Understanding Hadoop Resource Manager
Hadoop is a popular open-source framework for distributed storage and processing of large datasets. At the heart of Hadoop is the Resource Manager, which is responsible for managing and allocating resources across the Hadoop cluster.
The Hadoop Resource Manager is the central component that coordinates the execution of applications in a Hadoop cluster. It is responsible for:
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Resource Allocation: The Resource Manager is responsible for allocating resources (such as CPU, memory, and disk) to the various applications running on the Hadoop cluster.
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Application Scheduling: The Resource Manager schedules the execution of applications based on the available resources and the priority of the applications.
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Fault Tolerance: The Resource Manager monitors the health of the Hadoop cluster and takes appropriate actions in case of failures, such as restarting failed tasks or rescheduling applications on available resources.
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Security: The Resource Manager also handles security-related tasks, such as authenticating users and enforcing access control policies.
To interact with the Hadoop Resource Manager, clients use the YARN (Yet Another Resource Negotiator) API, which provides a set of interfaces for submitting, monitoring, and managing applications running on the Hadoop cluster.
graph TD
A[Client] --> B[YARN API]
B --> C[Resource Manager]
C --> D[Node Manager]
D --> E[Container]
The Resource Manager communicates with the Node Managers, which are responsible for managing the resources on individual nodes in the Hadoop cluster. The Node Managers launch and monitor the execution of tasks within containers, which are the basic units of resource allocation in Hadoop.
By understanding the role and functionality of the Hadoop Resource Manager, developers can effectively design and deploy their applications on the Hadoop platform, ensuring efficient resource utilization and reliable application execution.