mapreduce.jobtracker.jobhistory.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.
mapreduce.jobtracker.jobhistory.task.numberprogresssplits
12
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.
mapreduce.job.userhistorylocation
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".
mapreduce.jobtracker.jobhistory.completed.location
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.
mapreduce.job.committer.setup.cleanup.needed
true
true, if job needs job-setup and job-cleanup.
false, otherwise
mapreduce.task.io.sort.factor
10
The number of streams to merge at once while sorting
files. This determines the number of open file handles.
mapreduce.task.io.sort.mb
100
The total amount of buffer memory to use while sorting
files, in megabytes. By default, gives each merge stream 1MB, which
should minimize seeks.
mapreduce.map.sort.spill.percent
0.80
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
mapreduce.jobtracker.address
local
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.
mapreduce.local.clientfactory.class.name
org.apache.hadoop.mapred.LocalClientFactory
This the client factory that is responsible for
creating local job runner client
mapreduce.jobtracker.http.address
0.0.0.0:50030
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.
mapreduce.jobtracker.handler.count
10
The number of server threads for the JobTracker. This should be roughly
4% of the number of tasktracker nodes.
mapreduce.tasktracker.report.address
127.0.0.1:0
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.
mapreduce.cluster.local.dir
${hadoop.tmp.dir}/mapred/local
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.
mapreduce.jobtracker.system.dir
${hadoop.tmp.dir}/mapred/system
The directory where MapReduce stores control files.
mapreduce.jobtracker.staging.root.dir
${hadoop.tmp.dir}/mapred/staging
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)
mapreduce.cluster.temp.dir
${hadoop.tmp.dir}/mapred/temp
A shared directory for temporary files.
mapreduce.tasktracker.local.dir.minspacestart
0
If the space in mapreduce.cluster.local.dir drops under this,
do not ask for more tasks.
Value in bytes.
mapreduce.tasktracker.local.dir.minspacekill
0
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.
mapreduce.jobtracker.expire.trackers.interval
600000
Expert: The time-interval, in miliseconds, after which
a tasktracker is declared 'lost' if it doesn't send heartbeats.
mapreduce.tasktracker.instrumentation
org.apache.hadoop.mapred.TaskTrackerMetricsInst
Expert: The instrumentation class to associate with each TaskTracker.
mapreduce.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.
mapreduce.tasktracker.taskmemorymanager.monitoringinterval
5000
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.
mapreduce.tasktracker.tasks.sleeptimebeforesigkill
5000
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.
mapreduce.job.maps
2
The default number of map tasks per job.
Ignored when mapreduce.jobtracker.address is "local".
mapreduce.job.reduces
1
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".
mapreduce.jobtracker.restart.recover
false
"true" to enable (job) recovery upon restart,
"false" to start afresh
mapreduce.jobtracker.jobhistory.block.size
3145728
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.
mapreduce.jobtracker.taskscheduler
org.apache.hadoop.mapred.JobQueueTaskScheduler
The class responsible for scheduling the tasks.
mapreduce.job.running.map.limit
0
The maximum number of simultaneous map tasks per job.
There is no limit if this value is 0 or negative.
mapreduce.job.running.reduce.limit
0
The maximum number of simultaneous reduce tasks per job.
There is no limit if this value is 0 or negative.
mapreduce.job.reducer.preempt.delay.sec
0
The threshold in terms of seconds after which an unsatisfied mapper
request triggers reducer preemption to free space. Default 0 implies that the
reduces should be preempted immediately after allocation if there is currently no
room for newly allocated mappers.
mapreduce.job.max.split.locations
10
The max number of block locations to store for each split for
locality calculation.
mapreduce.job.split.metainfo.maxsize
10000000
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.
mapreduce.jobtracker.taskscheduler.maxrunningtasks.perjob
The maximum number of running tasks for a job before
it gets preempted. No limits if undefined.
mapreduce.map.maxattempts
4
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.
mapreduce.reduce.maxattempts
4
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.
mapreduce.reduce.shuffle.fetch.retry.enabled
${yarn.nodemanager.recovery.enabled}
Set to enable fetch retry during host restart.
mapreduce.reduce.shuffle.fetch.retry.interval-ms
1000
Time of interval that fetcher retry to fetch again when some
non-fatal failure happens because of some events like NM restart.
mapreduce.reduce.shuffle.fetch.retry.timeout-ms
30000
Timeout value for fetcher to retry to fetch again when some
non-fatal failure happens because of some events like NM restart.
mapreduce.reduce.shuffle.retry-delay.max.ms
60000
The maximum number of ms the reducer will delay before retrying
to download map data.
mapreduce.reduce.shuffle.parallelcopies
5
The default number of parallel transfers run by reduce
during the copy(shuffle) phase.
mapreduce.reduce.shuffle.connect.timeout
180000
Expert: The maximum amount of time (in milli seconds) reduce
task spends in trying to connect to a tasktracker for getting map output.
mapreduce.reduce.shuffle.read.timeout
180000
Expert: The maximum amount of time (in milli seconds) reduce
task waits for map output data to be available for reading after obtaining
connection.
mapreduce.shuffle.connection-keep-alive.enable
false
set to true to support keep-alive connections.
mapreduce.shuffle.connection-keep-alive.timeout
5
The number of seconds a shuffle client attempts to retain
http connection. Refer "Keep-Alive: timeout=" header in
Http specification
mapreduce.task.timeout
600000
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.
mapreduce.tasktracker.map.tasks.maximum
2
The maximum number of map tasks that will be run
simultaneously by a task tracker.
mapreduce.tasktracker.reduce.tasks.maximum
2
The maximum number of reduce tasks that will be run
simultaneously by a task tracker.
mapreduce.map.memory.mb
1024
The amount of memory to request from the scheduler for each
map task.
mapreduce.map.cpu.vcores
1
The number of virtual cores to request from the scheduler for
each map task.
mapreduce.reduce.memory.mb
1024
The amount of memory to request from the scheduler for each
reduce task.
mapreduce.reduce.cpu.vcores
1
The number of virtual cores to request from the scheduler for
each reduce task.
mapreduce.jobtracker.retiredjobs.cache.size
1000
The number of retired job status to keep in the cache.
mapreduce.tasktracker.outofband.heartbeat
false
Expert: Set this to true to let the tasktracker send an
out-of-band heartbeat on task-completion for better latency.
mapreduce.jobtracker.jobhistory.lru.cache.size
5
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.
mapreduce.jobtracker.instrumentation
org.apache.hadoop.mapred.JobTrackerMetricsInst
Expert: The instrumentation class to associate with each JobTracker.
mapred.child.java.opts
-Xmx200m
Java opts for the task 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/@[email protected]
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.
mapred.child.env
User added environment variables for the task 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.
mapreduce.admin.user.env
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.
mapreduce.map.log.level
INFO
The logging level for the map task. The allowed levels are:
OFF, FATAL, ERROR, WARN, INFO, DEBUG, TRACE and ALL.
The setting here could be overridden if "mapreduce.job.log4j-properties-file"
is set.
mapreduce.reduce.log.level
INFO
The logging level for the reduce task. The allowed levels are:
OFF, FATAL, ERROR, WARN, INFO, DEBUG, TRACE and ALL.
The setting here could be overridden if "mapreduce.job.log4j-properties-file"
is set.
mapreduce.map.cpu.vcores
1
The number of virtual cores required for each map task.
mapreduce.reduce.cpu.vcores
1
The number of virtual cores required for each reduce task.
mapreduce.reduce.merge.inmem.threshold
1000
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.
mapreduce.reduce.shuffle.merge.percent
0.66
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.
mapreduce.reduce.shuffle.input.buffer.percent
0.70
The percentage of memory to be allocated from the maximum heap
size to storing map outputs during the shuffle.
mapreduce.reduce.input.buffer.percent
0.0
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.
mapreduce.reduce.shuffle.memory.limit.percent
0.25
Expert: Maximum percentage of the in-memory limit that a
single shuffle can consume
mapreduce.shuffle.ssl.enabled
false
Whether to use SSL for for the Shuffle HTTP endpoints.
mapreduce.shuffle.ssl.file.buffer.size
65536
Buffer size for reading spills from file when using SSL.
mapreduce.shuffle.max.connections
0
Max allowed connections for the shuffle. Set to 0 (zero)
to indicate no limit on the number of connections.
mapreduce.shuffle.max.threads
0
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.
mapreduce.shuffle.transferTo.allowed
This option can enable/disable using nio transferTo method in
the shuffle phase. NIO transferTo does not perform well on windows in the
shuffle phase. Thus, with this configuration property it is possible to
disable it, in which case custom transfer method will be used. Recommended
value is false when running Hadoop on Windows. For Linux, it is recommended
to set it to true. If nothing is set then the default value is false for
Windows, and true for Linux.
mapreduce.shuffle.transfer.buffer.size
131072
This property is used only if
mapreduce.shuffle.transferTo.allowed is set to false. In that case,
this property defines the size of the buffer used in the buffer copy code
for the shuffle phase. The size of this buffer determines the size of the IO
requests.
mapreduce.reduce.markreset.buffer.percent
0.0
The percentage of memory -relative to the maximum heap size- to
be used for caching values when using the mark-reset functionality.
mapreduce.map.speculative
true
If true, then multiple instances of some map tasks
may be executed in parallel.
mapreduce.reduce.speculative
true
If true, then multiple instances of some reduce tasks
may be executed in parallel.
mapreduce.job.speculative.speculative-cap-running-tasks
0.1
The max percent (0-1) of running tasks that
can be speculatively re-executed at any time.
mapreduce.job.speculative.speculative-cap-total-tasks
0.01
The max percent (0-1) of all tasks that
can be speculatively re-executed at any time.
mapreduce.job.speculative.minimum-allowed-tasks
10
The minimum allowed tasks that
can be speculatively re-executed at any time.
mapreduce.job.speculative.retry-after-no-speculate
1000
The waiting time(ms) to do next round of speculation
if there is no task speculated in this round.
mapreduce.job.speculative.retry-after-speculate
15000
The waiting time(ms) to do next round of speculation
if there are tasks speculated in this round.
mapreduce.job.map.output.collector.class
org.apache.hadoop.mapred.MapTask$MapOutputBuffer
The MapOutputCollector implementation(s) to use. This may be a comma-separated
list of class names, in which case the map task will try to initialize each
of the collectors in turn. The first to successfully initialize will be used.
mapreduce.job.speculative.slowtaskthreshold
1.0
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.
mapreduce.job.jvm.numtasks
1
How many tasks to run per jvm. If set to -1, there is
no limit.
mapreduce.job.ubertask.enable
false
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. Note that configurations for application masters also affect
the "Small" definition - yarn.app.mapreduce.am.resource.mb must be
larger than both mapreduce.map.memory.mb and mapreduce.reduce.memory.mb,
and yarn.app.mapreduce.am.resource.cpu-vcores must be larger than
both mapreduce.map.cpu.vcores and mapreduce.reduce.cpu.vcores to enable
ubertask. Users may override this value.
mapreduce.job.ubertask.maxmaps
9
Threshold for number of maps, beyond which job is considered
too big for the ubertasking optimization. Users may override this value,
but only downward.
mapreduce.job.ubertask.maxreduces
1
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.
mapreduce.job.ubertask.maxbytes
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.
mapreduce.job.emit-timeline-data
false
Specifies if the Application Master should emit timeline data
to the timeline server. Individual jobs can override this value.
mapreduce.input.fileinputformat.split.minsize
0
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.
mapreduce.input.fileinputformat.list-status.num-threads
1
The number of threads to use to list and fetch block locations
for the specified input paths. Note: multiple threads should not be used
if a custom non thread-safe path filter is used.
mapreduce.jobtracker.maxtasks.perjob
-1
The maximum number of tasks for a single job.
A value of -1 indicates that there is no maximum.
mapreduce.input.lineinputformat.linespermap
1
When using NLineInputFormat, the number of lines of input data
to include in each split.
mapreduce.client.submit.file.replication
10
The replication level for submitted job files. This
should be around the square root of the number of nodes.
mapreduce.tasktracker.dns.interface
default
The name of the Network Interface from which a task
tracker should report its IP address.
mapreduce.tasktracker.dns.nameserver
default
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.
mapreduce.tasktracker.http.threads
40
The number of worker threads that for the http server. This is
used for map output fetching
mapreduce.tasktracker.http.address
0.0.0.0:50060
The task tracker http server address and port.
If the port is 0 then the server will start on a free port.
mapreduce.task.files.preserve.failedtasks
false
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.
mapreduce.output.fileoutputformat.compress
false
Should the job outputs be compressed?
mapreduce.output.fileoutputformat.compress.type
RECORD
If the job outputs are to compressed as SequenceFiles, how should
they be compressed? Should be one of NONE, RECORD or BLOCK.
mapreduce.output.fileoutputformat.compress.codec
org.apache.hadoop.io.compress.DefaultCodec
If the job outputs are compressed, how should they be compressed?
mapreduce.map.output.compress
false
Should the outputs of the maps be compressed before being
sent across the network. Uses SequenceFile compression.
mapreduce.map.output.compress.codec
org.apache.hadoop.io.compress.DefaultCodec
If the map outputs are compressed, how should they be
compressed?
map.sort.class
org.apache.hadoop.util.QuickSort
The default sort class for sorting keys.
mapreduce.task.userlog.limit.kb
0
The maximum size of user-logs of each task in KB. 0 disables the cap.
yarn.app.mapreduce.am.container.log.limit.kb
0
The maximum size of the MRAppMaster attempt container logs in KB.
0 disables the cap.
yarn.app.mapreduce.task.container.log.backups
0
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.
yarn.app.mapreduce.am.container.log.backups
0
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.
yarn.app.mapreduce.shuffle.log.separate
true
If enabled ('true') logging generated by the client-side shuffle
classes in a reducer will be written in a dedicated log file
'syslog.shuffle' instead of 'syslog'.
yarn.app.mapreduce.shuffle.log.limit.kb
0
Maximum size of the syslog.shuffle file in kilobytes
(0 for no limit).
yarn.app.mapreduce.shuffle.log.backups
0
If yarn.app.mapreduce.shuffle.log.limit.kb and
yarn.app.mapreduce.shuffle.log.backups are greater than zero
then a ContainerRollngLogAppender is used instead of ContainerLogAppender
for syslog.shuffle. See
org.apache.log4j.RollingFileAppender.maxBackupIndex
mapreduce.job.userlog.retain.hours
24
The maximum time, in hours, for which the user-logs are to be
retained after the job completion.
mapreduce.jobtracker.hosts.filename
Names a file that contains the list of nodes that may
connect to the jobtracker. If the value is empty, all hosts are
permitted.
mapreduce.jobtracker.hosts.exclude.filename
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.
mapreduce.jobtracker.heartbeats.in.second
100
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.
mapreduce.jobtracker.tasktracker.maxblacklists
4
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.
mapreduce.job.maxtaskfailures.per.tracker
3
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.
mapreduce.client.output.filter
FAILED
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.
mapreduce.client.completion.pollinterval
5000
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.
mapreduce.client.progressmonitor.pollinterval
1000
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.
mapreduce.jobtracker.persist.jobstatus.active
true
Indicates if persistency of job status information is
active or not.
mapreduce.jobtracker.persist.jobstatus.hours
1
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.
mapreduce.jobtracker.persist.jobstatus.dir
/jobtracker/jobsInfo
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.
mapreduce.task.profile
false
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.
mapreduce.task.profile.maps
0-2
To set the ranges of map tasks to profile.
mapreduce.task.profile has to be set to true for the value to be accounted.
mapreduce.task.profile.reduces
0-2
To set the ranges of reduce tasks to profile.
mapreduce.task.profile has to be set to true for the value to be accounted.
mapreduce.task.profile.params
-agentlib:hprof=cpu=samples,heap=sites,force=n,thread=y,verbose=n,file=%s
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.
mapreduce.task.profile.map.params
${mapreduce.task.profile.params}
Map-task-specific JVM profiler parameters. See
mapreduce.task.profile.params
mapreduce.task.profile.reduce.params
${mapreduce.task.profile.params}
Reduce-task-specific JVM profiler parameters. See
mapreduce.task.profile.params
mapreduce.task.skip.start.attempts
2
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.
mapreduce.map.skip.proc.count.autoincr
true
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.
mapreduce.reduce.skip.proc.count.autoincr
true
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.
mapreduce.job.skip.outdir
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".
mapreduce.map.skip.maxrecords
0
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.
mapreduce.reduce.skip.maxgroups
0
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.readahead
true
Configuration key to enable/disable IFile readahead.
mapreduce.ifile.readahead.bytes
4194304
Configuration key to set the IFile readahead length in bytes.
mapreduce.jobtracker.taskcache.levels
2
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.
mapreduce.job.queuename
default
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.
mapreduce.job.tags
Tags for the job that will be passed to YARN at submission
time. Queries to YARN for applications can filter on these tags.
mapreduce.cluster.acls.enabled
false
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.
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 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.
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 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.
mapreduce.tasktracker.indexcache.mb
10
The maximum memory that a task tracker allows for the
index cache that is used when serving map outputs to reducers.
mapreduce.job.token.tracking.ids.enabled
false
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
mapreduce.job.token.tracking.ids
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.
mapreduce.task.merge.progress.records
10000
The number of records to process during merge before
sending a progress notification to the TaskTracker.
mapreduce.task.combine.progress.records
10000
The number of records to process during combine output collection
before sending a progress notification.
mapreduce.job.reduce.slowstart.completedmaps
0.05
Fraction of the number of maps in the job which should be
complete before reduces are scheduled for the job.
mapreduce.job.complete.cancel.delegation.tokens
true
if false - do not unregister/cancel delegation tokens from
renewal, because same tokens may be used by spawned jobs
mapreduce.tasktracker.taskcontroller
org.apache.hadoop.mapred.DefaultTaskController
TaskController which is used to launch and manage task execution
mapreduce.tasktracker.group
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.
mapreduce.shuffle.port
13562
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.
mapreduce.job.reduce.shuffle.consumer.plugin.class
org.apache.hadoop.mapreduce.task.reduce.Shuffle
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.
mapreduce.tasktracker.healthchecker.script.path
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.
mapreduce.tasktracker.healthchecker.interval
60000
Frequency of the node health script to be run,
in milliseconds
mapreduce.tasktracker.healthchecker.script.timeout
600000
Time after node health script should be killed if
unresponsive and considered that the script has failed.
mapreduce.tasktracker.healthchecker.script.args
List of arguments which are to be passed to
node health script when it is being launched comma seperated.
mapreduce.job.counters.limit
120
Limit on the number of user counters allowed per job.
mapreduce.framework.name
local
The runtime framework for executing MapReduce jobs.
Can be one of local, classic or yarn.
yarn.app.mapreduce.am.staging-dir
/tmp/hadoop-yarn/staging
The staging dir used while submitting jobs.
mapreduce.am.max-attempts
2
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.
mapreduce.job.end-notification.url
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 $jobStatus.
If they are present in URI, then they will be replaced by their
respective values.
mapreduce.job.end-notification.retry.attempts
0
The number of times the submitter of the job wants to retry job
end notification if it fails. This is capped by
mapreduce.job.end-notification.max.attempts
mapreduce.job.end-notification.retry.interval
1000
The number of milliseconds the submitter of the job wants to
wait before job end notification is retried if it fails. This is capped by
mapreduce.job.end-notification.max.retry.interval
mapreduce.job.end-notification.max.attempts
5
true
The maximum number of times a URL will be read for providing job
end notification. Cluster administrators can set this to limit how long
after end of a job, the Application Master waits before exiting. Must be
marked as final to prevent users from overriding this.
mapreduce.job.log4j-properties-file
Used to override the default settings of log4j in container-log4j.properties
for NodeManager. Like container-log4j.properties, it requires certain
framework appenders properly defined in this overriden file. The file on the
path will be added to distributed cache and classpath. If no-scheme is given
in the path, it defaults to point to a log4j file on the local FS.
mapreduce.job.end-notification.max.retry.interval
5000
true
The maximum amount of time (in milliseconds) to wait before
retrying job end notification. Cluster administrators can set this to
limit how long the Application Master waits before exiting. Must be marked
as final to prevent users from overriding this.
yarn.app.mapreduce.am.env
User added environment variables for the MR App Master
processes. Example :
1) A=foo This will set the env variable A to foo
2) B=$B:c This is inherit tasktracker's B env variable.
yarn.app.mapreduce.am.admin.user.env
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.
yarn.app.mapreduce.am.command-opts
-Xmx1024m
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/@[email protected]
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.
yarn.app.mapreduce.am.admin-command-opts
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.
yarn.app.mapreduce.am.job.task.listener.thread-count
30
The number of threads used to handle RPC calls in the
MR AppMaster from remote tasks
yarn.app.mapreduce.am.job.client.port-range
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
yarn.app.mapreduce.am.job.committer.cancel-timeout
60000
The amount of time in milliseconds to wait for the output
committer to cancel an operation if the job is killed
yarn.app.mapreduce.am.job.committer.commit-window
10000
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.
mapreduce.fileoutputcommitter.algorithm.version
1
The file output committer algorithm version
valid algorithm version number: 1 or 2
default to 1, which is the original algorithm
In algorithm version 1,
1. commitTask will rename directory
$joboutput/_temporary/$appAttemptID/_temporary/$taskAttemptID/
to
$joboutput/_temporary/$appAttemptID/$taskID/
2. recoverTask will also do a rename
$joboutput/_temporary/$appAttemptID/$taskID/
to
$joboutput/_temporary/($appAttemptID + 1)/$taskID/
3. commitJob will merge every task output file in
$joboutput/_temporary/$appAttemptID/$taskID/
to
$joboutput/, then it will delete $joboutput/_temporary/
and write $joboutput/_SUCCESS
It has a performance regression, which is discussed in MAPREDUCE-4815.
If a job generates many files to commit then the commitJob
method call at the end of the job can take minutes.
the commit is single-threaded and waits until all
tasks have completed before commencing.
algorithm version 2 will change the behavior of commitTask,
recoverTask, and commitJob.
1. commitTask will rename all files in
$joboutput/_temporary/$appAttemptID/_temporary/$taskAttemptID/
to $joboutput/
2. recoverTask actually doesn't require to do anything, but for
upgrade from version 1 to version 2 case, it will check if there
are any files in
$joboutput/_temporary/($appAttemptID - 1)/$taskID/
and rename them to $joboutput/
3. commitJob can simply delete $joboutput/_temporary and write
$joboutput/_SUCCESS
This algorithm will reduce the output commit time for
large jobs by having the tasks commit directly to the final
output directory as they were completing and commitJob had
very little to do.
yarn.app.mapreduce.am.scheduler.heartbeat.interval-ms
1000
The interval in ms at which the MR AppMaster should send
heartbeats to the ResourceManager
yarn.app.mapreduce.client-am.ipc.max-retries
3
The number of client retries to the AM - before reconnecting
to the RM to fetch Application Status.
yarn.app.mapreduce.client-am.ipc.max-retries-on-timeouts
3
The number of client retries on socket timeouts to the AM - before
reconnecting to the RM to fetch Application Status.
yarn.app.mapreduce.client.max-retries
3
The number of client retries to the RM/HS before
throwing exception. This is a layer above the ipc.
yarn.app.mapreduce.am.resource.mb
1536
The amount of memory the MR AppMaster needs.
yarn.app.mapreduce.am.resource.cpu-vcores
1
The number of virtual CPU cores the MR AppMaster needs.
yarn.app.mapreduce.am.hard-kill-timeout-ms
10000
Number of milliseconds to wait before the job client kills the application.
yarn.app.mapreduce.client.job.max-retries
0
The number of retries the client will make for getJob and
dependent calls. The default is 0 as this is generally only needed for
non-HDFS DFS where additional, high level retries are required to avoid
spurious failures during the getJob call. 30 is a good value for
WASB
yarn.app.mapreduce.client.job.retry-interval
2000
The delay between getJob retries in ms for retries configured
with yarn.app.mapreduce.client.job.max-retries.
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.
If mapreduce.app-submission.cross-platform is false, platform-specific
environment vairable expansion syntax would be used to construct the default
CLASSPATH entries.
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/*.
If mapreduce.app-submission.cross-platform is true, platform-agnostic default
CLASSPATH for MR applications would be used:
{{HADOOP_MAPRED_HOME}}/share/hadoop/mapreduce/*,
{{HADOOP_MAPRED_HOME}}/share/hadoop/mapreduce/lib/*
Parameter expansion marker will be replaced by NodeManager on container
launch based on the underlying OS accordingly.
mapreduce.application.classpath
If enabled, user can submit an application cross-platform
i.e. submit an application from a Windows client to a Linux/Unix server or
vice versa.
mapreduce.app-submission.cross-platform
false
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.
mapreduce.application.framework.path
mapreduce.job.classloader
false
Whether to use a separate (isolated) classloader for
user classes in the task JVM.
mapreduce.job.classloader.system.classes
Used to override the default definition of the system classes for
the job classloader. The system classes are a comma-separated list of
patterns that indicate whether to load a class from the system classpath,
instead from the user-supplied JARs, when mapreduce.job.classloader is
enabled.
A positive pattern is defined as:
1. A single class name 'C' that matches 'C' and transitively all nested
classes 'C$*' defined in C;
2. A package name ending with a '.' (e.g., "com.example.") that matches
all classes from that package.
A negative pattern is defined by a '-' in front of a positive pattern
(e.g., "-com.example.").
A class is considered a system class if and only if it matches one of the
positive patterns and none of the negative ones. More formally:
A class is a member of the inclusion set I if it matches one of the positive
patterns. A class is a member of the exclusion set E if it matches one of
the negative patterns. The set of system classes S = I \ E.
mapreduce.jobhistory.address
0.0.0.0:10020
MapReduce JobHistory Server IPC host:port
mapreduce.jobhistory.webapp.address
0.0.0.0:19888
MapReduce JobHistory Server Web UI host:port
mapreduce.jobhistory.keytab
Location of the kerberos keytab file for the MapReduce
JobHistory Server.
/etc/security/keytab/jhs.service.keytab
mapreduce.jobhistory.principal
Kerberos principal name for the MapReduce JobHistory Server.
jhs/[email protected]
mapreduce.jobhistory.intermediate-done-dir
${yarn.app.mapreduce.am.staging-dir}/history/done_intermediate
mapreduce.jobhistory.done-dir
${yarn.app.mapreduce.am.staging-dir}/history/done
mapreduce.jobhistory.cleaner.enable
true
mapreduce.jobhistory.cleaner.interval-ms
86400000
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.
mapreduce.jobhistory.max-age-ms
604800000
Job history files older than this many milliseconds will
be deleted when the history cleaner runs. Defaults to 604800000 (1 week).
mapreduce.jobhistory.client.thread-count
10
The number of threads to handle client API requests
mapreduce.jobhistory.datestring.cache.size
200000
Size of the date string cache. Effects the number of directories
which will be scanned to find a job.
mapreduce.jobhistory.joblist.cache.size
20000
Size of the job list cache
mapreduce.jobhistory.loadedjobs.cache.size
5
Size of the loaded job cache
mapreduce.jobhistory.move.interval-ms
180000
Scan for history files to more from intermediate done dir to done
dir at this frequency.
mapreduce.jobhistory.move.thread-count
3
The number of threads used to move files.
mapreduce.jobhistory.store.class
The HistoryStorage class to use to cache history data.
mapreduce.jobhistory.minicluster.fixed.ports
false
Whether to use fixed ports with the minicluster
mapreduce.jobhistory.admin.address
0.0.0.0:10033
The address of the History server admin interface.
mapreduce.jobhistory.admin.acl
*
ACL of who can be admin of the History server.
mapreduce.jobhistory.recovery.enable
false
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.
mapreduce.jobhistory.recovery.store.class
org.apache.hadoop.mapreduce.v2.hs.HistoryServerFileSystemStateStoreService
The HistoryServerStateStoreService class to store history server
state for recovery.
mapreduce.jobhistory.recovery.store.fs.uri
${hadoop.tmp.dir}/mapred/history/recoverystore
The URI where history server state will be stored if
HistoryServerFileSystemStateStoreService is configured as the recovery
storage class.
mapreduce.jobhistory.recovery.store.leveldb.path
${hadoop.tmp.dir}/mapred/history/recoverystore
The URI where history server state will be stored if
HistoryServerLeveldbSystemStateStoreService is configured as the recovery
storage class.
mapreduce.jobhistory.http.policy
HTTP_ONLY
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
yarn.app.mapreduce.am.containerlauncher.threadpool-initial-size
10
The initial size of thread pool to launch containers in the
app master.
