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>Chapter 48. Genetic Query Optimizer</TD
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><A
NAME="GEQO-PG-INTRO"
>48.3. Genetic Query Optimization (<ACRONYM
CLASS="ACRONYM"
>GEQO</ACRONYM
>) in PostgreSQL</A
></H1
><P
>    The <ACRONYM
CLASS="ACRONYM"
>GEQO</ACRONYM
> module approaches the query
    optimization problem as though it were the well-known traveling salesman
    problem (<ACRONYM
CLASS="ACRONYM"
>TSP</ACRONYM
>).
    Possible query plans are encoded as integer strings. Each string
    represents the join order from one relation of the query to the next.
    For example, the join tree
</P><PRE
CLASS="LITERALLAYOUT"
>   /\
  /\ 2
 /\ 3
4  1</PRE
><P>
    is encoded by the integer string '4-1-3-2',
    which means, first join relation '4' and '1', then '3', and
    then '2', where 1, 2, 3, 4 are relation IDs within the
    <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> optimizer.
   </P
><P
>    Parts of the <ACRONYM
CLASS="ACRONYM"
>GEQO</ACRONYM
> module are adapted from D. Whitley's Genitor
    algorithm.
   </P
><P
>    Specific characteristics of the <ACRONYM
CLASS="ACRONYM"
>GEQO</ACRONYM
>
    implementation in <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>
    are:

    <P
></P
></P><UL
COMPACT="COMPACT"
><LI
STYLE="list-style-type: disc"
><P
>       Usage of a <I
CLASS="FIRSTTERM"
>steady state</I
> <ACRONYM
CLASS="ACRONYM"
>GA</ACRONYM
> (replacement of the least fit
       individuals in a population, not whole-generational replacement)
       allows fast convergence towards improved query plans. This is
       essential for query handling with reasonable time;
      </P
></LI
><LI
STYLE="list-style-type: disc"
><P
>       Usage of <I
CLASS="FIRSTTERM"
>edge recombination crossover</I
>
       which is especially suited to keep edge losses low for the
       solution of the <ACRONYM
CLASS="ACRONYM"
>TSP</ACRONYM
> by means of a
       <ACRONYM
CLASS="ACRONYM"
>GA</ACRONYM
>;
      </P
></LI
><LI
STYLE="list-style-type: disc"
><P
>       Mutation as genetic operator is deprecated so that no repair
       mechanisms are needed to generate legal <ACRONYM
CLASS="ACRONYM"
>TSP</ACRONYM
> tours.
      </P
></LI
></UL
><P>
   </P
><P
>    The <ACRONYM
CLASS="ACRONYM"
>GEQO</ACRONYM
> module allows
    the <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> query optimizer to
    support large join queries effectively through
    non-exhaustive search.
   </P
><DIV
CLASS="SECT2"
><H2
CLASS="SECT2"
><A
NAME="GEQO-FUTURE"
>48.3.1. Future Implementation Tasks for
    <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> <ACRONYM
CLASS="ACRONYM"
>GEQO</ACRONYM
></A
></H2
><P
>      Work is still needed to improve the genetic algorithm parameter
      settings.
      In file <TT
CLASS="FILENAME"
>src/backend/optimizer/geqo/geqo_main.c</TT
>,
      routines
      <CODE
CLASS="FUNCTION"
>gimme_pool_size</CODE
> and <CODE
CLASS="FUNCTION"
>gimme_number_generations</CODE
>,
      we have to find a compromise for the parameter settings
      to satisfy two competing demands:
      <P
></P
></P><UL
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><LI
><P
>         Optimality of the query plan
        </P
></LI
><LI
><P
>         Computing time
        </P
></LI
></UL
><P>
     </P
><P
>      At a more basic level, it is not clear that solving query optimization
      with a GA algorithm designed for TSP is appropriate.  In the TSP case,
      the cost associated with any substring (partial tour) is independent
      of the rest of the tour, but this is certainly not true for query
      optimization.  Thus it is questionable whether edge recombination
      crossover is the most effective mutation procedure.
     </P
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