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

6.5 Experimental Evaluation
Figure 6.17 illustrates the results using a log-scaled y-axis. The optimization time of
full join enumeration increases exponentially, while for both heuristic and directed reoptimization,
the optimization time increases almost linearly (slightly super-linearly). In
addition, directed re-optimization is even faster than the heuristic enumeration because
we only reorder quantifiers of violated optimality conditions. Due to randomly generated
statistics, on average, we take fewer operators into consideration, while still ensuring to
find the global optimal solution over multiple re-optimization steps. As a result, with
increasing plan complexity, the relative benefit of directed re-optimization increases.
On-Demand Re-Optimization Overhead
We additionally evaluated the overheads of statistics maintenance and of the PlanOptTree
algorithms. Here, all experiments were repeated 100 times. It is important to note that
both overheads were already included in the end-to-end comparison, where they have been
amortized by the execution time improvements in several orders of magnitude.
(a) Statistic Maintenance
(b) Algorithm Overhead
Figure 6.18: Overhead of PlanOptTrees
Figure 6.18(a) illustrates the statistic maintenance overhead comparing our Estimator
component (used for periodical re-optimization) versus our PlanOptTree (used for ondemand
re-optimization), which both use statistics of all 100,000 plan instances of the
simple-plan comparison scenario at the granularity of single operators (2,100,000 atomic
statistic tuples). For both models, we used the exponential moving average as aggregation
method. The costs include the transient maintenance of aggregates for all operators as
well as the aggregation itself. Full Monitoring refers to periodical re-optimization, where
all statistics of all operators are gathered by the Estimator. Min Monitoring refers to
a hypothetical scenario, where we know the required statistics and maintain only these
statistics with the Estimator. The relative improvement of Min Monitoring to Max Monitoring
is the benefit we achieve by maintaining only relevant statistics, where the absolute
benefit depends on the used workload aggregation method. In contrast, POT Monitoring
refers to the use of our PlanOptTree. Although the PlanOptTree declines unnecessary
statistics, it is slower than full monitoring because for each statistic tuple, we compute
the hierarchy of complex statistics and evaluate optimality conditions. Therefore, we distinguish
three variants of the POT monitoring: POT refers to the monitoring without
condition evaluation, while POT2 (without the MEMO structure) and POT3 (with the MEMO
structure) show the overhead of continuously evaluating the optimality conditions. It is
important to note that the use of the MEMO structure is counterproductive in this scenario
due to the rather simple optimality conditions and additional overhead for updating and
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