A Cost Model for a Fingered Join Operator in Relational Query Plans
Abstract
We introduce the finger aware cursor operator for relational join queries. It scans a list of
tuples in a finger enabled manner when a nested loop join operation is performed. Using
this scan operation, we improve the performance of nested loop join when compared to
when compared to conventional scan. To quantify the improvement in performance using
fingered scan, a statistic named runs that quantifies the degree of randomness in a list
of records is introduced. This statistic is vital in assessing the performance improvement
achieved using fingered scan. Using runs statistic as a key ingredient, we develop a cost
model that can assign a cost value to the join operation based on underlying fingered
scan. We then develop a cost formula and evaluate the cost model against a simulated
data set. We show that conventional System R cost model is not sufficient to capture the
performance improvement. We then evaluate using the new cost formula and show that it
predicts the cost of join operation correctly.
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Cite this version of the work
Vishnu Prathish
(2015).
A Cost Model for a Fingered Join Operator in Relational Query Plans. UWSpace.
http://hdl.handle.net/10012/9521
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