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

6 On-Demand Re-Optimization
ical re-optimization increases due to the increasing total execution time because more
re-optimization steps have taken place (Figure 6.16(h)).
Third, we varied the optimization interval ∆t ∈ {1 s, 3 s, 6 s, 12 s, 24 s, 48 s, 92 s, 184 s}.
Obviously, the execution time of unoptimized execution, and on-demand re-optimization
are independent of this optimization interval (Figure 6.16(c)). The same is also true for
the optimization time and the number of optimization steps of on-demand re-optimization
(Figures 6.16(f) and 6.16(i)). In contrast, the optimization interval is a high-influence parameter
for periodical re-optimization. For an increasing optimization interval, we observe
a degradation of the execution time because we miss more optimization opportunities due
to longer adaptation delays. Interestingly, for specific configurations, the execution time is
even significantly worse than the unoptimized execution because we switch to the optimal
plan just before the next workload shift occurs. However, if we further increase the optimization
interval, the execution time converges to the unoptimized case. The benefit of
on-demand re-optimization mainly depends the benefit of applied optimization techniques.
For really short optimization intervals of several seconds, we observe the best configuration
of periodical re-optimization, which is still slower than on-demand re-optimization. For
these configurations, we observe a significant increase of the cumulative re-optimization
time because the number of re-optimization steps increased up to 544.
Finally, we can summarize that on-demand re-optimization always lead to the lowest
execution time, where we observed execution time reductions compared to periodical
re-optimization of up-to 44.7%. However, the maximum benefit depends on applied optimization
techniques, where in this setting we only used selection re-ordering. Furthermore,
on-demand re-optimization shows much better robustness of execution time when varying
the tested influencing aspects.
Directed Re-Optimization Benefit
In addition, we evaluated the benefit of directed re-optimization, where we re-used the
different plans from Chapter 3 that included a varying number of join operators with
m = {2, 4, 6, 8, 10, 12, 14} as a clique query (all directly connected). We compared the
optimization time of (1) the full join enumeration using the standard DPSize algorithm
[Moe09], (2) our join enumeration heuristic with quadratic time complexity that we use
if the number of joins exceed eight joins (see Section 3.3.2), and (3) our directed reoptimization
that only takes operators into account that are included in violated optimality
conditions. Before optimization, we randomly generated statistics for input cardinalities
and join selectivities. For directed re-optimization, we used an optimized plan and
randomly changed the statistics of one join. The experiment was repeated 100 times.
Figure 6.17: Directed Join Enumeration Benefit
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