最近我设置了Hadoop集群进行测试，集群有两个任务节点，基于Yarn。
我知道Hadoop不适合这些示例，它在非常大的数据级别上具有良好的性能，但它仍然太慢。我的意思是非常慢。我的输入文件是500,000字的文件，减少的数字是2。
这是日志：
hadoop jar /home/hadoop/hadoopTest.jar com.hadoop.WordCountJob /wordcountest /wordcountresult
Job started: Mon Dec 23 12:38:13 CST 2013
13/12/23 12:38:13 INFO service.AbstractService: Service:org.apache.hadoop.yarn.client.YarnClientImpl is inited.
13/12/23 12:38:14 INFO service.AbstractService: Service:org.apache.hadoop.yarn.client.YarnClientImpl is started.
13/12/23 12:38:14 WARN mapreduce.JobSubmitter: Use GenericOptionsParser for parsing the arguments. Applications should implement Tool for the same.
13/12/23 12:38:27 INFO input.FileInputFormat: Total input paths to process : 1
13/12/23 12:38:27 INFO mapreduce.JobSubmitter: number of splits:1
13/12/23 12:38:27 WARN conf.Configuration: mapred.jar is deprecated. Instead, use mapreduce.job.jar
13/12/23 12:38:27 WARN conf.Configuration: mapred.reduce.tasks is deprecated. Instead, use mapreduce.job.reduces
13/12/23 12:38:27 WARN conf.Configuration: mapred.output.value.class is deprecated. Instead, use mapreduce.job.output.value.class
13/12/23 12:38:27 WARN conf.Configuration: mapreduce.map.class is deprecated. Instead, use mapreduce.job.map.class
13/12/23 12:38:27 WARN conf.Configuration: mapred.job.name is deprecated. Instead, use mapreduce.job.name
13/12/23 12:38:27 WARN conf.Configuration: mapreduce.reduce.class is deprecated. Instead, use mapreduce.job.reduce.class
13/12/23 12:38:27 WARN conf.Configuration: mapreduce.inputformat.class is deprecated. Instead, use mapreduce.job.inputformat.class
13/12/23 12:38:27 WARN conf.Configuration: mapred.input.dir is deprecated. Instead, use mapreduce.input.fileinputformat.inputdir
13/12/23 12:38:27 WARN conf.Configuration: mapred.output.dir is deprecated. Instead, use mapreduce.output.fileoutputformat.outputdir
13/12/23 12:38:27 WARN conf.Configuration: mapreduce.outputformat.class is deprecated. Instead, use mapreduce.job.outputformat.class
13/12/23 12:38:27 WARN conf.Configuration: mapred.map.tasks is deprecated. Instead, use mapreduce.job.maps
13/12/23 12:38:27 WARN conf.Configuration: mapred.output.key.class is deprecated. Instead, use mapreduce.job.output.key.class
13/12/23 12:38:27 WARN conf.Configuration: mapred.working.dir is deprecated. Instead, use mapreduce.job.working.dir
13/12/23 12:38:29 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1383617275312_0021
13/12/23 12:38:30 INFO client.YarnClientImpl: Submitted application application_1383617275312_0021 to ResourceManager at Hadoop1/111.11.11.11:8032
13/12/23 12:38:30 INFO mapreduce.Job: The url to track the job: http://kmHadoop1:8088/proxy/application_1383617275312_0021/
13/12/23 12:38:30 INFO mapreduce.Job: Running job: job_1383617275312_0021
13/12/23 12:43:22 INFO mapreduce.Job: Job job_1383617275312_0021 running in uber mode : false
13/12/23 12:43:22 INFO mapreduce.Job: map 0% reduce 0%
13/12/23 13:03:37 INFO mapreduce.Job: map 67% reduce 0%
13/12/23 13:03:43 INFO mapreduce.Job: map 100% reduce 0%
13/12/23 13:07:04 INFO mapreduce.Job: map 100% reduce 37%
13/12/23 13:07:07 INFO mapreduce.Job: map 100% reduce 51%
13/12/23 13:07:10 INFO mapreduce.Job: map 100% reduce 67%
13/12/23 13:07:51 INFO mapreduce.Job: map 100% reduce 69%
13/12/23 13:07:52 INFO mapreduce.Job: map 100% reduce 70%
13/12/23 13:07:54 INFO mapreduce.Job: map 100% reduce 85%
13/12/23 13:07:54 INFO mapreduce.Job: map 100% reduce 100%
13/12/23 13:07:54 INFO mapreduce.Job: Job job_1383617275312_0021 completed successfully
13/12/23 13:07:55 INFO mapreduce.Job: Counters: 43
File System Counters
FILE: Number of bytes read=519233
FILE: Number of bytes written=1254635
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=2356520
HDFS: Number of bytes written=427594
HDFS: Number of read operations=9
HDFS: Number of large read operations=0
HDFS: Number of write operations=4
Job Counters
Launched map tasks=1
Launched reduce tasks=2
Data-local map tasks=1
Total time spent by all maps in occupied slots (ms)=1225928
Total time spent by all reduces in occupied slots (ms)=495508
Map-Reduce Framework
Map input records=8646
Map output records=420146
Map output bytes=4187027
Map output materialized bytes=519225
Input split bytes=122
Combine input records=0
Combine output records=0
Reduce input groups=35430
Reduce shuffle bytes=519225
Reduce input records=420146
Reduce output records=35430
Spilled Records=840292
Shuffled Maps =2
Failed Shuffles=0
Merged Map outputs=2
GC time elapsed (ms)=263996
CPU time spent (ms)=222750
Physical memory (bytes) snapshot=529215488
Virtual memory (bytes) snapshot=4047876096
Total committed heap usage (bytes)=479268864
Shuffle Errors
BAD_ID=0
CONNECTION=0
IO_ERROR=0
WRONG_LENGTH=0
WRONG_MAP=0
WRONG_REDUCE=0
File Input Format Counters
Bytes Read=2356398
File Output Format Counters
Bytes Written=427594
Job ended: Mon Dec 23 13:07:55 CST 2013
The job took 1782 seconds.
我们可以在日志的每一行之前看到时间戳。
似乎每一步都很慢：init，检查输入路径，在Yarn，Mapreduce上启动等。
整个过程耗时1783秒。 发生了什么 ？我做错了吗？
我的hadoop版本是CDH4.3.0，集群有2个节点。 Hdfs中有数千个小文件，这是一个问题吗？
答案 0 :(得分：0)
我从你的输出中看到
Map output bytes=4187027
Map output materialized bytes=519225
...
您正在（至少）对中间地图输出数据进行压缩。您可以尝试在关闭压缩的情况下重新运行示例;众所周知，GZIP压缩会对您机器的处理器造成负担。也许在关闭压缩之前，您可能会考虑监控CPU负载以验证这确实是您的瓶颈。
在运行GZIP压缩时运行2或3个节点的群集时，我看到过长的工作时间。当您开始添加节点时，这会发生变化当我将该群集扩展到10个节点并重新开始相同的工作时，压缩实际上变得非常有益（对于100GB Terasort的整体工作时间与不使用压缩相比，大约提高了40％）。