kea/tests/tools/dhcp-ubench/dhcp-perf-guide.xml

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"http://www.oasis-open.org/docbook/xml/4.2/docbookx.dtd" [
<!ENTITY mdash  "&#x2014;" >
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<!--
 - Copyright (C) 2012  Internet Systems Consortium, Inc. ("ISC")
 -
 - Permission to use, copy, modify, and/or distribute this software for any
 - purpose with or without fee is hereby granted, provided that the above
 - copyright notice and this permission notice appear in all copies.
 -
 - THE SOFTWARE IS PROVIDED "AS IS" AND ISC DISCLAIMS ALL WARRANTIES WITH
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<book>
  <?xml-stylesheet href="bind10-guide.css" type="text/css"?>

  <bookinfo>
    <title>DHCP Performance Guide</title>
    <!-- <subtitle>Various aspects of DHCP Performance in BIND 10</subtitle> -->

    <copyright>
      <year>2012</year>
      <holder>Internet Systems Consortium, Inc. ("ISC")</holder>
    </copyright>
    <author>
      <firstname>Tomasz</firstname>
      <surname>Mrugalski</surname>
    </author>

    <abstract>
      <para>BIND 10 is a framework that features Domain Name System
      (DNS) and Dynamic Host Configuration Protocol (DHCP)
      software with development managed by Internet Systems Consortium (ISC).
      This document describes various aspects of DHCP performance,
      measurements and tuning. It covers BIND 10 DHCP (codename Kea),
      existing ISC DHCP4 software, perfdhcp (a DHCP performance
      measurement tool) and other related topics.</para>
    </abstract>

    <releaseinfo>This is a companion document for BIND 10 version
    &__VERSION__;.</releaseinfo>

  </bookinfo>

  <preface>
    <title>Preface</title>

    <section id="acknowledgements">
      <title>Acknowledgements</title>

      <para>ISC would like to acknowledge generous support for
      BIND 10 development of DHCPv4 and DHCPv6 components provided
      by <ulink url="http://www.comcast.com/">Comcast</ulink>.</para>

    </section>

  </preface>

  <chapter id="intro">
    <title>Introduction</title>
    <para>
      This document is in the early stages of development. It is
      expected to grow significantly in the near future. It will
      cover topics like database backend perfomance measurements,
      tools, and the pros an cons of various optimization techniques.
    </para>

  </chapter>

  <chapter id="dhcp4">
    <title>ISC DHCP 4.x</title>
    <para>
      TODO: Write something about ISC DHCP4 here.
    </para>
  </chapter>

  <chapter id="kea">
    <title>Kea</title>
    <para>

    </para>

    <section>
      <title>Backend performance evaluation</title>
      <para>
        Kea will support several different database backends, using
        both popular databases (like MySQL or SQLite) and
        custom-developed solutions (such as an in-memory database).
        To aid in the choice of backend, the BIND 10
        source code features a set of performance microbenchmarks.
         Written in C/C++, these are small tools that simulate expected
        DHCP server behaviour and evaluate the performance of
        considered databases. As implemented, the benchmarks are not really
        simulating DHCP operation, but rather use set of primitives
        that can be used by a real server.  For this reason, they are called
        micro-benchmarks.
      </para>

      <para>Although there are many operations and data types that
      server could store in a database, the most frequently used data
      type is lease information. Although the information held for IPv4
      and IPv6 leases differs slightly, it is expected that the performance
      differences will be minimal between IPv4 and IPv6 lease operations.
      Therefore each test uses the lease4 table (in which IPv4 leases are stored)
      for performance measurements.
      </para>

      <para>All benchmarks are implemented as single threaded applications
      that take advantage of a single database connection.</para>

      <para>
        Those benchmarks are stored in tests/tools/dhcp-ubench
        directory of the BIND 10 source tree. This directory contains simplified prototypes for
        the various database back-ends that are planned or considered as a
        possibly for BIND10 DHCP.  Athough trivial now, the benchmarks are
        expected to evolve into useful tools that will allow users to
        measure performance in their specific environment.
      </para>

    <para>
      Currently the following benchmarks are implemented:
      <itemizedlist>
        <listitem><para>In memory + flat file</para></listitem>
        <listitem><para>SQLite</para></listitem>
        <listitem><para>MySQL</para></listitem>
      </itemizedlist>
    </para>

    <para>
      As the benchmarks require additional (sometimes heavy) dependencies, they are not
      built by default. Actually, their build system is completely separate from that
      of the rest of BIND 10.
      It is anticipated that they will be eventually merged into the rest of BIND 10, but
      that is a low priority for now.
    </para>

    <para>
      All benchmarks will follow the same pattern:
      <orderedlist>
        <listitem><para>Prepare operation (connect to a database, create a file etc.)</para></listitem>
        <listitem><para>Measure timestamp 0</para></listitem>
        <listitem><para>Commit new lease4 record (repeated N times)</para></listitem>
        <listitem><para>Measure timestamp 1</para></listitem>
        <listitem><para>Search for random lease4 record (repeated N times)</para></listitem>
        <listitem><para>Measure timestamp 2</para></listitem>
        <listitem><para>Update existing lease4 record (repeated N times)</para></listitem>
        <listitem><para>Measure timestamp 3</para></listitem>
        <listitem><para>Delete existing lease4 record (repeated N times)</para></listitem>
        <listitem><para>Measure timestamp 4</para></listitem>
        <listitem><para>Print out statistics, based on N and measured timestamps.</para></listitem>
      </orderedlist>

      Although this approach does not attempt to simulate actual DHCP server
      operation that has mix of all steps, it answers the
      questions about basic database strengths and weak points. In particular
      it can show what is the impact of specific database optimizations, such as
      changing engine, optimizing for writes/reads etc.
    </para>

    <para>
      The framework attempts to do the same amount of work for every
      backend thus allowing fair complarison between them.
    </para>
    </section>

    <section id="mysql-backend">
      <title>MySQL backend</title>
      <para>The MySQL backend requires the MySQL client development libraries. It uses
      the mysql_config tool (similar to pkg-config) to discover required
      compilation and linking options. To install required packages on Ubuntu,
      use the following command:

      <screen>$ <userinput>sudo apt-get install mysql-client mysql-server libmysqlclient-dev</userinput></screen>

      Make sure that MySQL server is running. Make sure that you have your setup
      configured so there is a user that is able to modify used database.</para>

      <para>Before running tests, you need to initialize your database. You can
      use mysql.schema script for that purpose.</para>
      
      <para><emphasis>WARNING: It will drop existing
      Kea database. Do not run this on your production server. </emphasis></para>
      
      <para>Assuming your
      MySQL user is "kea", you can initialize your test database by:

      <screen>$ <userinput>mysql -u kea -p &lt; mysql.schema</userinput></screen>
      </para>

      <para>After the database is initialized, you are ready to run the test:
      <screen>$ <userinput>./mysql_ubench</userinput></screen>

      or

      <screen>$ <userinput>./mysql_ubench &gt; results-mysql.txt</userinput></screen>

      Redirecting output to a file is important, because for each operation
      there is a single character printed to show progress. If you have a slow
      terminal, this may considerably affect test performance. On the other hand,
      printing something after each operation is required as poor database settings
      may slow down operations to around 20 per second. (The observant user is expected
      to note that the initial dots are printed too slowly and abort the test.)</para>

      <para>Currently all default parameters are hardcoded. Default values can be
      overridden using command line switches. Although all benchmarks take
      the same list of parameters, some of them are specific to a given backend.
      To get a list of supported parameters, run the benchmark with the "-h" option:

      <screen>$ <userinput>./mysql_ubench -h</userinput>
This is a benchmark designed to measure expected performance
of several backends. This particular version identifies itself
as following:
MySQL client version is 5.5.24

Possible command-line parameters:
 -h - help (you are reading this)
 -m hostname - specifies MySQL server to connect (MySQL backend only)
 -u username - specifies MySQL user name (MySQL backend only)
 -p password - specifies MySQL passwod (MySQL backend only)
 -f name - database or filename (MySQL, SQLite and memfile)
 -n integer - number of test repetitions (MySQL, SQLite and memfile)
 -s yes|no - synchronous/asynchronous operation (MySQL, SQLite and memfile)
 -v yes|no - verbose mode (MySQL, SQLite and memfile)
 -c yes|no - should compiled statements be used (MySQL only)
</screen>

      </para>

      <para>Synchronous operation requires database backend to
      physically store changes to disk before proceeding. This
      property ensures that no data is lost in case of the server
      failure.  Unfortunately, it slows operation
      considerably. Asynchronous mode allows database to write data at
      a later time (usually controlled by the database engine on OS
      disk buffering mechanism).</para>

      <section>
        <title>MySQL tweaks</title>

        <para>One parameter that has huge impact on performance is the choice of backend engine.
        You can get a list of engines of your MySQL implementation by using

        <screen>&gt; <userinput>show engines;</userinput></screen>

        in your mysql client. Two notable engines are MyISAM and InnoDB. mysql_ubench uses
        use MyISAM for synchronous mode and InnoDB for asynchronous. Please use
        '-s yes|no' to choose whether you want synchronous or asynchronous operations.</para>

        <para>Another parameter that affects performance are precompiled statements.
        In a basic approach, the actual SQL query is passed as a text string that is
        then parsed by the database engine. Alternative is a so called precompiled
        statement. In this approach the SQL query is compiled an specific values are being
        bound to it. In the next iteration the query remains the same, only bound values
        are changing (e.g. searching for a different address). Usage of basic or precompiled
        statements is controlled with '-c no|yes'.</para>
    </section>
    </section>


    <section id="sqlite-ubench">
      <title>SQLite-ubench</title>
      <para>The SQLite backend requires both the sqlite3 development and run-time packages. Their
      names may vary from system to system, but on Ubuntu 12.04 they are called
      sqlite3 libsqlite3-dev. To install them, use the following command:

      <screen>&gt; <userinput>sudo apt-get install sqlite3 libsqlite3-dev</userinput></screen>

      Before running the test the database has to be created. Use the following command for that:
      <screen>&gt; <userinput>cat sqlite.schema | sqlite3 sqlite.db</userinput></screen>

      A new database called sqlite.db will be created. That is the default name used
      by sqlite_ubench test. If you prefer other name, make sure you update
      sqlite_ubench.cc accordingly.</para>

      <para>Once the database is created, you can run tests:
      <screen>&gt; <userinput>./sqlite_ubench</userinput></screen>
      or
      <screen>&gt; <userinput>./sqlite_ubench > results-sqlite.txt</userinput></screen>
      </para>

      <section id="sqlite-tweaks">
        <title>SQLite tweaks</title>
        <para>To modify default sqlite_ubench parameters, command line
        switches can be used. The currently supported switches are
        (default values specified in brackets):
        <orderedlist>
          <listitem><para>-f filename - name of the database file ("sqlite.db")</para></listitem>
          <listitem><para>-n num - number of iterations (100)</para></listitem>
          <listitem><para>-s yes|no - should the operations be performed in a synchronous (yes)
          or asynchronous (no) manner (yes)</para></listitem>
          <listitem><para>-v yes|no - verbose mode. Should the test print out progress? (yes)</para></listitem>
          <listitem><para>-c yes|no - precompiled statements. Should the SQL statements be precompiled?</para></listitem>
        </orderedlist>
        </para>

        <para>SQLite can run in asynchronous or synchronous mode. This
        mode can be controlled by using "synchronous" parameter. It is set
        using the SQLite command:</para>
        
        <para><command>PRAGMA synchronous = ON|OFF</command></para>

        <para>Another tweakable feature is journal mode. It can be
        turned to several modes of operation. Its value can be
        modified in SQLite_uBenchmark::connect().  See
        http://www.sqlite.org/pragma.html#pragma_journal_mode for
        detailed explanantion.</para>

        <para>sqlite_bench supports precompiled statements. Please use
        '-c no|yes' to define which should be used: basic SQL query (no) or
        precompiled statement (yes).</para>
      </section>
    </section>

    <section id="memfile-ubench">
      <title>memfile-ubench</title> <para>The memfile backend is a
      custom backend that somewhat mimics operation of ISC DHCP4. It
      implements in-memory storage using standard C++ and boost
      mechanisms (std::map and boost::shared_ptr&lt;&gt;). All
      database changes are also written to a lease file, which is
      strictly write-only. This approach takes advantage of the fact
      that file append operation is faster than modifications introduced
      in the middle of the file (as it often requires moving all data
      after modified point, effectively requiring writing large parts of
      the whole file, not just changed fragment).</para>

      <section id="memfile-tweaks">
        <title>memfile tweaks</title>
        <para>To modify default memfile_ubench parameters, command line
        switches can be used. Currently supported switches are
        (default values specified in brackets):
        <orderedlist>
          <listitem><para>-f filename - name of the database file ("dhcpd.leases")</para></listitem>
          <listitem><para>-n num - number of iterations (100)</para></listitem>
          <listitem><para>-s yes|no - should the operations be performend in a synchronous (yes)
          or asynchronous (no) manner (yes)</para></listitem>
          <listitem><para>-v yes|no - verbose mode. Should the test print out progress? (yes)</para></listitem>
        </orderedlist>
        </para>

        <para>memfile can run in asynchronous or synchronous mode. This
        mode can be controlled by using sync parameter. It uses
        fflush() and fsync() in synchronous mode to make sure that
        data is not buffered and physically stored on disk.</para>
      </section>
    </section>

    <section>
      <title>Basic performance measurements</title>
      <para>This section contains sample results for backend performance measurements,
      taken using microbenchmarks. Tests were conducted on reasonably powerful machine:
      <screen>
CPU: Quad-core Intel(R) Core(TM) i7-2600K CPU @ 3.40GHz (8 logical cores)
HDD: 1,5TB Seagate Barracuda ST31500341AS 7200rpm, ext4 partition
OS: Ubuntu 12.04, running kernel 3.2.0-26-generic SMP x86_64
compiler: g++ (Ubuntu/Linaro 4.6.3-1ubuntu5) 4.6.3
MySQL version: 5.5.24
SQLite version: 3.7.9sourceid version is 2011-11-01 00:52:41 c7c6050ef060877ebe77b41d959e9df13f8c9b5e</screen>
      </para>

      <para>The benchmarks were run without using precompiled statements.
      The code was compiled with the -O0 flag (no code optimizations).
      Each run was executed once.</para>

      <para>Two series of measures were made, synchronous and
      asynchronous. As those modes offer radically different
      performances, synchronous mode was conducted for one
      thousand repetitions and asynchronous mode was conducted for
      one hundred thousand repetitions.</para>

      <!-- raw results sync -->
      <table><title>Synchronous results (basic)</title>
      <tgroup cols='6' align='center' colsep='1' rowsep='1'>
        <colspec colname='Backend'/>
        <colspec colname='Num' />
        <colspec colname='Create'/>
        <colspec colname='Search'/>
        <colspec colname='Update'/>
        <colspec colname='Delete'/>
        <colspec colname='Average'/>
        <thead>
          <row>
            <entry>Backend</entry>
            <entry>Operations</entry>
            <entry>Create [s]</entry>
            <entry>Search [s]</entry>
            <entry>Update [s]</entry>
            <entry>Delete [s]</entry>
            <entry>Average [s]</entry>
          </row>
        </thead>
        <tbody>
          <row>
            <entry>MySQL</entry>
            <entry>1,000</entry>
            <entry>31.604</entry>
            <entry> 0.117</entry>
            <entry>27.964</entry>
            <entry>27.695</entry>
            <entry>21.845</entry>
          </row>

          <row>
            <entry>SQLite</entry>
            <entry>1,000</entry>
            <entry>61.421</entry>
            <entry> 0.033</entry>
            <entry>59.477</entry>
            <entry>56.034</entry>
            <entry>44.241</entry>
          </row>

          <row>
            <entry>memfile</entry>
            <entry>1,000</entry>
            <entry>38.224</entry>
            <entry> 0.001</entry>
            <entry>38.041</entry>
            <entry>38.017</entry>
            <entry>28.571</entry>
          </row>

        </tbody>
      </tgroup>
      </table>

      <para>The following parameters were measured for asynchronous mode.
      MySQL and SQLite were run with one hundred thousand repetitions. Memfile
      was run for one million repetitions due to its much higher performance.</para>

      <!-- raw results async -->
      <table><title>Asynchronous results (basic)</title>
      <tgroup cols='6' align='center' colsep='1' rowsep='1'>
        <colspec colname='Backend'/>
        <colspec colname='Num' />
        <colspec colname='Create'/>
        <colspec colname='Search'/>
        <colspec colname='Update'/>
        <colspec colname='Delete'/>
        <colspec colname='Average'/>
        <thead>
          <row>
            <entry>Backend</entry>
            <entry>Operations</entry>
            <entry>Create [s]</entry>
            <entry>Search [s]</entry>
            <entry>Update [s]</entry>
            <entry>Delete [s]</entry>
            <entry>Average [s]</entry>
          </row>
        </thead>
        <tbody>
          <row>
            <entry>MySQL</entry>
            <entry>100,000</entry>
            <entry>10.585</entry>
            <entry>10.386</entry>
            <entry>10.062</entry>
            <entry> 8.890</entry>
            <entry> 9.981</entry>
          </row>

          <row>
            <entry>SQLite</entry>
            <entry>100,000</entry>
            <entry> 3.710</entry>
            <entry> 3.159</entry>
            <entry> 2.865</entry>
            <entry> 2.439</entry>
            <entry> 3.044</entry>
          </row>

          <row>
            <entry>memfile</entry>
            <entry>1,000,000</entry>
            <entry> 1.300</entry>
            <entry> 0.039</entry>
            <entry> 1.307</entry>
            <entry> 1.278</entry>
            <entry> 0.981</entry>
          </row>

        </tbody>
      </tgroup>
      </table>

      <para>The presented performance results can be converted into operations per second metrics.
      It should be noted that due to large differences between various operations (sometimes
      over three orders of magnitude), it is difficult to create a simple, readable chart with
      that data.</para>

      <table id="tbl-basic-perf-results"><title>Estimated basic performance</title>
      <tgroup cols='6' align='center' colsep='1' rowsep='1'>
        <colspec colname='Backend'/>
        <colspec colname='Create'/>
        <colspec colname='Search'/>
        <colspec colname='Update'/>
        <colspec colname='Delete'/>
        <colspec colname='Average'/>
        <thead>
          <row>
            <entry>Backend</entry>
            <entry>Create [oper/s]</entry>
            <entry>Search [oper/s]</entry>
            <entry>Update [oper/s]</entry>
            <entry>Delete [oper/s]</entry>
            <entry>Average [oper/s]</entry>
          </row>
        </thead>
        <tbody>
          <row>
            <entry>MySQL (async)</entry>
            <entry>9447.47</entry>
            <entry>9627.97</entry>
            <entry>9938.00</entry>
            <entry>11248.34</entry>
            <entry>10065.45</entry>
          </row>

          <row>
            <entry>SQLite (async)</entry>
            <entry>26951.59</entry>
            <entry>31654.29</entry>
            <entry>34899.70</entry>
            <entry>40993.59</entry>
            <entry>33624.79</entry>
          </row>

          <row>
            <entry>memfile (async)</entry>
            <entry>76944.27</entry>
            <entry>2542588.35</entry>
            <entry>76504.54</entry>
            <entry>78269.25</entry>
            <entry>693576.60</entry>
          </row>


          <row>
            <entry>MySQL (sync)</entry>
            <entry>31.64</entry>
            <entry>8575.45</entry>
            <entry>35.76</entry>
            <entry>36.11</entry>
            <entry>2169.74</entry>
          </row>

          <row>
            <entry>SQLite (sync)</entry>
            <entry>16.28</entry>
            <entry>20045.37</entry>
            <entry>16.81</entry>
            <entry>17.85</entry>
            <entry>7524.08</entry>
          </row>

          <row>
            <entry>memfile (sync)</entry>
            <entry>26.16</entry>
            <entry>1223990.21</entry>
            <entry>26.29</entry>
            <entry>26.30</entry>
            <entry>306017.24</entry>
          </row>

        </tbody>
      </tgroup>
      </table>

      <mediaobject>
        <imageobject>
          <imagedata fileref="performance-results-graph1.png" format="PNG"/>
        </imageobject>
        <textobject>
          <phrase>Basic performance measurements</phrase>
        </textobject>
        <caption>
          <para>Graphical representation of the basic performance results
          presented in table <xref linkend="tbl-basic-perf-results" />.</para>
        </caption>
      </mediaobject>

    </section>

    <section>
      <title>Optimized performance measurements</title>
      <para>This section contains sample results for backend performance measurements,
      taken using microbenchmarks. Tests were conducted on reasonably powerful machine:
      <screen>
CPU: Quad-core Intel(R) Core(TM) i7-2600K CPU @ 3.40GHz (8 logical cores)
HDD: 1,5TB Seagate Barracuda ST31500341AS 7200rpm, ext4 partition
OS: Ubuntu 12.04, running kernel 3.2.0-26-generic SMP x86_64
compiler: g++ (Ubuntu/Linaro 4.6.3-1ubuntu5) 4.6.3
MySQL version: 5.5.24
SQLite version: 3.7.9sourceid version is 2011-11-01 00:52:41 c7c6050ef060877ebe77b41d959e9df13f8c9b5e</screen>
      </para>

      <para>The benchmarks were run with precompiled statements enabled.
      The code was compiled with the -Ofast flag (optimize compilation for speed).
      Each run was repeated three times and measured values were averaged.</para>

      <para>Again the benchmarks were run in two series, synchronous and
      asynchronous. As those modes offer radically different
      performances, synchronous mode was conducted for one
      thousand repetitions and asynchronous mode was conducted for
      one hundred thousand repetitions.</para>

      <!-- raw results sync -->
      <table><title>Synchronous results (optimized)</title>
      <tgroup cols='6' align='center' colsep='1' rowsep='1'>
        <colspec colname='Backend'/>
        <colspec colname='Num' />
        <colspec colname='Create'/>
        <colspec colname='Search'/>
        <colspec colname='Update'/>
        <colspec colname='Delete'/>
        <colspec colname='Average'/>
        <thead>
          <row>
            <entry>Backend</entry>
            <entry>Operations</entry>
            <entry>Create [s]</entry>
            <entry>Search [s]</entry>
            <entry>Update [s]</entry>
            <entry>Delete [s]</entry>
            <entry>Average [s]</entry>
          </row>
        </thead>
        <tbody>
          <row>
            <entry>MySQL</entry>
            <entry>1,000</entry>
            <entry>27.887</entry>
            <entry> 0.106</entry>
            <entry>28.223</entry>
            <entry>27.696</entry>
            <entry>20.978</entry>
          </row>

          <row>
            <entry>SQLite</entry>
            <entry>1,000</entry>
            <entry>61.299</entry>
            <entry> 0.015</entry>
            <entry>59.648</entry>
            <entry>61.098</entry>
            <entry>45.626</entry>
          </row>

          <row>
            <entry>memfile</entry>
            <entry>1,000</entry>
            <entry>39.564</entry>
            <entry> 0.001</entry>
            <entry>39.543</entry>
            <entry>39.326</entry>
            <entry>29.608</entry>
          </row>

        </tbody>
      </tgroup>
      </table>

      <para>The following parameters were measured for asynchronous mode.
      MySQL and SQLite were run with one hundred thousand repetitions. Memfile
      was run for one million repetitions due to its much higher performance.</para>

      <!-- raw results async -->
      <table><title>Asynchronous results (optimized)</title>
      <tgroup cols='6' align='center' colsep='1' rowsep='1'>
        <colspec colname='Backend'/>
        <colspec colname='Num' />
        <colspec colname='Create'/>
        <colspec colname='Search'/>
        <colspec colname='Update'/>
        <colspec colname='Delete'/>
        <colspec colname='Average'/>
        <thead>
          <row>
            <entry>Backend</entry>
            <entry>Operations</entry>
            <entry>Create [s]</entry>
            <entry>Search [s]</entry>
            <entry>Update [s]</entry>
            <entry>Delete [s]</entry>
            <entry>Average [s]</entry>
          </row>
        </thead>
        <tbody>
          <row>
            <entry>MySQL</entry>
            <entry>100,000</entry>
            <entry>8.507</entry>
            <entry>9.698</entry>
            <entry>7.785</entry>
            <entry>8.326</entry>
            <entry>8.579</entry>
          </row>

          <row>
            <entry>SQLite</entry>
            <entry>100,000</entry>
            <entry> 1.562</entry>
            <entry> 0.949</entry>
            <entry> 1.513</entry>
            <entry> 1.502</entry>
            <entry> 1.382</entry>
          </row>

          <row>
            <entry>memfile</entry>
            <entry>1,000,000</entry>
            <entry>1.302</entry>
            <entry>0.038</entry>
            <entry>1.306</entry>
            <entry>1.263</entry>
            <entry>0.977</entry>
          </row>

        </tbody>
      </tgroup>
      </table>

      <para>The presented performance results can be converted into operations per second metrics.
      It should be noted that due to large differences between various operations (sometime
      over three orders of magnitude), it is difficult to create a simple, readable chart with
      the data.</para>

      <table id="tbl-optim-perf-results"><title>Estimated optimized performance</title>
      <tgroup cols='6' align='center' colsep='1' rowsep='1'>
        <colspec colname='Backend'/>
        <colspec colname='Create'/>
        <colspec colname='Search'/>
        <colspec colname='Update'/>
        <colspec colname='Delete'/>
        <colspec colname='Average'/>
        <thead>
          <row>
            <entry>Backend</entry>
            <entry>Create [oper/s]</entry>
            <entry>Search [oper/s]</entry>
            <entry>Update [oper/s]</entry>
            <entry>Delete [oper/s]</entry>
            <entry>Average [oper/s]</entry>
          </row>
        </thead>
        <tbody>
          <row>
            <entry>MySQL (async)</entry>
            <entry>11754.84</entry>
            <entry>10311.34</entry>
            <entry>12845.35</entry>
            <entry>12010.24</entry>
            <entry>11730.44</entry>
          </row>

          <row>
            <entry>SQLite (async)</entry>
            <entry>64005.90</entry>
            <entry>105391.29</entry>
            <entry>66075.51</entry>
            <entry>66566.43</entry>
            <entry>75509.78</entry>
          </row>

          <row>
            <entry>memfile (async)</entry>
            <entry>76832.16</entry>
            <entry>2636018.56</entry>
            <entry>76542.50</entry>
            <entry>79188.81</entry>
            <entry>717145.51</entry>
          </row>


          <row>
            <entry>MySQL (sync)</entry>
            <entry>35.86</entry>
            <entry>9461.10</entry>
            <entry>35.43</entry>
            <entry>36.11</entry>
            <entry>2392.12</entry>
          </row>

          <row>
            <entry>SQLite (sync)</entry>
            <entry>16.31</entry>
            <entry>67036.11</entry>
            <entry>16.76</entry>
            <entry>16.37</entry>
            <entry>16771.39</entry>
          </row>

          <row>
            <entry>memfile (sync)</entry>
            <entry>25.28</entry>
            <entry>3460207.61</entry>
            <entry>25.29</entry>
            <entry>25.43</entry>
            <entry>865070.90</entry>
          </row>

        </tbody>
      </tgroup>
      </table>

      <mediaobject>
        <imageobject>
          <imagedata fileref="performance-results-graph2.png" format="PNG"/>
        </imageobject>
        <textobject>
          <phrase>Optimized performance measurements</phrase>
        </textobject>
        <caption>
          <para>Graphical representation of the optimized performance
          results presented in table <xref linkend="tbl-optim-perf-results"
          />.</para>
        </caption>
      </mediaobject>

    </section>

    <section>
      <title>Conclusions</title>
      <para>
        Improvements gained by introducing support for precompiled
        statements in MySQL is somewhat disappointing - between 6 and
        29%.  On the other hand, the improvement in SQLite is
        surprisingly high - the efficiency is more than doubled.
      </para>
      <para>
        Compiled statements do not have any measureable impact on
        synchronous operations. That is as expected, because the major
        bottleneck is the disk performance.
      </para>
      <para>
        Compilation flags yield surprisingly high improvements for C++
        STL code. The memfile backend is in some operations is almost
        twice as fast.
      </para>

      <para>
        If synchronous operation is required, the current performance
        results are likely to be deemed inadequate. The limiting
        factor here is a disk access time. Even migrating to high
        performance 15,000 rpm disk is expected to only roughly double
        number of leases per second, compared to the current results.
        The reason is that to write a file to disk, at least two writes
        are required: the new content and i-node modification of the
        file. The easiest way to boost synchronous performance is to
        switch to SSD disks. Memory-backed RAM disks are also a viable
        solution. However, care should be taken to properly engineer
        backup strategy for RAM disks.
      </para>

      <para>
        While the custom made backend (memfile) provides the best
        perfomance, it carries over all the limitations existing in
        the ISC DHCP4 code: there are no external tools to query or
        change database, the maintenance requires deep knowledge etc.
        Those flaws are not shared by usage of a proper database
        backend, like MySQL and SQLite. They both offer third party
        tools for administrative tasks, they are well documented and
        maintained. However, SQLite support for concurrent access is
        limiting in certain cases. Since all three investigated
        backends more than meet expected performance results, it is
        recommended to use MySQL as a first concrete database backend.
        Should this choice be rejected for any reason, the second
        recommended choice is SQLite.
      </para>

      <para>
        It should be emphaisized that obtained measurements indicate
        only database performance and they cannot be directly
        translated to expected leases per second or queries per second
        performance by an actual server. The DHCP server must do much
        more than just query the database to properly process client's
        message. The provided results should be considered as only rough
        estimates. They can also be used for relative comparisons
        between backends.
      </para>

    </section>

    <section>
      <title>Possible further optimizations</title>
      <para>
        For basic measurements the code was compiled with -g -O0
        flags. For optimized measurements the benchmarking code was
        compiled with -Ofast (optimize for speed). In both cases, the
        same backend (MySQL or SQLite) library was used. It may be
        useful to recompile the libraries (or the whole server in case
        of MySQL) with -Ofast.
      </para>
      <para>
        There are many MySQL parameters that various sources recommend
        to improve performance. They were not investigated further.
      </para>
      <para>
        Currently all operations are conducted on one by one
        basis. Each operation is treated as a separate
        transaction. Grouping N operations together will potentially
        bring almost N fold increase in synchronous operations. Such a
        feature is present in ISC DHCP4 and is called cache-threshold.
        Extension for this benchmark in this regard should be
        considered.  That affects only write operations (insert,
        update and delete). Read operations (search) are expected to
        be barely affected.
      </para>
      <para>
        Multi-threaded or multi-process benchmark may be considered in
        the future. It may be somewhat difficult as only some backends
        support concurrent access.
      </para>
    </section>

  </chapter>

  <chapter id="perfdhcp">
    <title>perfdhcp</title>
    <para>
      TODO: Write something about perfdhcp here.
    </para>
  </chapter>

</book>