hadoop/hadoop-mapreduce-project/hadoop-mapreduce-client/hadoop-mapreduce-client-core/src/main/resources/mapred-default.xml

<?xml version="1.0"?>
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<?xml-stylesheet type="text/xsl" href="configuration.xsl"?>

<!-- Do not modify this file directly.  Instead, copy entries that you -->
<!-- wish to modify from this file into mapred-site.xml and change them -->
<!-- there.  If mapred-site.xml does not already exist, create it.      -->

<configuration>

<property>
  <name>mapreduce.jobtracker.jobhistory.location</name>
  <value></value>
  <description> If job tracker is static the history files are stored 
  in this single well known place. If No value is set here, by default,
  it is in the local file system at ${hadoop.log.dir}/history.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.jobhistory.task.numberprogresssplits</name>
  <value>12</value>
  <description> Every task attempt progresses from 0.0 to 1.0 [unless
  it fails or is killed].  We record, for each task attempt, certain 
  statistics over each twelfth of the progress range.  You can change
  the number of intervals we divide the entire range of progress into
  by setting this property.  Higher values give more precision to the
  recorded data, but costs more memory in the job tracker at runtime.
  Each increment in this attribute costs 16 bytes per running task.
  </description>
</property>

<property>
  <name>mapreduce.job.userhistorylocation</name>
  <value></value>
  <description> User can specify a location to store the history files of 
  a particular job. If nothing is specified, the logs are stored in 
  output directory. The files are stored in "_logs/history/" in the directory.
  User can stop logging by giving the value "none". 
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.jobhistory.completed.location</name>
  <value></value>
  <description> The completed job history files are stored at this single well 
  known location. If nothing is specified, the files are stored at 
  ${mapreduce.jobtracker.jobhistory.location}/done.
  </description>
</property>

<property>
  <name>mapreduce.job.committer.setup.cleanup.needed</name>
  <value>true</value>
  <description> true, if job needs job-setup and job-cleanup.
                false, otherwise  
  </description>
</property>
<!-- i/o properties -->

<property>
  <name>mapreduce.task.io.sort.factor</name>
  <value>10</value>
  <description>The number of streams to merge at once while sorting
  files.  This determines the number of open file handles.</description>
</property>

<property>
  <name>mapreduce.task.io.sort.mb</name>
  <value>100</value>
  <description>The total amount of buffer memory to use while sorting 
  files, in megabytes.  By default, gives each merge stream 1MB, which
  should minimize seeks.</description>
</property>

<property>
  <name>mapreduce.map.sort.spill.percent</name>
  <value>0.80</value>
  <description>The soft limit in the serialization buffer. Once reached, a
  thread will begin to spill the contents to disk in the background. Note that
  collection will not block if this threshold is exceeded while a spill is
  already in progress, so spills may be larger than this threshold when it is
  set to less than .5</description>
</property>

<property>
  <name>mapreduce.jobtracker.address</name>
  <value>local</value>
  <description>The host and port that the MapReduce job tracker runs
  at.  If "local", then jobs are run in-process as a single map
  and reduce task.
  </description>
</property>

<property>
  <name>mapreduce.local.clientfactory.class.name</name>
  <value>org.apache.hadoop.mapred.LocalClientFactory</value>
  <description>This the client factory that is responsible for 
  creating local job runner client</description>
</property>

<property>
  <name>mapreduce.jobtracker.http.address</name>
  <value>0.0.0.0:50030</value>
  <description>
    The job tracker http server address and port the server will listen on.
    If the port is 0 then the server will start on a free port.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.handler.count</name>
  <value>10</value>
  <description>
    The number of server threads for the JobTracker. This should be roughly
    4% of the number of tasktracker nodes.
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.report.address</name>
  <value>127.0.0.1:0</value>
  <description>The interface and port that task tracker server listens on. 
  Since it is only connected to by the tasks, it uses the local interface.
  EXPERT ONLY. Should only be changed if your host does not have the loopback 
  interface.</description>
</property>

<property>
  <name>mapreduce.cluster.local.dir</name>
  <value>${hadoop.tmp.dir}/mapred/local</value>
  <description>The local directory where MapReduce stores intermediate
  data files.  May be a comma-separated list of
  directories on different devices in order to spread disk i/o.
  Directories that do not exist are ignored.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.system.dir</name>
  <value>${hadoop.tmp.dir}/mapred/system</value>
  <description>The directory where MapReduce stores control files.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.staging.root.dir</name>
  <value>${hadoop.tmp.dir}/mapred/staging</value>
  <description>The root of the staging area for users' job files
  In practice, this should be the directory where users' home 
  directories are located (usually /user)
  </description>
</property>

<property>
  <name>mapreduce.cluster.temp.dir</name>
  <value>${hadoop.tmp.dir}/mapred/temp</value>
  <description>A shared directory for temporary files.
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.local.dir.minspacestart</name>
  <value>0</value>
  <description>If the space in mapreduce.cluster.local.dir drops under this, 
  do not ask for more tasks.
  Value in bytes.
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.local.dir.minspacekill</name>
  <value>0</value>
  <description>If the space in mapreduce.cluster.local.dir drops under this, 
    do not ask more tasks until all the current ones have finished and 
    cleaned up. Also, to save the rest of the tasks we have running, 
    kill one of them, to clean up some space. Start with the reduce tasks,
    then go with the ones that have finished the least.
    Value in bytes.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.expire.trackers.interval</name>
  <value>600000</value>
  <description>Expert: The time-interval, in miliseconds, after which
  a tasktracker is declared 'lost' if it doesn't send heartbeats.
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.instrumentation</name>
  <value>org.apache.hadoop.mapred.TaskTrackerMetricsInst</value>
  <description>Expert: The instrumentation class to associate with each TaskTracker.
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.resourcecalculatorplugin</name>
  <value></value>
  <description>
   Name of the class whose instance will be used to query resource information
   on the tasktracker.
   
   The class must be an instance of 
   org.apache.hadoop.util.ResourceCalculatorPlugin. If the value is null, the
   tasktracker attempts to use a class appropriate to the platform. 
   Currently, the only platform supported is Linux.
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.taskmemorymanager.monitoringinterval</name>
  <value>5000</value>
  <description>The interval, in milliseconds, for which the tasktracker waits
   between two cycles of monitoring its tasks' memory usage. Used only if
   tasks' memory management is enabled via mapred.tasktracker.tasks.maxmemory.
   </description>
</property>

<property>
  <name>mapreduce.tasktracker.tasks.sleeptimebeforesigkill</name>
  <value>5000</value>
  <description>The time, in milliseconds, the tasktracker waits for sending a
  SIGKILL to a task, after it has been sent a SIGTERM. This is currently
  not used on WINDOWS where tasks are just sent a SIGTERM.
  </description>
</property>

<property>
  <name>mapreduce.job.maps</name>
  <value>2</value>
  <description>The default number of map tasks per job.
  Ignored when mapreduce.jobtracker.address is "local".  
  </description>
</property>

<property>
  <name>mapreduce.job.reduces</name>
  <value>1</value>
  <description>The default number of reduce tasks per job. Typically set to 99%
  of the cluster's reduce capacity, so that if a node fails the reduces can 
  still be executed in a single wave.
  Ignored when mapreduce.jobtracker.address is "local".
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.restart.recover</name>
  <value>false</value>
  <description>"true" to enable (job) recovery upon restart,
               "false" to start afresh
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.jobhistory.block.size</name>
  <value>3145728</value>
  <description>The block size of the job history file. Since the job recovery
               uses job history, its important to dump job history to disk as 
               soon as possible. Note that this is an expert level parameter.
               The default value is set to 3 MB.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.taskscheduler</name>
  <value>org.apache.hadoop.mapred.JobQueueTaskScheduler</value>
  <description>The class responsible for scheduling the tasks.</description>
</property>


<property>
  <name>mapreduce.job.split.metainfo.maxsize</name>
  <value>10000000</value>
  <description>The maximum permissible size of the split metainfo file. 
  The JobTracker won't attempt to read split metainfo files bigger than
  the configured value.
  No limits if set to -1.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.taskscheduler.maxrunningtasks.perjob</name>
  <value></value>
  <description>The maximum number of running tasks for a job before
  it gets preempted. No limits if undefined.
  </description>
</property>

<property>
  <name>mapreduce.map.maxattempts</name>
  <value>4</value>
  <description>Expert: The maximum number of attempts per map task.
  In other words, framework will try to execute a map task these many number
  of times before giving up on it.
  </description>
</property>

<property>
  <name>mapreduce.reduce.maxattempts</name>
  <value>4</value>
  <description>Expert: The maximum number of attempts per reduce task.
  In other words, framework will try to execute a reduce task these many number
  of times before giving up on it.
  </description>
</property>

<property>
  <name>mapreduce.reduce.shuffle.parallelcopies</name>
  <value>5</value>
  <description>The default number of parallel transfers run by reduce
  during the copy(shuffle) phase.
  </description>
</property>

<property>
  <name>mapreduce.reduce.shuffle.connect.timeout</name>
  <value>180000</value>
  <description>Expert: The maximum amount of time (in milli seconds) reduce
  task spends in trying to connect to a tasktracker for getting map output.
  </description>
</property>

<property>
  <name>mapreduce.reduce.shuffle.read.timeout</name>
  <value>180000</value>
  <description>Expert: The maximum amount of time (in milli seconds) reduce
  task waits for map output data to be available for reading after obtaining
  connection.
  </description>
</property>

<property>
  <name>mapreduce.task.timeout</name>
  <value>600000</value>
  <description>The number of milliseconds before a task will be
  terminated if it neither reads an input, writes an output, nor
  updates its status string.
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.map.tasks.maximum</name>
  <value>2</value>
  <description>The maximum number of map tasks that will be run
  simultaneously by a task tracker.
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.reduce.tasks.maximum</name>
  <value>2</value>
  <description>The maximum number of reduce tasks that will be run
  simultaneously by a task tracker.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.retiredjobs.cache.size</name>
  <value>1000</value>
  <description>The number of retired job status to keep in the cache.
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.outofband.heartbeat</name>
  <value>false</value>
  <description>Expert: Set this to true to let the tasktracker send an 
  out-of-band heartbeat on task-completion for better latency.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.jobhistory.lru.cache.size</name>
  <value>5</value>
  <description>The number of job history files loaded in memory. The jobs are 
  loaded when they are first accessed. The cache is cleared based on LRU.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.instrumentation</name>
  <value>org.apache.hadoop.mapred.JobTrackerMetricsInst</value>
  <description>Expert: The instrumentation class to associate with each JobTracker.
  </description>
</property>

<property>
  <name>mapred.child.java.opts</name>
  <value>-Xmx200m</value>
  <description>Java opts for the task tracker child processes.  
  The following symbol, if present, will be interpolated: @taskid@ is replaced 
  by current TaskID. Any other occurrences of '@' will go unchanged.
  For example, to enable verbose gc logging to a file named for the taskid in
  /tmp and to set the heap maximum to be a gigabyte, pass a 'value' of:
        -Xmx1024m -verbose:gc -Xloggc:/tmp/@taskid@.gc
  
  The configuration variable mapred.child.ulimit can be used to control the
  maximum virtual memory of the child processes. 
  </description>
</property>

<property>
  <name>mapred.child.env</name>
  <value></value>
  <description>User added environment variables for the task tracker child 
  processes. Example :
  1) A=foo  This will set the env variable A to foo
  2) B=$B:c This is inherit tasktracker's B env variable.  
  </description>
</property>

<property>
  <name>mapred.child.ulimit</name>
  <value></value>
  <description>The maximum virtual memory, in KB, of a process launched by the 
  Map-Reduce framework. This can be used to control both the Mapper/Reducer 
  tasks and applications using Hadoop Pipes, Hadoop Streaming etc. 
  By default it is left unspecified to let cluster admins control it via 
  limits.conf and other such relevant mechanisms.
  
  Note: mapred.child.ulimit must be greater than or equal to the -Xmx passed to
  JavaVM, else the VM might not start. 
  </description>
</property>

<property>
  <name>mapreduce.admin.user.env</name>
  <value>LD_LIBRARY_PATH=$HADOOP_COMMON_HOME/lib/native</value>
  <description>Expert: Additional execution environment entries for 
  map and reduce task processes. This is not an additive property.
  You must preserve the original value if you want your map and
  reduce tasks to have access to native libraries (compression, etc). 
  </description>
</property>

<property>
  <name>mapreduce.task.tmp.dir</name>
  <value>./tmp</value>
  <description> To set the value of tmp directory for map and reduce tasks.
  If the value is an absolute path, it is directly assigned. Otherwise, it is
  prepended with task's working directory. The java tasks are executed with
  option -Djava.io.tmpdir='the absolute path of the tmp dir'. Pipes and
  streaming are set with environment variable,
   TMPDIR='the absolute path of the tmp dir'
  </description>
</property>

<property>
  <name>mapreduce.map.log.level</name>
  <value>INFO</value>
  <description>The logging level for the map task. The allowed levels are:
  OFF, FATAL, ERROR, WARN, INFO, DEBUG, TRACE and ALL.
  </description>
</property>

<property>
  <name>mapreduce.reduce.log.level</name>
  <value>INFO</value>
  <description>The logging level for the reduce task. The allowed levels are:
  OFF, FATAL, ERROR, WARN, INFO, DEBUG, TRACE and ALL.
  </description>
</property>

<property>
  <name>mapreduce.reduce.merge.inmem.threshold</name>
  <value>1000</value>
  <description>The threshold, in terms of the number of files 
  for the in-memory merge process. When we accumulate threshold number of files
  we initiate the in-memory merge and spill to disk. A value of 0 or less than
  0 indicates we want to DON'T have any threshold and instead depend only on
  the ramfs's memory consumption to trigger the merge.
  </description>
</property>

<property>
  <name>mapreduce.reduce.shuffle.merge.percent</name>
  <value>0.66</value>
  <description>The usage threshold at which an in-memory merge will be
  initiated, expressed as a percentage of the total memory allocated to
  storing in-memory map outputs, as defined by
  mapreduce.reduce.shuffle.input.buffer.percent.
  </description>
</property>

<property>
  <name>mapreduce.reduce.shuffle.input.buffer.percent</name>
  <value>0.70</value>
  <description>The percentage of memory to be allocated from the maximum heap
  size to storing map outputs during the shuffle.
  </description>
</property>

<property>
  <name>mapreduce.reduce.input.buffer.percent</name>
  <value>0.0</value>
  <description>The percentage of memory- relative to the maximum heap size- to
  retain map outputs during the reduce. When the shuffle is concluded, any
  remaining map outputs in memory must consume less than this threshold before
  the reduce can begin.
  </description>
</property>

<property>
  <name>mapreduce.reduce.shuffle.memory.limit.percent</name>
  <value>0.25</value>
  <description>Expert: Maximum percentage of the in-memory limit that a
  single shuffle can consume</description>
</property>

<property>
  <name>mapreduce.reduce.markreset.buffer.percent</name>
  <value>0.0</value>
  <description>The percentage of memory -relative to the maximum heap size- to
  be used for caching values when using the mark-reset functionality.
  </description>
</property>

<property>
  <name>mapreduce.map.speculative</name>
  <value>true</value>
  <description>If true, then multiple instances of some map tasks 
               may be executed in parallel.</description>
</property>

<property>
  <name>mapreduce.reduce.speculative</name>
  <value>true</value>
  <description>If true, then multiple instances of some reduce tasks 
               may be executed in parallel.</description>
</property>
<property>
  <name>mapreduce.job.speculative.speculativecap</name>
  <value>0.1</value>
  <description>The max percent (0-1) of running tasks that
  can be speculatively re-executed at any time.</description>
</property>
 
<property>
  <name>mapreduce.job.speculative.slowtaskthreshold</name>
  <value>1.0</value>The number of standard deviations by which a task's 
  ave progress-rates must be lower than the average of all running tasks'
  for the task to be considered too slow.
  <description>
  </description>
</property>

<property>
  <name>mapreduce.job.speculative.slownodethreshold</name>
  <value>1.0</value>
  <description>The number of standard deviations by which a Task 
  Tracker's ave map and reduce progress-rates (finishTime-dispatchTime)
  must be lower than the average of all successful map/reduce task's for
  the TT to be considered too slow to give a speculative task to.
  </description>
</property>

<property>
  <name>mapreduce.job.jvm.numtasks</name>
  <value>1</value>
  <description>How many tasks to run per jvm. If set to -1, there is
  no limit. 
  </description>
</property>

<property>
  <name>mapreduce.job.ubertask.enable</name>
  <value>false</value>
  <description>Whether to enable the small-jobs "ubertask" optimization,
  which runs "sufficiently small" jobs sequentially within a single JVM.
  "Small" is defined by the following maxmaps, maxreduces, and maxbytes
  settings.  Users may override this value.
  </description>
</property>

<property>
  <name>mapreduce.job.ubertask.maxmaps</name>
  <value>9</value>
  <description>Threshold for number of maps, beyond which job is considered
  too big for the ubertasking optimization.  Users may override this value,
  but only downward.
  </description>
</property>

<property>
  <name>mapreduce.job.ubertask.maxreduces</name>
  <value>1</value>
  <description>Threshold for number of reduces, beyond which job is considered
  too big for the ubertasking optimization.  CURRENTLY THE CODE CANNOT SUPPORT
  MORE THAN ONE REDUCE and will ignore larger values.  (Zero is a valid max,
  however.)  Users may override this value, but only downward.
  </description>
</property>

<property>
  <name>mapreduce.job.ubertask.maxbytes</name>
  <value></value>
  <description>Threshold for number of input bytes, beyond which job is
  considered too big for the ubertasking optimization.  If no value is
  specified, dfs.block.size is used as a default.  Be sure to specify a
  default value in mapred-site.xml if the underlying filesystem is not HDFS.
  Users may override this value, but only downward.
  </description>
</property>

<property>
  <name>mapreduce.input.fileinputformat.split.minsize</name>
  <value>0</value>
  <description>The minimum size chunk that map input should be split
  into.  Note that some file formats may have minimum split sizes that
  take priority over this setting.</description>
</property>

<property>
  <name>mapreduce.jobtracker.maxtasks.perjob</name>
  <value>-1</value>
  <description>The maximum number of tasks for a single job.
  A value of -1 indicates that there is no maximum.  </description>
</property>

<property>
  <name>mapreduce.client.submit.file.replication</name>
  <value>10</value>
  <description>The replication level for submitted job files.  This
  should be around the square root of the number of nodes.
  </description>
</property>


<property>
  <name>mapreduce.tasktracker.dns.interface</name>
  <value>default</value>
  <description>The name of the Network Interface from which a task
  tracker should report its IP address.
  </description>
 </property>
 
<property>
  <name>mapreduce.tasktracker.dns.nameserver</name>
  <value>default</value>
  <description>The host name or IP address of the name server (DNS)
  which a TaskTracker should use to determine the host name used by
  the JobTracker for communication and display purposes.
  </description>
 </property>
 
<property>
  <name>mapreduce.tasktracker.http.threads</name>
  <value>40</value>
  <description>The number of worker threads that for the http server. This is
               used for map output fetching
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.http.address</name>
  <value>0.0.0.0:50060</value>
  <description>
    The task tracker http server address and port.
    If the port is 0 then the server will start on a free port.
  </description>
</property>

<property>
  <name>mapreduce.task.files.preserve.failedtasks</name>
  <value>false</value>
  <description>Should the files for failed tasks be kept. This should only be 
               used on jobs that are failing, because the storage is never
               reclaimed. It also prevents the map outputs from being erased
               from the reduce directory as they are consumed.</description>
</property>


<!-- 
  <property>
  <name>mapreduce.task.files.preserve.filepattern</name>
  <value>.*_m_123456_0</value>
  <description>Keep all files from tasks whose task names match the given
               regular expression. Defaults to none.</description>
  </property>
-->

<property>
  <name>mapreduce.output.fileoutputformat.compress</name>
  <value>false</value>
  <description>Should the job outputs be compressed?
  </description>
</property>

<property>
  <name>mapreduce.output.fileoutputformat.compression.type</name>
  <value>RECORD</value>
  <description>If the job outputs are to compressed as SequenceFiles, how should
               they be compressed? Should be one of NONE, RECORD or BLOCK.
  </description>
</property>

<property>
  <name>mapreduce.output.fileoutputformat.compression.codec</name>
  <value>org.apache.hadoop.io.compress.DefaultCodec</value>
  <description>If the job outputs are compressed, how should they be compressed?
  </description>
</property>

<property>
  <name>mapreduce.map.output.compress</name>
  <value>false</value>
  <description>Should the outputs of the maps be compressed before being
               sent across the network. Uses SequenceFile compression.
  </description>
</property>

<property>
  <name>mapreduce.map.output.compress.codec</name>
  <value>org.apache.hadoop.io.compress.DefaultCodec</value>
  <description>If the map outputs are compressed, how should they be 
               compressed?
  </description>
</property>

<property>
  <name>map.sort.class</name>
  <value>org.apache.hadoop.util.QuickSort</value>
  <description>The default sort class for sorting keys.
  </description>
</property>

<property>
  <name>mapreduce.task.userlog.limit.kb</name>
  <value>0</value>
  <description>The maximum size of user-logs of each task in KB. 0 disables the cap.
  </description>
</property>

<property>
  <name>mapreduce.job.userlog.retain.hours</name>
  <value>24</value>
  <description>The maximum time, in hours, for which the user-logs are to be 
               retained after the job completion.
  </description>
</property>

<property>
  <name>mapreduce.jobtracker.hosts.filename</name>
  <value></value>
  <description>Names a file that contains the list of nodes that may
  connect to the jobtracker.  If the value is empty, all hosts are
  permitted.</description>
</property>

<property>
  <name>mapreduce.jobtracker.hosts.exclude.filename</name>
  <value></value>
  <description>Names a file that contains the list of hosts that
  should be excluded by the jobtracker.  If the value is empty, no
  hosts are excluded.</description>
</property>

<property>
  <name>mapreduce.jobtracker.heartbeats.in.second</name>
  <value>100</value>
  <description>Expert: Approximate number of heart-beats that could arrive 
               at JobTracker in a second. Assuming each RPC can be processed 
               in 10msec, the default value is made 100 RPCs in a second.
  </description>
</property> 

<property>
  <name>mapreduce.jobtracker.tasktracker.maxblacklists</name>
  <value>4</value>
  <description>The number of blacklists for a taskTracker by various jobs
               after which the task tracker could be blacklisted across
               all jobs. The tracker will be given a tasks later
               (after a day). The tracker will become a healthy
               tracker after a restart.
  </description>
</property> 

<property>
  <name>mapreduce.job.maxtaskfailures.per.tracker</name>
  <value>4</value>
  <description>The number of task-failures on a tasktracker of a given job 
               after which new tasks of that job aren't assigned to it.
  </description>
</property>

<property>
  <name>mapreduce.client.output.filter</name>
  <value>FAILED</value>
  <description>The filter for controlling the output of the task's userlogs sent
               to the console of the JobClient. 
               The permissible options are: NONE, KILLED, FAILED, SUCCEEDED and 
               ALL.
  </description>
</property>

  <property>
    <name>mapreduce.client.completion.pollinterval</name>
    <value>5000</value>
    <description>The interval (in milliseconds) between which the JobClient
    polls the JobTracker for updates about job status. You may want to set this
    to a lower value to make tests run faster on a single node system. Adjusting
    this value in production may lead to unwanted client-server traffic.
    </description>
  </property>

  <property>
    <name>mapreduce.client.progressmonitor.pollinterval</name>
    <value>1000</value>
    <description>The interval (in milliseconds) between which the JobClient
    reports status to the console and checks for job completion. You may want to set this
    to a lower value to make tests run faster on a single node system. Adjusting
    this value in production may lead to unwanted client-server traffic.
    </description>
  </property>

  <property>
    <name>mapreduce.jobtracker.persist.jobstatus.active</name>
    <value>true</value>
    <description>Indicates if persistency of job status information is
      active or not.
    </description>
  </property>

  <property>
  <name>mapreduce.jobtracker.persist.jobstatus.hours</name>
  <value>1</value>
  <description>The number of hours job status information is persisted in DFS.
    The job status information will be available after it drops of the memory
    queue and between jobtracker restarts. With a zero value the job status
    information is not persisted at all in DFS.
  </description>
</property>

  <property>
    <name>mapreduce.jobtracker.persist.jobstatus.dir</name>
    <value>/jobtracker/jobsInfo</value>
    <description>The directory where the job status information is persisted
      in a file system to be available after it drops of the memory queue and
      between jobtracker restarts.
    </description>
  </property>

  <property>
    <name>mapreduce.task.profile</name>
    <value>false</value>
    <description>To set whether the system should collect profiler
     information for some of the tasks in this job? The information is stored
     in the user log directory. The value is "true" if task profiling
     is enabled.</description>
  </property>

  <property>
    <name>mapreduce.task.profile.maps</name>
    <value>0-2</value>
    <description> To set the ranges of map tasks to profile.
    mapreduce.task.profile has to be set to true for the value to be accounted.
    </description>
  </property>

  <property>
    <name>mapreduce.task.profile.reduces</name>
    <value>0-2</value>
    <description> To set the ranges of reduce tasks to profile.
    mapreduce.task.profile has to be set to true for the value to be accounted.
    </description>
  </property>

  <property>
    <name>mapreduce.task.skip.start.attempts</name>
    <value>2</value>
    <description> The number of Task attempts AFTER which skip mode 
    will be kicked off. When skip mode is kicked off, the 
    tasks reports the range of records which it will process 
    next, to the TaskTracker. So that on failures, TT knows which 
    ones are possibly the bad records. On further executions, 
    those are skipped.
    </description>
  </property>
  
  <property>
    <name>mapreduce.map.skip.proc.count.autoincr</name>
    <value>true</value>
    <description> The flag which if set to true, 
    SkipBadRecords.COUNTER_MAP_PROCESSED_RECORDS is incremented 
    by MapRunner after invoking the map function. This value must be set to 
    false for applications which process the records asynchronously 
    or buffer the input records. For example streaming. 
    In such cases applications should increment this counter on their own.
    </description>
  </property>
  
  <property>
    <name>mapreduce.reduce.skip.proc.count.autoincr</name>
    <value>true</value>
    <description> The flag which if set to true, 
    SkipBadRecords.COUNTER_REDUCE_PROCESSED_GROUPS is incremented 
    by framework after invoking the reduce function. This value must be set to 
    false for applications which process the records asynchronously 
    or buffer the input records. For example streaming. 
    In such cases applications should increment this counter on their own.
    </description>
  </property>
  
  <property>
    <name>mapreduce.job.skip.outdir</name>
    <value></value>
    <description> If no value is specified here, the skipped records are 
    written to the output directory at _logs/skip.
    User can stop writing skipped records by giving the value "none". 
    </description>
  </property>

  <property>
    <name>mapreduce.map.skip.maxrecords</name>
    <value>0</value>
    <description> The number of acceptable skip records surrounding the bad 
    record PER bad record in mapper. The number includes the bad record as well.
    To turn the feature of detection/skipping of bad records off, set the 
    value to 0.
    The framework tries to narrow down the skipped range by retrying  
    until this threshold is met OR all attempts get exhausted for this task. 
    Set the value to Long.MAX_VALUE to indicate that framework need not try to 
    narrow down. Whatever records(depends on application) get skipped are 
    acceptable.
    </description>
  </property>
  
  <property>
    <name>mapreduce.reduce.skip.maxgroups</name>
    <value>0</value>
    <description> The number of acceptable skip groups surrounding the bad 
    group PER bad group in reducer. The number includes the bad group as well.
    To turn the feature of detection/skipping of bad groups off, set the 
    value to 0.
    The framework tries to narrow down the skipped range by retrying  
    until this threshold is met OR all attempts get exhausted for this task. 
    Set the value to Long.MAX_VALUE to indicate that framework need not try to 
    narrow down. Whatever groups(depends on application) get skipped are 
    acceptable.
    </description>
  </property>
  
<!-- Job Notification Configuration -->

<!--
<property>
 <name>mapreduce.job.end-notification.url</name>
 <value>http://localhost:8080/jobstatus.php?jobId=$jobId&amp;jobStatus=$jobStatus</value>
 <description>Indicates url which will be called on completion of job to inform
              end status of job.
              User can give at most 2 variables with URI : $jobId and $jobStatus.
              If they are present in URI, then they will be replaced by their
              respective values.
</description>
</property>
-->

<property>
  <name>mapreduce.job.end-notification.retry.attempts</name>
  <value>0</value>
  <description>Indicates how many times hadoop should attempt to contact the
               notification URL </description>
</property>

<property>
  <name>mapreduce.job.end-notification.retry.interval</name>
   <value>30000</value>
   <description>Indicates time in milliseconds between notification URL retry
                calls</description>
</property>
  
<!-- Proxy Configuration -->
<property>
  <name>mapreduce.jobtracker.taskcache.levels</name>
  <value>2</value>
  <description> This is the max level of the task cache. For example, if
    the level is 2, the tasks cached are at the host level and at the rack
    level.
  </description>
</property>

<property>
  <name>mapreduce.job.queuename</name>
  <value>default</value>
  <description> Queue to which a job is submitted. This must match one of the
    queues defined in mapred-queues.xml for the system. Also, the ACL setup
    for the queue must allow the current user to submit a job to the queue.
    Before specifying a queue, ensure that the system is configured with 
    the queue, and access is allowed for submitting jobs to the queue.
  </description>
</property>

<property>
  <name>mapreduce.cluster.acls.enabled</name>
  <value>false</value>
  <description> Specifies whether ACLs should be checked
    for authorization of users for doing various queue and job level operations.
    ACLs are disabled by default. If enabled, access control checks are made by
    JobTracker and TaskTracker when requests are made by users for queue
    operations like submit job to a queue and kill a job in the queue and job
    operations like viewing the job-details (See mapreduce.job.acl-view-job)
    or for modifying the job (See mapreduce.job.acl-modify-job) using
    Map/Reduce APIs, RPCs or via the console and web user interfaces.
    For enabling this flag(mapreduce.cluster.acls.enabled), this is to be set
    to true in mapred-site.xml on JobTracker node and on all TaskTracker nodes.
  </description>
</property>

<property>
  <name>mapreduce.job.acl-modify-job</name>
  <value> </value>
  <description> Job specific access-control list for 'modifying' the job. It
    is only used if authorization is enabled in Map/Reduce by setting the
    configuration property mapreduce.cluster.acls.enabled to true.
    This specifies the list of users and/or groups who can do modification
    operations on the job. For specifying a list of users and groups the
    format to use is "user1,user2 group1,group". If set to '*', it allows all
    users/groups to modify this job. If set to ' '(i.e. space), it allows
    none. This configuration is used to guard all the modifications with respect
    to this job and takes care of all the following operations:
      o killing this job
      o killing a task of this job, failing a task of this job
      o setting the priority of this job
    Each of these operations are also protected by the per-queue level ACL
    "acl-administer-jobs" configured via mapred-queues.xml. So a caller should
    have the authorization to satisfy either the queue-level ACL or the
    job-level ACL.

    Irrespective of this ACL configuration, (a) job-owner, (b) the user who
    started the cluster, (c) members of an admin configured supergroup
    configured via mapreduce.cluster.permissions.supergroup and (d) queue
    administrators of the queue to which this job was submitted to configured
    via acl-administer-jobs for the specific queue in mapred-queues.xml can
    do all the modification operations on a job.

    By default, nobody else besides job-owner, the user who started the cluster,
    members of supergroup and queue administrators can perform modification
    operations on a job.
  </description>
</property>

<property>
  <name>mapreduce.job.acl-view-job</name>
  <value> </value>
  <description> Job specific access-control list for 'viewing' the job. It is
    only used if authorization is enabled in Map/Reduce by setting the
    configuration property mapreduce.cluster.acls.enabled to true.
    This specifies the list of users and/or groups who can view private details
    about the job. For specifying a list of users and groups the
    format to use is "user1,user2 group1,group". If set to '*', it allows all
    users/groups to modify this job. If set to ' '(i.e. space), it allows
    none. This configuration is used to guard some of the job-views and at
    present only protects APIs that can return possibly sensitive information
    of the job-owner like
      o job-level counters
      o task-level counters
      o tasks' diagnostic information
      o task-logs displayed on the TaskTracker web-UI and
      o job.xml showed by the JobTracker's web-UI
    Every other piece of information of jobs is still accessible by any other
    user, for e.g., JobStatus, JobProfile, list of jobs in the queue, etc.

    Irrespective of this ACL configuration, (a) job-owner, (b) the user who
    started the cluster, (c) members of an admin configured supergroup
    configured via mapreduce.cluster.permissions.supergroup and (d) queue
    administrators of the queue to which this job was submitted to configured
    via acl-administer-jobs for the specific queue in mapred-queues.xml can
    do all the view operations on a job.

    By default, nobody else besides job-owner, the user who started the
    cluster, memebers of supergroup and queue administrators can perform
    view operations on a job.
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.indexcache.mb</name>
  <value>10</value>
  <description> The maximum memory that a task tracker allows for the 
    index cache that is used when serving map outputs to reducers.
  </description>
</property>

<property>
  <name>mapreduce.task.merge.progress.records</name>
  <value>10000</value>
  <description> The number of records to process during merge before
   sending a progress notification to the TaskTracker.
  </description>
</property>

<property>
  <name>mapreduce.job.reduce.slowstart.completedmaps</name>
  <value>0.05</value>
  <description>Fraction of the number of maps in the job which should be 
  complete before reduces are scheduled for the job. 
  </description>
</property>

<property>
<name>mapreduce.job.complete.cancel.delegation.tokens</name>
  <value>true</value>
  <description> if false - do not unregister/cancel delegation tokens from 
    renewal, because same tokens may be used by spawned jobs
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.taskcontroller</name>
  <value>org.apache.hadoop.mapred.DefaultTaskController</value>
  <description>TaskController which is used to launch and manage task execution 
  </description>
</property>

<property>
  <name>mapreduce.tasktracker.group</name>
  <value></value>
  <description>Expert: Group to which TaskTracker belongs. If 
   LinuxTaskController is configured via mapreduce.tasktracker.taskcontroller,
   the group owner of the task-controller binary should be same as this group.
  </description>
</property>

<!--  Node health script variables -->

<property>
  <name>mapreduce.tasktracker.healthchecker.script.path</name>
  <value></value>
  <description>Absolute path to the script which is
  periodicallyrun by the node health monitoring service to determine if
  the node is healthy or not. If the value of this key is empty or the
  file does not exist in the location configured here, the node health
  monitoring service is not started.</description>
</property>

<property>
  <name>mapreduce.tasktracker.healthchecker.interval</name>
  <value>60000</value>
  <description>Frequency of the node health script to be run,
  in milliseconds</description>
</property>

<property>
  <name>mapreduce.tasktracker.healthchecker.script.timeout</name>
  <value>600000</value>
  <description>Time after node health script should be killed if 
  unresponsive and considered that the script has failed.</description>
</property>

<property>
  <name>mapreduce.tasktracker.healthchecker.script.args</name>
  <value></value>
  <description>List of arguments which are to be passed to 
  node health script when it is being launched comma seperated.
  </description>
</property>

<!--  end of node health script variables -->

<property>
 <name>mapreduce.job.counters.limit</name>
  <value>120</value>
  <description>Limit on the number of user counters allowed per job.
  </description>
</property>

<property>
  <name>mapreduce.framework.name</name>
  <value>local</value>
  <description>The runtime framework for executing MapReduce jobs.
  Can be one of local, classic or yarn.
  </description>
</property>

<property>
  <name>yarn.app.mapreduce.am.staging-dir</name>
  <value>/tmp/hadoop-yarn/staging</value>
  <description>The staging dir used while submitting jobs.
  </description>
</property>

<property>
  <name>mapreduce.job.end-notification.max.attempts</name>
  <value>5</value>
  <final>true</final>
  <description>The maximum number of times a URL will be read for providing job
    end notification. Cluster administrators can set this to limit how long
    after end of a job, the Application Master waits before exiting. Must be
    marked as final to prevent users from overriding this.
  </description>
</property>

<property>
  <name>mapreduce.job.end-notification.max.retry.interval</name>
  <value>5</value>
  <final>true</final>
  <description>The maximum amount of time (in seconds) to wait before retrying
    job end notification. Cluster administrators can set this to limit how long
    the Application Master waits before exiting. Must be marked as final to
    prevent users from overriding this.</description>
</property>

<property>
  <name>mapreduce.job.end-notification.url</name>
  <value></value>
  <description>The URL to send job end notification. It may contain sentinels
    $jobId and $jobStatus which will be replaced with jobId and jobStatus.
  </description>
</property>

<property>
  <name>mapreduce.job.end-notification.retry.attempts</name>
  <value>5</value>
  <description>The number of times the submitter of the job wants to retry job
    end notification if it fails. This is capped by
    mapreduce.job.end-notification.max.attempts</description>
</property>

<property>
  <name>mapreduce.job.end-notification.retry.interval</name>
  <value>1</value>
  <description>The number of seconds the submitter of the job wants to wait
    before job end notification is retried if it fails. This is capped by
    mapreduce.job.end-notification.max.retry.interval</description>
</property>

<property>
  <name>yarn.app.mapreduce.am.job.task.listener.thread-count</name>
  <value>30</value>
  <description>The number of threads used to handle RPC calls in the 
    MR AppMaster from remote tasks</description>
</property>

<property>
  <name>yarn.app.mapreduce.am.scheduler.heartbeat.interval-ms</name>
  <value>1000</value>
  <description>The interval in ms at which the MR AppMaster should send
    heartbeats to the ResourceManager</description>
</property>

</configuration>