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| author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-05-04 12:17:33 +0000 |
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| committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-05-04 12:17:33 +0000 |
| commit | 5e45211a64149b3c659b90ff2de6fa982a5a93ed (patch) | |
| tree | 739caf8c461053357daa9f162bef34516c7bf452 /doc/src/sgml/html/geqo-intro.html | |
| parent | Initial commit. (diff) | |
| download | postgresql-15-5e45211a64149b3c659b90ff2de6fa982a5a93ed.tar.xz postgresql-15-5e45211a64149b3c659b90ff2de6fa982a5a93ed.zip | |
Adding upstream version 15.5.upstream/15.5
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
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| -rw-r--r-- | doc/src/sgml/html/geqo-intro.html | 36 |
1 files changed, 36 insertions, 0 deletions
diff --git a/doc/src/sgml/html/geqo-intro.html b/doc/src/sgml/html/geqo-intro.html new file mode 100644 index 0000000..bfbf675 --- /dev/null +++ b/doc/src/sgml/html/geqo-intro.html @@ -0,0 +1,36 @@ +<?xml version="1.0" encoding="UTF-8" standalone="no"?> +<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8" /><title>62.1. Query Handling as a Complex Optimization Problem</title><link rel="stylesheet" type="text/css" href="stylesheet.css" /><link rev="made" href="pgsql-docs@lists.postgresql.org" /><meta name="generator" content="DocBook XSL Stylesheets Vsnapshot" /><link rel="prev" href="geqo.html" title="Chapter 62. Genetic Query Optimizer" /><link rel="next" href="geqo-intro2.html" title="62.2. Genetic Algorithms" /></head><body id="docContent" class="container-fluid col-10"><div class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="5" align="center">62.1. Query Handling as a Complex Optimization Problem</th></tr><tr><td width="10%" align="left"><a accesskey="p" href="geqo.html" title="Chapter 62. Genetic Query Optimizer">Prev</a> </td><td width="10%" align="left"><a accesskey="u" href="geqo.html" title="Chapter 62. Genetic Query Optimizer">Up</a></td><th width="60%" align="center">Chapter 62. Genetic Query Optimizer</th><td width="10%" align="right"><a accesskey="h" href="index.html" title="PostgreSQL 15.5 Documentation">Home</a></td><td width="10%" align="right"> <a accesskey="n" href="geqo-intro2.html" title="62.2. Genetic Algorithms">Next</a></td></tr></table><hr /></div><div class="sect1" id="GEQO-INTRO"><div class="titlepage"><div><div><h2 class="title" style="clear: both">62.1. Query Handling as a Complex Optimization Problem</h2></div></div></div><p> + Among all relational operators the most difficult one to process + and optimize is the <em class="firstterm">join</em>. 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 <em class="firstterm">join + methods</em> (e.g., nested loop, hash join, merge join in + <span class="productname">PostgreSQL</span>) to process individual joins + and a diversity of <em class="firstterm">indexes</em> (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 <em class="firstterm">near-exhaustive search</em> 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 + <em class="firstterm">genetic algorithm</em> to solve the join + ordering problem in a manner that is efficient for queries + involving large numbers of joins. + </p></div><div class="navfooter"><hr /><table width="100%" summary="Navigation footer"><tr><td width="40%" align="left"><a accesskey="p" href="geqo.html" title="Chapter 62. Genetic Query Optimizer">Prev</a> </td><td width="20%" align="center"><a accesskey="u" href="geqo.html" title="Chapter 62. Genetic Query Optimizer">Up</a></td><td width="40%" align="right"> <a accesskey="n" href="geqo-intro2.html" title="62.2. Genetic Algorithms">Next</a></td></tr><tr><td width="40%" align="left" valign="top">Chapter 62. Genetic Query Optimizer </td><td width="20%" align="center"><a accesskey="h" href="index.html" title="PostgreSQL 15.5 Documentation">Home</a></td><td width="40%" align="right" valign="top"> 62.2. Genetic Algorithms</td></tr></table></div></body></html>
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