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

4 Vectorizing Integration Flows
(a) Scalability with d
(b) Variance with d
(c) Scalability with n
(d) Scalability with m
(e) Scalability with t
(f) Scalability with q
Figure 4.21: Scalability Comparison with Different Influencing Factors
In Figure 4.21(a), we scaled the data size d of the XML input messages from 100 kB to
700 kB and measured the execution time (elapsed time) of 250 plan instances (n = 250)
needed by the three different execution models. There, we fixed m = 5, t = 0, n = 250 and
q = 50. We observe that all three execution models exhibit a linear scaling according to the
data size and that significant improvements can be achieved with vectorization. There, the
absolute improvement increases with increasing data size. Further, in Figure 4.21(b), we
illustrated the variance over all 20 repetitions of this sub-experiment. The variance of the
instance-based execution is minimal, while the variance of both vectorized models is worse
due to the unpredictable influence of thread scheduling by the operating system. Costbased
vectorization exhibits a significantly lower variance than full vectorization because
we use fewer threads and therefore reduce the thread scheduling influence.
Now, we fix d = 1 (lowest improvement in 4.21(a)), t = 0, n = 250 and q = 50 in order
to investigate the influence of m. In Figure 4.21(d), we vary m from 5 to 35 operators, as
already mentioned for the experimental setup. Interestingly, not only the absolute but also
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