h2o在Yarn上启动不起作用

hpxqektj  于 2021-05-29  发布在  Hadoop
关注(0)|答案(2)|浏览(493)

当我在cdh簇上启动h2o时,我得到以下错误。我下载了网站上的所有内容,并按照教程进行操作。我执行的命令是

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

它表明容器没有被使用。现在还不清楚这些在hadoop上是什么设置。我已经给了所有的设置记忆。内存的0.0没有意义,为什么容器不使用内存。集群现在还在运行吗?

----- 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'
ruyhziif

ruyhziif1#

您应该设置默认队列,使其具有运行2nodes集群的可用资源。
请参见警告: 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),但队列中没有可用的资源 WARNING: Job virtual cores request (2) exceeds queue available virtual cores capacity (0) 您请求2个虚拟核心,但默认队列中没有可用的核心
请检查Yarn文档-例如容量计划程序和最大可用资源的设置:https://hadoop.apache.org/docs/r2.4.1/hadoop-yarn/hadoop-yarn-site/capacityscheduler.html

njthzxwz

njthzxwz2#

我在cloudera管理器配置中做了以下更改

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

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