Help Center > > User Guide> Managing an Existing Cluster> Tenant Management> Overview

Overview

Updated at: Mar 25, 2021 GMT+08:00

Definition

An MRS cluster provides various resources and services for multiple organizations, departments, or applications to share. The cluster provides tenants as a logical entity to use these resources and services. A mode involving different tenants is called multi-tenant mode. Currently, only the analysis cluster supports tenant management.

Principles

The MRS cluster provides the multi-tenant function. It supports a layered tenant model and allows dynamic adding or deleting of tenants to isolate resources. It dynamically manages and configures tenants' computing and storage resources.

The computing resources indicate tenants' Yarn task queue resources. The task queue quota can be modified, and the task queue usage status and statistics can be viewed.

The storage resources can be stored on HDFS. You can add and delete the HDFS storage directories of tenants, and set the quotas of file quantity and the storage space of the directories.

Tenants can create and manage tenants in a cluster based on service requirements.

  • Roles, computing resources, and storage resources are automatically created when tenants are created. By default, all permissions of the new computing resources and storage resources are allocated to a tenant's roles.
  • Permissions to view the current tenant's resources, add a subtenant, and manage the subtenant's resources are granted to the tenant's roles by default.
  • After you have modified the tenant's computing or storage resources, permissions of the tenant's roles are automatically updated.

MRS supports a maximum of 512 tenants. The default tenants created by the system include default. Tenants that are in the topmost layer with the default tenant are called level-1 tenants.

Resource Pools

Yarn task queues support only the label-based scheduling policy. This policy enables Yarn task queues to associate NodeManagers that have specific node labels. In this way, Yarn tasks run on specified nodes so that tasks are scheduled and certain hardware resources are utilized. For example, Yarn tasks requiring a large memory capacity can run on nodes with a large memory capacity by means of label association, preventing poor service performance.

In an MRS cluster, the tenant logically divides Yarn cluster nodes to combine multiple NodeManagers into a resource pool. Yarn task queues can be associated with specified resource pools by configuring queue capacity policies, ensuring efficient and independent resource utilization in the resource pools.

MRS supports a maximum of 50 resource pools. By default, the system contains a Default resource pool.

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