mapred-default.xml
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mapred-default.xml
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			<?xml version="1.0"?>
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			<!-- 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.max.split.locations</name>
			<value>10</value>
			<description>The max number of block locations to store for each split for
			locality calculation.
			</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.retry-delay.max.ms</name>
			<value>60000</value>
			<description>The maximum number of ms the reducer will delay before retrying
			to download map data.
			</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. A value of 0 disables the timeout.
			</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

			Usage of -Djava.library.path can cause programs to no longer function if
			hadoop native libraries are used. These values should instead be set as part
			of LD_LIBRARY_PATH in the map / reduce JVM env using the mapreduce.map.env and
			mapreduce.reduce.env config settings.
			</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 nodemanager's B env variable on Unix.
			3) B=%B%;c This is inherit nodemanager's B env variable on Windows.
			</description>
			</property>

			<property>
			<name>mapreduce.admin.user.env</name>
			<value></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).
			When this value is empty, the command to set execution
			envrionment will be OS dependent:
			For linux, use LD_LIBRARY_PATH=$HADOOP_COMMON_HOME/lib/native.
			For windows, use PATH = %PATH%;%HADOOP_COMMON_HOME%\\bin.
			</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.map.cpu.vcores</name>
			<value>1</value>
			<description>
			The number of virtual cores required for each map task.
			</description>
			</property>

			<property>
			<name>mapreduce.reduce.cpu.vcores</name>
			<value>1</value>
			<description>
			The number of virtual cores required for each reduce task.
			</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.shuffle.ssl.enabled</name>
			<value>false</value>
			<description>
			Whether to use SSL for for the Shuffle HTTP endpoints.
			</description>
			</property>

			<property>
			<name>mapreduce.shuffle.ssl.file.buffer.size</name>
			<value>65536</value>
			<description>Buffer size for reading spills from file when using SSL.
			</description>
			</property>

			<property>
			<name>mapreduce.shuffle.max.connections</name>
			<value>0</value>
			<description>Max allowed connections for the shuffle. Set to 0 (zero)
			to indicate no limit on the number of connections.
			</description>
			</property>

			<property>
			<name>mapreduce.shuffle.max.threads</name>
			<value>0</value>
			<description>Max allowed threads for serving shuffle connections. Set to zero
			to indicate the default of 2 times the number of available
			processors (as reported by Runtime.availableProcessors()). Netty is used to
			serve requests, so a thread is not needed for each connection.
			</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.map.output.collector.class</name>
			<value>org.apache.hadoop.mapred.MapTask$MapOutputBuffer</value>
			<description>
			It defines the MapOutputCollector implementation to use.
			</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.compress.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.compress.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>yarn.app.mapreduce.task.container.log.backups</name>
			<value>0</value>
			<description>Number of backup files for task logs when using
			ContainerRollingLogAppender (CRLA). See
			org.apache.log4j.RollingFileAppender.maxBackupIndex. By default,
			ContainerLogAppender (CLA) is used, and container logs are not rolled. CRLA
			is enabled for tasks when both mapreduce.task.userlog.limit.kb and
			yarn.app.mapreduce.task.container.log.backups are greater than zero.
			</description>
			</property>

			<property>
			<name>yarn.app.mapreduce.am.container.log.backups</name>
			<value>0</value>
			<description>Number of backup files for the ApplicationMaster logs when using
			ContainerRollingLogAppender (CRLA). See
			org.apache.log4j.RollingFileAppender.maxBackupIndex. By default,
			ContainerLogAppender (CLA) is used, and container logs are not rolled. CRLA
			is enabled for the ApplicationMaster when both
			mapreduce.task.userlog.limit.kb and
			yarn.app.mapreduce.am.container.log.backups are greater than zero.
			</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>3</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. It
			MUST be less than mapreduce.map.maxattempts and
			mapreduce.reduce.maxattempts otherwise the failed task will
			never be tried on a different node.
			</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.profile.params</name>
			<value></value>
			<description>JVM profiler parameters used to profile map and reduce task
			attempts. This string may contain a single format specifier %s that will
			be replaced by the path to profile.out in the task attempt log directory.
			To specify different profiling options for map tasks and reduce tasks,
			more specific parameters mapreduce.task.profile.map.params and
			mapreduce.task.profile.reduce.params should be used.</description>
			</property>

			<property>
			<name>mapreduce.task.profile.map.params</name>
			<value>${mapreduce.task.profile.params}</value>
			<description>Map-task-specific JVM profiler parameters. See
			mapreduce.task.profile.params</description>
			</property>

			<property>
			<name>mapreduce.task.profile.reduce.params</name>
			<value>${mapreduce.task.profile.params}</value>
			<description>Reduce-task-specific JVM profiler parameters. See
			mapreduce.task.profile.params</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>

			<property>
			<name>mapreduce.ifile.readahead</name>
			<value>true</value>
			<description>Configuration key to enable/disable IFile readahead.
			</description>
			</property>

			<property>
			<name>mapreduce.ifile.readahead.bytes</name>
			<value>4194304</value>
			<description>Configuration key to set the IFile readahead length in bytes.
			</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.job.token.tracking.ids.enabled</name>
			<value>false</value>
			<description>Whether to write tracking ids of tokens to
			job-conf. When true, the configuration property
			"mapreduce.job.token.tracking.ids" is set to the token-tracking-ids of
			the job</description>
			</property>

			<property>
			<name>mapreduce.job.token.tracking.ids</name>
			<value></value>
			<description>When mapreduce.job.token.tracking.ids.enabled is
			set to true, this is set by the framework to the
			token-tracking-ids used by the job.</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>

			<property>
			<name>mapreduce.shuffle.port</name>
			<value>13562</value>
			<description>Default port that the ShuffleHandler will run on. ShuffleHandler
			is a service run at the NodeManager to facilitate transfers of intermediate
			Map outputs to requesting Reducers.
			</description>
			</property>

			<property>
			<name>mapreduce.job.reduce.shuffle.consumer.plugin.class</name>
			<value>org.apache.hadoop.mapreduce.task.reduce.Shuffle</value>
			<description>
			Name of the class whose instance will be used
			to send shuffle requests by reducetasks of this job.
			The class must be an instance of org.apache.hadoop.mapred.ShuffleConsumerPlugin.
			</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 -->

			<!-- MR YARN Application properties -->

			<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.am.max-attempts</name>
			<value>2</value>
			<description>The maximum number of application attempts. It is a
			application-specific setting. It should not be larger than the global number
			set by resourcemanager. Otherwise, it will be override. The default number is
			set to 2, to allow at least one retry for AM.</description>
			</property>

			<!-- Job Notification Configuration -->
			<property>
			<name>mapreduce.job.end-notification.url</name>
			<!--<value>http://localhost:8080/jobstatus.php?jobId=$jobId&jobStatus=$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>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>1000</value>
			<description>The number of milliseconds 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>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>5000</value>
			<final>true</final>
			<description>The maximum amount of time (in milliseconds) 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>yarn.app.mapreduce.am.env</name>
			<value></value>
			<description>User added environment variables for the MR App Master
			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>yarn.app.mapreduce.am.admin.user.env</name>
			<value></value>
			<description> Environment variables for the MR App Master
			processes for admin purposes. These values are set first and can be
			overridden by the user env (yarn.app.mapreduce.am.env) Example :
			1) A=foo This will set the env variable A to foo
			2) B=$B:c This is inherit app master's B env variable.
			</description>
			</property>

			<property>
			<name>yarn.app.mapreduce.am.command-opts</name>
			<value>-Xmx1024m</value>
			<description>Java opts for the MR App Master 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

			Usage of -Djava.library.path can cause programs to no longer function if
			hadoop native libraries are used. These values should instead be set as part
			of LD_LIBRARY_PATH in the map / reduce JVM env using the mapreduce.map.env and
			mapreduce.reduce.env config settings.
			</description>
			</property>

			<property>
			<name>yarn.app.mapreduce.am.admin-command-opts</name>
			<value></value>
			<description>Java opts for the MR App Master processes for admin purposes.
			It will appears before the opts set by yarn.app.mapreduce.am.command-opts and
			thus its options can be overridden user.

			Usage of -Djava.library.path can cause programs to no longer function if
			hadoop native libraries are used. These values should instead be set as part
			of LD_LIBRARY_PATH in the map / reduce JVM env using the mapreduce.map.env and
			mapreduce.reduce.env config settings.
			</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.job.client.port-range</name>
			<value></value>
			<description>Range of ports that the MapReduce AM can use when binding.
			Leave blank if you want all possible ports.
			For example 50000-50050,50100-50200</description>
			</property>

			<property>
			<name>yarn.app.mapreduce.am.job.committer.cancel-timeout</name>
			<value>60000</value>
			<description>The amount of time in milliseconds to wait for the output
			committer to cancel an operation if the job is killed</description>
			</property>

			<property>
			<name>yarn.app.mapreduce.am.job.committer.commit-window</name>
			<value>10000</value>
			<description>Defines a time window in milliseconds for output commit
			operations. If contact with the RM has occurred within this window then
			commits are allowed, otherwise the AM will not allow output commits until
			contact with the RM has been re-established.</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>

			<property>
			<name>yarn.app.mapreduce.client-am.ipc.max-retries</name>
			<value>3</value>
			<description>The number of client retries to the AM - before reconnecting
			to the RM to fetch Application Status.</description>
			</property>

			<property>
			<name>yarn.app.mapreduce.client-am.ipc.max-retries-on-timeouts</name>
			<value>3</value>
			<description>The number of client retries on socket timeouts to the AM - before
			reconnecting to the RM to fetch Application Status.</description>
			</property>

			<property>
			<name>yarn.app.mapreduce.client.max-retries</name>
			<value>3</value>
			<description>The number of client retries to the RM/HS before
			throwing exception. This is a layer above the ipc.</description>
			</property>

			<property>
			<name>yarn.app.mapreduce.am.resource.mb</name>
			<value>1536</value>
			<description>The amount of memory the MR AppMaster needs.</description>
			</property>

			<property>
			<name>yarn.app.mapreduce.am.resource.cpu-vcores</name>
			<value>1</value>
			<description>
			The number of virtual CPU cores the MR AppMaster needs.
			</description>
			</property>

			<property>
			<description>CLASSPATH for MR applications. A comma-separated list
			of CLASSPATH entries. If mapreduce.application.framework is set then this
			must specify the appropriate classpath for that archive, and the name of
			the archive must be present in the classpath.
			When this value is empty, the following default CLASSPATH for MR
			applications would be used.
			For Linux:
			$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/*,
			$HADOOP_MAPRED_HOME/share/hadoop/mapreduce/lib/*.
			For Windows:
			%HADOOP_MAPRED_HOME%/share/hadoop/mapreduce/*,
			%HADOOP_MAPRED_HOME%/share/hadoop/mapreduce/lib/*.
			</description>
			<name>mapreduce.application.classpath</name>
			<value></value>
			</property>

			<property>
			<description>Path to the MapReduce framework archive. If set, the framework
			archive will automatically be distributed along with the job, and this
			path would normally reside in a public location in an HDFS filesystem. As
			with distributed cache files, this can be a URL with a fragment specifying
			the alias to use for the archive name. For example,
			hdfs:/mapred/framework/hadoop-mapreduce-2.1.1.tar.gz#mrframework would
			alias the localized archive as "mrframework".

			Note that mapreduce.application.classpath must include the appropriate
			classpath for the specified framework. The base name of the archive, or
			alias of the archive if an alias is used, must appear in the specified
			classpath.
			</description>
			<name>mapreduce.application.framework.path</name>
			<value></value>
			</property>

			<property>
			<name>mapreduce.job.classloader</name>
			<value>false</value>
			<description>Whether to use a separate (isolated) classloader for
			user classes in the task JVM.</description>
			</property>

			<property>
			<name>mapreduce.job.classloader.system.classes</name>
			<value>java.,javax.,org.apache.commons.logging.,org.apache.log4j.,org.apache.hadoop.</value>
			<description>A comma-separated list of classes that should be loaded from the
			system classpath, not the user-supplied JARs, when mapreduce.job.classloader
			is enabled. Names ending in '.' (period) are treated as package names,
			and names starting with a '-' are treated as negative matches.
			</description>
			</property>

			<!-- jobhistory properties -->

			<property>
			<name>mapreduce.jobhistory.address</name>
			<value>0.0.0.0:10020</value>
			<description>MapReduce JobHistory Server IPC host:port</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.webapp.address</name>
			<value>0.0.0.0:19888</value>
			<description>MapReduce JobHistory Server Web UI host:port</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.keytab</name>
			<description>
			Location of the kerberos keytab file for the MapReduce
			JobHistory Server.
			</description>
			<value>/etc/security/keytab/jhs.service.keytab</value>
			</property>

			<property>
			<name>mapreduce.jobhistory.principal</name>
			<description>
			Kerberos principal name for the MapReduce JobHistory Server.
			</description>
			<value>jhs/_HOST@REALM.TLD</value>
			</property>

			<property>
			<name>mapreduce.jobhistory.intermediate-done-dir</name>
			<value>${yarn.app.mapreduce.am.staging-dir}/history/done_intermediate</value>
			<description></description>
			</property>

			<property>
			<name>mapreduce.jobhistory.done-dir</name>
			<value>${yarn.app.mapreduce.am.staging-dir}/history/done</value>
			<description></description>
			</property>

			<property>
			<name>mapreduce.jobhistory.cleaner.enable</name>
			<value>true</value>
			<description></description>
			</property>

			<property>
			<name>mapreduce.jobhistory.cleaner.interval-ms</name>
			<value>86400000</value>
			<description> How often the job history cleaner checks for files to delete,
			in milliseconds. Defaults to 86400000 (one day). Files are only deleted if
			they are older than mapreduce.jobhistory.max-age-ms.
			</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.max-age-ms</name>
			<value>604800000</value>
			<description> Job history files older than this many milliseconds will
			be deleted when the history cleaner runs. Defaults to 604800000 (1 week).
			</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.client.thread-count</name>
			<value>10</value>
			<description>The number of threads to handle client API requests</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.datestring.cache.size</name>
			<value>200000</value>
			<description>Size of the date string cache. Effects the number of directories
			which will be scanned to find a job.</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.joblist.cache.size</name>
			<value>20000</value>
			<description>Size of the job list cache</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.loadedjobs.cache.size</name>
			<value>5</value>
			<description>Size of the loaded job cache</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.move.interval-ms</name>
			<value>180000</value>
			<description>Scan for history files to more from intermediate done dir to done
			dir at this frequency.
			</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.move.thread-count</name>
			<value>3</value>
			<description>The number of threads used to move files.</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.store.class</name>
			<value></value>
			<description>The HistoryStorage class to use to cache history data.</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.minicluster.fixed.ports</name>
			<value>false</value>
			<description>Whether to use fixed ports with the minicluster</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.admin.address</name>
			<value>0.0.0.0:10033</value>
			<description>The address of the History server admin interface.</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.admin.acl</name>
			<value>*</value>
			<description>ACL of who can be admin of the History server.</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.recovery.enable</name>
			<value>false</value>
			<description>Enable the history server to store server state and recover
			server state upon startup. If enabled then
			mapreduce.jobhistory.recovery.store.class must be specified.</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.recovery.store.class</name>
			<value>org.apache.hadoop.mapreduce.v2.hs.HistoryServerFileSystemStateStoreService</value>
			<description>The HistoryServerStateStoreService class to store history server
			state for recovery.</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.recovery.store.fs.uri</name>
			<value>${hadoop.tmp.dir}/mapred/history/recoverystore</value>
			<!--value>hdfs://localhost:9000/mapred/history/recoverystore</value-->
			<description>The URI where history server state will be stored if
			HistoryServerFileSystemStateStoreService is configured as the recovery
			storage class.</description>
			</property>

			<property>
			<name>mapreduce.jobhistory.http.policy</name>
			<value>HTTP_ONLY</value>
			<description>
			This configures the HTTP endpoint for JobHistoryServer web UI.
			The following values are supported:
			- HTTP_ONLY : Service is provided only on http
			- HTTPS_ONLY : Service is provided only on https
			</description>
			</property>
			</configuration>

R3704/hadoop-2.3.0/share/hadoop/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapred-default.xmlc0a03c32f340master

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R3704/hadoop-2.3.0/share/hadoop/hadoop-mapreduce-client/hadoop-mapreduce-client-core/mapred-default.xml
c0a03c32f340master

mapred-default.xml
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