Cost-Based Optimization of Integration Flows - Datenbanken ...

3 Fundamentals of Optimizing Integration Flows
into optimization. Finally, the Action operator exhibits abstract costs in the form of
|ds in | + |ds out |. However, the Action operator will not be included in any rewriting (except
for parallelism) because it executes arbitrary code snippets and thus, is treated as a
black box by the optimizer.
2: Execution Times: In a second step, we monitor statistics (e.g., execution times
and cardinalities) in order to weight the mentioned abstract costs of interaction- and dataflow-oriented
operators. With the aim to estimate the costs for a newly created plan P ′ ,
we aggregate the costs C(o ′ i ) and C(o i) of the single operators weighted with the execution
statistics W (o i ) of the current plan P . Thus, we estimate missing statistics with
Ŵ (o ′ i) = C(o′ i )
C(o i ) · W (o i). (3.1)
For control-flow-oriented operators, we directly estimate the execution time. The costs for
the complex control-flow-oriented Switch operator can be computed by
⎛ ⎛
⎞⎞
n∑
i∑
⎝P (path i ) · ⎝ W ( ) ∑m i
expr pathj + W (o i,k ) ⎠⎠ , (3.2)
i=1
j=1
where we require switch path probabilities P (path i ) (relative frequencies) for all n paths,
weighted costs for path expression evaluation W ( )
expr pathj because the evaluation of
these expressions (e.g., XPath) can be cost-intensive as well as weighted costs for the
m i operators of each path. Here, the second summation goes only up to j = i because
the evaluation is aborted if we find a true condition due to the if-elseif-else semantic of
this operator. Similar, the costs for the complex Fork operator (concurrent subflows of
arbitrary operators) are computed by the most time-consuming subflow:
⎛
⎞
n
max
i=1
k=1
∑m i
⎝ W (o i,j ) + i · W (Start Thread) ⎠ , (3.3)
j=1
where W (Start T hread) denotes a constant, used to represent the required time for creation
and start of a thread. When computing the costs for the Iteration operator, with
r ·
n∑
W (o i ) , (3.4)
i=1
the average number of iteration loops r is required as well. Further, the waiting time of
the Delay operator is also taken into account. Finally, the Signal operator has to be
mentioned, where costs (needed for raising an exception) are represented as a constant.
Putting it all together, this cost model has several fundamental properties. Some of
these properties are used by different chapters of this thesis.
• Self-Adjustment: Due to weighting with monitored execution times, the cost model is
self-adjusting with regard to the behavior of different operators according to changing
workload characteristics. Thus, the cost model adjusts itself to the present environment
(hardware platform, behavior of external systems). Especially, this behavior
of external systems or different queries to these systems and thus, also of network
properties could not be taken into account by an empirical cost model that is only
based on cardinalities.
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