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- <!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>59.1. 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 V1.79.1" /><link rel="prev" href="geqo.html" title="Chapter 59. Genetic Query Optimizer" /><link rel="next" href="geqo-intro2.html" title="59.2. Genetic Algorithms" /></head><body><div xmlns="http://www.w3.org/TR/xhtml1/transitional" class="navheader"><table width="100%" summary="Navigation header"><tr><th colspan="5" align="center">59.1. Query Handling as a Complex Optimization Problem</th></tr><tr><td width="10%" align="left"><a accesskey="p" href="geqo.html" title="Chapter 59. Genetic Query Optimizer">Prev</a> </td><td width="10%" align="left"><a accesskey="u" href="geqo.html" title="Chapter 59. Genetic Query Optimizer">Up</a></td><th width="60%" align="center">Chapter 59. Genetic Query Optimizer</th><td width="10%" align="right"><a accesskey="h" href="index.html" title="PostgreSQL 12.4 Documentation">Home</a></td><td width="10%" align="right"> <a accesskey="n" href="geqo-intro2.html" title="59.2. Genetic Algorithms">Next</a></td></tr></table><hr></hr></div><div class="sect1" id="GEQO-INTRO"><div class="titlepage"><div><div><h2 class="title" style="clear: both">59.1. 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.
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