namevaluedescription
hadoop.job.history.location 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.
hadoop.job.history.user.location 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".
mapred.job.tracker.history.completed.location The completed job history files are stored at this single well known location. If nothing is specified, the files are stored at ${hadoop.job.history.location}/done.
io.sort.factor10The number of streams to merge at once while sorting files. This determines the number of open file handles.
io.sort.mb100The total amount of buffer memory to use while sorting files, in megabytes. By default, gives each merge stream 1MB, which should minimize seeks.
io.sort.record.percent0.05The percentage of io.sort.mb dedicated to tracking record boundaries. Let this value be r, io.sort.mb be x. The maximum number of records collected before the collection thread must block is equal to (r * x) / 4
io.sort.spill.percent0.80The soft limit in either the buffer or record collection buffers. Once reached, a thread will begin to spill the contents to disk in the background. Note that this does not imply any chunking of data to the spill. A value less than 0.5 is not recommended.
io.map.index.skip0Number of index entries to skip between each entry. Zero by default. Setting this to values larger than zero can facilitate opening large map files using less memory.
mapred.job.trackerlocalThe host and port that the MapReduce job tracker runs at. If "local", then jobs are run in-process as a single map and reduce task.
mapred.job.tracker.http.address0.0.0.0:50030 The job tracker http server address and port the server will listen on. If the port is 0 then the server will start on a free port.
mapred.job.tracker.handler.count10 The number of server threads for the JobTracker. This should be roughly 4% of the number of tasktracker nodes.
mapred.task.tracker.report.address127.0.0.1:0The interface and port that task tracker server listens on. Since it is only connected to by the tasks, it uses the local interface. EXPERT ONLY. Should only be changed if your host does not have the loopback interface.
mapred.local.dir${hadoop.tmp.dir}/mapred/localThe 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.
mapred.system.dir${hadoop.tmp.dir}/mapred/systemThe directory where MapReduce stores control files.
mapreduce.jobtracker.staging.root.dir${hadoop.tmp.dir}/mapred/stagingThe root of the staging area for users' job files In practice, this should be the directory where users' home directories are located (usually /user)
mapred.temp.dir${hadoop.tmp.dir}/mapred/tempA shared directory for temporary files.
mapred.local.dir.minspacestart0If the space in mapred.local.dir drops under this, do not ask for more tasks. Value in bytes.
mapred.local.dir.minspacekill0If the space in mapred.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.
mapred.tasktracker.expiry.interval600000Expert: The time-interval, in miliseconds, after which a tasktracker is declared 'lost' if it doesn't send heartbeats.
mapred.tasktracker.resourcecalculatorplugin 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.
mapred.tasktracker.taskmemorymanager.monitoring-interval5000The 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.
mapred.tasktracker.tasks.sleeptime-before-sigkill5000The time, in milliseconds, the tasktracker waits for sending a SIGKILL to a process, after it has been sent a SIGTERM.
mapred.map.tasks2The default number of map tasks per job. Ignored when mapred.job.tracker is "local".
mapred.reduce.tasks1The 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 mapred.job.tracker is "local".
mapreduce.tasktracker.outofband.heartbeatfalseExpert: Set this to true to let the tasktracker send an out-of-band heartbeat on task-completion for better latency.
mapreduce.tasktracker.outofband.heartbeat.damper1000000When out-of-band heartbeats are enabled, provides damping to avoid overwhelming the JobTracker if too many out-of-band heartbeats would occur. The damping is calculated such that the heartbeat interval is divided by (T*D + 1) where T is the number of completed tasks and D is the damper value. Setting this to a high value like the default provides no damping -- as soon as any task finishes, a heartbeat will be sent. Setting this parameter to 0 is equivalent to disabling the out-of-band heartbeat feature. A value of 1 would indicate that, after one task has completed, the time to wait before the next heartbeat would be 1/2 the usual time. After two tasks have finished, it would be 1/3 the usual time, etc.
mapred.jobtracker.restart.recoverfalse"true" to enable (job) recovery upon restart, "false" to start afresh
mapred.jobtracker.job.history.block.size3145728The 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.
mapreduce.job.split.metainfo.maxsize10000000The 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.
mapred.jobtracker.taskSchedulerorg.apache.hadoop.mapred.JobQueueTaskSchedulerThe class responsible for scheduling the tasks.
mapred.jobtracker.taskScheduler.maxRunningTasksPerJobThe maximum number of running tasks for a job before it gets preempted. No limits if undefined.
mapred.map.max.attempts4Expert: 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.
mapred.reduce.max.attempts4Expert: 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.
mapred.reduce.parallel.copies5The default number of parallel transfers run by reduce during the copy(shuffle) phase.
mapreduce.reduce.shuffle.maxfetchfailures10The maximum number of times a reducer tries to fetch a map output before it reports it.
mapreduce.reduce.shuffle.connect.timeout180000Expert: The maximum amount of time (in milli seconds) a reduce task spends in trying to connect to a tasktracker for getting map output.
mapreduce.reduce.shuffle.read.timeout180000Expert: The maximum amount of time (in milli seconds) a reduce task waits for map output data to be available for reading after obtaining connection.
mapred.task.timeout600000The number of milliseconds before a task will be terminated if it neither reads an input, writes an output, nor updates its status string.
mapred.tasktracker.map.tasks.maximum2The maximum number of map tasks that will be run simultaneously by a task tracker.
mapred.tasktracker.reduce.tasks.maximum2The maximum number of reduce tasks that will be run simultaneously by a task tracker.
mapred.jobtracker.completeuserjobs.maximum100The maximum number of complete jobs per user to keep around before delegating them to the job history.
mapreduce.reduce.input.limit-1The limit on the input size of the reduce. If the estimated input size of the reduce is greater than this value, job is failed. A value of -1 means that there is no limit set.
mapred.job.tracker.retiredjobs.cache.size1000The number of retired job status to keep in the cache.
mapred.job.tracker.jobhistory.lru.cache.size5The number of job history files loaded in memory. The jobs are loaded when they are first accessed. The cache is cleared based on LRU.
mapred.child.java.opts-Xmx200mJava opts for the task tracker child processes. The following symbol, if present, will be interpolated: @taskid@ is replaced by current TaskID. Any other occurrences of '@' will go unchanged. For example, to enable verbose gc logging to a file named for the taskid in /tmp and to set the heap maximum to be a gigabyte, pass a 'value' of: -Xmx1024m -verbose:gc -Xloggc:/tmp/@taskid@.gc The configuration variable mapred.child.ulimit can be used to control the maximum virtual memory of the child processes.
mapred.child.envUser 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.
mapred.child.ulimitThe maximum virtual memory, in KB, of a process launched by the Map-Reduce framework. This can be used to control both the Mapper/Reducer tasks and applications using Hadoop Pipes, Hadoop Streaming etc. By default it is left unspecified to let cluster admins control it via limits.conf and other such relevant mechanisms. Note: mapred.child.ulimit must be greater than or equal to the -Xmx passed to JavaVM, else the VM might not start.
mapred.cluster.map.memory.mb-1The size, in terms of virtual memory, of a single map slot in the Map-Reduce framework, used by the scheduler. A job can ask for multiple slots for a single map task via mapred.job.map.memory.mb, upto the limit specified by mapred.cluster.max.map.memory.mb, if the scheduler supports the feature. The value of -1 indicates that this feature is turned off.
mapred.cluster.reduce.memory.mb-1The size, in terms of virtual memory, of a single reduce slot in the Map-Reduce framework, used by the scheduler. A job can ask for multiple slots for a single reduce task via mapred.job.reduce.memory.mb, upto the limit specified by mapred.cluster.max.reduce.memory.mb, if the scheduler supports the feature. The value of -1 indicates that this feature is turned off.
mapred.cluster.max.map.memory.mb-1The maximum size, in terms of virtual memory, of a single map task launched by the Map-Reduce framework, used by the scheduler. A job can ask for multiple slots for a single map task via mapred.job.map.memory.mb, upto the limit specified by mapred.cluster.max.map.memory.mb, if the scheduler supports the feature. The value of -1 indicates that this feature is turned off.
mapred.cluster.max.reduce.memory.mb-1The maximum size, in terms of virtual memory, of a single reduce task launched by the Map-Reduce framework, used by the scheduler. A job can ask for multiple slots for a single reduce task via mapred.job.reduce.memory.mb, upto the limit specified by mapred.cluster.max.reduce.memory.mb, if the scheduler supports the feature. The value of -1 indicates that this feature is turned off.
mapred.job.map.memory.mb-1The size, in terms of virtual memory, of a single map task for the job. A job can ask for multiple slots for a single map task, rounded up to the next multiple of mapred.cluster.map.memory.mb and upto the limit specified by mapred.cluster.max.map.memory.mb, if the scheduler supports the feature. The value of -1 indicates that this feature is turned off iff mapred.cluster.map.memory.mb is also turned off (-1).
mapred.job.reduce.memory.mb-1The size, in terms of virtual memory, of a single reduce task for the job. A job can ask for multiple slots for a single map task, rounded up to the next multiple of mapred.cluster.reduce.memory.mb and upto the limit specified by mapred.cluster.max.reduce.memory.mb, if the scheduler supports the feature. The value of -1 indicates that this feature is turned off iff mapred.cluster.reduce.memory.mb is also turned off (-1).
mapred.child.tmp./tmp 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'
mapred.inmem.merge.threshold1000The 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.
mapred.job.shuffle.merge.percent0.66The 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 mapred.job.shuffle.input.buffer.percent.
mapred.job.shuffle.input.buffer.percent0.70The percentage of memory to be allocated from the maximum heap size to storing map outputs during the shuffle.
mapred.job.reduce.input.buffer.percent0.0The 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.
mapred.map.tasks.speculative.executiontrueIf true, then multiple instances of some map tasks may be executed in parallel.
mapred.reduce.tasks.speculative.executiontrueIf true, then multiple instances of some reduce tasks may be executed in parallel.
mapred.job.reuse.jvm.num.tasks1How many tasks to run per jvm. If set to -1, there is no limit.
mapred.min.split.size0The 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.
mapred.jobtracker.maxtasks.per.job-1The maximum number of tasks for a single job. A value of -1 indicates that there is no maximum.
mapred.submit.replication10The replication level for submitted job files. This should be around the square root of the number of nodes.
mapred.tasktracker.dns.interfacedefaultThe name of the Network Interface from which a task tracker should report its IP address.
mapred.tasktracker.dns.nameserverdefaultThe 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.
tasktracker.http.threads40The number of worker threads that for the http server. This is used for map output fetching
mapred.task.tracker.http.address0.0.0.0:50060 The task tracker http server address and port. If the port is 0 then the server will start on a free port.
keep.failed.task.filesfalseShould 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.
mapred.output.compressfalseShould the job outputs be compressed?
mapred.output.compression.typeRECORDIf the job outputs are to compressed as SequenceFiles, how should they be compressed? Should be one of NONE, RECORD or BLOCK.
mapred.output.compression.codecorg.apache.hadoop.io.compress.DefaultCodecIf the job outputs are compressed, how should they be compressed?
mapred.compress.map.outputfalseShould the outputs of the maps be compressed before being sent across the network. Uses SequenceFile compression.
mapred.map.output.compression.codecorg.apache.hadoop.io.compress.DefaultCodecIf the map outputs are compressed, how should they be compressed?
map.sort.classorg.apache.hadoop.util.QuickSortThe default sort class for sorting keys.
mapred.userlog.limit.kb0The maximum size of user-logs of each task in KB. 0 disables the cap.
mapred.userlog.retain.hours24The maximum time, in hours, for which the user-logs are to be retained after the job completion.
mapred.user.jobconf.limit5242880The maximum allowed size of the user jobconf. The default is set to 5 MB
mapred.hostsNames a file that contains the list of nodes that may connect to the jobtracker. If the value is empty, all hosts are permitted.
mapred.hosts.excludeNames a file that contains the list of hosts that should be excluded by the jobtracker. If the value is empty, no hosts are excluded.
mapred.heartbeats.in.second100Expert: 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.
mapred.max.tracker.blacklists4The number of blacklists for a tasktracker by various jobs after which the tasktracker will be marked as potentially faulty and is a candidate for graylisting across all jobs. (Unlike blacklisting, this is advisory; the tracker remains active. However, it is reported as graylisted in the web UI, with the expectation that chronically graylisted trackers will be manually decommissioned.) This value is tied to mapred.jobtracker.blacklist.fault-timeout-window; faults older than the window width are forgiven, so the tracker will recover from transient problems. It will also become healthy after a restart.
mapred.jobtracker.blacklist.fault-timeout-window180The timeout (in minutes) after which per-job tasktracker faults are forgiven. The window is logically a circular buffer of time-interval buckets whose width is defined by mapred.jobtracker.blacklist.fault-bucket-width; when the "now" pointer moves across a bucket boundary, the previous contents (faults) of the new bucket are cleared. In other words, the timeout's granularity is determined by the bucket width.
mapred.jobtracker.blacklist.fault-bucket-width15The width (in minutes) of each bucket in the tasktracker fault timeout window. Each bucket is reused in a circular manner after a full timeout-window interval (defined by mapred.jobtracker.blacklist.fault-timeout-window).
mapred.max.tracker.failures4The number of task-failures on a tasktracker of a given job after which new tasks of that job aren't assigned to it.
jobclient.output.filterFAILEDThe 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.
mapred.job.tracker.persist.jobstatus.activefalseIndicates if persistency of job status information is active or not.
mapred.job.tracker.persist.jobstatus.hours0The 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.
mapred.job.tracker.persist.jobstatus.dir/jobtracker/jobsInfoThe 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.
mapreduce.job.complete.cancel.delegation.tokenstrue if false - do not unregister/cancel delegation tokens from renewal, because same tokens may be used by spawned jobs
mapred.task.profilefalseTo 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.
mapred.task.profile.maps0-2 To set the ranges of map tasks to profile. mapred.task.profile has to be set to true for the value to be accounted.
mapred.task.profile.reduces0-2 To set the ranges of reduce tasks to profile. mapred.task.profile has to be set to true for the value to be accounted.
mapred.line.input.format.linespermap1 Number of lines per split in NLineInputFormat.
mapred.skip.attempts.to.start.skipping2 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.
mapred.skip.map.auto.incr.proc.counttrue 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.
mapred.skip.reduce.auto.incr.proc.counttrue 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.
mapred.skip.out.dir 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".
mapred.skip.map.max.skip.records0 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.
mapred.skip.reduce.max.skip.groups0 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.
mapreduce.ifile.readaheadtrueConfiguration key to enable/disable IFile readahead.
mapreduce.ifile.readahead.bytes4194304Configuration key to set the IFile readahead length in bytes.
job.end.retry.attempts0Indicates how many times hadoop should attempt to contact the notification URL
job.end.retry.interval30000Indicates time in milliseconds between notification URL retry calls
hadoop.rpc.socket.factory.class.JobSubmissionProtocol SocketFactory to use to connect to a Map/Reduce master (JobTracker). If null or empty, then use hadoop.rpc.socket.class.default.
mapred.task.cache.levels2 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.
mapred.queue.namesdefault Comma separated list of queues configured for this jobtracker. Jobs are added to queues and schedulers can configure different scheduling properties for the various queues. To configure a property for a queue, the name of the queue must match the name specified in this value. Queue properties that are common to all schedulers are configured here with the naming convention, mapred.queue.$QUEUE-NAME.$PROPERTY-NAME, for e.g. mapred.queue.default.submit-job-acl. The number of queues configured in this parameter could depend on the type of scheduler being used, as specified in mapred.jobtracker.taskScheduler. For example, the JobQueueTaskScheduler supports only a single queue, which is the default configured here. Before adding more queues, ensure that the scheduler you've configured supports multiple queues.
mapred.acls.enabledfalse 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.
mapred.queue.default.stateRUNNING This values defines the state , default queue is in. the values can be either "STOPPED" or "RUNNING" This value can be changed at runtime.
mapred.job.queue.namedefault Queue to which a job is submitted. This must match one of the queues defined in mapred.queue.names 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.
mapreduce.job.acl-modify-job 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 mapred.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, job-owner, the user who started the cluster, cluster administrators configured via mapreduce.cluster.administrators and queue administrators of the queue to which this job is submitted to configured via mapred.queue.queue-name.acl-administer-jobs in mapred-queue-acls.xml can do all the modification operations on a job. By default, nobody else besides job-owner, the user who started the cluster, cluster administrators and queue administrators can perform modification operations on a job.
mapreduce.job.acl-view-job 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 mapred.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, job-owner, the user who started the cluster, cluster administrators configured via mapreduce.cluster.administrators and queue administrators of the queue to which this job is submitted to configured via mapred.queue.queue-name.acl-administer-jobs in mapred-queue-acls.xml can do all the view operations on a job. By default, nobody else besides job-owner, the user who started the cluster, cluster administrators and queue administrators can perform view operations on a job.
mapred.tasktracker.indexcache.mb10 The maximum memory that a task tracker allows for the index cache that is used when serving map outputs to reducers.
mapred.combine.recordsBeforeProgress10000 The number of records to process during combine output collection before sending a progress notification to the TaskTracker.
mapred.merge.recordsBeforeProgress10000 The number of records to process during merge before sending a progress notification to the TaskTracker.
mapred.reduce.slowstart.completed.maps0.05Fraction of the number of maps in the job which should be complete before reduces are scheduled for the job.
mapred.task.tracker.task-controllerorg.apache.hadoop.mapred.DefaultTaskControllerTaskController which is used to launch and manage task execution
mapreduce.tasktracker.groupExpert: 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.
mapred.disk.healthChecker.interval60000How often the TaskTracker checks the health of its local directories. Configuring this to a value smaller than the heartbeat interval is equivalent to setting this to heartbeat interval value.
mapred.healthChecker.script.pathAbsolute 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.
mapred.healthChecker.interval60000Frequency of the node health script to be run, in milliseconds
mapred.healthChecker.script.timeout600000Time after node health script should be killed if unresponsive and considered that the script has failed.
mapred.healthChecker.script.argsList of arguments which are to be passed to node health script when it is being launched comma seperated.
mapreduce.job.counters.max120Limit on the number of counters allowed per job.
mapreduce.job.counters.groups.max50Limit on the number of counter groups allowed per job.
mapreduce.job.counters.counter.name.max64Limit on the length of counter names in jobs. Names exceeding this limit will be truncated.
mapreduce.job.counters.group.name.max128Limit on the length of counter group names in jobs. Names exceeding this limit will be truncated.