MartinUtesch
University of Mining and Technology
Institute of Automatic Control
Freiberg
Germany
1997-10-02Genetic Query OptimizationAuthor
Written by Martin Utesch (utesch@aut.tu-freiberg.de)
for the Institute of Automatic Control at the University of Mining and Technology in Freiberg, Germany.
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 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.
Genetic Algorithms
The genetic algorithm (GA) is a heuristic optimization method which
operates through
determined, randomized search. The set of possible solutions for the
optimization problem is considered as a
population of individuals.
The degree of adaptation of an individual to its environment is specified
by its fitness.
The coordinates of an individual in the search space are represented
by chromosomes, in essence a set of character
strings. A gene is a
subsection of a chromosome which encodes the value of a single parameter
being optimized. Typical encodings for a gene could be binary or
integer.
Through simulation of the evolutionary operations recombination,
mutation, and
selection new generations of search points are found
that show a higher average fitness than their ancestors.
According to the comp.ai.genetic> FAQ it cannot be stressed too
strongly that a GA is not a pure random search for a solution to a
problem. A GA uses stochastic processes, but the result is distinctly
non-random (better than random).
Genetic Query Optimization (GEQO) in PostgreSQL
The GEQO module is intended for the solution of the query
optimization problem similar to a traveling salesman problem (TSP).
Possible query plans are encoded as integer strings. Each string
represents the join order from one relation of the query to the next.
E. g., the query tree
/\
/\ 2
/\ 3
4 1
is encoded by the integer string '4-1-3-2',
which means, first join relation '4' and '1', then '3', and
then '2', where 1, 2, 3, 4 are relation IDs within the
PostgreSQL optimizer.
Parts of the GEQO module are adapted from D. Whitley's Genitor
algorithm.
Specific characteristics of the GEQO
implementation in PostgreSQL
are:
Usage of a steady state GA (replacement of the least fit
individuals in a population, not whole-generational replacement)
allows fast convergence towards improved query plans. This is
essential for query handling with reasonable time;
Usage of edge recombination crossover which is
especially suited
to keep edge losses low for the solution of the
TSP by means of a GA;
Mutation as genetic operator is deprecated so that no repair
mechanisms are needed to generate legal TSP tours.
The GEQO module allows
the PostgreSQL query optimizer to
support large join queries effectively through
non-exhaustive search.
Future Implementation Tasks for
PostgreSQL> GEQO
Work is still needed to improve the genetic algorithm parameter
settings.
In file backend/optimizer/geqo/geqo_params.c, routines
gimme_pool_size and gimme_number_generations,
we have to find a compromise for the parameter settings
to satisfy two competing demands:
Optimality of the query plan
Computing time
Further Readings
The following resources contain additional information about
genetic algorithms:
The Hitch-Hiker's
Guide to Evolutionary Computation (FAQ for comp.ai.genetic)
Evolutionary
Computation and its application to art and design by
Craig Reynolds