hadoop2.2.0 mapred-site.xml--i/o properties
2014-05-03 14:25
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<!-- i/o properties -->
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<property> <name>mapreduce.cluster.local.dir</name> <value>${hadoop.tmp.dir}/mapred/local</value> <description>The local directory where MapReduce stores intermediate data files. May be a comma-separated list of directories on different devices in order to spread disk i/o. Directories that do not exist are ignored. </description> </property>
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<property> <name>mapreduce.jobtracker.system.dir</name> <value>${hadoop.tmp.dir}/mapred/system</value> <description>The directory where MapReduce stores control files. </description> </property>
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<property> <name>mapreduce.jobtracker.staging.root.dir</name> <value>${hadoop.tmp.dir}/mapred/staging</value> <description>The root of the staging area for users' job files In practice, this should be the directory where users' home directories are located (usually /user) </description> </property>
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<property> <name>mapreduce.cluster.temp.dir</name> <value>${hadoop.tmp.dir}/mapred/temp</value> <description>A shared directory for temporary files. </description> </property>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<property> <name>mapreduce.jobtracker.restart.recover</name> <value>false</value> <description>"true" to enable (job) recovery upon restart, "false" to start afresh </description> </property>
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<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>
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<property> <name>mapreduce.jobtracker.taskscheduler</name> <value>org.apache.hadoop.mapred.JobQueueTaskScheduler</value> <description>The class responsible for scheduling the tasks.</description> </property>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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=$B:c This is inherit tasktracker's B env variable. </description> </property>
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<property> <name>mapreduce.admin.user.env</name> <value>LD_LIBRARY_PATH=$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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<!-- <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> -->
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<property> <name>mapreduce.output.fileoutputformat.compress</name> <value>false</value> <description>Should the job outputs be compressed? </description> </property>
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<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>
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<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>
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<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>
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<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>
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<property> <name>map.sort.class</name> <value>org.apache.hadoop.util.QuickSort</value> <description>The default sort class for sorting keys. </description> </property>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
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<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>
注释:
<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>
注释:
<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>
注释:
<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>
注释:
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