On Parallel Processing of Aggregate and Scalar Functions
in Object-Relational DBMS
Michael Jaedicke (Technische Universität München)
Bernhard Mitschang (Technische Universität München)
Nowadays parallel object-relational DBMS are
envisioned as the next great wave, but there is
still a lack of efficient implementation concepts
for some parts of the proposed functionality.
Thus one of the current goals for parallel
object-relational DBMS is to move towards
higher performance. In this paper we develop a
framework that allows to process user-defined
functions with data parallelism. We will
describe the class of partitionable functions
that can be processed parallelly. We will also
propose an extension which allows to speed up
the processing of another large class of functions
by means of parallel sorting. Functions
that can be processed by means of our
techniques are often used in decision support
queries on large data volumes, for example. Hence
a parallel execution is indispensable.