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<!DOCTYPE document PUBLIC "-//APACHE//DTD Documentation V2.0//EN" "http://forrest.apache.org/dtd/document-v20.dtd">
<document>
<header>
<title>Fair Scheduler</title>
</header>
<body>
<section>
<title>Purpose</title>
<p>This document describes the Fair Scheduler, a pluggable
MapReduce scheduler for Hadoop which provides a way to share
large clusters.</p>
</section>
<section>
<title>Introduction</title>
<p>Fair scheduling is a method of assigning resources to jobs
such that all jobs get, on average, an equal share of resources
over time. When there is a single job running, that job uses the
entire cluster. When other jobs are submitted, tasks slots that
free up are assigned to the new jobs, so that each job gets
roughly the same amount of CPU time. Unlike the default Hadoop
scheduler, which forms a queue of jobs, this lets short jobs finish
in reasonable time while not starving long jobs. It is also an easy
way to share a cluster between multiple of users.
Fair sharing can also work with job priorities - the priorities are
used as weights to determine the fraction of total compute time that
each job gets.
</p>
<p>
The fair scheduler organizes jobs into <em>pools</em>, and
divides resources fairly between these pools. By default, there is a
separate pool for each user, so that each user gets an equal share
of the cluster. It is also possible to set a job's pool based on the
user's Unix group or any jobconf property.
Within each pool, jobs can be scheduled using either fair sharing or
first-in-first-out (FIFO) scheduling.
</p>
<p>
In addition to providing fair sharing, the Fair Scheduler allows
assigning guaranteed <em>minimum shares</em> to pools, which is useful
for ensuring that certain users, groups or production applications
always get sufficient resources. When a pool contains jobs, it gets
at least its minimum share, but when the pool does not need its full
guaranteed share, the excess is split between other pools.
</p>
<p>
If a pool's minimum share is not met for some period of time, the
scheduler optionally supports <em>preemption</em> of jobs in other
pools. The pool will be allowed to kill tasks from other pools to make
room to run. Preemption can be used to guarantee
that "production" jobs are not starved while also allowing
the Hadoop cluster to also be used for experimental and research jobs.
In addition, a pool can also be allowed to preempt tasks if it is
below half of its fair share for a configurable timeout (generally
set larger than the minimum share preemption timeout).
When choosing tasks to kill, the fair scheduler picks the
most-recently-launched tasks from over-allocated jobs,
to minimize wasted computation.
Preemption does not cause the preempted jobs to fail, because Hadoop
jobs tolerate losing tasks; it only makes them take longer to finish.
</p>
<p>
Finally, the Fair Scheduler can limit the number of concurrent
running jobs per user and per pool. This can be useful when a
user must submit hundreds of jobs at once, or for ensuring that
intermediate data does not fill up disk space on a cluster when too many
concurrent jobs are running.
Setting job limits causes jobs submitted beyond the limit to wait
until some of the user/pool's earlier jobs finish.
Jobs to run from each user/pool are chosen in order of priority and then
submit time.
</p>
</section>
<section>
<title>Installation</title>
<p>
To run the fair scheduler in your Hadoop installation, you need to put
it on the CLASSPATH. The easiest way is to copy the
<em>hadoop-*-fairscheduler.jar</em> from
<em>HADOOP_HOME/build/contrib/fairscheduler</em> to <em>HADOOP_HOME/lib</em>.
Alternatively you can modify <em>HADOOP_CLASSPATH</em> to include this jar, in
<em>HADOOP_CONF_DIR/hadoop-env.sh</em>
</p>
<p>
You will also need to set the following property in the Hadoop config
file <em>HADOOP_CONF_DIR/mapred-site.xml</em> to have Hadoop use
the fair scheduler:
</p>
<source>
&lt;property&gt;
&lt;name&gt;mapreduce.jobtracker.taskscheduler&lt;/name&gt;
&lt;value&gt;org.apache.hadoop.mapred.FairScheduler&lt;/value&gt;
&lt;/property&gt;
</source>
<p>
Once you restart the cluster, you can check that the fair scheduler
is running by going to <em>http://&lt;jobtracker URL&gt;/scheduler</em>
on the JobTracker's web UI. A &quot;job scheduler administration&quot; page should
be visible there. This page is described in the Administration section.
</p>
<p>
If you wish to compile the fair scheduler from source, run <em> ant
package</em> in your HADOOP_HOME directory. This will build
<em>build/contrib/fair-scheduler/hadoop-*-fairscheduler.jar</em>.
</p>
</section>
<section>
<title>Configuration</title>
<p>
The Fair Scheduler contains configuration in two places -- algorithm
parameters are set in <em>HADOOP_CONF_DIR/mapred-site.xml</em>, while
a separate XML file called the <em>allocation file</em>,
located by default in
<em>HADOOP_CONF_DIR/fair-scheduler.xml</em>, is used to configure
pools, minimum shares, running job limits and preemption timeouts.
The allocation file is reloaded periodically at runtime,
allowing you to change pool settings without restarting
your Hadoop cluster.
</p>
<p>
For a minimal installation, to just get equal sharing between users,
you will not need to edit the allocation file.
</p>
<section>
<title>Scheduler Parameters in mapred-site.xml</title>
<p>
The following parameters can be set in <em>mapred-site.xml</em>
to affect the behavior of the fair scheduler:
</p>
<p><strong>Basic Parameters</strong></p>
<table>
<tr>
<th>Name</th><th>Description</th>
</tr>
<tr>
<td>
mapred.fairscheduler.preemption
</td>
<td>
Boolean property for enabling preemption. Default: false.
</td>
</tr>
<tr>
<td>
mapred.fairscheduler.poolnameproperty
</td>
<td>
Specify which jobconf property is used to determine the pool that a
job belongs in. String, default: <em>mapreduce.job.user.name</em>
(i.e. one pool for each user).
Another useful value is <em>group.name</em> to create a
pool per Unix group.
Finally, a common setting is to use a non-standard property
such as <em>pool.name</em> as the pool name property, and make it
default to <em>mapreduce.job.mapreduce.job.user.name</em> through the following setting:<br/>
<code>&lt;property&gt;</code><br/>
<code>&nbsp;&nbsp;&lt;name&gt;pool.name&lt;/name&gt;</code><br/>
<code>&nbsp;&nbsp;&lt;value&gt;${mapreduce.job.mapreduce.job.user.name}&lt;/value&gt;</code><br/>
<code>&lt;/property&gt;</code><br/>
This allows you to specify the pool name explicitly for some jobs
through the jobconf (e.g. passing <em>-Dpool.name=&lt;name&gt;</em>
to <em>bin/hadoop jar</em>, while having the default be the user's
pool.
</td>
</tr>
<tr>
<td>
mapred.fairscheduler.allocation.file
</td>
<td>
Can be used to have the scheduler use a different allocation file
than the default one (<em>HADOOP_CONF_DIR/fair-scheduler.xml</em>).
Must be an absolute path to the allocation file.
</td>
</tr>
</table>
<p> <br></br></p>
<p><strong>Advanced Parameters</strong> </p>
<table>
<tr>
<th>Name</th><th>Description</th>
</tr>
<tr>
<td>
mapred.fairscheduler.sizebasedweight
</td>
<td>
Take into account job sizes in calculating their weights for fair
sharing. By default, weights are only based on job priorities.
Setting this flag to true will make them based on the size of the
job (number of tasks needed) as well,though not linearly
(the weight will be proportional to the log of the number of tasks
needed). This lets larger jobs get larger fair shares while still
providing enough of a share to small jobs to let them finish fast.
Boolean value, default: false.
</td>
</tr>
<tr>
<td>
mapred.fairscheduler.preemption.only.log
</td>
<td>
This flag will cause the scheduler to run through the preemption
calculations but simply log when it wishes to preempt a task,
without actually preempting the task.
Boolean property, default: false.
This property can be useful for
doing a "dry run" of preemption before enabling it to make sure
that you have not set timeouts too aggressively.
You will see preemption log messages in your JobTracker's output
log (<em>HADOOP_LOG_DIR/hadoop-jobtracker-*.log</em>).
The messages look as follows:<br/>
<code>Should preempt 2 tasks for job_20090101337_0001: tasksDueToMinShare = 2, tasksDueToFairShare = 0</code>
</td>
</tr>
<tr>
<td>
mapred.fairscheduler.update.interval
</td>
<td>
Interval at which to update fair share calculations. The default
of 500ms works well for clusters with fewer than 500 nodes,
but larger values reduce load on the JobTracker for larger clusters.
Integer value in milliseconds, default: 500.
</td>
</tr>
<tr>
<td>
mapred.fairscheduler.preemption.interval
</td>
<td>
Interval at which to check for tasks to preempt. The default
of 15s works well for timeouts on the order of minutes.
It is not recommended to set timeouts much smaller than this
amount, but you can use this value to make preemption computations
run more often if you do set such timeouts. A value of less than
5s will probably be too small, however, as it becomes less than
the inter-heartbeat interval.
Integer value in milliseconds, default: 15000.
</td>
</tr>
<tr>
<td>
mapred.fairscheduler.weightadjuster
</td>
<td>
An extension point that lets you specify a class to adjust the
weights of running jobs. This class should implement the
<em>WeightAdjuster</em> interface. There is currently one example
implementation - <em>NewJobWeightBooster</em>, which increases the
weight of jobs for the first 5 minutes of their lifetime to let
short jobs finish faster. To use it, set the weightadjuster
property to the full class name,
<code>org.apache.hadoop.mapred.NewJobWeightBooster</code>.
NewJobWeightBooster itself provides two parameters for setting the
duration and boost factor.
<ul>
<li><em>mapred.newjobweightbooster.factor</em>
Factor by which new jobs weight should be boosted.
Default is 3.</li>
<li><em>mapred.newjobweightbooster.duration</em>
Boost duration in milliseconds. Default is 300000 for 5 minutes.</li>
</ul>
</td>
</tr>
<tr>
<td>
mapred.fairscheduler.loadmanager
</td>
<td>
An extension point that lets you specify a class that determines
how many maps and reduces can run on a given TaskTracker. This class
should implement the LoadManager interface. By default the task caps
in the Hadoop config file are used, but this option could be used to
make the load based on available memory and CPU utilization for example.
</td>
</tr>
<tr>
<td>
mapred.fairscheduler.taskselector
</td>
<td>
An extension point that lets you specify a class that determines
which task from within a job to launch on a given tracker. This can be
used to change either the locality policy (e.g. keep some jobs within
a particular rack) or the speculative execution algorithm (select
when to launch speculative tasks). The default implementation uses
Hadoop's default algorithms from JobInProgress.
</td>
</tr>
<!--
<tr>
<td>
mapred.fairscheduler.eventlog.enabled
</td>
<td>
Enable a detailed log of fair scheduler events, useful for
debugging.
This log is stored in <em>HADOOP_LOG_DIR/fairscheduler</em>.
Boolean value, default: false.
</td>
</tr>
<tr>
<td>
mapred.fairscheduler.dump.interval
</td>
<td>
If using the event log, this is the interval at which to dump
complete scheduler state (list of pools and jobs) to the log.
Integer value in milliseconds, default: 10000.
</td>
</tr>
-->
</table>
</section>
<section>
<title>Allocation File (fair-scheduler.xml)</title>
<p>
The allocation file configures minimum shares, running job
limits, weights and preemption timeouts for each pool.
Only users/pools whose values differ from the defaults need to be
explicitly configured in this file.
The allocation file is located in
<em>HADOOP_HOME/conf/fair-scheduler.xml</em>.
It can contain the following types of elements:
</p>
<ul>
<li><em>pool</em> elements, which configure each pool.
These may contain the following sub-elements:
<ul>
<li><em>minMaps</em> and <em>minReduces</em>,
to set the pool's minimum share of task slots.</li>
<li><em>schedulingMode</em>, the pool's internal scheduling mode,
which can be <em>fair</em> for fair sharing or <em>fifo</em> for
first-in-first-out.</li>
<li><em>maxRunningJobs</em>,
to limit the number of jobs from the
pool to run at once (defaults to infinite).</li>
<li><em>weight</em>, to share the cluster
non-proportionally with other pools. For example, a pool with weight 2.0 will get a 2x higher share than other pools. The default weight is 1.0.</li>
<li><em>minSharePreemptionTimeout</em>, the
number of seconds the pool will wait before
killing other pools' tasks if it is below its minimum share
(defaults to infinite).</li>
</ul>
</li>
<li><em>user</em> elements, which may contain a
<em>maxRunningJobs</em> element to limit
jobs. Note that by default, there is a pool for each
user, so per-user limits are not necessary.</li>
<li><em>poolMaxJobsDefault</em>, which sets the default running
job limit for any pools whose limit is not specified.</li>
<li><em>userMaxJobsDefault</em>, which sets the default running
job limit for any users whose limit is not specified.</li>
<li><em>defaultMinSharePreemptionTimeout</em>,
which sets the default minimum share preemption timeout
for any pools where it is not specified.</li>
<li><em>fairSharePreemptionTimeout</em>,
which sets the preemption timeout used when jobs are below half
their fair share.</li>
<li><em>defaultPoolSchedulingMode</em>, which sets the default scheduling
mode (<em>fair</em> or <em>fifo</em>) for pools whose mode is
not specified.</li>
</ul>
<p>
Pool and user elements only required if you are setting
non-default values for the pool/user. That is, you do not need to
declare all users and all pools in your config file before running
the fair scheduler. If a user or pool is not listed in the config file,
the default values for limits, preemption timeouts, etc will be used.
</p>
<p>
An example allocation file is given below : </p>
<source>
&lt;?xml version="1.0"?&gt;
&lt;allocations&gt;
&lt;pool name="sample_pool"&gt;
&lt;minMaps&gt;5&lt;/minMaps&gt;
&lt;minReduces&gt;5&lt;/minReduces&gt;
&lt;minSharePreemptionTimeout&gt;300&lt;/minSharePreemptionTimeout&gt;
&lt;/pool&gt;
&lt;mapreduce.job.user.name="sample_user"&gt;
&lt;maxRunningJobs&gt;6&lt;/maxRunningJobs&gt;
&lt;/user&gt;
&lt;userMaxJobsDefault&gt;3&lt;/userMaxJobsDefault&gt;
&lt;fairSharePreemptionTimeout&gt;600&lt;/fairSharePreemptionTimeout&gt;
&lt;/allocations&gt;
</source>
<p>
This example creates a pool sample_pool with a guarantee of 5 map
slots and 5 reduce slots. The pool also has a minimum share preemption
timeout of 300 seconds (5 minutes), meaning that if it does not get its
guaranteed share within this time, it is allowed to kill tasks from
other pools to achieve its share.
The example also limits the number of running jobs
per user to 3, except for sample_user, who can run 6 jobs concurrently.
Finally, the example sets a fair share preemption timeout of 600 seconds
(10 minutes). If a job is below half its fair share for 10 minutes, it
will be allowed to kill tasks from other jobs to achieve its share.
Note that the preemption settings require preemption to be
enabled in <em>mapred-site.xml</em> as described earlier.
</p>
<p>
Any pool not defined in the allocation file will have no guaranteed
capacity and no preemption timeout. Also, any pool or user with no max
running jobs set in the file will be allowed to run an unlimited
number of jobs.
</p>
</section>
</section>
<section>
<title> Administration</title>
<p>
The fair scheduler provides support for administration at runtime
through two mechanisms:
</p>
<ol>
<li>
It is possible to modify minimum shares, limits, weights, preemption
timeouts and pool scheduling modes at runtime by editing the allocation
file. The scheduler will reload this file 10-15 seconds after it
sees that it was modified.
</li>
<li>
Current jobs, pools, and fair shares can be examined through the
JobTracker's web interface, at
<em>http://&lt;JobTracker URL&gt;/scheduler</em>.
On this interface, it is also possible to modify jobs' priorities or
move jobs from one pool to another and see the effects on the fair
shares (this requires JavaScript).
</li>
</ol>
<p>
The following fields can be seen for each job on the web interface:
</p>
<ul>
<li><em>Submitted</em> - Date and time job was submitted.</li>
<li><em>JobID, User, Name</em> - Job identifiers as on the standard
web UI.</li>
<li><em>Pool</em> - Current pool of job. Select another value to move job to
another pool.</li>
<li><em>Priority</em> - Current priority. Select another value to change the
job's priority</li>
<li><em>Maps/Reduces Finished</em>: Number of tasks finished / total tasks.</li>
<li><em>Maps/Reduces Running</em>: Tasks currently running.</li>
<li><em>Map/Reduce Fair Share</em>: The average number of task slots that this
job should have at any given time according to fair sharing. The actual
number of tasks will go up and down depending on how much compute time
the job has had, but on average it will get its fair share amount.</li>
</ul>
<p>
In addition, it is possible to view an "advanced" version of the web
UI by going to <em>http://&lt;JobTracker URL&gt;/scheduler?advanced</em>.
This view shows two more columns:
</p>
<ul>
<li><em>Maps/Reduce Weight</em>: Weight of the job in the fair sharing
calculations. This depends on priority and potentially also on
job size and job age if the <em>sizebasedweight</em> and
<em>NewJobWeightBooster</em> are enabled.</li>
</ul>
</section>
<!--
<section>
<title>Implementation</title>
<p>There are two aspects to implementing fair scheduling: Calculating
each job's fair share, and choosing which job to run when a task slot
becomes available.</p>
<p>To select jobs to run, the scheduler then keeps track of a
&quot;deficit&quot; for each job - the difference between the amount of
compute time it should have gotten on an ideal scheduler, and the amount
of compute time it actually got. This is a measure of how
&quot;unfair&quot; we've been to the job. Every few hundred
milliseconds, the scheduler updates the deficit of each job by looking
at how many tasks each job had running during this interval vs. its
fair share. Whenever a task slot becomes available, it is assigned to
the job with the highest deficit. There is one exception - if there
were one or more jobs who were not meeting their pool capacity
guarantees, we only choose among these &quot;needy&quot; jobs (based
again on their deficit), to ensure that the scheduler meets pool
guarantees as soon as possible.</p>
<p>
The fair shares are calculated by dividing the capacity of the cluster
among runnable jobs according to a &quot;weight&quot; for each job. By
default the weight is based on priority, with each level of priority
having 2x higher weight than the next (for example, VERY_HIGH has 4x the
weight of NORMAL). However, weights can also be based on job sizes and ages,
as described in the Configuring section. For jobs that are in a pool,
fair shares also take into account the minimum guarantee for that pool.
This capacity is divided among the jobs in that pool according again to
their weights.
</p>
<p>When limits on a user's running jobs or a pool's running jobs
are in place, we choose which jobs get to run by sorting all jobs in order
of priority and then submit time, as in the standard Hadoop scheduler. Any
jobs that fall after the user/pool's limit in this ordering are queued up
and wait idle until they can be run. During this time, they are ignored
from the fair sharing calculations and do not gain or lose deficit (their
fair share is set to zero).</p>
<p>
Preemption is implemented by periodically checking whether jobs are
below their minimum share or below half their fair share. If a job has
been below its share for sufficiently long, it is allowed to kill
other jobs' tasks. The tasks chosen are the most-recently-launched
tasks from over-allocated jobs, to minimize the amount of wasted
computation.
</p>
<p>
Finally, the fair scheduler provides several extension points where
the basic functionality can be extended. For example, the weight
calculation can be modified to give a priority boost to new jobs,
implementing a "shortest job first" policy which reduces response
times for interactive jobs even further.
These extension points are listed in
<a href="#Scheduler+Parameters+in+mapred-site.xml">Advanced Parameters</a>.
</p>
</section>
-->
</body>
</document>