<!-- doc/src/sgml/geqo.sgml -->

 <chapter id="geqo">
  <title>Genetic Query Optimizer</title>

  <para>
   <note>
    <title>Author</title>
    <para>
     Written by Martin Utesch (<email>utesch@aut.tu-freiberg.de</email>)
     for the Institute of Automatic Control at the University of Mining and Technology in Freiberg, Germany.
    </para>
   </note>
  </para>

  <sect1 id="geqo-intro">
   <title>Query Handling as a Complex Optimization Problem</title>

   <para>
    Among all relational operators the most difficult one to process
    and optimize is the <firstterm>join</firstterm>. The number of
    possible query plans grows exponentially with the
    number of joins in the query. Further optimization effort is
    caused by the support of a variety of <firstterm>join
    methods</firstterm> (e.g., nested loop, hash join, merge join in
    <productname>PostgreSQL</productname>) to process individual joins
    and a diversity of <firstterm>indexes</firstterm> (e.g.,
    B-tree, hash, GiST and GIN in <productname>PostgreSQL</productname>) as
    access paths for relations.
   </para>

   <para>
    The normal <productname>PostgreSQL</productname> query optimizer
    performs a <firstterm>near-exhaustive search</firstterm> over the
    space of alternative strategies. This algorithm, first introduced
    in IBM's System R database, produces a near-optimal join order,
    but can take an enormous amount of time and memory space when the
    number of joins in the query grows large. This makes the ordinary
    <productname>PostgreSQL</productname> query optimizer
    inappropriate for queries that join a large number of tables.
   </para>

   <para>
    The Institute of Automatic Control at the University of Mining and
    Technology, in Freiberg, Germany, encountered some problems when
    it wanted to use <productname>PostgreSQL</productname> as the
    backend for a decision support knowledge based system for the
    maintenance of an electrical power grid. The DBMS needed to handle
    large join queries for the inference machine of the knowledge
    based system. The number of joins in these queries made using the
    normal query optimizer infeasible.
   </para>

   <para>
    In the following we describe the implementation of a
    <firstterm>genetic algorithm</firstterm> to solve the join
    ordering problem in a manner that is efficient for queries
    involving large numbers of joins.
   </para>
  </sect1>

  <sect1 id="geqo-intro2">
   <title>Genetic Algorithms</title>

   <para>
    The genetic algorithm (<acronym>GA</acronym>) is a heuristic optimization method which
    operates through randomized search. The set of possible solutions for the
    optimization problem is considered as a
    <firstterm>population</firstterm> of <firstterm>individuals</firstterm>.
    The degree of adaptation of an individual to its environment is specified
    by its <firstterm>fitness</firstterm>.
   </para>

   <para>
    The coordinates of an individual in the search space are represented
    by <firstterm>chromosomes</firstterm>, in essence a set of character
    strings. A <firstterm>gene</firstterm> is a
    subsection of a chromosome which encodes the value of a single parameter
    being optimized. Typical encodings for a gene could be <firstterm>binary</firstterm> or
    <firstterm>integer</firstterm>.
   </para>

   <para>
    Through simulation of the evolutionary operations <firstterm>recombination</firstterm>,
    <firstterm>mutation</firstterm>, and
    <firstterm>selection</firstterm> new generations of search points are found
    that show a higher average fitness than their ancestors. <xref linkend="geqo-figure"/>
    illustrates these steps.
   </para>

   <figure id="geqo-figure">
    <title>Structure of a Genetic Algorithm</title>
    <mediaobject>
     <imageobject>
      <imagedata fileref="images/genetic-algorithm.svg" format="SVG" width="100%"/>
     </imageobject>
    </mediaobject>
   </figure>

   <para>