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

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5 Multi-Flow Optimization • P 5 : The plan P 5 shows the lowest benefit. There, the Switch operator o 5 is executed only once for a batch because the switch expression attribute /resultsets/ resultset/row/A1 Orderdate is used as partitioning attribute. Additional benefit comes from the Assign and Invoke operators o 8 and o 9 . However, we achieved only an improvement of 25% because the Selection operators in front of the operators that benefit from partitioning consume most of the time and significantly reduces the cardinality of intermediate results. • P 7 : In contrast to the other plans, plan P 7 does not contain any partitioning attribute candidate. Therefore, a system partitioning with sel = 1.0 is used (this case is similar to the time-base batch creation strategy but without the drawback of distinct messages in a batch). Many operators benefit from partitioning. First, the queries to external systems are prepared only once (Assign operators o 3 , o 6 , o 9 , and o 11 ). Second, also the external queries and the subsequent schema mapping is only executed once (Invoke operators o 4 , o 7 , o 10 , and o 12 as well as Translation operators o 5 , o 8 , o 13 ). Third, additional benefit is achieved by the final Assign and Invoke operators o 18 and o 19 . In total, this led to an improvement of 30%. Figure 5.15(b) illustrates the optimization overhead imposed by the cost-based multiflow optimization. This includes the derivation of partitioning attributes, the creation of a partitioning scheme, the plan rewriting, as well as the continuous waiting time computation. Essentially, we observe that the overhead is moderate, where the differences are mainly reasoned by the different numbers of operators. Finally, based on the observed results, we can conclude that the multi-flow optimization technique can be used by default (if small additional latency for single messages is acceptable) because the throughput improvements clearly amortize the optimization overhead. This is true for arbitrary asynchronous, data-driven integration flows because each flow has at least one combination of writing Assign and Invoke operators. Scalability We now investigate the scalability of plan execution, which includes (1) the scalability with increasing input data sizes and (2) the scalability with increasing batch sizes. First, we used our example plans in order to investigate the scalability of optimization benefits with increasing input data size. We reused the scalability experiment with increasing data size from Section 3.5. In contrast to the already presented scalability results, we now disabled all optimization techniques except multi-flow optimization. In detail, we executed 20,000 plan instances for the plans P 1 , P 2 , P 5 , and P 7 and compared the optimized plans with their unoptimized counterparts varying the input data size d ∈ {1, 2, 3, 4, 5, 6, 7} (in 100 kB). Again, we varied the input data size of these plans (the size of the received message) only but did not change the size of externally loaded data. Further, we fixed a batch size of k ′ = 10, an optimization interval of ∆t = 5 min, a sliding window size of ∆w = 5 min and EMA as the workload aggregation method. The results are shown in Figure 5.16. In general, the plans scale with increasing data size but with a decreasing relative improvement. With regard to the different plans, we observe different scalability behavior. The plan P 1 scales best with increasing data size and shows almost constant relative improvement because this plan mainly benefits from reduced costs for writing interactions that linearly depend on the data size. Further, also plan P 2 shows good scalability with increasing data size. However, the relative improvement is decreasing because 158
5.5 Experimental Evaluation (a) Plan P 1 (b) Plan P 2 (c) Plan P 5 (d) Plan P 7 Figure 5.16: Use Case Scalability Comparison with Varying Data Size d (a) Plan P 1 (b) Plan P 2 (c) Plan P 5 (d) Plan P 7 Figure 5.17: Use Case Scalability Comparison with Varying Batch Size k ′ the size of loaded data (that would have an influence on the benefit) was not changed. In contrast, plan P 5 shows only a constant absolute improvement, which resulted in a 159
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Cost-Based Optimization of Integrat
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of traditional data management syst
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Contents 1 Introduction 1 2 Prelimi
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Contents 6.5 Experimental Evaluatio
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1 Introduction for integration flow
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1 Introduction violated. We present
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2 Preliminaries and Existing Techni
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3 Fundamentals of Optimizing Integr
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4 Vectorizing Integration Flows •
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Page 212 and 213: Bibliography [BBD05a] Shivnath Babu
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Bibliography [GZ08] [HA03] [Haa07]
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Bibliography [IKNG09] [INSS92] [Ioa
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Bibliography [LX09] [LZ05] Rubao Le
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Bibliography [OMG07] OMG. XML Metad
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Bibliography [Sto02] Michael Stoneb
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Bibliography [ZRH04] Yali Zhu, Elke
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List of Figures 3.27 Workload Adapt
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List of Tables 2.1 Interaction-Orie
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Selbstständigkeitserklärung Hierm
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