.
  2. Application Scheduling:

    • It schedules applications (jobs) to run on the cluster. The ResourceManager ensures that multiple applications can coexist on the same cluster without resource conflicts.
  3. NodeManager Communication:

    • The ResourceManager communicates with NodeManagers, which run on individual nodes in the cluster. NodeManagers report resource utilization and health status to the ResourceManager.
  4. Fault Tolerance:

    • The ResourceManager itself is a single point of failure. To address this, Hadoop provides a standby ResourceManager that can take over in case the primary ResourceManager fails.
  5. Dynamic Resource Allocation:

    • The ResourceManager supports dynamic allocation of resources, which means it can allocate and deallocate resources from applications as needed, allowing for efficient resource utilization.
  6. Fair Scheduler and Capacity Scheduler:

    • Hadoop YARN includes two commonly used schedulers: the Fair Scheduler and the Capacity Scheduler. These schedulers are pluggable and allow for fine-grained control over resource allocation policies based on user-defined rules.
  7. Web UI:

    • The ResourceManager provides a web-based user interface that allows administrators and users to monitor the cluster’s resource usage, application status, and logs.
  8. REST API:

    • ResourceManager exposes a REST API that can be used to interact programmatically with the cluster, including submitting and monitoring applications.

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