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>Chapter 49. Genetic Query Optimizer</TD
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>49.1. Query Handling as a Complex Optimization Problem</A
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><P
>    Among all relational operators the most difficult one to process
    and optimize is the <I
CLASS="FIRSTTERM"
>join</I
>. 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 <I
CLASS="FIRSTTERM"
>join
    methods</I
> (e.g., nested loop, hash join, merge join in
    <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>) to process individual joins
    and a diversity of <I
CLASS="FIRSTTERM"
>indexes</I
> (e.g.,
    B-tree, hash, GiST and GIN in <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
>) as 
    access paths for relations.
   </P
><P
>    The normal <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> query optimizer
    performs a <I
CLASS="FIRSTTERM"
>near-exhaustive search</I
> 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
    <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> query optimizer
    inappropriate for queries that join a large number of tables.
   </P
><P
>    The Institute of Automatic Control at the University of Mining and
    Technology, in Freiberg, Germany, encountered some problems when
    it wanted to use <SPAN
CLASS="PRODUCTNAME"
>PostgreSQL</SPAN
> 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.
   </P
><P
>    In the following we describe the implementation of a
    <I
CLASS="FIRSTTERM"
>genetic algorithm</I
> to solve the join
    ordering problem in a manner that is efficient for queries
    involving large numbers of joins.
   </P
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