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
algorithm, first introduced in the "System R"
database, produces a near-optimal join order, but can take an
enormous amount of time and memory space when the number of joins
in the query grows large. This makes the ordinary
PostgreSQL query optimizer
inappropriate for queries that join a large number of tables.
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 to be developed.
In the following we describe the implementation of a
Genetic Algorithm to solve the join
ordering problem in a manner that is efficient for queries
involving large numbers of joins.
