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 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 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 being developed.
In the following we propose the implementation of a Genetic Algorithm
as an option for the database query optimization problem.
