419 lines
12 KiB
Plaintext
419 lines
12 KiB
Plaintext
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$Header: /cvsroot/pgsql/doc/src/sgml/geqo.sgml,v 1.8 1999/03/30 15:25:56 thomas Exp $
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Genetic Optimizer
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$Log: geqo.sgml,v $
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Revision 1.8 1999/03/30 15:25:56 thomas
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Fix up small markup problems. Force omit-tags to nil so we have tag
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completion as required by the newest DocBook conventions.
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Revision 1.7 1999/02/19 01:57:08 thomas
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Fix SGML markup from last content changes.
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Revision 1.6 1999/02/18 05:26:17 momjian
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Enable bushy plans by default.
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Revision 1.5 1998/12/29 02:24:15 thomas
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Clean up to ensure tag completion as required by the newest versions
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of Norm's Modular Style Sheets and jade/docbook.
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From Vince Vielhaber <vev@michvhf.com>.
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Revision 1.4 1998/08/15 06:55:05 thomas
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Change Id field in chapter tag to change html output file name.
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-->
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<Chapter Id="geqo">
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<DocInfo>
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<Author>
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<FirstName>Martin</FirstName>
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<SurName>Utesch</SurName>
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<Affiliation>
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<Orgname>
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University of Mining and Technology
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</Orgname>
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<Orgdiv>
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Institute of Automatic Control
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</Orgdiv>
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<Address>
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<City>
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Freiberg
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</City>
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<Country>
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Germany
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</Country>
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</Address>
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</Affiliation>
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</Author>
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<Date>1997-10-02</Date>
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</DocInfo>
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<Title>Genetic Query Optimization in Database Systems</Title>
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<Para>
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<Note>
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<Title>Author</Title>
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<Para>
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Written by <ULink url="utesch@aut.tu-freiberg.de">Martin Utesch</ULink>
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for the Institute of Automatic Control at the University of Mining and Technology in Freiberg, Germany.
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</Para>
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</Note>
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</para>
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<Sect1>
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<Title>Query Handling as a Complex Optimization Problem</Title>
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<Para>
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Among all relational operators the most difficult one to process and
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optimize is the <FirstTerm>join</FirstTerm>. The number of alternative plans to answer a query
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grows exponentially with the number of <Command>join</Command>s included in it. Further
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optimization effort is caused by the support of a variety of <FirstTerm>join methods</FirstTerm>
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(e.g., nested loop, index scan, merge join in <ProductName>Postgres</ProductName>) to
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process individual <Command>join</Command>s and a diversity of <FirstTerm>indices</FirstTerm> (e.g., r-tree,
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b-tree, hash in <ProductName>Postgres</ProductName>) as access paths for relations.
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</para>
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<Para>
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The current <ProductName>Postgres</ProductName> optimizer implementation performs a <FirstTerm>near-
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exhaustive search</FirstTerm> over the space of alternative strategies. This query
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optimization technique is inadequate to support database application
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domains that involve the need for extensive queries, such as artificial
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intelligence.
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</para>
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<Para>
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The Institute of Automatic Control at the University of Mining and
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Technology, in Freiberg, Germany, encountered the described problems as its
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folks wanted to take the <ProductName>Postgres</ProductName> DBMS as the backend for a decision
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support knowledge based system for the maintenance of an electrical
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power grid. The DBMS needed to handle large <Command>join</Command> queries for the
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inference machine of the knowledge based system.
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</para>
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<Para>
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Performance difficulties within exploring the space of possible query
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plans arose the demand for a new optimization technique being developed.
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</para>
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<Para>
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In the following we propose the implementation of a <FirstTerm>Genetic Algorithm</FirstTerm>
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as an option for the database query optimization problem.
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</para>
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</sect1>
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<Sect1>
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<Title>Genetic Algorithms (<Acronym>GA</Acronym>)</Title>
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<Para>
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The <Acronym>GA</Acronym> is a heuristic optimization method which operates through
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determined, randomized search. The set of possible solutions for the
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optimization problem is considered as a <FirstTerm>population</FirstTerm> of <FirstTerm>individuals</FirstTerm>.
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The degree of adaption of an individual to its environment is specified
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by its <FirstTerm>fitness</FirstTerm>.
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</para>
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<Para>
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The coordinates of an individual in the search space are represented
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by <FirstTerm>chromosomes</FirstTerm>, in essence a set of character strings. A <FirstTerm>gene</FirstTerm> is a
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subsection of a chromosome which encodes the value of a single parameter
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being optimized. Typical encodings for a gene could be <FirstTerm>binary</FirstTerm> or
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<FirstTerm>integer</FirstTerm>.
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</para>
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<Para>
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Through simulation of the evolutionary operations <FirstTerm>recombination</FirstTerm>,
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<FirstTerm>mutation</FirstTerm>, and <FirstTerm>selection</FirstTerm> new generations of search points are found
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that show a higher average fitness than their ancestors.
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</para>
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<Para>
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According to the "comp.ai.genetic" <Acronym>FAQ</Acronym> it cannot be stressed too
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strongly that a <Acronym>GA</Acronym> is not a pure random search for a solution to a
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problem. A <Acronym>GA</Acronym> uses stochastic processes, but the result is distinctly
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non-random (better than random).
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<ProgramListing>
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Structured Diagram of a <Acronym>GA</Acronym>:
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---------------------------
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P(t) generation of ancestors at a time t
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P''(t) generation of descendants at a time t
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+=========================================+
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|>>>>>>>>>>> Algorithm GA <<<<<<<<<<<<<<|
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+=========================================+
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| INITIALIZE t := 0 |
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+=========================================+
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| INITIALIZE P(t) |
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+=========================================+
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| evalute FITNESS of P(t) |
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+=========================================+
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| while not STOPPING CRITERION do |
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| +-------------------------------------+
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| | P'(t) := RECOMBINATION{P(t)} |
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| +-------------------------------------+
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| | P''(t) := MUTATION{P'(t)} |
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| +-------------------------------------+
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| | P(t+1) := SELECTION{P''(t) + P(t)} |
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| +-------------------------------------+
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| | evalute FITNESS of P''(t) |
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| +-------------------------------------+
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| | t := t + 1 |
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+===+=====================================+
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</ProgramListing>
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</para>
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</sect1>
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<Sect1>
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<Title>Genetic Query Optimization (<Acronym>GEQO</Acronym>) in Postgres</Title>
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<Para>
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The <Acronym>GEQO</Acronym> module is intended for the solution of the query
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optimization problem similar to a traveling salesman problem (<Acronym>TSP</Acronym>).
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Possible query plans are encoded as integer strings. Each string
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represents the <Command>join</Command> order from one relation of the query to the next.
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E. g., the query tree
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<ProgramListing>
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/\
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/\ 2
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/\ 3
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4 1
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</ProgramListing>
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is encoded by the integer string '4-1-3-2',
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which means, first join relation '4' and '1', then '3', and
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then '2', where 1, 2, 3, 4 are relids in <ProductName>Postgres</ProductName>.
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</para>
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<Para>
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Parts of the <Acronym>GEQO</Acronym> module are adapted from D. Whitley's Genitor
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algorithm.
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</para>
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<Para>
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Specific characteristics of the <Acronym>GEQO</Acronym> implementation in <ProductName>Postgres</ProductName>
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are:
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<ItemizedList Mark="bullet" Spacing="compact">
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<ListItem>
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<Para>
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Usage of a <FirstTerm>steady state</FirstTerm> <Acronym>GA</Acronym> (replacement of the least fit
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individuals in a population, not whole-generational replacement)
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allows fast convergence towards improved query plans. This is
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essential for query handling with reasonable time;
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</Para>
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</ListItem>
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<ListItem>
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<Para>
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Usage of <FirstTerm>edge recombination crossover</FirstTerm> which is especially suited
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to keep edge losses low for the solution of the <Acronym>TSP</Acronym> by means of a <Acronym>GA</Acronym>;
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</Para>
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</ListItem>
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<ListItem>
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<Para>
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Mutation as genetic operator is deprecated so that no repair
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mechanisms are needed to generate legal <Acronym>TSP</Acronym> tours.
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</Para>
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</ListItem>
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</ItemizedList>
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</para>
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<Para>
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The <Acronym>GEQO</Acronym> module gives the following benefits to the <ProductName>Postgres</ProductName> DBMS
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compared to the <ProductName>Postgres</ProductName> query optimizer implementation:
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<ItemizedList Mark="bullet" Spacing="compact">
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<ListItem>
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<Para>
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Handling of large <Command>join</Command> queries through non-exhaustive search;
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</Para>
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</ListItem>
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<ListItem>
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<Para>
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Improved cost size approximation of query plans since no longer
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plan merging is needed (the <Acronym>GEQO</Acronym> module evaluates the cost for a
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query plan as an individual).
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</Para>
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</ListItem>
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</ItemizedList>
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</para>
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</Sect1>
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<Sect1>
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<Title>Future Implementation Tasks for <ProductName>Postgres</ProductName> <Acronym>GEQO</Acronym></Title>
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<Sect2>
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<Title>Basic Improvements</Title>
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<Sect3>
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<Title>Improve freeing of memory when query is already processed</Title>
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<Para>
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With large <Command>join</Command> queries the computing time spent for the genetic query
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optimization seems to be a mere <Emphasis>fraction</Emphasis> of the time
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<ProductName>Postgres</ProductName>
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needs for freeing memory via routine <Function>MemoryContextFree</Function>,
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file <FileName>backend/utils/mmgr/mcxt.c</FileName>.
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Debugging showed that it get stucked in a loop of routine
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<Function>OrderedElemPop</Function>, file <FileName>backend/utils/mmgr/oset.c</FileName>.
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The same problems arise with long queries when using the normal
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<ProductName>Postgres</ProductName> query optimization algorithm.
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</para>
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</sect3>
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<Sect3>
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<Title>Improve genetic algorithm parameter settings</Title>
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<Para>
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In file <FileName>backend/optimizer/geqo/geqo_params.c</FileName>, routines
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<Function>gimme_pool_size</Function> and <Function>gimme_number_generations</Function>,
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we have to find a compromise for the parameter settings
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to satisfy two competing demands:
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<ItemizedList Spacing="compact">
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<ListItem>
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<Para>
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Optimality of the query plan
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</Para>
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</ListItem>
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<ListItem>
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<Para>
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Computing time
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</Para>
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</ListItem>
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</ItemizedList>
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</para>
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</sect3>
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<Sect3>
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<Title>Find better solution for integer overflow</Title>
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<Para>
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In file <FileName>backend/optimizer/geqo/geqo_eval.c</FileName>, routine
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<Function>geqo_joinrel_size</Function>,
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the present hack for MAXINT overflow is to set the <ProductName>Postgres</ProductName> integer
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value of <StructField>rel->size</StructField> to its logarithm.
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Modifications of <StructName>Rel</StructName> in <FileName>backend/nodes/relation.h</FileName> will
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surely have severe impacts on the whole <ProductName>Postgres</ProductName> implementation.
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</para>
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</sect3>
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<Sect3>
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<Title>Find solution for exhausted memory</Title>
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<Para>
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Memory exhaustion may occur with more than 10 relations involved in a query.
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In file <FileName>backend/optimizer/geqo/geqo_eval.c</FileName>, routine
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<Function>gimme_tree</Function> is recursively called.
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Maybe I forgot something to be freed correctly, but I dunno what.
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Of course the <StructName>rel</StructName> data structure of the <Command>join</Command> keeps growing and
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growing the more relations are packed into it.
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Suggestions are welcome :-(
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</para>
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</sect3>
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</sect2>
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<BIBLIOGRAPHY Id="geqo-biblio">
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<TITLE>
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References
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</TITLE>
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<PARA>Reference information for <Acronym>GEQ</Acronym> algorithms.
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</PARA>
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<BIBLIOENTRY>
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<BOOKBIBLIO>
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<TITLE>
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The Hitch-Hiker's Guide to Evolutionary Computation
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</TITLE>
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<AUTHORGROUP>
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<AUTHOR>
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<FIRSTNAME>Jörg</FIRSTNAME>
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<SURNAME>Heitkötter</SURNAME>
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</AUTHOR>
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<AUTHOR>
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<FIRSTNAME>David</FIRSTNAME>
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<SURNAME>Beasley</SURNAME>
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</AUTHOR>
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</AUTHORGROUP>
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<PUBLISHER>
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<PUBLISHERNAME>
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InterNet resource
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</PUBLISHERNAME>
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</PUBLISHER>
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<ABSTRACT>
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<Para>
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FAQ in <ULink url="news://comp.ai.genetic">comp.ai.genetic</ULink>
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is available at <ULink url="ftp://ftp.Germany.EU.net/pub/research/softcomp/EC/Welcome.html">Encore</ULink>.
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</Para>
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</ABSTRACT>
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</BOOKBIBLIO>
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<BOOKBIBLIO>
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<TITLE>
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The Design and Implementation of the Postgres Query Optimizer
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</TITLE>
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<AUTHORGROUP>
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<AUTHOR>
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<FIRSTNAME>Z.</FIRSTNAME>
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<SURNAME>Fong</SURNAME>
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</AUTHOR>
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</AUTHORGROUP>
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<PUBLISHER>
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<PUBLISHERNAME>
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University of California, Berkeley Computer Science Department
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</PUBLISHERNAME>
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</PUBLISHER>
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<ABSTRACT>
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<Para>
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File <FileName>planner/Report.ps</FileName> in the 'postgres-papers' distribution.
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</Para>
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</ABSTRACT>
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</BOOKBIBLIO>
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<BOOKBIBLIO>
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<TITLE>
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Fundamentals of Database Systems
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</TITLE>
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<AUTHORGROUP>
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<AUTHOR>
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<FIRSTNAME>R.</FIRSTNAME>
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<SURNAME>Elmasri</SURNAME>
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</AUTHOR>
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<AUTHOR>
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<FIRSTNAME>S.</FIRSTNAME>
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<SURNAME>Navathe</SURNAME>
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</AUTHOR>
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</AUTHORGROUP>
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<PUBLISHER>
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<PUBLISHERNAME>
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The Benjamin/Cummings Pub., Inc.
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</PUBLISHERNAME>
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</PUBLISHER>
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</BOOKBIBLIO>
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</BIBLIOENTRY>
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</BIBLIOGRAPHY>
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</sect1>
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</Chapter>
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<!-- Keep this comment at the end of the file
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Local variables:
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sgml-parent-document:nil
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sgml-default-dtd-file:"./reference.ced"
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sgml-local-catalogs:"/usr/lib/sgml/catalog"
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End:
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