실행에 표시 지속적인 로그 스토리지 기반 하둡 클러스터, 8042 / 로그 / userlogs / 종료에 사라

내지도가 작업을 줄일 실행 한 후에, 나는 나의 슬레이브 노드 모두에서 디렉토리 / usr / 지방 / 하둡 / 로그 / userlogs을 탐색.

작업이 작았 따라 결과는 관심의 them- 중 하나에 나타난 두 번 연속 ll 명령을 실행하면 먼저 실행중인 작업을 보여줍니다 사실이지만, 작업이 사라질 로그의 들어있는 디렉토리를 terminated- 후, 참조 subsequently- 이리:

내 문제가 해결되지 않았다 드디어 권장하지만 나는 내 원사를 site.xml 및 mapred-site.xml의 파일을 다음과 같이 수정했습니다.

이 결과의라면, 내가 log4j를 사용하고, 그것은과 같이 구성되어있다 :

# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you 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.

# Define some default values that can be overridden by system properties
# hadoop.root.logger=INFO,console
hadoop.root.logger=INFO
hadoop.log.dir=.
hadoop.log.file=hadoop.log

# Define the root logger to the system property "hadoop.root.logger".
log4j.rootLogger=DEBUG, DebugAppender
#
# log4j.rootLogger=${hadoop.root.logger}, EventCounter
#

# Logging Threshold
log4j.threshold=ALL

# Null Appender
log4j.appender.NullAppender=org.apache.log4j.varia.NullAppender

#
# Rolling File Appender - cap space usage at 5gb.
#
hadoop.log.maxfilesize=256MB
hadoop.log.maxbackupindex=20
log4j.appender.RFA=org.apache.log4j.RollingFileAppender
log4j.appender.RFA.File=${hadoop.log.dir}/${hadoop.log.file}

log4j.appender.RFA.MaxFileSize=${hadoop.log.maxfilesize}
log4j.appender.RFA.MaxBackupIndex=${hadoop.log.maxbackupindex}

log4j.appender.RFA.layout=org.apache.log4j.PatternLayout

# Pattern format: Date LogLevel LoggerName LogMessage
log4j.appender.RFA.layout.ConversionPattern=%d{ISO8601} %p %c: %m%n
# Debugging Pattern format
#log4j.appender.RFA.layout.ConversionPattern=%d{ISO8601} %-5p %c{2} (%F:%M(%L)) - %m%n


#
# Daily Rolling File Appender
#

log4j.appender.DRFA=org.apache.log4j.DailyRollingFileAppender
log4j.appender.DRFA.File=${hadoop.log.dir}/${hadoop.log.file}

# Rollover at midnight
log4j.appender.DRFA.DatePattern=.yyyy-MM-dd

log4j.appender.DRFA.layout=org.apache.log4j.PatternLayout

# Pattern format: Date LogLevel LoggerName LogMessage
log4j.appender.DRFA.layout.ConversionPattern=%d{ISO8601} %p %c: %m%n
# Debugging Pattern format
#log4j.appender.DRFA.layout.ConversionPattern=%d{ISO8601} %-5p %c{2} (%F:%M(%L)) - %m%n


#
# console
# Add "console" to rootlogger above if you want to use this
#

log4j.appender.console=org.apache.log4j.ConsoleAppender
log4j.appender.console.target=System.err
log4j.appender.console.layout=org.apache.log4j.PatternLayout
log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{2}: %m%n

#
# TaskLog Appender
#

#Default values
hadoop.tasklog.taskid=null
hadoop.tasklog.iscleanup=false
hadoop.tasklog.noKeepSplits=4
hadoop.tasklog.totalLogFileSize=100
hadoop.tasklog.purgeLogSplits=true
hadoop.tasklog.logsRetainHours=12

log4j.appender.TLA=org.apache.hadoop.mapred.TaskLogAppender
log4j.appender.TLA.taskId=${hadoop.tasklog.taskid}
log4j.appender.TLA.isCleanup=${hadoop.tasklog.iscleanup}
log4j.appender.TLA.totalLogFileSize=${hadoop.tasklog.totalLogFileSize}

log4j.appender.TLA.layout=org.apache.log4j.PatternLayout
log4j.appender.TLA.layout.ConversionPattern=%d{ISO8601} %p %c: %m%n

#
# HDFS block state change log from block manager
#
# Uncomment the following to log normal block state change
# messages from BlockManager in NameNode.
log4j.logger.BlockStateChange=DEBUG

#
#Security appender
#
hadoop.security.logger=INFO,NullAppender
hadoop.security.log.maxfilesize=256MB
hadoop.security.log.maxbackupindex=20
log4j.category.SecurityLogger=${hadoop.security.logger}
hadoop.security.log.file=SecurityAuth-${user.name}.audit
log4j.appender.RFAS=org.apache.log4j.RollingFileAppender
log4j.appender.RFAS.File=${hadoop.log.dir}/${hadoop.security.log.file}
log4j.appender.RFAS.layout=org.apache.log4j.PatternLayout
log4j.appender.RFAS.layout.ConversionPattern=%d{ISO8601} %p %c: %m%n
log4j.appender.RFAS.MaxFileSize=${hadoop.security.log.maxfilesize}
log4j.appender.RFAS.MaxBackupIndex=${hadoop.security.log.maxbackupindex}

#
# Daily Rolling Security appender
#
log4j.appender.DRFAS=org.apache.log4j.DailyRollingFileAppender
log4j.appender.DRFAS.File=${hadoop.log.dir}/${hadoop.security.log.file}
log4j.appender.DRFAS.layout=org.apache.log4j.PatternLayout
log4j.appender.DRFAS.layout.ConversionPattern=%d{ISO8601} %p %c: %m%n
log4j.appender.DRFAS.DatePattern=.yyyy-MM-dd

#
# hadoop configuration logging
#

# Uncomment the following line to turn off configuration deprecation warnings.
log4j.logger.org.apache.hadoop.conf.Configuration.deprecation=WARN

#
# hdfs audit logging
#
hdfs.audit.logger=INFO,NullAppender
hdfs.audit.log.maxfilesize=256MB
hdfs.audit.log.maxbackupindex=20
log4j.logger.org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit=${hdfs.audit.logger}
log4j.additivity.org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit=false
log4j.appender.RFAAUDIT=org.apache.log4j.RollingFileAppender
log4j.appender.RFAAUDIT.File=${hadoop.log.dir}/hdfs-audit.log
log4j.appender.RFAAUDIT.layout=org.apache.log4j.PatternLayout
log4j.appender.RFAAUDIT.layout.ConversionPattern=%d{ISO8601} %p %c{2}: %m%n
log4j.appender.RFAAUDIT.MaxFileSize=${hdfs.audit.log.maxfilesize}
log4j.appender.RFAAUDIT.MaxBackupIndex=${hdfs.audit.log.maxbackupindex}

#
# NameNode metrics logging.
# The default is to retain two namenode-metrics.log files up to 64MB each.
#
namenode.metrics.logger=INFO,NullAppender
log4j.logger.NameNodeMetricsLog=${namenode.metrics.logger}
log4j.additivity.NameNodeMetricsLog=false
log4j.appender.NNMETRICSRFA=org.apache.log4j.RollingFileAppender
log4j.appender.NNMETRICSRFA.File=${hadoop.log.dir}/namenode-metrics.log
log4j.appender.NNMETRICSRFA.layout=org.apache.log4j.PatternLayout
log4j.appender.NNMETRICSRFA.layout.ConversionPattern=%d{ISO8601} %m%n
log4j.appender.NNMETRICSRFA.MaxBackupIndex=1
log4j.appender.NNMETRICSRFA.MaxFileSize=64MB

#
# DataNode metrics logging.
# The default is to retain two datanode-metrics.log files up to 64MB each.
#
datanode.metrics.logger=INFO,NullAppender
log4j.logger.DataNodeMetricsLog=${datanode.metrics.logger}
log4j.additivity.DataNodeMetricsLog=false
log4j.appender.DNMETRICSRFA=org.apache.log4j.RollingFileAppender
log4j.appender.DNMETRICSRFA.File=${hadoop.log.dir}/datanode-metrics.log
log4j.appender.DNMETRICSRFA.layout=org.apache.log4j.PatternLayout
log4j.appender.DNMETRICSRFA.layout.ConversionPattern=%d{ISO8601} %m%n
log4j.appender.DNMETRICSRFA.MaxBackupIndex=1
log4j.appender.DNMETRICSRFA.MaxFileSize=64MB

#
# mapred audit logging
#
mapred.audit.logger=INFO,NullAppender
mapred.audit.log.maxfilesize=256MB
mapred.audit.log.maxbackupindex=20
log4j.logger.org.apache.hadoop.mapred.AuditLogger=${mapred.audit.logger}
log4j.additivity.org.apache.hadoop.mapred.AuditLogger=false
log4j.appender.MRAUDIT=org.apache.log4j.RollingFileAppender
log4j.appender.MRAUDIT.File=${hadoop.log.dir}/mapred-audit.log
log4j.appender.MRAUDIT.layout=org.apache.log4j.PatternLayout
log4j.appender.MRAUDIT.layout.ConversionPattern=%d{ISO8601} %p %c{2}: %m%n
log4j.appender.MRAUDIT.MaxFileSize=${mapred.audit.log.maxfilesize}
log4j.appender.MRAUDIT.MaxBackupIndex=${mapred.audit.log.maxbackupindex}

# Custom Logging levels

log4j.logger.org.apache.hadoop.mapred.JobTracker=DEBUG
log4j.logger.org.apache.hadoop.mapred.TaskTracker=DEBUG
log4j.logger.org.apache.hadoop.hdfs.server.namenode.FSNamesystem.audit=DEBUG

# Jets3t library
log4j.logger.org.jets3t.service.impl.rest.httpclient.RestS3Service=ERROR

# AWS SDK & S3A FileSystem
log4j.logger.com.amazonaws=ERROR
log4j.logger.com.amazonaws.http.AmazonHttpClient=ERROR
log4j.logger.org.apache.hadoop.fs.s3a.S3AFileSystem=WARN

#
# Event Counter Appender
# Sends counts of logging messages at different severity levels to Hadoop Metrics.
#
log4j.appender.EventCounter=org.apache.hadoop.log.metrics.EventCounter

#
# Job Summary Appender
#
# Use following logger to send summary to separate file defined by
# hadoop.mapreduce.jobsummary.log.file :
# hadoop.mapreduce.jobsummary.logger=INFO,JSA
# 
hadoop.mapreduce.jobsummary.logger=${hadoop.root.logger}
hadoop.mapreduce.jobsummary.log.file=hadoop-mapreduce.jobsummary.log
hadoop.mapreduce.jobsummary.log.maxfilesize=256MB
hadoop.mapreduce.jobsummary.log.maxbackupindex=20
log4j.appender.JSA=org.apache.log4j.RollingFileAppender
log4j.appender.JSA.File=${hadoop.log.dir}/${hadoop.mapreduce.jobsummary.log.file}
log4j.appender.JSA.MaxFileSize=${hadoop.mapreduce.jobsummary.log.maxfilesize}
log4j.appender.JSA.MaxBackupIndex=${hadoop.mapreduce.jobsummary.log.maxbackupindex}
log4j.appender.JSA.layout=org.apache.log4j.PatternLayout
log4j.appender.JSA.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{2}: %m%n
log4j.logger.org.apache.hadoop.mapred.JobInProgress$JobSummary=${hadoop.mapreduce.jobsummary.logger}
log4j.additivity.org.apache.hadoop.mapred.JobInProgress$JobSummary=false

#
# shuffle connection log from shuffleHandler
# Uncomment the following line to enable logging of shuffle connections
# log4j.logger.org.apache.hadoop.mapred.ShuffleHandler.audit=DEBUG

#
# Yarn ResourceManager Application Summary Log
#
# Set the ResourceManager summary log filename
yarn.server.resourcemanager.appsummary.log.file=rm-appsummary.log
# Set the ResourceManager summary log level and appender
yarn.server.resourcemanager.appsummary.logger=${hadoop.root.logger}
#yarn.server.resourcemanager.appsummary.logger=INFO,RMSUMMARY

# To enable AppSummaryLogging for the RM,
# set yarn.server.resourcemanager.appsummary.logger to
# <LEVEL>,RMSUMMARY in hadoop-env.sh

# Appender for ResourceManager Application Summary Log
# Requires the following properties to be set
#    - hadoop.log.dir (Hadoop Log directory)
#    - yarn.server.resourcemanager.appsummary.log.file (resource manager app summary log filename)
#    - yarn.server.resourcemanager.appsummary.logger (resource manager app summary log level and appender)

log4j.logger.org.apache.hadoop.yarn.server.resourcemanager.RMAppManager$ApplicationSummary=${yarn.server.resourcemanager.appsummary.logger}
log4j.additivity.org.apache.hadoop.yarn.server.resourcemanager.RMAppManager$ApplicationSummary=false
log4j.appender.RMSUMMARY=org.apache.log4j.RollingFileAppender
log4j.appender.RMSUMMARY.File=${hadoop.log.dir}/${yarn.server.resourcemanager.appsummary.log.file}
log4j.appender.RMSUMMARY.MaxFileSize=256MB
log4j.appender.RMSUMMARY.MaxBackupIndex=20
log4j.appender.RMSUMMARY.layout=org.apache.log4j.PatternLayout
log4j.appender.RMSUMMARY.layout.ConversionPattern=%d{ISO8601} %p %c{2}: %m%n

# HS audit log configs
#mapreduce.hs.audit.logger=INFO,HSAUDIT
#log4j.logger.org.apache.hadoop.mapreduce.v2.hs.HSAuditLogger=${mapreduce.hs.audit.logger}
#log4j.additivity.org.apache.hadoop.mapreduce.v2.hs.HSAuditLogger=false
#log4j.appender.HSAUDIT=org.apache.log4j.DailyRollingFileAppender
#log4j.appender.HSAUDIT.File=${hadoop.log.dir}/hs-audit.log
#log4j.appender.HSAUDIT.layout=org.apache.log4j.PatternLayout
#log4j.appender.HSAUDIT.layout.ConversionPattern=%d{ISO8601} %p %c{2}: %m%n
#log4j.appender.HSAUDIT.DatePattern=.yyyy-MM-dd

# Http Server Request Logs
#log4j.logger.http.requests.namenode=INFO,namenoderequestlog
#log4j.appender.namenoderequestlog=org.apache.hadoop.http.HttpRequestLogAppender
#log4j.appender.namenoderequestlog.Filename=${hadoop.log.dir}/jetty-namenode-yyyy_mm_dd.log
#log4j.appender.namenoderequestlog.RetainDays=3

#log4j.logger.http.requests.datanode=INFO,datanoderequestlog
#log4j.appender.datanoderequestlog=org.apache.hadoop.http.HttpRequestLogAppender
#log4j.appender.datanoderequestlog.Filename=${hadoop.log.dir}/jetty-datanode-yyyy_mm_dd.log
#log4j.appender.datanoderequestlog.RetainDays=3

#log4j.logger.http.requests.resourcemanager=INFO,resourcemanagerrequestlog
#log4j.appender.resourcemanagerrequestlog=org.apache.hadoop.http.HttpRequestLogAppender
#log4j.appender.resourcemanagerrequestlog.Filename=${hadoop.log.dir}/jetty-resourcemanager-yyyy_mm_dd.log
#log4j.appender.resourcemanagerrequestlog.RetainDays=3

#log4j.logger.http.requests.jobhistory=INFO,jobhistoryrequestlog
#log4j.appender.jobhistoryrequestlog=org.apache.hadoop.http.HttpRequestLogAppender
#log4j.appender.jobhistoryrequestlog.Filename=${hadoop.log.dir}/jetty-jobhistory-yyyy_mm_dd.log
#log4j.appender.jobhistoryrequestlog.RetainDays=3

#log4j.logger.http.requests.nodemanager=INFO,nodemanagerrequestlog
#log4j.appender.nodemanagerrequestlog=org.apache.hadoop.http.HttpRequestLogAppender
#log4j.appender.nodemanagerrequestlog.Filename=${hadoop.log.dir}/jetty-nodemanager-yyyy_mm_dd.log
#log4j.appender.nodemanagerrequestlog.RetainDays=3


# WebHdfs request log on datanodes
# Specify -Ddatanode.webhdfs.logger=INFO,HTTPDRFA on datanode startup to
# direct the log to a separate file.
#datanode.webhdfs.logger=INFO,console
#log4j.logger.datanode.webhdfs=${datanode.webhdfs.logger}
#log4j.appender.HTTPDRFA=org.apache.log4j.DailyRollingFileAppender
#log4j.appender.HTTPDRFA.File=${hadoop.log.dir}/hadoop-datanode-webhdfs.log
#log4j.appender.HTTPDRFA.layout=org.apache.log4j.PatternLayout
#log4j.appender.HTTPDRFA.layout.ConversionPattern=%d{ISO8601} %m%n
#log4j.appender.HTTPDRFA.DatePattern=.yyyy-MM-dd


# Appender for viewing information for errors and warnings
yarn.ewma.cleanupInterval=300
yarn.ewma.messageAgeLimitSeconds=86400
yarn.ewma.maxUniqueMessages=250
log4j.appender.EWMA=org.apache.hadoop.yarn.util.Log4jWarningErrorMetricsAppender
log4j.appender.EWMA.cleanupInterval=${yarn.ewma.cleanupInterval}
log4j.appender.EWMA.messageAgeLimitSeconds=${yarn.ewma.messageAgeLimitSeconds}
log4j.appender.EWMA.maxUniqueMessages=${yarn.ewma.maxUniqueMessages}

이것은 내가 실행하기 위해 노력하고있어 프로그램입니다 :

import org.apache.log4j.Logger;
import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class OpcodeCount {

  public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{

    private final static IntWritable one = new IntWritable(1);
    private Text word = new Text();

    // deek logger
    private final Logger LOG = org.apache.log4j.Logger.getLogger(this.getClass());

    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {

      // debugging output
      LOG.warn("anything :D");

      StringTokenizer itr = new StringTokenizer(value.toString());
      while (itr.hasMoreTokens()) {
        word.set(itr.nextToken());
        context.write(word, one);
      }
    }
  }

  public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> {
    private IntWritable result = new IntWritable();

    // deek logger
    private final Logger LOG = org.apache.log4j.Logger.getLogger(this.getClass());

      public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {

      // debugging output
      LOG.warn("anything :D");

      int sum = 0;
      for (IntWritable val : values) {
        sum += val.get();
      }
      result.set(sum);
      context.write(key, result);
    }
  }

  public static void main(String[] args) throws Exception {
    Configuration conf = new Configuration();
    Job job = Job.getInstance(conf, "opcode count: 03");
    job.setJarByClass(OpcodeCount.class);
    job.setMapperClass(TokenizerMapper.class);
    job.setCombinerClass(IntSumReducer.class);
    job.setReducerClass(IntSumReducer.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class);
    FileInputFormat.addInputPath(job, new Path(args[0]));
    FileOutputFormat.setOutputPath(job, new Path(args[1]));
    System.exit(job.waitForCompletion(true) ? 0 : 1);
  }
}

무엇 아주 이상한 것은 드리려고 이전에 내 로그 persistent-하지 않음을하지만 내 네임 노드를 다시 포맷 후 더 이상 사건이다.

해결법

==============================
1.그래서 분명히, 작업 *을 실행하는 슬레이브 노드에서 그들을 파고 이외에, 당신은 또한 단지 갈 수 있고 (예를 들어)이 장소에서, HDFS 떨어져 그들을 잡아 :

그래서 분명히, 작업 *을 실행하는 슬레이브 노드에서 그들을 파고 이외에, 당신은 또한 단지 갈 수 있고 (예를 들어)이 장소에서, HDFS 떨어져 그들을 잡아 :
```
hadoop fs -cat /tmp/logs/ubuntu/logs/application_1507243913606_0002/slave2_46063
```
방법을 보여주는 클라우 데라에서이 친구들까지 큰.

* 작업이 완료되고 그 자체를 삭제할 때까지

from https://stackoverflow.com/questions/46594989/persistent-log-storage-hadoop-cluster-8042-logs-userlogs-visible-in-execution by cc-by-sa and MIT license

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복붙노트

[HADOOP] 실행에 표시 지속적인 로그 스토리지 기반 하둡 클러스터, 8042 / 로그 / userlogs / 종료에 사라

실행에 표시 지속적인 로그 스토리지 기반 하둡 클러스터, 8042 / 로그 / userlogs / 종료에 사라

해결법

1.그래서 분명히, 작업 *을 실행하는 슬레이브 노드에서 그들을 파고 이외에, 당신은 또한 단지 갈 수 있고 (예를 들어)이 장소에서, HDFS 떨어져 그들을 잡아 :

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