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<!DOCTYPE concept PUBLIC "-//OASIS//DTD DITA Concept//EN" "concept.dtd">
<concept rev="1.3.0" id="admission_control">

  <title>Admission Control and Query Queuing</title>
  <prolog>
    <metadata>
      <data name="Category" value="Impala"/>
      <data name="Category" value="Querying"/>
      <data name="Category" value="Admission Control"/>
      <data name="Category" value="Resource Management"/>
    </metadata>
  </prolog>
  <conbody>
    <p id="admission_control_intro"> Admission control is an Impala feature that
      imposes limits on concurrent SQL queries, to avoid resource usage spikes
      and out-of-memory conditions on busy clusters. The admission control
      feature lets you set an upper limit on the number of concurrent Impala
      queries and on the memory used by those queries. Any additional queries
      are queued until the earlier ones finish, rather than being cancelled or
      running slowly and causing contention. As other queries finish, the queued
      queries are allowed to proceed. </p>
    <p rev="2.5.0"> In <keyword keyref="impala25_full"/> and higher, you can
      specify these limits and thresholds for each pool rather than globally.
      That way, you can balance the resource usage and throughput between steady
      well-defined workloads, rare resource-intensive queries, and ad-hoc
      exploratory queries. </p>
    <p> In addition to the threshold values for currently executing queries, you
      can place limits on the maximum number of queries that are queued
      (waiting) and a limit on the amount of time they might wait before
      returning with an error. These queue settings let you ensure that queries
      do not wait indefinitely so that you can detect and correct
        <q>starvation</q> scenarios. </p>
    <p> Queries, DML statements, and some DDL statements, including
        <codeph>CREATE TABLE AS SELECT</codeph> and <codeph>COMPUTE
        STATS</codeph> are affected by admission control. </p>
    <p> On a busy cluster, you might find there is an optimal number of Impala
      queries that run concurrently. For example, when the I/O capacity is fully
      utilized by I/O-intensive queries, you might not find any throughput
      benefit in running more concurrent queries. By allowing some queries to
      run at full speed while others wait, rather than having all queries
      contend for resources and run slowly, admission control can result in
      higher overall throughput. </p>
    <p> For another example, consider a memory-bound workload such as many large
      joins or aggregation queries. Each such query could briefly use many
      gigabytes of memory to process intermediate results. Because Impala by
      default cancels queries that exceed the specified memory limit, running
      multiple large-scale queries at once might require re-running some queries
      that are cancelled. In this case, admission control improves the
      reliability and stability of the overall workload by only allowing as many
      concurrent queries as the overall memory of the cluster can accommodate. </p>
    <p outputclass="toc inpage"/>
  </conbody>

  <concept id="admission_concurrency">
    <title>Concurrent Queries and Admission Control</title>
    <conbody>
      <p> One way to limit resource usage through admission control is to set an
        upper limit on the number of concurrent queries. This is the initial
        technique you might use when you do not have extensive information about
        memory usage for your workload. The settings can be specified separately
        for each dynamic resource pool. </p>
      <dl>
        <dlentry>
          <dt> Max Running Queries </dt>
          <dd><p>Maximum number of concurrently running queries in this pool.
              The default value is unlimited for Impala 2.5 or higher.
              (optional)</p> The maximum number of queries that can run
            concurrently in this pool. The default value is unlimited. Any
            queries for this pool that exceed <uicontrol>Max Running
              Queries</uicontrol> are added to the admission control queue until
            other queries finish. You can use <uicontrol>Max Running
              Queries</uicontrol> in the early stages of resource management,
            when you do not have extensive data about query memory usage, to
            determine if the cluster performs better overall if throttling is
            applied to Impala queries. <p> For a workload with many small
              queries, you typically specify a high value for this setting, or
              leave the default setting of <q>unlimited</q>. For a workload with
              expensive queries, where some number of concurrent queries
              saturate the memory, I/O, CPU, or network capacity of the cluster,
              set the value low enough that the cluster resources are not
              overcommitted for Impala. </p><p>Once you have enabled
              memory-based admission control using other pool settings, you can
              still use <uicontrol>Max Running Queries</uicontrol> as a
              safeguard. If queries exceed either the total estimated memory or
              the maximum number of concurrent queries, they are added to the
              queue. </p><p>If <uicontrol>Max Running Queries
                Multiple</uicontrol> is set, the <uicontrol>Max Running
                Queries</uicontrol> setting is ignored.</p>
          </dd>
        </dlentry>
        <dlentry>
          <dt>Max Running Queries Multiple</dt>
          <dd>This floating point number is multiplied by the current total
            number of executors at runtime to give the maximum number of
            concurrently running queries allowed in the pool. The effect of this
            setting scales with the number of executors in the resource
              pool.<p>This calculation is rounded up to the nearest integer, so
              the result will always be at least one. </p><p>If set to zero or a
              negative number, the setting is ignored.</p></dd>
        </dlentry>
        <dlentry>
          <dt> Max Queued Queries </dt>
          <dd> Maximum number of queries that can be queued in this pool. The
            default value is 200 for Impala 2.1 or higher and 50 for previous
            versions of Impala. (optional)<p>If <uicontrol>Max Queued Queries
                Multiple</uicontrol> is set, the <uicontrol>Max Queued
                Queries</uicontrol> setting is ignored.</p></dd>
        </dlentry>
        <dlentry>
          <dt>Max Queued Queries Multiple</dt>
          <dd>This floating point number is multiplied by the current total
            number of executors at runtime to give the maximum number of queries
            that can be queued in the pool. The effect of this setting scales
            with the number of executors in the resource pool.<p>This
              calculation is rounded up to the nearest integer, so the result
              will always be at least one. </p><p>If set to zero or a negative
              number, the setting is ignored.</p></dd>
        </dlentry>
        <dlentry>
          <dt> Queue Timeout </dt>
          <dd> The amount of time, in milliseconds, that a query waits in the
            admission control queue for this pool before being canceled. The
            default value is 60,000 milliseconds. <p>In the following cases,
                <uicontrol>Queue Timeout</uicontrol> is not significant, and you
              can specify a high value to avoid canceling queries
                unexpectedly:<ul id="ul_kzr_rbg_gw">
                <li>In a low-concurrency workload where few or no queries are
                  queued</li>
                <li>In an environment without a strict SLA, where it does not
                  matter if queries occasionally take longer than usual because
                  they are held in admission control</li>
              </ul>You might also need to increase the value to use Impala with
              some business intelligence tools that have their own timeout
              intervals for queries. </p><p>In a high-concurrency workload,
              especially for queries with a tight SLA, long wait times in
              admission control can cause a serious problem. For example, if a
              query needs to run in 10 seconds, and you have tuned it so that it
              runs in 8 seconds, it violates its SLA if it waits in the
              admission control queue longer than 2 seconds. In a case like
              this, set a low timeout value and monitor how many queries are
              cancelled because of timeouts. This technique helps you to
              discover capacity, tuning, and scaling problems early, and helps
              avoid wasting resources by running expensive queries that have
              already missed their SLA. </p><p> If you identify some queries
              that can have a high timeout value, and others that benefit from a
              low timeout value, you can create separate pools with different
              values for this setting. </p>
          </dd>
        </dlentry>
      </dl>
      <p> You can combine these settings with the memory-based approach
        described in <xref href="impala_admission.xml#admission_memory"/>. If
        either the maximum number of or the expected memory usage of the
        concurrent queries is exceeded, subsequent queries are queued until the
        concurrent workload falls below the threshold again. </p>
    </conbody>
  </concept>

  <concept id="admission_memory">
    <title>Memory Limits and Admission Control</title>
    <conbody>
      <p>
        Each dynamic resource pool can have an upper limit on the cluster-wide memory used by queries executing in that pool.
        This is the technique to use once you have a stable workload with well-understood memory requirements.
      </p>
      <p>Use the following settings to manage memory-based admission
        control.</p>
      <dl>
        <dlentry>
          <dt>Max Memory</dt>
          <dd>
            <p>
              The maximum amount of aggregate memory available across the
              cluster to all queries executing in this pool. This should be a
              portion of the aggregate configured memory for Impala daemons,
              which will be shown in the settings dialog next to this option for
              convenience. Setting this to a non-zero value enables memory based
              admission control.
            </p>
            <p>
              Impala determines the expected maximum memory used by all
              queries in the pool and holds back any further queries that would
              result in Max Memory being exceeded.
            </p>
            <p>
              You set Max Memory in <codeph>fair-scheduler.xml</codeph> file
              with the <codeph>maxResources</codeph> tag. For example:
                <codeph>&lt;maxResources>2500 mb&lt;/maxResources></codeph>
            </p>
            <p>
              If you specify Max Memory, you should specify the amount of
              memory to allocate to each query in this pool. You can do this in
              two ways:
            </p>
            <ul>
              <li>By setting Maximum Query Memory Limit and Minimum Query Memory
                Limit. This is preferred in <keyword keyref="impala31_full"/>
                and greater and gives Impala flexibility to set aside more
                memory to queries that are expected to be memory-hungry.</li>
              <li>By setting Default Query Memory Limit to the exact amount of
                memory that Impala should set aside for queries in that
                pool.</li>
            </ul>
            <p>
              Note that in the following cases, Impala will rely entirely on
              memory estimates to determine how much memory to set aside for
              each query. This is not recommended because it can result in
              queries not running or being starved for memory if the estimates
              are inaccurate. And it can affect other queries running on the
              same node.
              <ul>
                <li>Max Memory, Maximum Query Memory Limit, and Minimum Query
                  Memory Limit are not set, and the <codeph>MEM_LIMIT</codeph>
                  query option is not set for the query.</li>
                <li>Default Query Memory Limit is set to 0, and the
                    <codeph>MEM_LIMIT</codeph> query option is not set for the
                  query.</li>
              </ul>
            </p>
            <p>If <uicontrol>Max Memory Multiple</uicontrol> is set, the
                <uicontrol>Max Memory</uicontrol> setting is ignored.</p>
          </dd>
        </dlentry>
        <dlentry>
          <dt>Max Memory Multiple</dt>
          <dd> This number of bytes is multiplied by the current total number of
            executors at runtime to give the maximum memory available across the
            cluster for the pool. The effect of this setting scales with the
            number of executors in the resource pool.<p>If set to zero or a
              negative number, the setting is ignored.</p></dd>
        </dlentry>
        <dlentry>
          <dt>Minimum Query Memory Limit and Maximum Query Memory Limit</dt>
          <dd>
            <p>These two options determine the minimum and maximum per-host
              memory limit that will be chosen by Impala Admission control for
              queries in this resource pool. If set, Impala Admission Control
              will choose a memory limit between the minimum and maximum values
              based on the per-host memory estimate for the query. The memory
              limit chosen determines the amount of memory that Impala Admission
              control will set aside for this query on each host that the query
              is running on. The aggregate memory across all of the hosts that
              the query is running on is counted against the pool’s Max
              Memory.</p>
            <p>Minimum Query Memory Limit must be less than or equal to Maximum
              Query Memory Limit and Max Memory.</p>
            <p>A user can override Impala’s choice of memory limit by setting
              the <codeph>MEM_LIMIT</codeph> query option. If the Clamp
              MEM_LIMIT Query Option setting is set to <codeph>TRUE</codeph> and
              the user sets <codeph>MEM_LIMIT</codeph> to a value that is
              outside of the range specified by these two options, then the
              effective memory limit will be either the minimum or maximum,
              depending on whether <codeph>MEM_LIMIT</codeph> is lower than or
              higher the range.</p>
            <p>For example, assume a resource pool with the following parameters
              set: <ul>
                <li>Minimum Query Memory Limit = 2GB</li>
                <li>Maximum Query Memory Limit = 10GB</li>
              </ul>If a user tries to submit a query with the
                <codeph>MEM_LIMIT</codeph> query option set to 14 GB, the
              following would happen:<ul>
                <li>If Clamp MEM_LIMIT Query Option = true, admission controller
                  would override <codeph>MEM_LIMIT</codeph> with 10 GB and
                  attempt admission using that value.</li>
                <li>If Clamp MEM_LIMIT Query Option = false, the admission
                  controller will retain the <codeph>MEM_LIMIT</codeph> of 14 GB
                  set by the user and will attempt admission using the
                  value.</li>
              </ul></p>
          </dd>
        </dlentry>
        <dlentry>
          <dt>Default Query Memory Limit</dt>
          <dd>The default memory limit applied to queries executing in this pool
            when no explicit <codeph>MEM_LIMIT</codeph> query option is set. The
            memory limit chosen determines the amount of memory that Impala
            Admission control will set aside for this query on each host that
            the query is running on. The aggregate memory across all of the
            hosts that the query is running on is counted against the pool’s Max
            Memory. This option is deprecated in <keyword keyref="impala31_full"
            /> and higher and is replaced by Maximum Query Memory Limit and
            Minimum Query Memory Limit. Do not set this if either Maximum Query
            Memory Limit or Minimum Query Memory Limit is set.</dd>
        </dlentry>
      </dl>
      <dl>
        <dlentry>
          <dt> Clamp MEM_LIMIT Query Option</dt>
          <dd>If this field is not selected, the <codeph>MEM_LIMIT</codeph>
            query option will not be bounded by the <b>Maximum Query Memory
              Limit</b> and the <b>Minimum Query Memory Limit</b> values
            specified for this resource pool. By default, this field is selected
            in Impala 3.1 and higher. The field is disabled if both <b>Minimum
              Query Memory Limit</b> and <b>Maximum Query Memory Limit</b> are
            not set.</dd>
        </dlentry>
      </dl>
      <p
        conref="../shared/impala_common.xml#common/admission_control_mem_limit_interaction"/>
      <p>
        You can combine the memory-based settings with the upper limit on concurrent queries described in
        <xref href="impala_admission.xml#admission_concurrency"/>. If either the maximum number of
        or the expected memory usage of the concurrent queries is exceeded, subsequent queries
        are queued until the concurrent workload falls below the threshold again.
      </p>
    </conbody>
  </concept>
  <concept id="set_per_query_memory_limits">
    <title>Setting Per-query Memory Limits</title>
    <conbody>
      <p>Use per-query memory limits to prevent queries from consuming excessive
        memory resources that impact other queries. We recommends that you set
        the query memory limits whenever possible.</p>
      <p>If you set the <b>Max Memory</b> for a resource pool, Impala attempts
        to throttle queries if there is not enough memory to run them within the
        specified resources.</p>
      <p>Only use admission control with maximum memory resources if you can
        ensure there are query memory limits. Set the pool <b>Maximum Query
          Memory Limit</b> to be certain. You can override this setting with the
          <codeph>MEM_LIMIT</codeph> query option, if necessary.</p>
      <p>Typically, you set query memory limits using the <codeph>set
          MEM_LIMIT=Xg;</codeph> query option. When you find the right value for
        your business case, memory-based admission control works well. The
        potential downside is that queries that attempt to use more memory might
        perform poorly or even be cancelled.</p>
    </conbody>
  </concept>

  <concept id="admission_yarn">

    <title>How Impala Admission Control Relates to Other Resource Management Tools</title>
  <prolog>
    <metadata>
      <data name="Category" value="Concepts"/>
    </metadata>
  </prolog>

    <conbody>

      <p>
        The admission control feature is similar in some ways to the YARN resource management framework. These features
        can be used separately or together. This section describes some similarities and differences, to help you
        decide which combination of resource management features to use for Impala.
      </p>

      <p>
        Admission control is a lightweight, decentralized system that is suitable for workloads consisting
        primarily of Impala queries and other SQL statements. It sets <q>soft</q> limits that smooth out Impala
        memory usage during times of heavy load, rather than taking an all-or-nothing approach that cancels jobs
        that are too resource-intensive.
      </p>

      <p>
        Because the admission control system does not interact with other Hadoop workloads such as MapReduce jobs, you
        might use YARN with static service pools on clusters where resources are shared between
        Impala and other Hadoop components. This configuration is recommended when using Impala in a
        <term>multitenant</term> cluster. Devote a percentage of cluster resources to Impala, and allocate another
        percentage for MapReduce and other batch-style workloads. Let admission control handle the concurrency and
        memory usage for the Impala work within the cluster, and let YARN manage the work for other components within the
        cluster. In this scenario, Impala's resources are not managed by YARN.
      </p>

      <p>
        The Impala admission control feature uses the same configuration mechanism as the YARN resource manager to map users to
        pools and authenticate them.
      </p>

      <p>
        Although the Impala admission control feature uses a <codeph>fair-scheduler.xml</codeph> configuration file
        behind the scenes, this file does not depend on which scheduler is used for YARN. You still use this file
        even when YARN is using the capacity scheduler.
      </p>

    </conbody>
  </concept>

  <concept id="admission_architecture">

    <title>How Impala Schedules and Enforces Limits on Concurrent Queries</title>
  <prolog>
    <metadata>
      <data name="Category" value="Concepts"/>
      <data name="Category" value="Scheduling"/>
    </metadata>
  </prolog>

    <conbody>

      <p>
        The admission control system is decentralized, embedded in each Impala daemon and communicating through the
        statestore mechanism. Although the limits you set for memory usage and number of concurrent queries apply
        cluster-wide, each Impala daemon makes its own decisions about whether to allow each query to run
        immediately or to queue it for a less-busy time. These decisions are fast, meaning the admission control
        mechanism is low-overhead, but might be imprecise during times of heavy load across many coordinators. There could be times when the
        more queries were queued (in aggregate across the cluster) than the specified limit, or when number of admitted queries
        exceeds the expected number. Thus, you typically err on the
        high side for the size of the queue, because there is not a big penalty for having a large number of queued
        queries; and you typically err on the low side for configuring memory resources, to leave some headroom in case more
        queries are admitted than expected, without running out of memory and being cancelled as a result.
      </p>

      <p>
        To avoid a large backlog of queued requests, you can set an upper limit on the size of the queue for
        queries that are queued. When the number of queued queries exceeds this limit, further queries are
        cancelled rather than being queued. You can also configure a timeout period per pool, after which queued queries are
        cancelled, to avoid indefinite waits. If a cluster reaches this state where queries are cancelled due to
        too many concurrent requests or long waits for query execution to begin, that is a signal for an
        administrator to take action, either by provisioning more resources, scheduling work on the cluster to
        smooth out the load, or by doing <xref href="impala_performance.xml#performance">Impala performance
        tuning</xref> to enable higher throughput.
      </p>
    </conbody>
  </concept>

  <concept id="admission_jdbc_odbc">

    <title>How Admission Control works with Impala Clients (JDBC, ODBC, HiveServer2)</title>
  <prolog>
    <metadata>
      <data name="Category" value="JDBC"/>
      <data name="Category" value="ODBC"/>
      <data name="Category" value="HiveServer2"/>
      <data name="Category" value="Concepts"/>
    </metadata>
  </prolog>

    <conbody>

      <p>
        Most aspects of admission control work transparently with client interfaces such as JDBC and ODBC:
      </p>

      <ul>
        <li>
          If a SQL statement is put into a queue rather than running immediately, the API call blocks until the
          statement is dequeued and begins execution. At that point, the client program can request to fetch
          results, which might also block until results become available.
        </li>

        <li>
          If a SQL statement is cancelled because it has been queued for too long or because it exceeded the memory
          limit during execution, the error is returned to the client program with a descriptive error message.
        </li>

      </ul>

      <p rev=""> In Impala 2.0 and higher, you can submit a SQL
          <codeph>SET</codeph> statement from the client application to change
        the <codeph>REQUEST_POOL</codeph> query option. This option lets you
        submit queries to different resource pools, as described in <xref
          href="impala_request_pool.xml#request_pool"/>.  </p>

      <p>
        At any time, the set of queued queries could include queries submitted through multiple different Impala
        daemon hosts. All the queries submitted through a particular host will be executed in order, so a
        <codeph>CREATE TABLE</codeph> followed by an <codeph>INSERT</codeph> on the same table would succeed.
        Queries submitted through different hosts are not guaranteed to be executed in the order they were
        received. Therefore, if you are using load-balancing or other round-robin scheduling where different
        statements are submitted through different hosts, set up all table structures ahead of time so that the
        statements controlled by the queuing system are primarily queries, where order is not significant. Or, if a
        sequence of statements needs to happen in strict order (such as an <codeph>INSERT</codeph> followed by a
        <codeph>SELECT</codeph>), submit all those statements through a single session, while connected to the same
        Impala daemon host.
      </p>

      <p>
        Admission control has the following limitations or special behavior when used with JDBC or ODBC
        applications:
      </p>

      <ul>
        <li>
          The other resource-related query options,
          <codeph>RESERVATION_REQUEST_TIMEOUT</codeph> and <codeph>V_CPU_CORES</codeph>, are no longer used. Those query options only
          applied to using Impala with Llama, which is no longer supported.
        </li>
      </ul>
    </conbody>
  </concept>

  <concept id="admission_schema_config">
    <title>SQL and Schema Considerations for Admission Control</title>
    <conbody>
      <p>
        When queries complete quickly and are tuned for optimal memory usage, there is less chance of
        performance or capacity problems during times of heavy load. Before setting up admission control,
        tune your Impala queries to ensure that the query plans are efficient and the memory estimates
        are accurate. Understanding the nature of your workload, and which queries are the most
        resource-intensive, helps you to plan how to divide the queries into different pools and
        decide what limits to define for each pool.
      </p>
      <p>
        For large tables, especially those involved in join queries, keep their statistics up to date
        after loading substantial amounts of new data or adding new partitions.
        Use the <codeph>COMPUTE STATS</codeph> statement for unpartitioned tables, and
        <codeph>COMPUTE INCREMENTAL STATS</codeph> for partitioned tables.
      </p>
      <p>
        When you use dynamic resource pools with a <uicontrol>Max Memory</uicontrol> setting enabled,
        you typically override the memory estimates that Impala makes based on the statistics from the
        <codeph>COMPUTE STATS</codeph> statement.
        You either set the <codeph>MEM_LIMIT</codeph> query option within a particular session to
        set an upper memory limit for queries within that session, or a default <codeph>MEM_LIMIT</codeph>
        setting for all queries processed by the <cmdname>impalad</cmdname> instance, or
        a default <codeph>MEM_LIMIT</codeph> setting for all queries assigned to a particular
        dynamic resource pool. By designating a consistent memory limit for a set of similar queries
        that use the same resource pool, you avoid unnecessary query queuing or out-of-memory conditions
        that can arise during high-concurrency workloads when memory estimates for some queries are inaccurate.
      </p>
      <p>
        Follow other steps from <xref href="impala_performance.xml#performance"/> to tune your queries.
      </p>
    </conbody>
  </concept>
  <concept id="admission_guidelines">
    <title>Guidelines for Using Admission Control</title>
    <prolog>
      <metadata>
        <data name="Category" value="Planning"/>
        <data name="Category" value="Guidelines"/>
        <data name="Category" value="Best Practices"/>
      </metadata>
    </prolog>
    <conbody>
      <p> The limits imposed by admission control are de-centrally managed
          <q>soft</q> limits. Each Impala coordinator node makes its own
        decisions about whether to allow queries to run immediately or to queue
        them. These decisions rely on information passed back and forth between
        nodes by the StateStore service. If a sudden surge in requests causes
        more queries than anticipated to run concurrently, then the throughput
        could decrease due to queries spilling to disk or contending for
        resources. Or queries could be cancelled if they exceed the
          <codeph>MEM_LIMIT</codeph> setting while running. </p>
      <p> In <cmdname>impala-shell</cmdname>, you can also specify which
        resource pool to direct queries to by setting the
          <codeph>REQUEST_POOL</codeph> query option. </p>
      <p> To see how admission control works for particular queries, examine the
        profile output or the summary output for the query. <ul>
          <li>Profile<p>The information is available through the
                <codeph>PROFILE</codeph> statement in
                <cmdname>impala-shell</cmdname> immediately after running a
              query in the shell, on the <uicontrol>queries</uicontrol> page of
              the Impala debug web UI, or in the Impala log file (basic
              information at log level 1, more detailed information at log level
              2). </p><p>The profile output contains details about the admission
              decision, such as whether the query was queued or not and which
              resource pool it was assigned to. It also includes the estimated
              and actual memory usage for the query, so you can fine-tune the
              configuration for the memory limits of the resource pools.
            </p></li>
          <li>Summary<p>Starting in <keyword keyref="impala31"/>, the
              information is available in <cmdname>impala-shell</cmdname> when
              the <codeph>LIVE_PROGRESS</codeph> or
                <codeph>LIVE_SUMMARY</codeph> query option is set to
                <codeph>TRUE</codeph>.</p><p>You can also start an
                <codeph>impala-shell</codeph> session with the
                <codeph>--live_progress</codeph> or
                <codeph>--live_summary</codeph> flags to monitor all queries in
              that <codeph>impala-shell</codeph> session.</p><p>The summary
              output includes the queuing status consisting of whether the query
              was queued and what was the latest queuing reason.</p></li>
        </ul></p>
      <p> For details about all the Fair Scheduler configuration settings, see
          <xref keyref="FairScheduler">Fair Scheduler Configuration</xref>, in
        particular the tags such as <codeph>&lt;queue&gt;</codeph> and
          <codeph>&lt;aclSubmitApps&gt;</codeph> to map users and groups to
        particular resource pools (queues). </p>
    </conbody>
  </concept>
</concept>