Hadoop之YARN命令 -

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Hadoop之YARN命令

概述

YARN命令是调用bin/yarn脚本文件，如果运行yarn脚本没有带任何参数，则会打印yarn所有命令的描述。

使用: yarn [--config confdir] COMMAND [--loglevel loglevel] [GENERIC_OPTIONS] [COMMAND_OPTIONS]

YARN有一个参数解析框架，采用解析泛型参数以及运行类。

命令参数描述

`--config confdir`	指定一个默认的配置文件目录，默认值是： `${HADOOP_PREFIX}/conf`.
`--loglevel loglevel`	重载Log级别。有效的日志级别包含：FATAL, ERROR, WARN, INFO, DEBUG, and TRACE。默认是INFO。
GENERIC_OPTIONS	YARN支持表A的通用命令项。
COMMAND COMMAND_OPTIONS	YARN分为用户命令和管理员命令。

表A：

通用项 Description

`-archives <comma separated list of archives>`	用逗号分隔计算中未归档的文件。仅仅针对JOB。
`-conf <configuration file>`	制定应用程序的配置文件。
`-D <property>=<value>`	使用给定的属性值。
`-files <comma separated list of files>`	用逗号分隔的文件,拷贝到Map reduce机器，仅仅针对JOB
`-jt <local> or <resourcemanager:port>`	指定一个ResourceManager. 仅仅针对JOB。
`-libjars <comma seperated list of jars>`	将用逗号分隔的jar路径包含到classpath中去，仅仅针对JOB。

用户命令：

对于Hadoop集群用户很有用的命令：

`application`

使用:yarn application [options]

命令选项描述

-appStates <States>	使用-list命令，基于应用程序的状态来过滤应用程序。如果应用程序的状态有多个，用逗号分隔。有效的应用程序状态包含如下： ALL, NEW, NEW_SAVING, SUBMITTED, ACCEPTED, RUNNING, FINISHED, FAILED, KILLED
-appTypes <Types>	使用-list命令，基于应用程序类型来过滤应用程序。如果应用程序的类型有多个，用逗号分隔。
-list	从RM返回的应用程序列表，使用-appTypes参数，支持基于应用程序类型的过滤，使用-appStates参数，支持对应用程序状态的过滤。
-kill <ApplicationId>	kill掉指定的应用程序。
-status <ApplicationId>	打印应用程序的状态。

示例1：

[hduser@hadoop0 bin]$ ./yarn application -list -appStates ACCEPTED
15/08/10 11:48:43 INFO client.RMProxy: Connecting to ResourceManager at hadoop1/10.0.1.41:8032
Total number of applications (application-types: [] and states: [ACCEPTED]):1
Application-Id	                Application-Name Application-Type User	 Queue	 State	  Final-State Progress Tracking-URL
application_1438998625140_1703	MAC_STATUS	 MAPREDUCE	  hduser default ACCEPTED UNDEFINED   0%       N/A

示例2：

[hduser@hadoop0 bin]$ ./yarn application -list
15/08/10 11:43:01 INFO client.RMProxy: Connecting to ResourceManager at hadoop1/10.0.1.41:8032
Total number of applications (application-types: [] and states: [SUBMITTED, ACCEPTED, RUNNING]):1
Application-Id	               Application-Name	Application-Type  User   Queue   State    Final-State   Progress Tracking-URL
application_1438998625140_1701 MAC_STATUS	MAPREDUCE	  hduser default ACCEPTED UNDEFINED	0%	 N/A

示例3：

[hduser@hadoop0 bin]$ ./yarn application -kill application_1438998625140_1705
15/08/10 11:57:41 INFO client.RMProxy: Connecting to ResourceManager at hadoop1/10.0.1.41:8032
Killing application application_1438998625140_1705
15/08/10 11:57:42 INFO impl.YarnClientImpl: Killed application application_1438998625140_1705

`applicationattempt`

使用:yarn applicationattempt [options]

命令选项描述

-help	帮助
-list <ApplicationId>	获取到应用程序尝试的列表，其返回值ApplicationAttempt-Id 等于<Application Attempt Id>
-status <Application Attempt Id>	打印应用程序尝试的状态。

打印应用程序尝试的报告。

示例1：

[hadoop@hadoopcluster78 bin]$ yarn applicationattempt -list application_1437364567082_0106
15/08/10 20:58:28 INFO client.RMProxy: Connecting to ResourceManager at hadoopcluster79/10.0.1.79:8032
Total number of application attempts :1
ApplicationAttempt-Id	               State	AM-Container-Id	                       Tracking-URL
appattempt_1437364567082_0106_000001   RUNNING	container_1437364567082_0106_01_000001 http://hadoopcluster79:8088/proxy/application_1437364567082_0106/

示例2：

[hadoop@hadoopcluster78 bin]$ yarn applicationattempt -status appattempt_1437364567082_0106_000001
15/08/10 21:01:41 INFO client.RMProxy: Connecting to ResourceManager at hadoopcluster79/10.0.1.79:8032
Application Attempt Report : 
	ApplicationAttempt-Id : appattempt_1437364567082_0106_000001
	State : FINISHED
	AMContainer : container_1437364567082_0106_01_000001
	Tracking-URL : http://hadoopcluster79:8088/proxy/application_1437364567082_0106/jobhistory/job/job_1437364567082_0106
	RPC Port : 51911
	AM Host : hadoopcluster80
	Diagnostics :

`classpath`

使用: yarn classpath

打印需要得到Hadoop的jar和所需要的lib包路径

[hadoop@hadoopcluster78 bin]$ yarn classpath
/home/hadoop/apache/hadoop-2.4.1/etc/hadoop:/home/hadoop/apache/hadoop-2.4.1/etc/hadoop:/home/hadoop/apache/hadoop-2.4.1/etc/hadoop:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/common/lib/*:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/common/*:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/hdfs:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/hdfs/lib/*:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/hdfs/*:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/yarn/lib/*:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/yarn/*:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/mapreduce/lib/*:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/mapreduce/*:/home/hadoop/apache/hadoop-2.4.1/contrib/capacity-scheduler/*.jar:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/yarn/*:/home/hadoop/apache/hadoop-2.4.1/share/hadoop/yarn/lib/*

container

使用: yarn container [options]

命令选项描述

-help	帮助
-list <Application Attempt Id>	应用程序尝试的Containers列表
-status <ContainerId>	打印Container的状态

打印container(s)的报告

示例1：

[hadoop@hadoopcluster78 bin]$ yarn container -list appattempt_1437364567082_0106_01 
15/08/10 20:45:45 INFO client.RMProxy: Connecting to ResourceManager at hadoopcluster79/10.0.1.79:8032
Total number of containers :25
                  Container-Id	          Start Time	         Finish Time	               State	                Host	                            LOG-URL
container_1437364567082_0106_01_000028	       1439210458659	                   0	             RUNNING	hadoopcluster83:37140	//hadoopcluster83:8042/node/containerlogs/container_1437364567082_0106_01_000028/hadoop
container_1437364567082_0106_01_000016	       1439210314436	                   0	             RUNNING	hadoopcluster84:43818	//hadoopcluster84:8042/node/containerlogs/container_1437364567082_0106_01_000016/hadoop
container_1437364567082_0106_01_000019	       1439210338598	                   0	             RUNNING	hadoopcluster83:37140	//hadoopcluster83:8042/node/containerlogs/container_1437364567082_0106_01_000019/hadoop
container_1437364567082_0106_01_000004	       1439210314130	                   0	             RUNNING	hadoopcluster82:48622	//hadoopcluster82:8042/node/containerlogs/container_1437364567082_0106_01_000004/hadoop
container_1437364567082_0106_01_000008	       1439210314130	                   0	             RUNNING	hadoopcluster82:48622	//hadoopcluster82:8042/node/containerlogs/container_1437364567082_0106_01_000008/hadoop
container_1437364567082_0106_01_000031	       1439210718604	                   0	             RUNNING	hadoopcluster83:37140	//hadoopcluster83:8042/node/containerlogs/container_1437364567082_0106_01_000031/hadoop
container_1437364567082_0106_01_000020	       1439210339601	                   0	             RUNNING	hadoopcluster83:37140	//hadoopcluster83:8042/node/containerlogs/container_1437364567082_0106_01_000020/hadoop
container_1437364567082_0106_01_000005	       1439210314130	                   0	             RUNNING	hadoopcluster82:48622	//hadoopcluster82:8042/node/containerlogs/container_1437364567082_0106_01_000005/hadoop
container_1437364567082_0106_01_000013	       1439210314435	                   0	             RUNNING	hadoopcluster84:43818	//hadoopcluster84:8042/node/containerlogs/container_1437364567082_0106_01_000013/hadoop
container_1437364567082_0106_01_000022	       1439210368679	                   0	             RUNNING	hadoopcluster84:43818	//hadoopcluster84:8042/node/containerlogs/container_1437364567082_0106_01_000022/hadoop
container_1437364567082_0106_01_000021	       1439210353626	                   0	             RUNNING	hadoopcluster83:37140	//hadoopcluster83:8042/node/containerlogs/container_1437364567082_0106_01_000021/hadoop
container_1437364567082_0106_01_000014	       1439210314435	                   0	             RUNNING	hadoopcluster84:43818	//hadoopcluster84:8042/node/containerlogs/container_1437364567082_0106_01_000014/hadoop
container_1437364567082_0106_01_000029	       1439210473726	                   0	             RUNNING	hadoopcluster80:42366	//hadoopcluster80:8042/node/containerlogs/container_1437364567082_0106_01_000029/hadoop
container_1437364567082_0106_01_000006	       1439210314130	                   0	             RUNNING	hadoopcluster82:48622	//hadoopcluster82:8042/node/containerlogs/container_1437364567082_0106_01_000006/hadoop
container_1437364567082_0106_01_000003	       1439210314129	                   0	             RUNNING	hadoopcluster82:48622	//hadoopcluster82:8042/node/containerlogs/container_1437364567082_0106_01_000003/hadoop
container_1437364567082_0106_01_000015	       1439210314436	                   0	             RUNNING	hadoopcluster84:43818	//hadoopcluster84:8042/node/containerlogs/container_1437364567082_0106_01_000015/hadoop
container_1437364567082_0106_01_000009	       1439210314130	                   0	             RUNNING	hadoopcluster82:48622	//hadoopcluster82:8042/node/containerlogs/container_1437364567082_0106_01_000009/hadoop
container_1437364567082_0106_01_000030	       1439210708467	                   0	             RUNNING	hadoopcluster83:37140	//hadoopcluster83:8042/node/containerlogs/container_1437364567082_0106_01_000030/hadoop
container_1437364567082_0106_01_000012	       1439210314435	                   0	             RUNNING	hadoopcluster84:43818	//hadoopcluster84:8042/node/containerlogs/container_1437364567082_0106_01_000012/hadoop
container_1437364567082_0106_01_000027	       1439210444354	                   0	             RUNNING	hadoopcluster84:43818	//hadoopcluster84:8042/node/containerlogs/container_1437364567082_0106_01_000027/hadoop
container_1437364567082_0106_01_000026	       1439210428514	                   0	             RUNNING	hadoopcluster83:37140	//hadoopcluster83:8042/node/containerlogs/container_1437364567082_0106_01_000026/hadoop
container_1437364567082_0106_01_000017	       1439210314436	                   0	             RUNNING	hadoopcluster84:43818	//hadoopcluster84:8042/node/containerlogs/container_1437364567082_0106_01_000017/hadoop
container_1437364567082_0106_01_000001	       1439210306902	                   0	             RUNNING	hadoopcluster80:42366	//hadoopcluster80:8042/node/containerlogs/container_1437364567082_0106_01_000001/hadoop
container_1437364567082_0106_01_000002	       1439210314129	                   0	             RUNNING	hadoopcluster82:48622	//hadoopcluster82:8042/node/containerlogs/container_1437364567082_0106_01_000002/hadoop
container_1437364567082_0106_01_000025	       1439210414171	                   0	             RUNNING	hadoopcluster83:37140	//hadoopcluster83:8042/node/containerlogs/container_1437364567082_0106_01_000025/hadoop

示例2：

[hadoop@hadoopcluster78 bin]$ yarn container -status container_1437364567082_0105_01_000020 
15/08/10 20:28:00 INFO client.RMProxy: Connecting to ResourceManager at hadoopcluster79/10.0.1.79:8032
Container Report : 
	Container-Id : container_1437364567082_0105_01_000020
	Start-Time : 1439208779842
	Finish-Time : 0
	State : RUNNING
	LOG-URL : //hadoopcluster83:8042/node/containerlogs/container_1437364567082_0105_01_000020/hadoop
	Host : hadoopcluster83:37140
	Diagnostics : null

jar

使用: yarn jar <jar> [mainClass] args...

运行jar文件，用户可以将写好的YARN代码打包成jar文件，用这个命令去运行它。

logs

使用: yarn logs -applicationId <application ID> [options]

注：应用程序没有完成，该命令是不能打印日志的。

命令选项描述

-applicationId <application ID>	指定应用程序ID，应用程序的ID可以在yarn.resourcemanager.webapp.address配置的路径查看（即：ID）
-appOwner <AppOwner>	应用的所有者（如果没有指定就是当前用户）应用程序的ID可以在yarn.resourcemanager.webapp.address配置的路径查看（即：User）
-containerId <ContainerId>	Container Id
-help	帮助
-nodeAddress <NodeAddress>	节点地址的格式：nodename:port （端口是配置文件中:yarn.nodemanager.webapp.address参数指定）

转存container的日志。

示例：

[hadoop@hadoopcluster78 bin]$ yarn logs -applicationId application_1437364567082_0104  -appOwner hadoop
15/08/10 17:59:19 INFO client.RMProxy: Connecting to ResourceManager at hadoopcluster79/10.0.1.79:8032


Container: container_1437364567082_0104_01_000003 on hadoopcluster82_48622
============================================================================
LogType: stderr
LogLength: 0
Log Contents:

LogType: stdout
LogLength: 0
Log Contents:

LogType: syslog
LogLength: 3673
Log Contents:
2015-08-10 17:24:01,565 WARN [main] org.apache.hadoop.conf.Configuration: job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.retry.interval;  Ignoring.
2015-08-10 17:24:01,580 WARN [main] org.apache.hadoop.conf.Configuration: job.xml:an attempt to override final parameter: mapreduce.job.end-notification.max.attempts;  Ignoring.
。。。。。。此处省略N万个字符
// 下面的命令，根据APP的所有者查看LOG日志，因为application_1437364567082_0104任务我是用hadoop用户启动的，所以打印的是如下信息：
[hadoop@hadoopcluster78 bin]$ yarn logs -applicationId application_1437364567082_0104  -appOwner root
15/08/10 17:59:25 INFO client.RMProxy: Connecting to ResourceManager at hadoopcluster79/10.0.1.79:8032
Logs not available at /tmp/logs/root/logs/application_1437364567082_0104
Log aggregation has not completed or is not enabled.

node

使用: yarn node [options]

命令选项描述

-all	所有的节点，不管是什么状态的。
-list	列出所有RUNNING状态的节点。支持-states选项过滤指定的状态，节点的状态包含：NEW，RUNNING，UNHEALTHY，DECOMMISSIONED，LOST，REBOOTED。支持--all显示所有的节点。
-states <States>	和-list配合使用，用逗号分隔节点状态，只显示这些状态的节点信息。
-status <NodeId>	打印指定节点的状态。

示例1：

[hadoop@hadoopcluster78 bin]$ ./yarn node -list -all
15/08/10 17:34:17 INFO client.RMProxy: Connecting to ResourceManager at hadoopcluster79/10.0.1.79:8032
Total Nodes:4
         Node-Id	     Node-State	Node-Http-Address	Number-of-Running-Containers
hadoopcluster82:48622	        RUNNING	hadoopcluster82:8042	                           0
hadoopcluster84:43818	        RUNNING	hadoopcluster84:8042	                           0
hadoopcluster83:37140	        RUNNING	hadoopcluster83:8042	                           0
hadoopcluster80:42366	        RUNNING	hadoopcluster80:8042	                           0

示例2：

[hadoop@hadoopcluster78 bin]$ ./yarn node -list -states RUNNING
15/08/10 17:39:55 INFO client.RMProxy: Connecting to ResourceManager at hadoopcluster79/10.0.1.79:8032
Total Nodes:4
         Node-Id	     Node-State	Node-Http-Address	Number-of-Running-Containers
hadoopcluster82:48622	        RUNNING	hadoopcluster82:8042	                           0
hadoopcluster84:43818	        RUNNING	hadoopcluster84:8042	                           0
hadoopcluster83:37140	        RUNNING	hadoopcluster83:8042	                           0
hadoopcluster80:42366	        RUNNING	hadoopcluster80:8042	                           0

示例3：

[hadoop@hadoopcluster78 bin]$ ./yarn node -status hadoopcluster82:48622
15/08/10 17:52:52 INFO client.RMProxy: Connecting to ResourceManager at hadoopcluster79/10.0.1.79:8032
Node Report : 
	Node-Id : hadoopcluster82:48622
	Rack : /default-rack
	Node-State : RUNNING
	Node-Http-Address : hadoopcluster82:8042
	Last-Health-Update : 星期一 10/八月/15 05:52:09:601CST
	Health-Report : 
	Containers : 0
	Memory-Used : 0MB
	Memory-Capacity : 10240MB
	CPU-Used : 0 vcores
	CPU-Capacity : 8 vcores

打印节点的报告。

queue

使用: yarn queue [options]

命令选项描述

-help	帮助
-status <QueueName>	打印队列的状态

打印队列信息。

version

使用: yarn version

打印hadoop的版本。

管理员命令：

下列这些命令对hadoop集群的管理员是非常有用的。

`daemonlog`

使用:

   yarn daemonlog -getlevel <host:httpport> <classname> 
   yarn daemonlog -setlevel <host:httpport> <classname> <level>

参数选项描述

-getlevel `<host:httpport>` `<classname>`	打印运行在<host:port>的守护进程的日志级别。这个命令内部会连接http://<host:port>/logLevel?log=<name>
-setlevel `<host:httpport> <classname> <level>`	设置运行在<host:port>的守护进程的日志级别。这个命令内部会连接http://<host:port>/logLevel?log=<name>

针对指定的守护进程，获取/设置日志级别.

示例1：

[root@hadoopcluster78 ~]# hadoop daemonlog -getlevel hadoopcluster82:50075 org.apache.hadoop.hdfs.server.datanode.DataNode
Connecting to http://hadoopcluster82:50075/logLevel?log=org.apache.hadoop.hdfs.server.datanode.DataNode
Submitted Log Name: org.apache.hadoop.hdfs.server.datanode.DataNode
Log Class: org.apache.commons.logging.impl.Log4JLogger
Effective level: INFO

[root@hadoopcluster78 ~]# yarn daemonlog -getlevel hadoopcluster79:8088 org.apache.hadoop.yarn.server.resourcemanager.rmapp.RMAppImpl
Connecting to http://hadoopcluster79:8088/logLevel?log=org.apache.hadoop.yarn.server.resourcemanager.rmapp.RMAppImpl
Submitted Log Name: org.apache.hadoop.yarn.server.resourcemanager.rmapp.RMAppImpl
Log Class: org.apache.commons.logging.impl.Log4JLogger
Effective level: INFO

[root@hadoopcluster78 ~]# yarn daemonlog -getlevel hadoopcluster78:19888 org.apache.hadoop.mapreduce.v2.hs.JobHistory
Connecting to http://hadoopcluster78:19888/logLevel?log=org.apache.hadoop.mapreduce.v2.hs.JobHistory
Submitted Log Name: org.apache.hadoop.mapreduce.v2.hs.JobHistory
Log Class: org.apache.commons.logging.impl.Log4JLogger
Effective level: INFO

`nodemanager`

使用: yarn nodemanager

启动NodeManager

`proxyserver`

使用: yarn proxyserver

启动web proxy server

`resourcemanager`

使用: yarn resourcemanager [-format-state-store]

参数选项描述

-format-state-store

RMStateStore的格式. 如果过去的应用程序不再需要，则清理RMStateStore， RMStateStore仅仅在ResourceManager没有运行的时候，才运行RMStateStore

启动ResourceManager

`rmadmin`

使用:

  yarn rmadmin [-refreshQueues]
               [-refreshNodes]
               [-refreshUserToGroupsMapping] 
               [-refreshSuperUserGroupsConfiguration]
               [-refreshAdminAcls] 
               [-refreshServiceAcl]
               [-getGroups [username]]
               [-transitionToActive [--forceactive] [--forcemanual] <serviceId>]
               [-transitionToStandby [--forcemanual] <serviceId>]
               [-failover [--forcefence] [--forceactive] <serviceId1> <serviceId2>]
               [-getServiceState <serviceId>]
               [-checkHealth <serviceId>]
               [-help [cmd]]

参数选项描述

-refreshQueues	重载队列的ACL，状态和调度器特定的属性，ResourceManager将重载mapred-queues配置文件
-refreshNodes	动态刷新dfs.hosts和dfs.hosts.exclude配置，无需重启NameNode。 dfs.hosts：列出了允许连入NameNode的datanode清单（IP或者机器名） dfs.hosts.exclude：列出了禁止连入NameNode的datanode清单（IP或者机器名）重新读取hosts和exclude文件，更新允许连到Namenode的或那些需要退出或入编的Datanode的集合。
-refreshUserToGroupsMappings	刷新用户到组的映射。
-refreshSuperUserGroupsConfiguration	刷新用户组的配置
-refreshAdminAcls	刷新ResourceManager的ACL管理
-refreshServiceAcl	ResourceManager重载服务级别的授权文件。
-getGroups [username]	获取指定用户所属的组。
-transitionToActive [–forceactive] [–forcemanual] <serviceId>	尝试将目标服务转为 Active 状态。如果使用了–forceactive选项，不需要核对非Active节点。如果采用了自动故障转移，这个命令不能使用。虽然你可以重写–forcemanual选项，你需要谨慎。
-transitionToStandby [–forcemanual] <serviceId>	将服务转为 Standby 状态. 如果采用了自动故障转移，这个命令不能使用。虽然你可以重写–forcemanual选项，你需要谨慎。
-failover [–forceactive] <serviceId1> <serviceId2>	启动从serviceId1 到 serviceId2的故障转移。如果使用了-forceactive选项，即使服务没有准备，也会尝试故障转移到目标服务。如果采用了自动故障转移，这个命令不能使用。
-getServiceState <serviceId>	返回服务的状态。（注：ResourceManager不是HA的时候，时不能运行该命令的）
-checkHealth <serviceId>	请求服务器执行健康检查，如果检查失败，RMAdmin将用一个非零标示退出。（注：ResourceManager不是HA的时候，时不能运行该命令的）
-help [cmd]	显示指定命令的帮助，如果没有指定，则显示命令的帮助。

scmadmin

使用: yarn scmadmin [options]

参数选项描述

-help	Help
-runCleanerTask	Runs the cleaner task

Runs Shared Cache Manager admin client

sharedcachemanager

使用: yarn sharedcachemanager

启动Shared Cache Manager

timelineserver

之前yarn运行框架只有Job history server，这是hadoop2.4版本之后加的通用Job History Server，命令为Application Timeline Server，详情请看：The YARN Timeline Server

使用: yarn timelineserver

启动TimeLineServer

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Hadoop之MapReduce命令 | Python之Window环境搭建

2015-08-11 09:41
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Hadoop之YARN命令

概述

用户命令：

`application`

`applicationattempt`

`classpath`

container

jar

logs

node

queue

version

管理员命令：

`daemonlog`

`nodemanager`

`proxyserver`

`resourcemanager`

`rmadmin`

scmadmin

sharedcachemanager

timelineserver

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最近访客 更多访客>>

博主相关

文章分类

社区版块

存档分类

最新评论

Hadoop之YARN命令

概述

用户命令：

application

applicationattempt

classpath

container

jar

logs

node

queue

version

管理员命令：

daemonlog

nodemanager

proxyserver

resourcemanager

rmadmin

scmadmin

sharedcachemanager

timelineserver

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`application`

`applicationattempt`

`classpath`

`daemonlog`

`nodemanager`

`proxyserver`

`resourcemanager`

`rmadmin`