Author: Written by Martin Utesch (<firstname.lastname@example.org>) for the Institute of Automatic Control at the University of Mining and Technology in Freiberg, Germany.
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 Postgres) to process individual joins and a diversity of indices (e.g., r-tree, b-tree, hash in Postgres) as access paths for relations.
The current Postgres optimizer implementation performs a near-exhaustive search over the space of alternative strategies. This query optimization technique is inadequate to support database application domains that involve the need for extensive queries, such as artificial intelligence.
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 Postgres 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 being developed.
In the following we propose the implementation of a Genetic Algorithm as an option for the database query optimization problem.