Hadoop MapReduce Next Generation - Capacity Scheduler

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Purpose

This document describes the CapacityScheduler, a pluggable scheduler for Hadoop which allows for multiple-tenants to securely share a large cluster such that their applications are allocated resources in a timely manner under constraints of allocated capacities.

Overview

The CapacityScheduler is designed to run Hadoop applications as a shared, multi-tenant cluster in an operator-friendly manner while maximizing the throughput and the utilization of the cluster.

Traditionally each organization has it own private set of compute resources that have sufficient capacity to meet the organization's SLA under peak or near peak conditions. This generally leads to poor average utilization and overhead of managing multiple independent clusters, one per each organization. Sharing clusters between organizations is a cost-effective manner of running large Hadoop installations since this allows them to reap benefits of economies of scale without creating private clusters. However, organizations are concerned about sharing a cluster because they are worried about others using the resources that are critical for their SLAs.

The CapacityScheduler is designed to allow sharing a large cluster while giving each organization capacity guarantees. The central idea is that the available resources in the Hadoop cluster are shared among multiple organizations who collectively fund the cluster based on their computing needs. There is an added benefit that an organization can access any excess capacity not being used by others. This provides elasticity for the organizations in a cost-effective manner.

Sharing clusters across organizations necessitates strong support for multi-tenancy since each organization must be guaranteed capacity and safe-guards to ensure the shared cluster is impervious to single rouge application or user or sets thereof. The CapacityScheduler provides a stringent set of limits to ensure that a single application or user or queue cannot consume disproportionate amount of resources in the cluster. Also, the CapacityScheduler provides limits on initialized/pending applications from a single user and queue to ensure fairness and stability of the cluster.

The primary abstraction provided by the CapacityScheduler is the concept of queues. These queues are typically setup by administrators to reflect the economics of the shared cluster.

To provide further control and predictability on sharing of resources, the CapacityScheduler supports hierarchical queues to ensure resources are shared among the sub-queues of an organization before other queues are allowed to use free resources, there-by providing affinity for sharing free resources among applications of a given organization.

Features

The CapacityScheduler supports the following features:

  • Hierarchical Queues - Hierarchy of queues is supported to ensure resources are shared among the sub-queues of an organization before other queues are allowed to use free resources, there-by providing more control and predictability.
  • Capacity Guarantees - Queues are allocated a fraction of the capacity of the grid in the sense that a certain capacity of resources will be at their disposal. All applications submitted to a queue will have access to the capacity allocated to the queue. Adminstrators can configure soft limits and optional hard limits on the capacity allocated to each queue.
  • Security - Each queue has strict ACLs which controls which users can submit applications to individual queues. Also, there are safe-guards to ensure that users cannot view and/or modify applications from other users. Also, per-queue and system administrator roles are supported.
  • Elasticity - Free resources can be allocated to any queue beyond it's capacity. When there is demand for these resources from queues running below capacity at a future point in time, as tasks scheduled on these resources complete, they will be assigned to applications on queues running below the capacity (pre-emption is not supported). This ensures that resources are available in a predictable and elastic manner to queues, thus preventing artifical silos of resources in the cluster which helps utilization.
  • Multi-tenancy - Comprehensive set of limits are provided to prevent a single application, user and queue from monopolizing resources of the queue or the cluster as a whole to ensure that the cluster isn't overwhelmed.
  • Operability
    • Runtime Configuration - The queue definitions and properties such as capacity, ACLs can be changed, at runtime, by administrators in a secure manner to minimize disruption to users. Also, a console is provided for users and administrators to view current allocation of resources to various queues in the system. Administrators can add additional queues at runtime, but queues cannot be deleted at runtime.
    • Drain applications - Administrators can stop queues at runtime to ensure that while existing applications run to completion, no new applications can be submitted. If a queue is in STOPPED state, new applications cannot be submitted to itself or any of its child queueus. Existing applications continue to completion, thus the queue can be drained gracefully. Administrators can also start the stopped queues.
  • Resource-based Scheduling - Support for resource-intensive applications, where-in a application can optionally specify higher resource-requirements than the default, there-by accomodating applications with differing resource requirements. Currently, memory is the the resource requirement supported.

Configuration

  • Setting up ResourceManager to use CapacityScheduler

    To configure the ResourceManager to use the CapacityScheduler, set the following property in the conf/yarn-site.xml:

    PropertyValue
    yarn.resourcemanager.scheduler.classorg.apache.hadoop.yarn.server.resourcemanager.scheduler.capacity.CapacityScheduler
  • Setting up queues

    conf/capacity-scheduler.xml is the configuration file for the CapacityScheduler.

    The CapacityScheduler has a pre-defined queue called root. All queueus in the system are children of the root queue.

    Further queues can be setup by configuring yarn.scheduler.capacity.root.queues with a list of comma-separated child queues.

    The configuration for CapacityScheduler uses a concept called queue path to configure the hierarchy of queues. The queue path is the full path of the queue's hierarchy, starting at root, with . (dot) as the delimiter.

    A given queue's children can be defined with the configuration knob: yarn.scheduler.capacity.<queue-path>.queues. Children do not inherit properties directly from the parent unless otherwise noted.

    Here is an example with three top-level child-queues a, b and c and some sub-queues for a and b:

    <property>
      <name>yarn.scheduler.capacity.root.queues</name>
      <value>a,b,c</value>
      <description>The queues at the this level (root is the root queue).
      </description>
    </property>
    
    <property>
      <name>yarn.scheduler.capacity.root.a.queues</name>
      <value>a1,a2</value>
      <description>The queues at the this level (root is the root queue).
      </description>
    </property>
    
    <property>
      <name>yarn.scheduler.capacity.root.b.queues</name>
      <value>b1,b2,b3</value>
      <description>The queues at the this level (root is the root queue).
      </description>
    </property>
  • Queue Properties
    • Resource Allocation
      PropertyDescription
      yarn.scheduler.capacity.<queue-path>.capacityQueue capacity in percentage (%) as a float (e.g. 12.5). The sum of capacities for all queues, at each level, must be equal to 100. Applications in the queue may consume more resources than the queue's capacity if there are free resources, providing elasticity.
      yarn.scheduler.capacity.<queue-path>.maximum-capacityMaximum queue capacity in percentage (%) as a float. This limits the elasticity for applications in the queue. Defaults to -1 which disables it.
      yarn.scheduler.capacity.<queue-path>.minimum-user-limit-percentEach queue enforces a limit on the percentage of resources allocated to a user at any given time, if there is demand for resources. The user limit can vary between a minimum and maximum value. The the former (the minimum value) is set to this property value and the latter (the maximum value) depends on the number of users who have submitted applications. For e.g., suppose the value of this property is 25. If two users have submitted applications to a queue, no single user can use more than 50% of the queue resources. If a third user submits an application, no single user can use more than 33% of the queue resources. With 4 or more users, no user can use more than 25% of the queues resources. A value of 100 implies no user limits are imposed. The default is 100.
      yarn.scheduler.capacity.<queue-path>.user-limit-factorThe multiple of the queue capacity which can be configured to allow a single user to acquire more resources. By default this is set to 1 which ensures that a single user can never take more than the queue's configured capacity irrespective of how idle th cluster is. Value is specified as a float.
    • Running and Pending Application Limits

      The CapacityScheduler supports the following parameters to control the running and pending applications:

      PropertyDescription
      yarn.scheduler.capacity.maximum-applications / yarn.scheduler.capacity.<queue-path>.maximum-applicationsMaximum number of applications in the system which can be concurrently active both running and pending. Limits on each queue are directly proportional to their queue capacities and user limits. This is a hard limit and any applications submitted when this limit is reached will be rejected. Default is 10000. This can be set for all queues with yarn.scheduler.capacity.maximum-applications and can also be overridden on a per queue basis by setting yarn.scheduler.capacity.<queue-path>.maximum-applications.
      yarn.scheduler.capacity.maximum-am-resource-percent / yarn.scheduler.capacity.<queue-path>.maximum-am-resource-percentMaximum percent of resources in the cluster which can be used to run application masters - controls number of concurrent active applications. Limits on each queue are directly proportional to their queue capacities and user limits. Specified as a float - ie 0.5 = 50%. Default is 10%. This can be set for all queues with yarn.scheduler.capacity.maximum-am-resource-percent and can also be overridden on a per queue basis by setting yarn.scheduler.capacity.<queue-path>.maximum-am-resource-percent
    • Queue Administration & Permissions

      The CapacityScheduler supports the following parameters to the administer the queues:

      PropertyDescription
      yarn.scheduler.capacity.<queue-path>.stateThe state of the queue. Can be one of RUNNING or STOPPED. If a queue is in STOPPED state, new applications cannot be submitted to itself or any of its child queues. Thus, if the root queue is STOPPED no applications can be submitted to the entire cluster. Existing applications continue to completion, thus the queue can be drained gracefully.
      yarn.scheduler.capacity.root.<queue-path>.acl_submit_applicationsThe ACL which controls who can submit applications to the given queue. If the given user/group has necessary ACLs on the given queue or one of the parent queues in the hierarchy they can submit applications. ACLs for this property are inherited from the parent queue if not specified.
      yarn.scheduler.capacity.root.<queue-path>.acl_administer_queueThe ACL which controls who can administer applications on the given queue. If the given user/group has necessary ACLs on the given queue or one of the parent queues in the hierarchy they can administer applications. ACLs for this property are inherited from the parent queue if not specified.

      Note: An ACL is of the form user1, user2spacegroup1, group2. The special value of * implies anyone. The special value of space implies no one. The default is * for the root queue if not specified.

    • Reviewing the configuration of the CapacityScheduler

      Once the installation and configuration is completed, you can review it after starting the YARN cluster from the web-ui.

      • Start the YARN cluster in the normal manner.
      • Open the ResourceManager web UI.
      • The /scheduler web-page should show the resource usages of individual queues.

Changing Queue Configuration

Changing queue properties and adding new queues is very simple. You need to edit conf/capacity-scheduler.xml and run yarn rmadmin -refreshQueues.

$ vi $HADOOP_CONF_DIR/capacity-scheduler.xml
$ $YARN_HOME/bin/yarn rmadmin -refreshQueues

Note: Queues cannot be deleted, only addition of new queues is supported - the updated queue configuration should be a valid one i.e. queue-capacity at each level should be equal to 100%.