cat /etc/ecm/hadoop-conf/fair-scheduler.xml

<?xml version="1.0"?>

<allocations>

<aclSubmitApps>*</aclSubmitApps>

<weight>2</weight>

<minResources>10000 mb, 10vcores</minResources>

<maxChildResources>34000 mb,24 vcores</maxChildResources>

<maxRunningApps>50</maxRunningApps>

<maxAMShare>1</maxAMShare>

<maxResources>400000 mb, 200vcores</maxResources> #限制队列最大使用资源

<aclAdministerApps>*</aclAdministerApps>

<schedulingPolicy>fair</schedulingPolicy>

<queue name="default">

<aclSubmitApps>*</aclSubmitApps>

<minResources>10000 mb, 10vcores</minResources>

<aclAdministerApps>*</aclAdministerApps>

<weight>1</weight>

<maxRunningApps>10</maxRunningApps>

<maxAMShare>0.5</maxAMShare>

<maxResources>200000 mb, 100vcores</maxResources>

</queue>

<queue name="collects">

<schedulingPolicy>fair</schedulingPolicy>

<aclSubmitApps>*</aclSubmitApps>

<aclAdministerApps>*</aclAdministerApps>

<weight>8</weight>

<maxAMShare>0.8</maxAMShare>

<minResources>50 mb, 2vcores</minResources>

<maxResources>400000 mb, 200vcores</maxResources>

<maxRunningApps>50</maxRunningApps>

</queue>

<queue name="data_bi">

<schedulingPolicy>fair</schedulingPolicy>

<aclSubmitApps>*</aclSubmitApps>

<aclAdministerApps>*</aclAdministerApps>

<weight>4</weight>

<minResources>100 mb, 1vcores</minResources>

<maxResources>30000 mb, 50vcores</maxResources>

<maxRunningApps>5</maxRunningApps>

</queue>

<queue name="opay_collects">

<weight>20</weight>

<minResources>10 mb, 1vcores</minResources>

<maxResources>400000 mb, 200vcores</maxResources>

<maxRunningApps>20</maxRunningApps>

<maxAMShare>0.5</maxAMShare>

<schedulingPolicy>fair</schedulingPolicy>

<aclSubmitApps>*</aclSubmitApps>

<aclAdministerApps>*</aclAdministerApps>

</queue>

<queue name="opos_collects">

<weight>5</weight>

<minResources>10 mb, 1vcores</minResources>

<maxResources>80000 mb, 50vcores</maxResources>

<maxRunningApps>10</maxRunningApps>

</queue>

<queue name="users" type="parent">

<weight>5</weight>

<minResources>10 mb, 1vcores</minResources>

<maxResources>10000 mb, 150vcores</maxResources>

<maxRunningApps>30</maxRunningApps>

<schedulingPolicy>fair</schedulingPolicy>

<aclSubmitApps>*</aclSubmitApps>

<aclAdministerApps>*</aclAdministerApps>

</queue>

<queue name="airflow">

<schedulingPolicy>fair</schedulingPolicy>

<aclSubmitApps>*</aclSubmitApps>

<aclAdministerApps>*</aclAdministerApps>

<weight>8</weight>

<minResources>10 mb, 2vcores</minResources>

<maxResources>200000 mb, 150vcores</maxResources>

<maxRunningApps>30</maxRunningApps>

</queue>

<defaultQueueSchedulingPolicy>fair</defaultQueueSchedulingPolicy>

<userMaxAppsDefault>50</userMaxAppsDefault>

<queueMaxAppsDefault>50</queueMaxAppsDefault>

<queueMaxAMShareDefault>0.5</queueMaxAMShareDefault>

<defaultFairSharePreemptionThreshold>0.5</defaultFairSharePreemptionThreshold>

<queueMaxResourcesDefault>34000 mb,24vcores</queueMaxResourcesDefault>

<defaultFairSharePreemptionTimeout>9223372036854775807</defaultFairSharePreemptionTimeout>

<defaultMinSharePreemptionTimeout>9223372036854775807</defaultMinSharePreemptionTimeout>

</allocations>

#新的xml, 不带root限制: 放emr的yarn-配置-fair-scheduler

<?xml version="1.0" encoding="utf-8"?>
<allocations>
<queue name="root">
<queue name="default">
<aclSubmitApps>*</aclSubmitApps>
<minResources>10000 mb, 10vcores</minResources>
<aclAdministerApps>*</aclAdministerApps>
<weight>1</weight>
<maxRunningApps>10</maxRunningApps>
<maxAMShare>0.5</maxAMShare>
<maxResources>200000 mb, 100vcores</maxResources>
</queue>
<queue name="collects">
<schedulingPolicy>fair</schedulingPolicy>
<aclSubmitApps>*</aclSubmitApps>
<aclAdministerApps>*</aclAdministerApps>
<weight>8</weight>
<maxAMShare>0.8</maxAMShare>
<minResources>50 mb, 2vcores</minResources>
<maxResources>400000 mb, 200vcores</maxResources>
<maxRunningApps>50</maxRunningApps>
</queue>
<queue name="data_bi">
<schedulingPolicy>fair</schedulingPolicy>
<aclSubmitApps>*</aclSubmitApps>
<aclAdministerApps>*</aclAdministerApps>
<weight>4</weight>
<minResources>100 mb, 1vcores</minResources>
<maxResources>30000 mb, 50vcores</maxResources>
<maxRunningApps>5</maxRunningApps>
</queue>
<queue name="opay_collects">
<weight>20</weight>
<minResources>10 mb, 1vcores</minResources>
<maxResources>400000 mb, 200vcores</maxResources>
<maxRunningApps>20</maxRunningApps>
<maxAMShare>0.5</maxAMShare>
<schedulingPolicy>fair</schedulingPolicy>
<aclSubmitApps>*</aclSubmitApps>
<aclAdministerApps>*</aclAdministerApps>
</queue>
<queue name="opos_collects">
<weight>5</weight>
<minResources>10 mb, 1vcores</minResources>
<maxResources>80000 mb, 50vcores</maxResources>
<maxRunningApps>10</maxRunningApps>
</queue>
<queue name="users" type="parent">
<weight>5</weight>
<minResources>10 mb, 1vcores</minResources>
<maxResources>10000 mb, 150vcores</maxResources>
<maxRunningApps>30</maxRunningApps>
<schedulingPolicy>fair</schedulingPolicy>
<aclSubmitApps>*</aclSubmitApps>
<aclAdministerApps>*</aclAdministerApps>
</queue>
<queue name="airflow">
<schedulingPolicy>fair</schedulingPolicy>
<aclSubmitApps>*</aclSubmitApps>
<aclAdministerApps>*</aclAdministerApps>
<weight>8</weight>
<minResources>10 mb, 2vcores</minResources>
<maxResources>200000 mb, 150vcores</maxResources>
<maxRunningApps>30</maxRunningApps>
</queue>
</queue>
<defaultQueueSchedulingPolicy>fair</defaultQueueSchedulingPolicy>
<userMaxAppsDefault>50</userMaxAppsDefault>
<queueMaxAppsDefault>50</queueMaxAppsDefault>
<queueMaxAMShareDefault>0.5</queueMaxAMShareDefault>
<defaultFairSharePreemptionThreshold>0.5</defaultFairSharePreemptionThreshold>
<queueMaxResourcesDefault>34000 mb,24vcores</queueMaxResourcesDefault>
<defaultFairSharePreemptionTimeout>9223372036854775807</defaultFairSharePreemptionTimeout>
<defaultMinSharePreemptionTimeout>9223372036854775807</defaultMinSharePreemptionTimeout>
</allocations>

EMR的fair-scheduler.xml的更多相关文章

  1. Fair Scheduler 队列设置经验总结

    Fair Scheduler 队列设置经验总结 由于公司的hadoop集群的计算资源不是很充足,需要开启yarn资源队列的资源抢占.在使用过程中,才明白资源抢占的一些特点.在这里总结一下. 只有一个队 ...

  2. 三：Fair Scheduler 公平调度器

    参考资料: http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/FairScheduler.html http://h ...

  3. Fair Scheduler中的Delay Schedule分析

    延迟调度的主要目的是提高数据本地性(data locality),减少数据在网络中的传输.对于那些输入数据不在本地的MapTask,调度器将会延迟调度他们,而把slot分配给那些具备本地性的MapTa ...

  4. Hadoop学习之--Fair Scheduler作业调度分析

    Fair Scheduler调度器同步心跳分配任务的过程简单来讲会经历以下环节: 1. 对map/reduce是否已经达到资源上限的循环判断 2. 对pool队列根据Fair算法排序 3.然后循环po ...

  5. YARN的Fair Scheduler和Capacity Scheduler

    关于Scheduler YARN有四种调度机制:Fair Schedule,Capacity Schedule,FIFO以及Priority: 其中Fair Scheduler是资源池机制,进入到里面 ...

  6. Hadoop的三种调度器FIFO、Capacity Scheduler、Fair Scheduler（转载）

    目前Hadoop有三种比较流行的资源调度器:FIFO .Capacity Scheduler.Fair Scheduler.目前Hadoop2.7默认使用的是Capacity Scheduler容量调 ...

  7. fair scheduler配置

    <property>    <name>yarn.resourcemanager.scheduler.class</name>    <value>or ...

  8. Yarn参数优化(Fair Scheduler版本)

    YARN 自从hadoop2.0之后, 我们可以使用apache yarn 来对集群资源进行管理.yarn把可以把资源(内存,CPU)以Container的方式进行划分隔离.YARN会管理集群中所有机 ...

  9. Linux 2.6 完全公平调度算法CFS（Completely Fair Scheduler） 分析

    转会http://www.ibm.com/developerworks/cn/linux/l-completely-fair-scheduler/index.html? ca=drs-cn-0125 ...

  10. 利用yarn capacity scheduler在EMR集群上实现大集群的多租户的集群资源隔离和quota限制

    转自:https://m.aliyun.com/yunqi/articles/79700 背景 使用过hadoop的人基本都会考虑集群里面资源的调度和优先级的问题,假设你现在所在的公司有一个大hado ...

随机推荐

  1. Gym - 102346G Getting Confidence 最小费用最大流

    Gym - 102346GGetting Confidence 题意:n*n的格子,每个格子上有一个数,要求每行每列都只能拿一个数,使得乘积最大,然后输出每列选择的是第几行的数. 如果是加法的话,那么 ...

  2. 【概率论】5-3:超几何分布(The Hypergeomtric Distribution)

    title: [概率论]5-3:超几何分布(The Hypergeomtric Distribution) categories: - Mathematic - Probability keyword ...

  3. 服务器之poll

    poll服务器方法采用将监听端口用数组存放起来,这样就不需要轮询的监听整个文件描述符了 #include <poll.h> int poll(struct pollfd *fds, nfd ...

  4. express+mongoDB（mLab）做一个todolist小项目

    这是在网课上学习的,先建立一个express-todolist文件夹作为项目跟目录 另外,我们直接把项目上用到的css文件和js文件下载下来放在项目里 这里直接贴出来 先建立一个public文件夹,放 ...

  5. windows下java环境变量标准配置

    配置步骤 1.“此电脑”右键,选择“属性”,点击“高级系统设置”,点击“环境变量”. 2.在“系统变量”这一栏,点击“新建”,变量名:JAVA_HOME,变量值:C:\Program Files\Ja ...

  6. [WEB安全]给BurpSuite设置非本地的网络代理

    目录 0x01 一般情况 0x02 移动端流量抓取 0x03 多重代理的情形 0x04 参考链接 在Web渗透测试过程中,BurpSuite是不可或缺的神器之一. BurpSuite的核心是代理Pro ...

  7. Node.js 文件操作

    1.新建一个文件a.txt,并写入"你好,这是一个新文件.". writeFile 代码 demo1.js var fs = require('fs'); console.log( ...

  8. docker pull 失败： server misbehaving

    在docker pull 镜像时一直报错: Error response from daemon: Get https://registry-1.docker.io/v2/: dial tcp: lo ...

  9. python 设计模式之解释器(Interpreter)模式

    #写在前面 关于解释器模式,我在网上转了两三圈,心中有了那么一点概念 ,也不知道自己理解的是对还是错. 其实关于每一种设计模式,我总想找出一个答案,那就是为什么要用这种设计模式, 如果不用会怎么样,会 ...

  10. 批量转换Excel转CSV文件

    本文为Excel VBA代码,可以实现将某一文件夹内的Excel文件(xls或者xlsx)另存为“逗号分隔的csv文件”.   使用条件: 1. Windows系统: 2. 已安装 MS 2007或以 ...