Question

我有一个3节点集群（1个资源管理器，每个2个32GB的节点管理器）。当我启动YARN会话时，我只是按照apache flink doc YARN Setup中的说明进行操作，Flink运行报告没有足够的资源来启动AM。然后我停止了YARN会话，Flink运行正常。

已激活应用程序，等待为AM分配资源。为该应用程序处理的最后一个节点：flink02.myminda.com:39826（分区：[]，总资源：，可用资源：）。详细信息：AM分区=;分区资源=;队列的绝对容量= 100.0％;队列的绝对使用容量= 18.333334％;队列的绝对最大容量= 100.0％;

yarn-site.xml

<?xml version="1.0"?>
<!--
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
  You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

  Unless required by applicable law or agreed to in writing, software
  distributed under the License is distributed on an "AS IS" BASIS,
  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  See the License for the specific language governing permissions and
  limitations under the License. See accompanying LICENSE file.
-->
<configuration>

<!-- Site specific YARN configuration properties -->
    <property>
            <name>yarn.acl.enable</name>
            <value>0</value>
    </property>

    <property>
            <name>yarn.resourcemanager.hostname</name>
            <value>flink01</value>
    </property>

    <property>
            <name>yarn.nodemanager.aux-services</name>
            <value>mapreduce_shuffle</value>
    </property>


    <property>
            <name>yarn.nodemanager.resource.memory-mb</name>
            <value>30720</value>
    </property>

    <property>
            <name>yarn.scheduler.maximum-allocation-mb</name>
            <value>30720</value>
    </property>

    <property>
            <name>yarn.scheduler.minimum-allocation-mb</name>
            <value>1024</value>
    </property>

    <property>
            <name>yarn.nodemanager.vmem-check-enabled</name>
            <value>false</value>
    </property>

    <property>
            <name>yarn.log.aggregation-enable</name>
            <value>false</value>
    </property>


</configuration>

mapred-site.xml

<configuration>
    <property>
        <name>mapreduce.framework.name</name>
        <value>yarn</value>
    </property>

    <property>
        <name>mapreduce.map.cpu.vcores</name>
        <value>4</value>
    </property>

    <property>
        <name>yarn.app.mapreduce.am.resource.mb</name>
        <value>10240</value>
    </property>

    <property>
        <name>yarn.app.mapreduce.am.command-opts</name>
        <value>-Xmx8192m</value>
    </property>



    <property>
        <name>mapreduce.map.memory.mb</name>
        <value>10240</value>
    </property>

    <property>
        <name>mapreduce.map.java.opts</name>
        <value>-Xmx8192m</value>
    </property>

    <property>
        <name>mapreduce.reduce.memory.mb</name>
        <value>10240</value>
    </property>

    <property>
        <name>mapreduce.reduce.java.opts</name>
        <value>-Xmx8192m</value>
    </property>
</configuration>

资源没有用尽，为什么YARN等待资源

0 个答案: