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

6.5 Experimental Evaluation
(a) Selectivity Variations
(b) Execution Time
(c) Optimization Time
(d) Cumulative Execution Time
Figure 6.19: Simple-Flow Correlation Comparison Scenario
times with and without the use of our correlation table. We observe that without the use
of the correlation table, the optimization technique selection reordering assumes statistical
independence and thus, changed the plan back and forth, even in case of constant
workload characteristics. Due to the direct triggering of re-optimization by on-demand reoptimization,
overall 1,345 re-optimization steps have been executed. In addition to this
re-optimization overhead, the permanent change between suboptimal and optimal plans
led to a degradation of the execution time because non-optimal plans were used for long
time horizons. In contrast, when using our correlation table, the required re-optimization
steps were reduced to nine. It is important to note that, in case of using the correlation table,
a single workload change triggers several re-optimization steps because we allow only
one operator reordering per optimization step in order to learn the conditional selectivities
stepwise, which reduce the risk for cyclic re-optimizations. However, over time, the conditional
selectivity estimates converge to the real selectivities. Finally, as a combination of
the reduced number of re-optimization steps and the prevention of using suboptimal plans
due to unknown correlation, we achieved a 7% overall improvement with regard to the
cumulative execution time (see Figure 6.19(d)). In conclusion, the use of our correlation
table ensures robustness in the presence of correlated data or conditional probabilities,
which has high importance when using on-demand re-optimization.
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