The GEQO module approaches the query
optimization problem as though it were the well-known 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.
For example, the join 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.
Work is still needed to improve the genetic algorithm parameter
settings.
In file src/backend/optimizer/geqo/geqo_main.c,
routines
gimme_pool_size and gimme_number_generations,
we have to find a compromise for the parameter settings
to satisfy two competing demands:
At a more basic level, it is not clear that solving query optimization
with a GA algorithm designed for TSP is appropriate. In the TSP case,
the cost associated with any substring (partial tour) is independent
of the rest of the tour, but this is certainly not true for query
optimization. Thus it is questionable whether edge recombination
crossover is the most effective mutation procedure.
