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Genetic Query Optimization in Database Systems

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Martin Utesch

<utesch@aut.tu-freiberg.de>

Institute of Automatic Control

University of Mining and Technology

Freiberg, Germany

02/10/1997

1.) 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, index scan, 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 evolve the need for extensive queries, such as artifcial

intelligence.

The Institute of Automatic Control at the University of Mining and

Technology 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 within exploring the space of possible query

plans arose 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.

2.) Genetic Algorithms (GA)

===========================

The 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 adaption 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).

Structured Diagram of a GA:

---------------------------

P(t) generation of ancestors at a time t

P''(t) generation of descendants at a time t

+=========================================+

|>>>>>>>>>>> Algorithm GA <<<<<<<<<<<<<<|

+=========================================+

| INITIALIZE t := 0 |

+=========================================+

| INITIALIZE P(t) |

+=========================================+

| evalute FITNESS of P(t) |

+=========================================+

| while not STOPPING CRITERION do |

| +-------------------------------------+

| | P'(t) := RECOMBINATION{P(t)} |

| +-------------------------------------+

| | P''(t) := MUTATION{P'(t)} |

| +-------------------------------------+

| | P(t+1) := SELECTION{P''(t) + P(t)} |

| +-------------------------------------+

| | evalute FITNESS of P''(t) |

| +-------------------------------------+

| | t := t + 1 |

+===+=====================================+

3.) 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 relids in PostgreSQL.

Parts of the GEQO module are adapted from D. Whitley's Genitor

algorithm.

Specific characteristics of the GEQO implementation in PostgreSQL

are:

o 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;

o 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;

o mutation as genetic operator is deprecated so that no repair

mechanisms are needed to generate legal TSP tours.

The GEQO module gives the following benefits to the PostgreSQL DBMS

compared to the Postgres query optimizer implementation:

o handling of large JOIN queries through non-exhaustive search;

o improved cost size approximation of query plans since no longer

plan merging is needed (the GEQO module evaluates the cost for a

query plan as an individual).

References

==========

J. Heitk"otter, D. Beasley:

---------------------------

"The Hitch-Hicker's Guide to Evolutionary Computation",

FAQ in 'comp.ai.genetic',

'ftp://ftp.Germany.EU.net/pub/research/softcomp/EC/Welcome.html'

Z. Fong:

--------

"The Design and Implementation of the Postgres Query Optimizer",

file 'planner/Report.ps' in the 'postgres-papers' distribution

R. Elmasri, S. Navathe:

-----------------------

"Fundamentals of Database Systems",

The Benjamin/Cummings Pub., Inc.