Follow Techotopia on Twitter

On-line Guides
All Guides
eBook Store
iOS / Android
Linux for Beginners
Office Productivity
Linux Installation
Linux Security
Linux Utilities
Linux Virtualization
Linux Kernel
System/Network Admin
Scripting Languages
Development Tools
Web Development
GUI Toolkits/Desktop
Mail Systems
Eclipse Documentation

How To Guides
General System Admin
Linux Security
Linux Filesystems
Web Servers
Graphics & Desktop
PC Hardware
Problem Solutions
Privacy Policy




Chapter 47. Genetic Query Optimizer

Author: Written by Martin Utesch () for the Institute of Automatic Control at the University of Mining and Technology in Freiberg, Germany.

47.1. Query Handling as a Complex Optimization Problem

Among all relational operators the most difficult one to process and optimize is the join. The number of alternative plans to answer a query grows exponentially with the number of joins included in it. Further optimization effort is caused by the support of a variety of join methods (e.g., nested loop, hash join, merge join in PostgreSQL) to process individual joins and a diversity of indexes (e.g., R-tree, B-tree, hash in PostgreSQL) as access paths for relations.

The current PostgreSQL optimizer implementation performs a near-exhaustive search over the space of alternative strategies. This algorithm, first introduced in the "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 PostgreSQL query optimizer inappropriate for queries that join a large number of tables.

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 PostgreSQL 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.

Performance difficulties in exploring the space of possible query plans created the demand for a new optimization technique to be developed.

In the following we describe the implementation of a Genetic Algorithm to solve the join ordering problem in a manner that is efficient for queries involving large numbers of joins.

  Published courtesy of The PostgreSQL Global Development Group Design by Interspire