A Novel Approach of Query Optimization for Distributed Database Systems
Query optimization in distributed databases
explicitly needed in many aspects of the
optimization process, often making it imperative
for the optimizer to consult underlying data
sources while doing cost based optimization. This
is not only increases the cost of optimization, but
also changes the trade-offs involved in the
optimization process significantly. The leading cost
in this optimization process is the “cost of costing”
that traditionally has been considered
insignificant. The optimizer can only afford a few
rounds of messages to the under- lying data
sources and hence the optimization techniques in
this environment must be geared toward gathering
all the required cost information with minimal
communication.
In this paper, we explore the design and search
space for a query optimizer in distributed
environment and demonstrate the need for this
optimization approach in various aspects of the
optimization process. This work present minimumcommunication
cost query cost variants of various
query optimization techniques, and discuss tradeoffs
in their performance in the present
development. We have implemented a novel
optimization approach in the distributed database
environment, somewhat unexpectedly, indicate that
a simple two-phase optimization scheme performs
fairly well as long as the physical database design
is known to the optimizer, though more determined
algorithms are required.
Keywords: Query Optimization, Query Optimization approach, Query processing in DDBMS
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