YARN时间线服务v2无法启动

Question

我在AWS上设置了一个测试HDP集群，用于评估项目。Ambari UI报告了一些错误，当我通过它们重启必要的服务时，我遇到了使用YARN的麻烦。启动YARN的时间轴服务阅读器V2时，出现错误

2018-08-10 15:51:06,400 INFO  [main] client.RpcRetryingCallerImpl: Call exception, tries=15, retries=15, started=129034 ms ago, cancelled=false, msg=Call to HOSTNAME/IPADDRESS:17020 failed on connection exception: org.apache.hbase.thirdparty.io.netty.channel.AbstractChannel$AnnotatedConnectException: Connection refused: HOSTNAME/IPADDRESS:17020, details=row 'prod.timelineservice.entity' on table 'hbase:meta' at region=hbase:meta,,1.1588230740, hostname=HOSTNAME,17020,1533827052949, seqNum=-1

这最终导致了

stderr: 
Traceback (most recent call last):
  File "/usr/lib/ambari-agent/lib/resource_management/libraries/script/script.py", line 982, in restart
    self.status(env)
  File "/var/lib/ambari-agent/cache/stacks/HDP/3.0/services/YARN/package/scripts/timelinereader.py", line 88, in status
    check_process_status(pid_file)
  File "/usr/lib/ambari-agent/lib/resource_management/libraries/functions/check_process_status.py", line 43, in check_process_status
    raise ComponentIsNotRunning()
ComponentIsNotRunning

The above exception was the cause of the following exception:

Traceback (most recent call last):
  File "/var/lib/ambari-agent/cache/stacks/HDP/3.0/services/YARN/package/scripts/timelinereader.py", line 108, in <module>
    ApplicationTimelineReader().execute()
  File "/usr/lib/ambari-agent/lib/resource_management/libraries/script/script.py", line 353, in execute
    method(env)
  File "/usr/lib/ambari-agent/lib/resource_management/libraries/script/script.py", line 993, in restart
    self.start(env, upgrade_type=upgrade_type)
  File "/var/lib/ambari-agent/cache/stacks/HDP/3.0/services/YARN/package/scripts/timelinereader.py", line 51, in start
    hbase(action='start')
  File "/var/lib/ambari-agent/cache/stacks/HDP/3.0/services/YARN/package/scripts/hbase_service.py", line 80, in hbase
    createTables()
  File "/var/lib/ambari-agent/cache/stacks/HDP/3.0/services/YARN/package/scripts/hbase_service.py", line 147, in createTables
    logoutput=True)
  File "/usr/lib/ambari-agent/lib/resource_management/core/base.py", line 166, in __init__
    self.env.run()
  File "/usr/lib/ambari-agent/lib/resource_management/core/environment.py", line 160, in run
    self.run_action(resource, action)
  File "/usr/lib/ambari-agent/lib/resource_management/core/environment.py", line 124, in run_action
    provider_action()
  File "/usr/lib/ambari-agent/lib/resource_management/core/providers/system.py", line 263, in action_run
    returns=self.resource.returns)
  File "/usr/lib/ambari-agent/lib/resource_management/core/shell.py", line 72, in inner
    result = function(command, **kwargs)
  File "/usr/lib/ambari-agent/lib/resource_management/core/shell.py", line 102, in checked_call
    tries=tries, try_sleep=try_sleep, timeout_kill_strategy=timeout_kill_strategy, returns=returns)
  File "/usr/lib/ambari-agent/lib/resource_management/core/shell.py", line 150, in _call_wrapper
    result = _call(command, **kwargs_copy)
  File "/usr/lib/ambari-agent/lib/resource_management/core/shell.py", line 308, in _call
    raise ExecuteTimeoutException(err_msg)
resource_management.core.exceptions.ExecuteTimeoutException: Execution of 'ambari-sudo.sh su yarn-ats -l -s /bin/bash -c 'export  PATH='"'"'/usr/sbin:/sbin:/usr/lib/ambari-server/*:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/var/lib/ambari-agent'"'"' ; sleep 10;export HBASE_CLASSPATH_PREFIX=/usr/hdp/3.0.0.0-1634/hadoop-yarn/timelineservice/*; /usr/hdp/3.0.0.0-1634/hbase/bin/hbase --config /usr/hdp/3.0.0.0-1634/hadoop/conf/embedded-yarn-ats-hbase org.apache.hadoop.yarn.server.timelineservice.storage.TimelineSchemaCreator -Dhbase.client.retries.number=35 -create -s'' was killed due timeout after 300 seconds

哪个组件需要重新启动才能使纱线恢复健康状态?将来调试该问题的正确方法是什么？

Answer 1

如果您进入“后台操作”( Ambari UI中的齿轮图标)，然后转到Timeline Service V2 start链接(您可能必须先单击运行Timeline服务的计算机才能到达)，您的右上角应该有链接，上面写着“复制”和“打开”。这些将有望更详细地向您显示错误日志。

在我的例子中，时间轴服务V2无法启动，因为系统没有足够的内存。这是一个很小的VM集群，每台机器上只有2 2GB的RAM。我通过更详细的错误日志发现它给出了内存不足的错误，所以当我将VM内存提升到4 4GB时，它就能够运行了。我最好的猜测是你在运行Ambari UI的主NameNode上的内存不够。根据您在主NameNode上运行的服务的数量，似乎需要一些关于4GB+的东西。