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 relids within the Postgres optimizer.
Parts of the GEQO module are adapted from D. Whitley's Genitor algorithm.
Specific characteristics of the GEQO implementation in Postgres 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 Postgres query optimizer to support large join queries effectively through non-exhaustive search.
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
Reference information for GEQ algorithms.
The Hitch-Hiker's Guide to Evolutionary Computation , Jörg Heitkötter and David Beasley, InterNet resource , The Design and Implementation of the Postgres Query Optimizer , Z. Fong, University of California, Berkeley Computer Science Department , Fundamentals of Database Systems , R. Elmasri and S. Navathe, The Benjamin/Cummings Pub., Inc. .
FAQ in comp.ai.genetic is available at Encore.
File planner/Report.ps in the 'postgres-papers' distribution.