从YARN开始不起作用

时间:2016-05-08 05:36:18

标签: hadoop yarn h2o

当我在cdh集群上启动H2o时,我收到以下错误。我从wbesite下载了所有内容,并按照教程进行操作。我跑的命令是

hadoop jar h2odriver.jar -nodes 2 -mapperXmx 1g -output hdfsOutputDirName  

它表明没有使用容器。目前尚不清楚这些设置将在hadoop上进行。我已经给了所有设置记忆。内存的0.0是没有意义的,为什么容器没有使用内存。集群现在是否正在运行?

----- YARN cluster metrics -----
Number of YARN worker nodes: 3

----- Nodes -----
Node: http://data-node-3:8042 Rack: /default, RUNNING, 1 containers used, 1.0 / 6.0 GB used, 1 / 4 vcores used
Node: http://data-node-1:8042 Rack: /default, RUNNING, 0 containers used, 0.0 / 6.0 GB used, 0 / 4 vcores used
Node: http://data-node-2:8042 Rack: /default, RUNNING, 0 containers used, 0.0 / 6.0 GB used, 0 / 4 vcores used

----- Queues -----
Queue name:            root.default
    Queue state:       RUNNING
    Current capacity:  0.00
    Capacity:          0.00
    Maximum capacity:  -1.00
    Application count: 0

Queue 'root.default' approximate utilization: 0.0 / 0.0 GB used, 0 / 0 vcores used

----------------------------------------------------------------------

WARNING: Job memory request (2.2 GB) exceeds queue available memory capacity (0.0 GB)
WARNING: Job virtual cores request (2) exceeds queue available virtual cores capacity (0)

----------------------------------------------------------------------

For YARN users, logs command is 'yarn logs -applicationId application_1462681033282_0008'

2 个答案:

答案 0 :(得分:3)

您应该将默认队列设置为具有运行2nodes群集的可用资源。

见警告:

  1. WARNING: Job memory request (2.2 GB) exceeds queue available memory capacity (0.0 GB)

    • 您要求每个节点1GB(+开销),但YARN队列中没有可用资源
  2. WARNING: Job virtual cores request (2) exceeds queue available virtual cores capacity (0)

    • 您要求2个虚拟内核,但默认队列中没有可用内核
  3. 请检查YARN文档 - 例如容量调度程序的设置和最大可用资源: https://hadoop.apache.org/docs/r2.4.1/hadoop-yarn/hadoop-yarn-site/CapacityScheduler.html

答案 1 :(得分:0)

我在Cloudera Manager纱线配置中进行了以下更改

Setting                                     Value
yarn.scheduler.maximum-allocation-vcores    8 
yarn.nodemanager.resource.cpu-vcores        4
yarn.nodemanager.resource.cpu-vcores        4
yarn.scheduler.maximum-allocation-mb        16 GB