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

5 Multi-Flow Optimization
partition b1.3
[“[10,200)“]
msg4 [„CustA“]
msg8 [„CustA“]
msg10 [„CustA“]
Message Partition
partition b1
partition b1.2
[“>200“]
msg3 [„CustA“]
msg7 [„CustA“]
[“CustA“]
partition b1.1
[“ 200
Translation (o7)
[in: msg2, out: msg4]
ba2:
@totalprice
< 10
Translation (o5)
[in: x1(m), out: y1(m)]
Switch (o4)
[in: x1(m)]
@totalprice
[10,200]
Translation (o6)
[in: x1(m), out: y1(m)]
PMerge (o-3)
[in: y1, out: msg4]
@totalprice
> 200
Translation (o7)
[in: x1(m), out: y1(m)]
(a) Original Plan P
(b) Rewritten Plan P ′
Figure 5.9: Example Plan Rewriting Using the Split and Merge Approach
If we use the partitioning scheme (ba 1 , ba 2 ), we rewrite the plan as shown in Figure 5.9(b).
First, we insert a PSplit and an Iteration operator just before the Switch operator. As
a result, o 2 and o 3 benefit from the top-level partition, while o 4 is executed for each subpartition
of the top-level partition. Second, right after this Switch operator, a PMerge is
inserted as well, because subsequent operators benefit from the top level partition.
Algorithm 5.2 illustrates the plan rewriting. For simplicity of presentation, we use a
linear representation of the algorithm, while the realization of this is recursive according
to the hierarchy of sequences of complex and atomic operators. Essentially, this algorithm
consists of two parts. First, there is the dependency analysis that we have already described
in Subsection 3.2.1. Second, plan rewriting requires as input the operator sequence
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