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

6.4 Optimization Techniques
o 1 o 4 o 2 o 3 o 5
R
W sel(ba 1)
sel(ba 2)
W
W W W
≥
(oc1)
W - W +
∆tw
W(P’,R·∆tw)
0
lc
T L(M’,R·∆tw)
≥ (oc2)
≥ (oc3)
≥ (oc4)
≥ (oc5)
Figure 6.13: Example PlanOptTree of Multi-Flow Optimization
operators, we monitor the execution time. In addition, the message rate R is monitored
for the first operator of this plan. We use oc 1 to express the ordering of the partition
tree. In contrast, for the validity condition, we require a hierarchy of complex statistic
nodes. First, we determine the cost components W − (P ′ ) and W + (P ′ ) according to the
defined cost model extension. Then, ∆tw is computed from these cost components and the
message rate. Furthermore, we compute the latency time and the execution time using
the determined waiting time ∆tw. Finally, we use the optimality conditions oc 2 -oc 5 in
order to express the mentioned validity condition, where two more complex statistic nodes
(latency constraint lc and 0) are used as constant-value operands.
Once we triggered re-optimization, we can use the PlanOptTree for directed re-optimization
as well. There are three facets, where we can exploit the PlanOptTree. First, we
use the violated optimality conditions for the directed reordering of partitioning attributes
similarly to the ordering of selective operators. Second, with regard to the different cases of
computing ∆tw (minimum, default, exceeded latency), we can directly derive the case from
the violated optimality conditions and compute the waiting time ∆tw accordingly. Third,
after each executed partitioned plan instance, we determine the current (continuously
adapted) waiting time ∆tw by querying the corresponding complex statistic node. Most
importantly this allows for a workload adaptation with almost no adaptation delay as it
was introduced by the optimization interval ∆t. Therefore, we also solved the problem
that the maximum message latency time cannot be guaranteed if workload characteristics
change abruptly and if we use long optimization intervals.
6.4.4 Discussion
We generalize the main findings from the given example optimization techniques. First,
even when considering techniques with a large search space, only few optimality conditions
are required because we do not model the complete plan search space but only the conditions
of the optimal plan. Hence, all conditions are binary decisions of subplans and they
exploit the fact that costs of operators in front of and after that subplan are independent
of the rewriting decision. Second, only one optimality condition per dependency (plus one
condition for binary operators) is required to model plan optimality. As a result, those
conditions can be seamlessly integrated into existing memo structures that are typically
used during query optimization in DBMS (for an example, see the cascades framework in
MS SQL Server [BN08]). Third, due to the possibility of arbitrarily complex optimality
conditions, all kinds of optimization techniques can be integrated. For example, our optimizer
uses (1) reordering techniques (e.g., switch path reordering, early selection, early
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