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MapReduce REST APIs

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总览

MapReduce应用程序主REST API允许用户获取正在运行的MapReduce应用程序主控的状态。当前,这等效于正在运行的MapReduce作业。该信息包括应用程序主机正在运行的作业以及所有作业详细信息,例如任务,计数器,配置,尝试等。应通过代理访问应用程序主机。可将该代理配置为在资源管理器或单独的主机上运行。代理URL通常如下所示:http:// proxy-http-address:port / proxy / appid

Mapreduce应用程序主信息API

MapReduce应用程序主信息资源提供有关该Mapreduce应用程序主信息的总体信息。这包括应用程序ID,启动时间,用户,名称等。

URI

以下两个URI均从由appid值标识的应用程序ID中为您提供MapReduce应用程序主信息。

支持HTTP操作

  • 得到

查询参数支持

  没有

信息对象的元素

当您请求mapreduce应用程序主信息时,该信息将作为信息对象返回。

项目 数据类型 描述
appId 申请编号
startsOn 应用程序启动的时间(从纪元开始以毫秒为单位)
名称 应用程序名称
用户 启动应用程序的用户的用户名
经过时间 自应用程序启动以来的时间(以毫秒为单位)

回应范例

JSON回应

HTTP请求:

  获取http:// proxy-http-address:port / proxy / application_1326232085508_0003 / ws / v1 / mapreduce / info

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

{
  “信息”:{
      “ appId”:“ application_1326232085508_0003”,
      “ startedOn”:1326238244047,
      “ user”:“ user1”,
      “ name”:“睡眠工作”,
      “ elapsedTime”:32374
   }
}

XML回应

HTTP请求:

  接受:application / xml
  获取http:// proxy-http-address:port / proxy / application_1326232085508_0003 / ws / v1 / mapreduce / info

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:223
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<信息>
  <appId> application_1326232085508_0003 </ appId>
  <name>睡眠作业</ name>
  <user> user1 </ user>
  <startedOn> 1326238244047 </ startedOn>
  <elapsedTime> 32407 </ elapsedTime>
</ info>

Jobs API

作业资源提供了在此应用程序主机上运行的作业的列表。另请参见Job API,以获取作业对象的语法。

支持HTTP操作

  • 得到

查询参数支持

  没有

工作对象的元素

当您请求作业列表时,该信息将作为作业对象的集合返回。另请参见Job API,以获取作业对象的语法。

项目 数据类型 描述
工作 作业对象数组(JSON)/零个或多个作业对象(XML) 作业对象的集合

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

{
  “职位” : {
      “工作”:[
         {
            “ runningReduceAttempts”:1,
            “ reduceProgress”:100,
            “ failedReduceAttempts”:0,
            “ newMapAttempts”:0,
            “ mapsRunning”:0,
            “ state”:“ RUNNING”,
            “ successfulReduceAttempts”:0,
            “ reducesRunning”:1,
            “ acls”:[
               {
                  “ value”:“”,
                  “名称”:“ mapreduce.job.acl-modify-job”
               },
               {
                  “ value”:“”,
                  “名称”:“ mapreduce.job.acl-view-job”
               }
            ],
            “ reducesPending”:0,
            “ user”:“ user1”,
            “ reducesTotal”:1
            “ mapsCompleted”:1
            “ startTime”:1326238769379,
            “ id”:“ job_1326232085508_4_4”,
            “ successfulMapAttempts”:1
            “ runningMapAttempts”:0,
            “ newReduceAttempts”:0,
            “ name”:“睡眠工作”,
            “ mapsPending”:0,
            “ elapsedTime”:59377,
            “ reducesCompleted”:0,
            “ mapProgress”:100,
            “诊断”:“”,
            “ failedMapAttempts”:0,
            “ killedReduceAttempts”:0,
            “ mapsTotal”:1
            “ uberized”:错误,
            “ killedMapAttempts”:0,
            “ finishTime”:0
         }
     ]
   }
 }

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:1214
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<职位>
  <工作>
    <startTime> 1326238769379 </ startTime>
    <finishTime> 0 </ finishTime>
    <elapsedTime> 59416 </ elapsedTime>
    <id> job_1326232085508_4_4 </ id>
    <name>睡眠作业</ name>
    <user> user1 </ user>
    <状态>运行中</状态>
    <mapsTotal> 1 </ mapsTotal>
    <mapsCompleted> 1 </ mapsCompleted>
    <reducesTotal> 1 </ reducesTotal>
    <reducesCompleted> 0 </ reducesCompleted>
    <mapProgress> 100.0 </ mapProgress>
    <reduceProgress> 100.0 </ reduceProgress>
    <mapsPending> 0 </ mapsPending>
    <mapsRunning> 0 </ mapsRunning>
    <reducesPending> 0 </ reducesPending>
    <reducesRunning> 1 </ reducesRunning>
    <uberized> false </ uberized>
    <diagnostics />
    <newReduceAttempts> 0 </ newReduceAttempts>
    <runningReduceAttempts> 1 </ runningReduceAttempts>
    <failedReduceAttempts> 0 </ failedReduceAttempts>
    <killedReduceAttempts> 0 </ killedReduceAttempts>
    <successfulReduceAttempts> 0 </ successfulReduceAttempts>
    <newMapAttempts> 0 </ newMapAttempts>
    <runningMapAttempts> 0 </ runningMapAttempts>
    <failedMapAttempts> 0 </ failedMapAttempts>
    <killedMapAttempts> 0 </ killedMapAttempts>
    <successfulMapAttempts> 1 </ successfulMapAttempts>
    <acls>
      <name> mapreduce.job.acl-modify-job </ name>
      <value> </ value>
    </ acls>
    <acls>
      <name> mapreduce.job.acl-view-job </ name>
      <value> </ value>
    </ acls>
  </ job>
</ jobs>

作业API

作业资源包含有关由该应用程序主机启动的特定作业的信息。某些字段仅在用户具有权限时才可访问-取决于acl设置。

支持HTTP操作

  • 得到

查询参数支持

  没有

工作对象的元素

项目 数据类型 描述
ID 工作编号
名称 工作名称
用户 用户名
作业状态-有效值为:NEW,INITED,RUNNING,Succeeded,FAILED,KILL_WAIT,KILLED,ERROR
开始时间 作业开始的时间(从纪元开始以毫秒为单位)
finishTime 作业完成的时间(从开始到现在的毫秒数)
经过时间 自作业开始以来经过的时间(毫秒)
mapsTotal 整型 地图总数
mapsCompleted 整型 完成的地图数
reduceTotal 整型 减少总数
reduceCompleted 整型 完成数量减少
诊断 诊断消息
超级 布尔值 指示该作业是否为超级作业-完全在应用程序主服务器中运行
mapsPending 整型 仍要运行的地图数量
mapsRunning 整型 正在运行的地图数
reducePending 整型 减少数量仍需运行
reduceRunning 整型 运行次数减少
newReduce尝试 整型 新的减少尝试次数
runningReduceAttempts 整型 运行减少尝试的次数
failedReduce尝试 整型 减少尝试失败的次数
减少尝试 整型 减少尝试的失败次数
成功减少尝试 整型 成功减少尝试的次数
newMapAttempts 整型 新地图尝试次数
runningMapAttempts 整型 正在运行的地图尝试次数
failedMapAttempts 整型 失败的映射尝试次数
KilledMapAttempts 整型 被杀死的地图尝试次数
successMapAttempts 整型 成功的地图尝试次数
ACL acls(json)/零个或多个acls对象(xml)的数组 ACLS对象的集合

acls对象的元素

项目 数据类型 描述
ACL值
名称 ACL名称

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  服务器:码头(6.1.26)
  内容长度:720

响应主体:

{
   “工作”:{
      “ runningReduceAttempts”:1,
      “ reduceProgress”:100,
      “ failedReduceAttempts”:0,
      “ newMapAttempts”:0,
      “ mapsRunning”:0,
      “ state”:“ RUNNING”,
      “ successfulReduceAttempts”:0,
      “ reducesRunning”:1,
      “ acls”:[
         {
            “ value”:“”,
            “名称”:“ mapreduce.job.acl-modify-job”
         },
         {
            “ value”:“”,
            “名称”:“ mapreduce.job.acl-view-job”
         }
      ],
      “ reducesPending”:0,
      “ user”:“ user1”,
      “ reducesTotal”:1
      “ mapsCompleted”:1
      “ startTime”:1326238769379,
      “ id”:“ job_1326232085508_4_4”,
      “ successfulMapAttempts”:1
      “ runningMapAttempts”:0,
      “ newReduceAttempts”:0,
      “ name”:“睡眠工作”,
      “ mapsPending”:0,
      “ elapsedTime”:59437,
      “ reducesCompleted”:0,
      “ mapProgress”:100,
      “诊断”:“”,
      “ failedMapAttempts”:0,
      “ killedReduceAttempts”:0,
      “ mapsTotal”:1
      “ uberized”:错误,
      “ killedMapAttempts”:0,
      “ finishTime”:0
   }
}

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:1201
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<工作>
  <startTime> 1326238769379 </ startTime>
  <finishTime> 0 </ finishTime>
  <elapsedTime> 59474 </ elapsedTime>
  <id> job_1326232085508_4_4 </ id>
  <name>睡眠作业</ name>
  <user> user1 </ user>
  <状态>运行中</状态>
  <mapsTotal> 1 </ mapsTotal>
  <mapsCompleted> 1 </ mapsCompleted>
  <reducesTotal> 1 </ reducesTotal>
  <reducesCompleted> 0 </ reducesCompleted>
  <mapProgress> 100.0 </ mapProgress>
  <reduceProgress> 100.0 </ reduceProgress>
  <mapsPending> 0 </ mapsPending>
  <mapsRunning> 0 </ mapsRunning>
  <reducesPending> 0 </ reducesPending>
  <reducesRunning> 1 </ reducesRunning>
  <uberized> false </ uberized>
  <diagnostics />
  <newReduceAttempts> 0 </ newReduceAttempts>
  <runningReduceAttempts> 1 </ runningReduceAttempts>
  <failedReduceAttempts> 0 </ failedReduceAttempts>
  <killedReduceAttempts> 0 </ killedReduceAttempts>
  <successfulReduceAttempts> 0 </ successfulReduceAttempts>
  <newMapAttempts> 0 </ newMapAttempts>
  <runningMapAttempts> 0 </ runningMapAttempts>
  <failedMapAttempts> 0 </ failedMapAttempts>
  <killedMapAttempts> 0 </ killedMapAttempts>
  <successfulMapAttempts> 1 </ successfulMapAttempts>
  <acls>
    <name> mapreduce.job.acl-modify-job </ name>
    <value> </ value>
  </ acls>
  <acls>
    <name> mapreduce.job.acl-view-job </ name> <value> </ value>
  </ acls>
</ job>

工作尝试API

使用作业尝试API,您可以获得代表作业尝试的资源集合。在此资源上运行GET操作时,您将获得一个Job Attempt对象的集合。

支持HTTP操作

  • 得到

查询参数支持

  没有

jobAttempts对象的元素

当您请求作业尝试列表时,该信息将作为作业尝试对象数组返回。

项目 数据类型 描述
工作尝试 作业尝试对象数组(JSON)/零个或多个作业尝试对象(XML) 作业尝试对象的集合

jobAttempt对象的元素

项目 数据类型 描述
ID 工作尝试编号
nodeId 尝试运行的节点的节点ID
nodeHttpAddress 尝试运行的节点的节点http地址
logsLink 作业尝试日志的http链接
containerId 尝试作业的容器的ID
开始时间 尝试的开始时间(自纪元以来以毫秒为单位)

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / jobattempts

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

{
   “ jobAttempts”:{
      “ jobAttempt”:[
         {
            “ nodeId”:“ host.domain.com:8041”,
            “ nodeHttpAddress”:“ host.domain.com:8042”,
            “ startTime”:1326238773493,
            “ id”:1
            “ logsLink”:“ http://host.domain.com:8042/node/containerlogs/container_1326232085508_0004_01_000001”,
            “ containerId”:“ container_1326232085508_0004_01_000001”
         }
      ]
   }
}

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / jobattempts
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:498
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<jobAttempts>
  <jobAttempt>
    <nodeHttpAddress> host.domain.com:8042 </ nodeHttpAddress>
    <nodeId> host.domain.com:8041 </ nodeId>
    <id> 1 </ id>
    <startTime> 1326238773493 </ startTime>
    <containerId> container_1326232085508_0004_01_000001 </ containerId>
    <logsLink> http://host.domain.com:8042/node/containerlogs/container_1326232085508_0004_01_000001 </ logsLink>
  </ jobAttempt>
</ jobAttempts>

作业计数器API

使用作业计数器API,您可以反对代表该作业的所有计数器的资源集合。

支持HTTP操作

  • 得到

查询参数支持

  没有

jobCounters对象的元素

项目 数据类型 描述
ID 工作编号
柜台集团 counterGroup对象的数组(JSON)/零个或多个counterGroup对象的XML 计数器组对象的集合

counterGroup对象的元素

项目 数据类型 描述
counterGroupName 柜台组名称
计数器 计数器对象数组(JSON)/零个或多个计数器对象(XML) 柜台对象的集合

计数器对象的元素

项目 数据类型 描述
名称 柜台名称
reduceCounterValue 减少任务的计数器值
mapCounterValue 地图任务的对价
totalCounterValue 所有任务的对价

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / counters

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

{
   “ jobCounters”:{
      “ id”:“ job_1326232085508_4_4”,
      “ counterGroup”:[
         {
            “ counterGroupName”:“随机播放错误”,
            “柜台”:[
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ BAD_ID”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“连接”
               },
              {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ IO_ERROR”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ WRONG_LENGTH”
               },{
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ WRONG_MAP”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ WRONG_REDUCE”
               }
            ]
         },
         {
            “ counterGroupName”:“ org.apache.hadoop.mapreduce.FileSystemCounter”,
            “柜台”:[
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:2483,
                  “名称”:“ FILE_BYTES_READ”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:108763,
                  “名称”:“ FILE_BYTES_WRITTEN”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ FILE_READ_OPS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ FILE_LARGE_READ_OPS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ FILE_WRITE_OPS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:48​​,
                  “名称”:“ HDFS_BYTES_READ”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ HDFS_BYTES_WRITTEN”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:1
                  “名称”:“ HDFS_READ_OPS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ HDFS_LARGE_READ_OPS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ HDFS_WRITE_OPS”
               }
            ]
         },
         {
            “ counterGroupName”:“ org.apache.hadoop.mapreduce.TaskCounter”,
            “柜台”:[
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:1
                  “名称”:“ MAP_INPUT_RECORDS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:1200,
                  “名称”:“ MAP_OUTPUT_RECORDS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:48​​00,
                  “名称”:“ MAP_OUTPUT_BYTES”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:2235,
                  “名称”:“ MAP_OUTPUT_MATERIALIZED_BYTES”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:48​​,
                  “名称”:“ SPLIT_RAW_BYTES”
               },
              {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ COMBINE_INPUT_RECORDS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ COMBINE_OUTPUT_RECORDS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:460,
                  “名称”:“ REDUCE_INPUT_GROUPS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:2235,
                  “名称”:“ REDUCE_SHUFFLE_BYTES”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:460,
                  “名称”:“ REDUCE_INPUT_RECORDS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ REDUCE_OUTPUT_RECORDS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:1200,
                  “名称”:“ SPILLED_RECORDS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:1
                  “名称”:“ SHUFFLED_MAPS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ FAILED_SHUFFLE”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:1
                  “名称”:“ MERGED_MAP_OUTPUTS”
               },{
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:58
                  “名称”:“ GC_TIME_MILLIS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:1580,
                  “名称”:“ CPU_MILLISECONDS”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:462643200,
                  “名称”:“ PHYSICAL_MEMORY_BYTES”
               },
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:2149728256,
                  “名称”:“ VIRTUAL_MEMORY_BYTES”
               },
              {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:357957632,
                  “名称”:“ COMMITTED_HEAP_BYTES”
               }
            ]
         },
         {
            “ counterGroupName”:“ org.apache.hadoop.mapreduce.lib.input.FileInputFormatCounter”,
            “柜台”:[
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ BYTES_READ”
               }
            ]
         },
         {
            “ counterGroupName”:“ org.apache.hadoop.mapreduce.lib.output.FileOutputFormatCounter”,
            “柜台”:[
               {
                  “ reduceCounterValue”:0,
                  “ mapCounterValue”:0,
                  “ totalCounterValue”:0,
                  “名称”:“ BYTES_WRITTEN”
               }
            ]
         }
      ]
   }
}

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / counters
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:7027
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<jobCounters>
  <id> job_1326232085508_4_4 </ id>
  <counterGroup>
    <counterGroupName>随机播放错误</ counterGroupName>
    <计数器>
      <name> BAD_ID </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> CONNECTION </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> IO_ERROR </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> WRONG_LENGTH </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> WRONG_MAP </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> WRONG_REDUCE </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
  </ counterGroup>
  <counterGroup>
    <counterGroupName> org.apache.hadoop.mapreduce.FileSystemCounter </ counterGroupName>
    <计数器>
      <name> FILE_BYTES_READ </ name>
      <totalCounterValue> 2483 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> FILE_BYTES_WRITTEN </ name>
      <totalCounterValue> 108763 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <名称> FILE_READ_OPS </名称>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <名称> FILE_LARGE_READ_OPS </名称>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <名称> FILE_WRITE_OPS </名称>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> HDFS_BYTES_READ </ name>
      <totalCounterValue> 48 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> HDFS_BYTES_WRITTEN </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> HDFS_READ_OPS </ name>
      <totalCounterValue> 1 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> HDFS_LARGE_READ_OPS </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> HDFS_WRITE_OPS </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
  </ counterGroup>
  <counterGroup>
    <counterGroupName> org.apache.hadoop.mapreduce.TaskCounter </ counterGroupName>
    <计数器>
      <名称> MAP_INPUT_RECORDS </名称>
      <totalCounterValue> 1 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <名称> MAP_OUTPUT_RECORDS </名称>
      <totalCounterValue> 1200 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <名称> MAP_OUTPUT_BYTES </名称>
      <totalCounterValue> 4800 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> MAP_OUTPUT_MATERIALIZED_BYTES </ name>
      <totalCounterValue> 2235 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> SPLIT_RAW_BYTES </ name>
      <totalCounterValue> 48 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> COMBINE_INPUT_RECORDS </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> COMBINE_OUTPUT_RECORDS </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> REDUCE_INPUT_GROUPS </ name>
      <totalCounterValue> 460 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> REDUCE_SHUFFLE_BYTES </ name>
      <totalCounterValue> 2235 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> REDUCE_INPUT_RECORDS </ name>
      <totalCounterValue> 460 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> REDUCE_OUTPUT_RECORDS </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> SPILLED_RECORDS </ name>
      <totalCounterValue> 1200 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> SHUFFLED_MAPS </ name>
      <totalCounterValue> 1 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> FAILED_SHUFFLE </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> MERGED_MAP_OUTPUTS </ name>
      <totalCounterValue> 1 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> GC_TIME_MILLIS </ name>
      <totalCounterValue> 58 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <名称> CPU_MILLISECONDS </名称>
      <totalCounterValue> 1580 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> PHYSICAL_MEMORY_BYTES </ name>
      <totalCounterValue> 462643200 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> VIRTUAL_MEMORY_BYTES </ name>
      <totalCounterValue> 2149728256 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
    <计数器>
      <name> COMMITTED_HEAP_BYTES </ name>
      <totalCounterValue> 357957632 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
  </ counterGroup>
  <counterGroup>
    <counterGroupName> org.apache.hadoop.mapreduce.lib.input.FileInputFormatCounter </ counterGroupName>
    <计数器>
      <name> BYTES_READ </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter> </ counterGroup> <counterGroup>
    <counterGroupName> org.apache.hadoop.mapreduce.lib.output.FileOutputFormatCounter </ counterGroupName>
    <counter> <name> BYTES_WRITTEN </ name>
      <totalCounterValue> 0 </ totalCounterValue>
      <mapCounterValue> 0 </ mapCounterValue>
      <reduceCounterValue> 0 </ reduceCounterValue>
    </ counter>
  </ counterGroup>
</ jobCounters>

Job Conf API

作业配置资源包含有关此作业的作业配置的信息。

URI

使用以下URI从Jobid值标识的作业中获取作业配置信息。

支持HTTP操作

  • 得到

查询参数支持

  没有

conf对象的元素

项目 数据类型 描述
路径 作业配置文件的路径
属性 配置属性的数组(JSON)/零个或多个属性对象(XML) 属性对象的集合

属性对象的元素

项目 数据类型 描述
名称 配置属性的名称
配置属性的值
资源 此配置对象来自的位置。如果有多个,则在列表末尾显示历史记录,并带有最新来源。

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / conf

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

如果输出很大,这是输出的一小段。实际输出包含作业配置文件中的每个属性。

{
   “ conf”:{
      “ path”:“ hdfs://host.domain.com:9000 / user / user1 / .staging / job_1326232085508_0004 / job.xml”,
      “财产”:[
         {
            “ value”:“ / home / hadoop / hdfs / data”,
            “ name”:“ dfs.datanode.data.dir”,
            “源”:[“ hdfs-site.xml”,“ job.xml”]
         },
         {
            “ value”:“ org.apache.hadoop.yarn.server.webproxy.amfilter.AmFilterInitializer”,
            “ name”:“ hadoop.http.filter.initializers”
            “源”:[“以编程方式”,“ job.xml”]
         },
         {
            “ value”:“ / home / hadoop / tmp”,
            “名称”:“ mapreduce.cluster.temp.dir”
            “源”:[“ mapred-site.xml”]
         },
         ...
      ]
   }
}

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / conf
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:552
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<conf>
  <path> hdfs://host.domain.com:9000 / user / user1 / .staging / job_1326232085508_0004 / job.xml </ path>
  <属性>
    <name> dfs.datanode.data.dir </ name>
    <value> / home / hadoop / hdfs / data </ value>
    <source> hdfs-site.xml </ source>
    <source> job.xml </ source>
  </ property>
  <属性>
    <name> hadoop.http.filter.initializers </ name>
    <value> org.apache.hadoop.yarn.server.webproxy.amfilter.AmFilterInitializer </ value>
    <source>以编程方式</ source>
    <source> job.xml </ source>
  </ property>
  <属性>
    <name> mapreduce.cluster.temp.dir </ name>
    <值> / home / hadoop / tmp </值>
    <source> mapred-site.xml </ source>
  </ property>
  ...
</ conf>

任务API

使用任务API,您可以获得代表作业的所有任务的资源集合。在此资源上运行GET操作时,您将获得任务对象的集合。

支持HTTP操作

  • 得到

查询参数支持

  • type-任务类型,有效值为m或r。m表示地图任务,r表示缩小任务。

任务对象的元素

当您请求任务列表时,信息将作为任务对象数组返回。另请参阅任务API,以获取任务对象的语法。

项目 数据类型 描述
任务 任务对象数组(JSON)/零个或多个任务对象(XML) 任务对象的集合

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

{
   “任务” : {
      “任务”:[
         {
            “进度”:100,
            “ elapsedTime”:2768,
            “ state”:“ Succeeded”,
            “ startTime”:1326238773493,
            “ id”:“ task_1326232085508_4_4_m_0”,
            “ type”:“ MAP”,
            “ successfulAttempt”:“ attempt_1326232085508_4_4_m_0_0”,
            “ finishTime”:1326238776261
         },
         {
            “进度”:100,
            “ elapsedTime”:0,
            “ state”:“ RUNNING”,
            “ startTime”:1326238777460,
            “ id”:“ task_1326232085508_4_4_r_0”,
            “ type”:“ REDUCE”,
            “ successfulAttempt”:“”,
            “ finishTime”:0
         }
      ]
   }
}

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:603
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<任务>
  <任务>
    <startTime> 1326238773493 </ startTime>
    <finishTime> 1326238776261 </ finishTime>
    <elapsedTime> 2768 </ elapsedTime>
    <progress> 100.0 </ progress>
    <id> task_1326232085508_4_4_m_0 </ id>
    <state>成功</ state>
    <type> MAP </ type>
    <successfulAttempt> attempt_1326232085508_4_4_m_0_0 </ successfulAttempt>
  </ task>
  <任务>
    <startTime> 1326238777460 </ startTime>
    <finishTime> 0 </ finishTime>
    <elapsedTime> 0 </ elapsedTime>
    <progress> 100.0 </ progress>
    <id> task_1326232085508_4_4_r_0 </ id>
    <状态>运行中</状态>
    <type> REDUCE </ type>
    <successfulAttempt />
  </ task>
</ tasks>

任务API

任务资源包含有关作业中特定任务的信息。

支持HTTP操作

  • 得到

查询参数支持

  没有

任务对象的元素

项目 数据类型 描述
ID 任务ID
任务的状态-有效值为:NEW,SCHEDULED,RUNNING,Succeeded,FAILED,KILL_WAIT,KILLED
类型 任务类型-MAP或REDUCE
成功尝试 上次成功尝试的ID
进展 浮动 任务进度百分比
开始时间 任务开始的时间(从纪元开始以毫秒为单位)
finishTime 任务完成的时间(从纪元开始以毫秒为单位)
经过时间 自应用程序启动以来经过的时间(以毫秒为单位)

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks / task_1326232085508_4_4_r_0

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

{
   “任务”:{
      “进度”:100,
      “ elapsedTime”:0,
      “ state”:“ RUNNING”,
      “ startTime”:1326238777460,
      “ id”:“ task_1326232085508_4_4_r_0”,
      “ type”:“ REDUCE”,
      “ successfulAttempt”:“”,
      “ finishTime”:0
   }
}

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks / task_1326232085508_4_4_r_0
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:299
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<任务>
  <startTime> 1326238777460 </ startTime>
  <finishTime> 0 </ finishTime>
  <elapsedTime> 0 </ elapsedTime>
  <progress> 100.0 </ progress>
  <id> task_1326232085508_4_4_r_0 </ id>
  <状态>运行中</状态>
  <type> REDUCE </ type>
  <successfulAttempt />
</ task>

任务计数器API

使用任务计数器API,您可以反对代表该任务所有计数器的资源集合。

支持HTTP操作

  • 得到

查询参数支持

  没有

jobTaskCounters对象的元素

项目 数据类型 描述
ID 任务ID
taskcounterGroup counterGroup对象的数组(JSON)/零个或多个counterGroup对象的XML 计数器组对象的集合

counterGroup对象的元素

项目 数据类型 描述
counterGroupName 柜台组名称
计数器 计数器对象数组(JSON)/零个或多个计数器对象(XML) 柜台对象的集合

计数器对象的元素

项目 数据类型 描述
名称 柜台名称
柜台的价值

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks / task_1326232085508_4_4_r_0 / counters

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

{
   “ jobTaskCounters”:{
      “ id”:“ task_1326232085508_4_4_r_0”,
      “ taskCounterGroup”:[
         {
            “ counterGroupName”:“ org.apache.hadoop.mapreduce.FileSystemCounter”,
            “柜台”:[
               {
                  “值”:2363,
                  “名称”:“ FILE_BYTES_READ”
               },
               {
                  “值”:54372,
                  “名称”:“ FILE_BYTES_WRITTEN”
               },
               {
                  “值”:0,
                  “名称”:“ FILE_READ_OPS”
               },
               {
                  “值”:0,
                  “名称”:“ FILE_LARGE_READ_OPS”
               },
               {
                  “值”:0,
                  “名称”:“ FILE_WRITE_OPS”
               },
               {
                  “值”:0,
                  “名称”:“ HDFS_BYTES_READ”
               },
               {
                  “值”:0,
                  “名称”:“ HDFS_BYTES_WRITTEN”
               },
               {
                  “值”:0,
                  “名称”:“ HDFS_READ_OPS”
               },
               {
                  “值”:0,
                  “名称”:“ HDFS_LARGE_READ_OPS”
               },
               {
                  “值”:0,
                  “名称”:“ HDFS_WRITE_OPS”
               }
            ]
         },
         {
            “ counterGroupName”:“ org.apache.hadoop.mapreduce.TaskCounter”,
            “柜台”:[
               {
                  “值”:0,
                  “名称”:“ COMBINE_INPUT_RECORDS”
               },
               {
                  “值”:0,
                  “名称”:“ COMBINE_OUTPUT_RECORDS”
               },
               {
                  “值”:460,
                  “名称”:“ REDUCE_INPUT_GROUPS”
               },
               {
                  “值”:2235,
                  “名称”:“ REDUCE_SHUFFLE_BYTES”
               },
               {
                  “值”:460,
                  “名称”:“ REDUCE_INPUT_RECORDS”
               },
               {
                  “值”:0,
                  “名称”:“ REDUCE_OUTPUT_RECORDS”
               },
               {
                  “值”:0,
                  “名称”:“ SPILLED_RECORDS”
               },
               {
                  “值”:1
                  “名称”:“ SHUFFLED_MAPS”
               },
               {
                  “值”:0,
                  “名称”:“ FAILED_SHUFFLE”
               },
               {
                  “值”:1
                  “名称”:“ MERGED_MAP_OUTPUTS”
               },
               {
                  “值”:26,
                  “名称”:“ GC_TIME_MILLIS”
               },
               {
                  “值”:860,
                  “名称”:“ CPU_MILLISECONDS”
               },
               {
                  “值”:107839488,
                  “名称”:“ PHYSICAL_MEMORY_BYTES”
               },
               {
                  “值”:1123147776,
                  “名称”:“ VIRTUAL_MEMORY_BYTES”
               },
               {
                  “值”:57475072,
                  “名称”:“ COMMITTED_HEAP_BYTES”
               }
            ]
         },
         {
            “ counterGroupName”:“随机播放错误”,
            “柜台”:[
               {
                  “值”:0,
                  “名称”:“ BAD_ID”
               },
               {
                  “值”:0,
                  “名称”:“连接”
               },
               {
                  “值”:0,
                  “名称”:“ IO_ERROR”
               },
               {
                  “值”:0,
                  “名称”:“ WRONG_LENGTH”
               },
               {
                  “值”:0,
                  “名称”:“ WRONG_MAP”
               },
               {
                  “值”:0,
                  “名称”:“ WRONG_REDUCE”
               }
            ]
         },
         {
            “ counterGroupName”:“ org.apache.hadoop.mapreduce.lib.output.FileOutputFormatCounter”,
            “柜台”:[
               {
                  “值”:0,
                  “名称”:“ BYTES_WRITTEN”
               }
            ]
         }
      ]
   }
}

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks / task_1326232085508_4_4_r_0 / counters
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:2660
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<jobTaskCounters>
  <id> task_1326232085508_4_4_r_0 </ id>
  <taskCounterGroup>
    <counterGroupName> org.apache.hadoop.mapreduce.FileSystemCounter </ counterGroupName>
    <计数器>
      <name> FILE_BYTES_READ </ name>
      <value> 2363 </ value>
    </ counter>
    <计数器>
      <name> FILE_BYTES_WRITTEN </ name>
      <value> 54372 </ value>
    </ counter>
    <计数器>
      <名称> FILE_READ_OPS </名称>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <名称> FILE_LARGE_READ_OPS </名称>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <名称> FILE_WRITE_OPS </名称>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> HDFS_BYTES_READ </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> HDFS_BYTES_WRITTEN </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> HDFS_READ_OPS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> HDFS_LARGE_READ_OPS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> HDFS_WRITE_OPS </ name>
      <value> 0 </ value>
    </ counter>
  </ taskCounterGroup>
  <taskCounterGroup>
    <counterGroupName> org.apache.hadoop.mapreduce.TaskCounter </ counterGroupName>
    <计数器>
      <name> COMBINE_INPUT_RECORDS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> COMBINE_OUTPUT_RECORDS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> REDUCE_INPUT_GROUPS </ name>
      <value> 460 </ value>
    </ counter>
    <计数器>
      <name> REDUCE_SHUFFLE_BYTES </ name>
      <value> 2235 </ value>
    </ counter>
    <计数器>
      <name> REDUCE_INPUT_RECORDS </ name>
      <value> 460 </ value>
    </ counter>
    <计数器>
      <name> REDUCE_OUTPUT_RECORDS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> SPILLED_RECORDS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> SHUFFLED_MAPS </ name>
      <value> 1 </ value>
    </ counter>
    <计数器>
      <name> FAILED_SHUFFLE </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> MERGED_MAP_OUTPUTS </ name>
      <value> 1 </ value>
    </ counter>
    <计数器>
      <name> GC_TIME_MILLIS </ name>
      <value> 26 </ value>
    </ counter>
    <计数器>
      <名称> CPU_MILLISECONDS </名称>
      <value> 860 </ value>
    </ counter>
    <计数器>
      <name> PHYSICAL_MEMORY_BYTES </ name>
      <value> 107839488 </ value>
    </ counter>
    <计数器>
      <name> VIRTUAL_MEMORY_BYTES </ name>
      <value> 1123147776 </ value>
    </ counter>
    <计数器>
      <name> COMMITTED_HEAP_BYTES </ name>
      <value> 57475072 </ value>
    </ counter>
  </ taskCounterGroup>
  <taskCounterGroup>
    <counterGroupName>随机播放错误</ counterGroupName>
    <计数器>
      <name> BAD_ID </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> CONNECTION </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> IO_ERROR </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> WRONG_LENGTH </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> WRONG_MAP </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> WRONG_REDUCE </ name>
      <value> 0 </ value>
    </ counter>
  </ taskCounterGroup>
  <taskCounterGroup>
    <counterGroupName> org.apache.hadoop.mapreduce.lib.output.FileOutputFormatCounter </ counterGroupName>
    <计数器>
      <name> BYTES_WRITTEN </ name>
      <value> 0 </ value>
    </ counter>
  </ taskCounterGroup>
</ jobTaskCounters>

任务尝试API

使用任务尝试API,您可以获得代表作业中任务尝试的资源集合。在此资源上运行GET操作时,将获得“任务尝试对象”的集合。

支持HTTP操作

  • 得到

查询参数支持

  没有

taskAttempts对象的元素

当您请求任务尝试列表时,该信息将作为任务尝试对象数组返回。另请参阅Task Attempt API,以获取任务对象的语法。

项目 数据类型 描述
taskAttempt 任务尝试对象(JSON)/零个或多个任务尝试对象(XML)的数组 任务尝试对象的集合

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks / task_1326232085508_4_4_r_0 /尝试

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

{
   “ taskAttempts”:{
      “ taskAttempt”:[
         {
            “ elapsedMergeTime”:47,
            “ shuffleFinishTime”:1326238780052,
            “ assignedContainerId”:“ container_1326232085508_0004_01_000003”,
            “进度”:100,
            “ elapsedTime”:0,
            “ state”:“ RUNNING”,
            “ elapsedShuffleTime”:2592,
            “ mergeFinishTime”:1326238780099,
            “ rack”:“ / 98.139.92.0”,
            “ elapsedReduceTime”:0,
            “ nodeHttpAddress”:“ host.domain.com:8042”,
            “ type”:“ REDUCE”,
            “ startTime”:1326238777460,
            “ id”:“ attempt_1326232085508_4_4_r_0_0”,
            “ finishTime”:0
         }
      ]
   }
}

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks / task_1326232085508_4_4_r_0 /尝试
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:807
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<taskAttempts>
  <taskAttempt>
    <startTime> 1326238777460 </ startTime>
    <finishTime> 0 </ finishTime>
    <elapsedTime> 0 </ elapsedTime>
    <progress> 100.0 </ progress>
    <id> attempt_1326232085508_4_4_r_0_0 </ id>
    <rack> /98.139.92.0 </ rack>
    <状态>运行中</状态>
    <nodeHttpAddress> host.domain.com:8042 </ nodeHttpAddress>
    <type> REDUCE </ type>
    <assignedContainerId> container_1326232085508_0004_01_000003 </ assignedContainerId>
    <shuffleFinishTime> 1326238780052 </ shuffleFinishTime>
    <mergeFinishTime> 1326238780099 </ mergeFinishTime>
    <elapsedShuffleTime> 2592 </ elapsedShuffleTime>
    <elapsedMergeTime> 47 </ elapsedMergeTime>
    <elapsedReduceTime> 0 </ elapsedReduceTime>
  </ taskAttempt>
</ taskAttempts>

任务尝试API

任务尝试资源包含有关作业中特定任务尝试的信息。

支持HTTP操作

  • 得到

查询参数支持

  没有

taskAttempt对象的元素

项目 数据类型 描述
ID 任务ID
机架
任务尝试的状态-有效值为:NEW,UNASSIGNED,ASSIGNED,RUNNING,COMMIT_PENDING,SUCCESS_CONTAINER_CLEANUP,SUCCEEDED,FAIL_CONTAINER_CLEANUP,FAIL_TASK_CLEANUP,FAILED,KILL_CONTAINER_CLEANUP,KILL_TAIL_
类型 任务类型
AssignedContainerId 此尝试分配给的容器ID
nodeHttpAddress 尝试执行此任务的节点的http地址
诊断 诊断消息
进展 浮动 任务尝试的进度百分比
开始时间 任务尝试开始的时间(自时期起以毫秒为单位)
finishTime 任务尝试完成的时间(自纪元以来以毫秒为单位)
经过时间 自任务开始尝试以来经过的时间(以毫秒为单位)

对于减少任务尝试,您还具有以下字段:

项目 数据类型 描述
shuffleFinishTime 随机播放结束的时间(自纪元以来以毫秒为单位)
mergeFinishTime 合并完成的时间(自纪元以来以毫秒为单位)
经过的ShuffleTime 随机播放阶段完成所需的时间(减少任务开始和随机播放完成之间的时间,以毫秒为单位)
经过的合并时间 合并阶段完成所需的时间(混洗完成和合并完成之间的时间,以毫秒为单位)
经过减少时间 还原阶段完成所需的时间(从合并完成到还原任务结束之间的时间,以毫秒为单位)

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks / task_1326232085508_4_4_r_0 / attempts / attempt_1326232085508_4_4_r_0_0

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

{
   “ taskAttempt”:{
      “ elapsedMergeTime”:47,
      “ shuffleFinishTime”:1326238780052,
      “ assignedContainerId”:“ container_1326232085508_0004_01_000003”,
      “进度”:100,
      “ elapsedTime”:0,
      “ state”:“ RUNNING”,
      “ elapsedShuffleTime”:2592,
      “ mergeFinishTime”:1326238780099,
      “ rack”:“ / 98.139.92.0”,
      “ elapsedReduceTime”:0,
      “ nodeHttpAddress”:“ host.domain.com:8042”,
      “ startTime”:1326238777460,
      “ id”:“ attempt_1326232085508_4_4_r_0_0”,
      “ type”:“ REDUCE”,
      “ finishTime”:0
   }
}

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks / task_1326232085508_4_4_r_0 / attempts / attempt_1326232085508_4_4_r_0_0
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:691
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<taskAttempt>
  <startTime> 1326238777460 </ startTime>
  <finishTime> 0 </ finishTime>
  <elapsedTime> 0 </ elapsedTime>
  <progress> 100.0 </ progress>
  <id> attempt_1326232085508_4_4_r_0_0 </ id>
  <rack> /98.139.92.0 </ rack>
  <状态>运行中</状态>
  <nodeHttpAddress> host.domain.com:8042 </ nodeHttpAddress>
  <type> REDUCE </ type>
  <assignedContainerId> container_1326232085508_0004_01_000003 </ assignedContainerId>
  <shuffleFinishTime> 1326238780052 </ shuffleFinishTime>
  <mergeFinishTime> 1326238780099 </ mergeFinishTime>
  <elapsedShuffleTime> 2592 </ elapsedShuffleTime>
  <elapsedMergeTime> 47 </ elapsedMergeTime>
  <elapsedReduceTime> 0 </ elapsedReduceTime>
</ taskAttempt>

任务尝试状态API

使用任务尝试状态API,您可以使用状态设置为“ KILLED”的PUT请求修改运行任务尝试的状态,从而查询提交的任务尝试的状态以及终止正在运行的任务尝试。要执行PUT操作,必须为AM Web服务设置身份验证。另外,您必须被授权杀死任务尝试。当前,您只能将状态更改为“已杀死”。尝试将状态更改为其他任何状态都会导致400错误响应。下面是未授权和错误请求错误的示例。当您执行成功的PUT时,初始响应可能是202。您可以通过重复PUT请求直到得到200,使用GET方法查询状态或查询任务尝试信息并检查,来确认该应用已被杀死。状态。在以下示例中,

请注意,为了终止任务尝试,必须为HTTP接口设置身份验证筛选器。该功能要求在HttpServletRequest中设置用户名。如果未设置任何过滤器,则响应将为“未经授权”响应。

此功能目前处于Alpha阶段,将来可能会更改。

支持HTTP操作

  • 得到
    • 开机自检

查询参数支持

  没有

jobTaskAttemptState对象的元素

当您请求应用程序状态时,返回的信息包含以下字段

项目 数据类型 描述
应用程序状态-可以是“ NEW”,“ STARTING”,“ RUNNING”,“ COMMIT_PENDING”,“ Succeeded”,“ FAILED”,“ KILLED”之一

回应范例

JSON回应

HTTP请求

  GET http:// proxy-http-address:port / proxy / application_1429692837321_0001 / ws / v1 / mapreduce / jobs / job_1429692837321_0001 / tasks / task_1429692837321_0001_m_000000 / attempts / attempt_1429692837321_0001_m_000000_0 / state

响应标题:

HTTP / 1.1 200 OK
内容类型:application / json
服务器:码头(6.1.26)
内容长度:20

响应主体:

{
  “ state”:“ STARTING”
}

HTTP请求

  PUT http:// proxy-http-address:port / proxy / application_1429692837321_0001 / ws / v1 / mapreduce / jobs / job_1429692837321_0001 / tasks / task_1429692837321_0001_m_000000 / attempts / attempt_1429692837321_0001_m_000000_0 / state

请求正文:

{
  “ state”:“ KILLED”
}

响应标题:

HTTP / 1.1 200 OK
内容类型:application / json
服务器:码头(6.1.26)
内容长度:18

响应主体:

{
  “ state”:“ KILLED”
}

XML回应

HTTP请求

  GET http:// proxy-http-address:port / proxy / application_1429692837321_0001 / ws / v1 / mapreduce / jobs / job_1429692837321_0001 / tasks / task_1429692837321_0001_m_000000 / attempts / attempt_1429692837321_0001_m_000000_0 / state

响应标题:

HTTP / 1.1 200 OK
内容类型:application / xml
服务器:码头(6.1.26)
内容长度:121

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<jobTaskAttemptState>
  <状态>开始</状态>
</ jobTaskAttemptState>

HTTP请求

  PUT http:// proxy-http-address:port / proxy / application_1429692837321_0001 / ws / v1 / mapreduce / jobs / job_1429692837321_0001 / tasks / task_1429692837321_0001_m_000000 / attempts / attempt_1429692837321_0001_m_000000_0 / state

请求正文:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<jobTaskAttemptState>
  <state> KILLED </ state>
</ jobTaskAttemptState>

响应标题:

HTTP / 1.1 200 OK
内容类型:application / xml
服务器:码头(6.1.26)
内容长度:121

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<jobTaskAttemptState>
  <state> KILLED </ state>
</ jobTaskAttemptState>

未经授权的错误响应

HTTP请求

  PUT http:// proxy-http-address:port / proxy / application_1429692837321_0001 / ws / v1 / mapreduce / jobs / job_1429692837321_0001 / tasks / task_1429692837321_0001_m_000000 / attempts / attempt_1429692837321_0001_m_000000_0 / state

请求正文:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<jobTaskAttemptState>
  <state> KILLED </ state>
</ jobTaskAttemptState>

响应标题:

HTTP / 1.1 403未经授权
内容类型:application / json
服务器:码头(6.1.26)

错误的请求错误响应

HTTP请求

  PUT http:// proxy-http-address:port / proxy / application_1429692837321_0001 / ws / v1 / mapreduce / jobs / job_1429692837321_0001 / tasks / task_1429692837321_0001_m_000000 / attempts / attempt_1429692837321_0001_m_000000_0 / state

请求正文:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<jobTaskAttemptState>
  <状态>运行中</状态>
</ jobTaskAttemptState>

响应标题:

HTTP / 1.1 400
内容长度:295
内容类型:application / xml
服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<RemoteException>
  <exception> BadRequestException </ exception>
  <message> java.lang.Exception:仅允许将“ KILLED”作为目标状态。</ message>
  <javaClassName> org.apache.hadoop.yarn.webapp.BadRequestException </ javaClassName>
</ RemoteException>

任务尝试计数器API

使用任务尝试计数器API,您可以反对代表该任务尝试的所有计数器的资源集合。

支持HTTP操作

  • 得到

查询参数支持

  没有

jobTaskAttemptCounters对象的元素

项目 数据类型 描述
ID 任务尝试ID
taskAttemptcounterGroup 任务尝试counterGroup对象(JSON)/零个或多个任务尝试counterGroup对象(XML)的数组 任务尝试计数器组对象的集合

taskAttemptCounterGroup对象的元素

项目 数据类型 描述
counterGroupName 柜台组名称
计数器 计数器对象数组(JSON)/零个或多个计数器对象(XML) 柜台对象的集合

计数器对象的元素

项目 数据类型 描述
名称 柜台名称
柜台的价值

回应范例

JSON回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks / task_1326232085508_4_4_r_0 / attempts / attempt_1326232085508_4_4_r_0_0 / counters

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / json
  传输编码:分块
  服务器:码头(6.1.26)

响应主体:

{
   “ jobTaskAttemptCounters”:{
      “ taskAttemptCounterGroup”:[
         {
            “ counterGroupName”:“ org.apache.hadoop.mapreduce.FileSystemCounter”,
            “柜台”:[
               {
                  “值”:2363,
                  “名称”:“ FILE_BYTES_READ”
               },
               {
                  “值”:54372,
                  “名称”:“ FILE_BYTES_WRITTEN”
               },
               {
                  “值”:0,
                  “名称”:“ FILE_READ_OPS”
               },
               {
                  “值”:0,
                  “名称”:“ FILE_LARGE_READ_OPS”
               },
               {
                  “值”:0,
                  “名称”:“ FILE_WRITE_OPS”
               },
               {
                  “值”:0,
                  “名称”:“ HDFS_BYTES_READ”
               },
               {
                  “值”:0,
                  “名称”:“ HDFS_BYTES_WRITTEN”
               },
              {
                  “值”:0,
                  “名称”:“ HDFS_READ_OPS”
               },
               {
                  “值”:0,
                  “名称”:“ HDFS_LARGE_READ_OPS”
               },
               {
                  “值”:0,
                  “名称”:“ HDFS_WRITE_OPS”
               }
            ]
         },
         {
            “ counterGroupName”:“ org.apache.hadoop.mapreduce.TaskCounter”,
            “柜台”:[
               {
                  “值”:0,
                  “名称”:“ COMBINE_INPUT_RECORDS”
               },
               {
                  “值”:0,
                  “名称”:“ COMBINE_OUTPUT_RECORDS”
               },
               {
                  “值”:460,
                  “名称”:“ REDUCE_INPUT_GROUPS”
               },
               {
                  “值”:2235,
                  “名称”:“ REDUCE_SHUFFLE_BYTES”
               },
               {
                  “值”:460,
                  “名称”:“ REDUCE_INPUT_RECORDS”
               },
               {
                  “值”:0,
                  “名称”:“ REDUCE_OUTPUT_RECORDS”
               },
               {
                  “值”:0,
                  “名称”:“ SPILLED_RECORDS”
               },
               {
                  “值”:1
                  “名称”:“ SHUFFLED_MAPS”
               },
               {
                  “值”:0,
                  “名称”:“ FAILED_SHUFFLE”
               },
               {
                  “值”:1
                  “名称”:“ MERGED_MAP_OUTPUTS”
               },
               {
                  “值”:26,
                  “名称”:“ GC_TIME_MILLIS”
               },
               {
                  “值”:860,
                  “名称”:“ CPU_MILLISECONDS”
               },
               {
                  “值”:107839488,
                  “名称”:“ PHYSICAL_MEMORY_BYTES”
               },
               {
                  “值”:1123147776,
                  “名称”:“ VIRTUAL_MEMORY_BYTES”
               },
               {
                  “值”:57475072,
                  “名称”:“ COMMITTED_HEAP_BYTES”
               }
            ]
         },
         {
            “ counterGroupName”:“随机播放错误”,
            “柜台”:[
               {
                  “值”:0,
                  “名称”:“ BAD_ID”
               },
               {
                  “值”:0,
                  “名称”:“连接”
               },
               {
                  “值”:0,
                  “名称”:“ IO_ERROR”
               },
               {
                  “值”:0,
                  “名称”:“ WRONG_LENGTH”
               },
               {
                  “值”:0,
                  “名称”:“ WRONG_MAP”
               },
               {
                  “值”:0,
                  “名称”:“ WRONG_REDUCE”
               }
            ]
         },
         {
            “ counterGroupName”:“ org.apache.hadoop.mapreduce.lib.output.FileOutputFormatCounter”,
            “柜台”:[
               {
                  “值”:0,
                  “名称”:“ BYTES_WRITTEN”
               }
            ]
         }
      ],
      “ id”:“ attempt_1326232085508_4_4_r_0_0”
   }
}

XML回应

HTTP请求:

  GET http:// proxy-http-address:port / proxy / application_1326232085508_0004 / ws / v1 / mapreduce / jobs / job_1326232085508_4_4 / tasks / task_1326232085508_4_4_r_0 / attempts / attempt_1326232085508_4_4_r_0_0 / counters
  接受:application / xml

响应标题:

  HTTP / 1.1 200 OK
  内容类型:application / xml
  内容长度:2735
  服务器:码头(6.1.26)

响应主体:

<?xml版本=“ 1.0”编码=“ UTF-8”独立=“是”?>
<jobTaskAttemptCounters>
  <id> attempt_1326232085508_4_4_r_0_0 </ id>
  <taskAttemptCounterGroup>
    <counterGroupName> org.apache.hadoop.mapreduce.FileSystemCounter </ counterGroupName>
    <计数器>
      <name> FILE_BYTES_READ </ name>
      <value> 2363 </ value>
    </ counter>
    <计数器>
      <name> FILE_BYTES_WRITTEN </ name>
      <value> 54372 </ value>
    </ counter>
    <计数器>
      <名称> FILE_READ_OPS </名称>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <名称> FILE_LARGE_READ_OPS </名称>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <名称> FILE_WRITE_OPS </名称>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> HDFS_BYTES_READ </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> HDFS_BYTES_WRITTEN </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> HDFS_READ_OPS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> HDFS_LARGE_READ_OPS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> HDFS_WRITE_OPS </ name>
      <value> 0 </ value>
    </ counter>
  </ taskAttemptCounterGroup>
  <taskAttemptCounterGroup>
    <counterGroupName> org.apache.hadoop.mapreduce.TaskCounter </ counterGroupName>
    <计数器>
      <name> COMBINE_INPUT_RECORDS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> COMBINE_OUTPUT_RECORDS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> REDUCE_INPUT_GROUPS </ name>
      <value> 460 </ value>
    </ counter>
    <计数器>
      <name> REDUCE_SHUFFLE_BYTES </ name>
      <value> 2235 </ value>
    </ counter>
    <计数器>
      <name> REDUCE_INPUT_RECORDS </ name>
      <value> 460 </ value>
    </ counter>
    <计数器>
      <name> REDUCE_OUTPUT_RECORDS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> SPILLED_RECORDS </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> SHUFFLED_MAPS </ name>
      <value> 1 </ value>
    </ counter>
    <计数器>
      <name> FAILED_SHUFFLE </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> MERGED_MAP_OUTPUTS </ name>
      <value> 1 </ value>
    </ counter>
    <计数器>
      <name> GC_TIME_MILLIS </ name>
      <value> 26 </ value>
    </ counter>
    <计数器>
      <名称> CPU_MILLISECONDS </名称>
      <value> 860 </ value>
    </ counter>
    <计数器>
      <name> PHYSICAL_MEMORY_BYTES </ name>
      <value> 107839488 </ value>
    </ counter>
    <计数器>
      <name> VIRTUAL_MEMORY_BYTES </ name>
      <value> 1123147776 </ value>
    </ counter>
    <计数器>
      <name> COMMITTED_HEAP_BYTES </ name>
      <value> 57475072 </ value>
    </ counter>
  </ taskAttemptCounterGroup>
  <taskAttemptCounterGroup>
    <counterGroupName>随机播放错误</ counterGroupName>
    <计数器>
      <name> BAD_ID </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> CONNECTION </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> IO_ERROR </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> WRONG_LENGTH </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> WRONG_MAP </ name>
      <value> 0 </ value>
    </ counter>
    <计数器>
      <name> WRONG_REDUCE </ name>
      <value> 0 </ value>
    </ counter>
  </ taskAttemptCounterGroup>
  <taskAttemptCounterGroup>
    <counterGroupName> org.apache.hadoop.mapreduce.lib.output.FileOutputFormatCounter </ counterGroupName>
    <计数器>
      <name> BYTES_WRITTEN </ name>
      <value> 0 </ value>
    </ counter>
  </ taskAttemptCounterGroup>
</ jobTaskAttemptCounters>