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$PostgreSQL: pgsql/doc/src/sgml/geqo.sgml,v 1.34 2005/11/07 17:36:44 tgl Exp $

$PostgreSQL: pgsql/doc/src/sgml/geqo.sgml,v 1.35 2006/01/22 03:56:58 neilc Exp $

Genetic Optimizer

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<para>

Among all relational operators the most difficult one to process

and optimize is the <firstterm>join</firstterm>. The number of

alternative plans to answer aquery grows exponentially with the

number of joinsincludedinit. Further optimization effort is

possiblequery plans grows exponentially with the

number of joins inthe 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

</para>

<para>

The current <productname>PostgreSQL</productname> optimizer

implementation performs a <firstterm>near-exhaustive

search</firstterm> over the space of alternative strategies. This

algorithm, first introduced in the <quote>System R</quote>

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

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 the described problems as its

folks wanted to take the <productname>PostgreSQL</productname> DBMS 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.

</para>

<para>

Performance difficulties in exploring the space of possible query

plans created the demand for a new optimization technique to be developed.

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

<firstterm>genetic algorithm</firstterm> to solve the join

ordering problem in a manner that is efficient for queries

involving large numbers of joins.

</para>