Resource Distribution Workflow Example
To understand the resource distribution workflow, consider the example of a 100-node cluster, each with 10 GB of memory allocated for YARN containers, for a total cluster capacity of 1000 GB (1 TB).
According to the previously described configuration, the Engineering organization is assigned 60% of the cluster capacity, i.e., an absolute capacity of 600 GB. Similarly, the Support organization is assigned 100 GB, and the Marketing organization gets 300 GB.
Under the Engineering organization, capacity is distributed between the Development team and the QA team in a in a 1:4 ratio. So Development gets 120 GB, and 480 GB is assigned to QA.
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Initially, the entire "engineering" queue is free with no applications running, while the "support" and "marketing" queues are utilizing their full capacities.
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Users Sid and Hitesh first submit applications to the "development" leaf queue. Their applications are elastic and can run with either all of the resources available in the cluster, or with a subset of cluster resources (depending upon the state of the resource-usage).
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Even though the "development" queue is allocated 120 GB, Sid and Hitesh are each allowed to occupy 120 GB, for a total of 240 GB.
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This can happen despite the fact that the "development" queue is configured to be run with a capacity of 120 GB. Capacity Scheduler allows elastic sharing of cluster resources for better utilization of available cluster resources. Since there are no other users in the "engineering" queue, Sid and Hitesh are allowed to use the available free resources.
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Next, users Jian, Zhijie and Xuan submit more applications to the "development" leaf queue. Even though each is restricted to 120 GB, the overall used capacity in the queue becomes 600 GB -- essentially taking over all of the resources allocated to the "qa" leaf queue.
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User Gupta now submits an application to the "qa" queue. With no free resources available in the cluster, his application must wait.
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Given that the "development" queue is utilizing all of the available cluster resources, Gupta may or may not be able to immediately get back the guaranteed capacity of his "qa" queue -- depending upon whether or not preemption is enabled.
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As the applications of Sid, Hitesh, Jian, Zhijie, and Xuan finish running and resources become available, the newly available Containers will be allocated to Gupta’s application.
This will continue until the cluster stabilizes at the intended 1:4 resource usage ratio for the "development" and "qa" queues.
From this example, you can see that it is possible for abusive users to submit applications continuously, and thereby lock out other queues from resource allocation until Containers finish running or get preempted. To avoid this scenario, Capacity Scheduler supports limits on the elastic growth of any queue. For example, to restrict the "development" queue from monopolizing the "engineering" queue capacity, an administrator can set a the maximum- capacity property:
Property:
yarn.scheduler.capacity.root.engineering.development.maximum-capacity
Value: 40
Once this is set, users of the "development" queue can still go beyond their capacity of 120 GB, but they will not be allocated any more than 40% of the "engineering" parent queue's capacity (i.e., 40% of 600 GB = 240 GB).
The capacity and maximum-capacity properties can be used to control sharing and elasticity across the organizations and sub-organizations utilizing a YARN cluster. Administrators should balance these properties to avoid strict limits that result in a loss of utilization, and to avoid excessive cross-organization sharing.
Capacity and maximum capacity settings can be dynamically changed at run-time using yarn rmadmin -refreshQueues.