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hadoopワードカウントプログラムをdocから実行したいとします。しかし、プログラムがrunning jobhadoopが「実行中のジョブ」でスタックしました
16/09/02 10:51:13 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
16/09/02 10:51:13 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
16/09/02 10:51:13 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
16/09/02 10:51:14 INFO input.FileInputFormat: Total input paths to process : 1
16/09/02 10:51:14 INFO mapreduce.JobSubmitter: number of splits:2
16/09/02 10:51:14 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1472783047951_0003
16/09/02 10:51:14 INFO impl.YarnClientImpl: Submitted application application_1472783047951_0003
16/09/02 10:51:14 INFO mapreduce.Job: The url to track the job: http://hadoop-master:8088/proxy/application_1472783047951_0003/
16/09/02 10:51:14 INFO mapreduce.Job: Running job: job_1472783047951_0003
で立ち往生そしてhttp://hadoop-master:8088/proxy/application_1472783047951_0003/ 
を示し、そして、それはhttp://hadoop-slave2:8042にAppMasterを実行し、
Hive
hive (default)> select a, b, count(1) as cnt from newtb group by a, b;
Query ID = hadoop_20160902110124_d2b2680b-c493-4986-aa84-f65794bfd8e4
Total jobs = 1
Launching Job 1 out of 1
Number of reduce tasks not specified. Estimated from input data size: 1
In order to change the average load for a reducer (in bytes):
set hive.exec.reducers.bytes.per.reducer=<number>
In order to limit the maximum number of reducers:
set hive.exec.reducers.max=<number>
In order to set a constant number of reducers:
set mapreduce.job.reduces=<number>
Starting Job = job_1472783047951_0004, Tracking URL = http://hadoop-master:8088/proxy/application_1472783047951_0004/
Kill Command = /opt/hadoop-2.6.4/bin/hadoop job -kill job_1472783047951_0004
select *で何も問題ありません。
hive (default)> select * from newtb;
OK
1 2 3
1 3 4
2 3 4
5 6 7
8 9 0
1 8 3
Time taken: 0.101 seconds, Fetched: 6 row(s)
私はMapReduceに何か問題があると思います。ディスクには十分なメモリがあります。だから、それを解決する方法は？