title: Hadoop error in shuffle in fetcher: Exceeded MAX_FAILED_UNIQUE_FETCHES
body: <p>I am new to hadoop. I have a kerberos security enabled hadoop cluster (master and 1 slave) set up on a virtual box. I am trying to run a job from the hadoop examples 'pi'. The job terminates with the error Exceeded MAX_FAILED_UNIQUE_FETCHES. I tried searching for this error but the solutions given on the internet do not seem to be working for me. Perhaps I am missing something obvious. I even tried removing the slave from the etc/hadoop/slaves file to see if the job can run only on the master but that fails as well with the same error. Below is the log. I am running this on 64-bit Ubuntu 14.04 virtual box. Any help appreciated.</p>
  
  <pre><code>montauk@montauk-vmaster:/usr/local/hadoop$ sudo -u yarn bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.4.0.jar pi 2 10
  Number of Maps  = 2
  Samples per Map = 10
  OpenJDK 64-Bit Server VM warning: You have loaded library /usr/local/hadoop/lib/native/libhadoop.so.1.0.0 which might have disabled stack guard. The VM will try to fix the stack guard now.
  It's highly recommended that you fix the library with 'execstack -c &lt;libfile&gt;', or link it with '-z noexecstack'.
  14/06/05 12:04:43 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
  Wrote input for Map #0
  Wrote input for Map #1
  Starting Job
  14/06/05 12:04:49 INFO client.RMProxy: Connecting to ResourceManager at /192.168.0.29:8040
  14/06/05 12:04:50 INFO hdfs.DFSClient: Created HDFS_DELEGATION_TOKEN token 17 for yarn on 192.168.0.29:54310
  14/06/05 12:04:50 INFO security.TokenCache: Got dt for hdfs://192.168.0.29:54310; Kind: HDFS_DELEGATION_TOKEN, Service: 192.168.0.29:54310, Ident: (HDFS_DELEGATION_TOKEN token 17 for yarn)
  14/06/05 12:04:50 INFO input.FileInputFormat: Total input paths to process : 2
  14/06/05 12:04:51 INFO mapreduce.JobSubmitter: number of splits:2
  14/06/05 12:04:51 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1401975262053_0007
  14/06/05 12:04:51 INFO mapreduce.JobSubmitter: Kind: HDFS_DELEGATION_TOKEN, Service: 192.168.0.29:54310, Ident: (HDFS_DELEGATION_TOKEN token 17 for yarn)
  14/06/05 12:04:53 INFO impl.YarnClientImpl: Submitted application application_1401975262053_0007
  14/06/05 12:04:53 INFO mapreduce.Job: The url to track the job: http://montauk-vmaster:8088/proxy/application_1401975262053_0007/
  14/06/05 12:04:53 INFO mapreduce.Job: Running job: job_1401975262053_0007
  14/06/05 12:05:29 INFO mapreduce.Job: Job job_1401975262053_0007 running in uber mode : false
  14/06/05 12:05:29 INFO mapreduce.Job:  map 0% reduce 0%
  14/06/05 12:06:04 INFO mapreduce.Job:  map 50% reduce 0%
  14/06/05 12:06:06 INFO mapreduce.Job:  map 100% reduce 0%
  14/06/05 12:06:34 INFO mapreduce.Job:  map 100% reduce 100%
  14/06/05 12:06:34 INFO mapreduce.Job: Task Id : attempt_1401975262053_0007_r_000000_0, Status : FAILED
  Error: org.apache.hadoop.mapreduce.task.reduce.Shuffle$ShuffleError: error in shuffle in fetcher#4
      at org.apache.hadoop.mapreduce.task.reduce.Shuffle.run(Shuffle.java:134)
      at org.apache.hadoop.mapred.ReduceTask.run(ReduceTask.java:376)
      at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:167)
      at java.security.AccessController.doPrivileged(Native Method)
      at javax.security.auth.Subject.doAs(Subject.java:415)
      at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1548)
      at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:162)
  Caused by: java.io.IOException: Exceeded MAX_FAILED_UNIQUE_FETCHES; bailing-out.
      at org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl.checkReducerHealth(ShuffleSchedulerImpl.java:323)
      at org.apache.hadoop.mapreduce.task.reduce.ShuffleSchedulerImpl.copyFailed(ShuffleSchedulerImpl.java:245)
      at org.apache.hadoop.mapreduce.task.reduce.Fetcher.copyFromHost(Fetcher.java:347)
      at org.apache.hadoop.mapreduce.task.reduce.Fetcher.run(Fetcher.java:165)
  </code></pre>