我一直在使用jobclient.monitorandprintjob()方法将map reduce作业的输出打印到控制台。我的用法是这样的:
job_client.monitorAndPrintJob(job_conf, job_client.getJob(j.getAssignedJobID()))
其输出如下所示(打印在控制台上):
13/03/04 07:20:00 INFO mapred.JobClient: Running job: job_201302211725_10139<br>
13/03/04 07:20:01 INFO mapred.JobClient: map 0% reduce 0%<br>
13/03/04 07:20:08 INFO mapred.JobClient: map 100% reduce 0%<br>
13/03/04 07:20:13 INFO mapred.JobClient: map 100% reduce 100%<br>
13/03/04 07:20:13 INFO mapred.JobClient: Job complete: job_201302211725_10139<br>
13/03/04 07:20:13 INFO mapred.JobClient: Counters: 26<br>
13/03/04 07:20:13 INFO mapred.JobClient: Job Counters<br>
13/03/04 07:20:13 INFO mapred.JobClient: Launched reduce tasks=1<br>
13/03/04 07:20:13 INFO mapred.JobClient: Aggregate execution time of mappers(ms)=5539<br>
13/03/04 07:20:13 INFO mapred.JobClient: Total time spent by all reduces waiting after reserving slots (ms)=0<br>
13/03/04 07:20:13 INFO mapred.JobClient: Total time spent by all maps waiting after reserving slots (ms)=0<br>
13/03/04 07:20:13 INFO mapred.JobClient: Launched map tasks=2<br>
13/03/04 07:20:13 INFO mapred.JobClient: Data-local map tasks=2<br>
13/03/04 07:20:13 INFO mapred.JobClient: Aggregate execution time of reducers(ms)=4337<br>
13/03/04 07:20:13 INFO mapred.JobClient: FileSystemCounters<br>
13/03/04 07:20:13 INFO mapred.JobClient: MAPRFS_BYTES_READ=583<br>
13/03/04 07:20:13 INFO mapred.JobClient: MAPRFS_BYTES_WRITTEN=394<br>
13/03/04 07:20:13 INFO mapred.JobClient: FILE_BYTES_WRITTEN=140219<br>
13/03/04 07:20:13 INFO mapred.JobClient: Map-Reduce Framework<br>
13/03/04 07:20:13 INFO mapred.JobClient: Map input records=6<br>
13/03/04 07:20:13 INFO mapred.JobClient: Reduce shuffle bytes=136<br>
13/03/04 07:20:13 INFO mapred.JobClient: Spilled Records=22<br>
13/03/04 07:20:13 INFO mapred.JobClient: Map output bytes=116<br>
13/03/04 07:20:13 INFO mapred.JobClient: CPU_MILLISECONDS=1320<br>
13/03/04 07:20:13 INFO mapred.JobClient: Map input bytes=64<br>
13/03/04 07:20:13 INFO mapred.JobClient: Combine input records=13<br>
13/03/04 07:20:13 INFO mapred.JobClient: SPLIT_RAW_BYTES=180<br>
13/03/04 07:20:13 INFO mapred.JobClient: Reduce input records=11<br>
13/03/04 07:20:13 INFO mapred.JobClient: Reduce input groups=11<br>
13/03/04 07:20:13 INFO mapred.JobClient: Combine output records=11<br>
13/03/04 07:20:13 INFO mapred.JobClient: PHYSICAL_MEMORY_BYTES=734961664<br>
13/03/04 07:20:13 INFO mapred.JobClient: Reduce output records=11<br>
13/03/04 07:20:13 INFO mapred.JobClient: VIRTUAL_MEMORY_BYTES=9751805952<br>
13/03/04 07:20:13 INFO mapred.JobClient: Map output records=13<br>
13/03/04 07:20:13 INFO mapred.JobClient: GC time elapsed (ms)=0<br>
我希望上面的输出/日志打印到一个文本文件中,而不是打印到控制台。有什么建议吗?
在您的HADOOP_HOME/conf中,您可能会发现一个名为:log4j.properties
的文件。我相信你可以配置在哪里以及如何登录。
确切地说,您应该使用滚动文件附加器,因此您应该取消注释(仅删除#
) log4j.properties
文件中的以下行:
# Rolling File Appender
#
#log4j.appender.RFA=org.apache.log4j.RollingFileAppender
#log4j.appender.RFA.File=${hadoop.log.dir}/${hadoop.log.file}
# Logfile size and and 30-day backups
#log4j.appender.RFA.MaxFileSize=1MB
#log4j.appender.RFA.MaxBackupIndex=30
#log4j.appender.RFA.layout=org.apache.log4j.PatternLayout
#log4j.appender.RFA.layout.ConversionPattern=%d{ISO8601} %-5p %c{2} - %m%n
#log4j.appender.RFA.layout.ConversionPattern=%d{ISO8601} %-5p %c{2} (%F:%M(%L)) - %m%n
并根据您的喜好自定义其他参数。
有关log4j配置的更多信息,请阅读此处。