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

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6 On-Demand Re-Optimization (a) Selectivity Variations (b) Input Data Size Variations (c) Optimization Time (d) Execution Time (e) Cumulative Optimization Time (f) Cumulative Execution Time Figure 6.14: Simple-Flow Comparison Scenario cardinalities and operator selectivities. When using periodical re-optimization, the often re-occurring small peaks are caused by the numerous asynchronous re-optimization steps. A major characteristic of periodical re-optimization is that after a certain workload shift, it takes a while until periodical re-optimization is triggered. During this time, the plan execution time is much longer than the execution time of the optimal plan. In contrast, on- 190
6.5 Experimental Evaluation demand re-optimization directly reacts to the workload change and switches plans. Hence, over time, execution time reductions are yielded due to the fast adaptation. In order to achieve this with periodical re-optimization, a really small ∆t is required. However, this would increase the total re-optimization time. Finally, note that the small differences of absolute execution times compared to Chapter 3 are caused by randomly generated messages for this experiment according to the given selectivities. Complex-Plan End-to-End Comparison In addition to the simple-plan scenario, we executed a second series of experiments using the more complex example plan P 7 ′. The difference of plan P 7 ′ compared to the already introduced plan P 7 is that we explicitly changed the join query type chain to a clique type in order to have the possibility of arbitrary join reordering. This plan receives a data set, loads data from four different systems, and executes schema transformations with translation operators (XSLT scripts). Finally, the five data sets are joined using four join operators and the result is sent to a fifth system. We executed 20,000 plan instances using the input cardinalities shown in Figure 6.15(b) and the same parameter configurations as for the simple plan scenario. For the four loaded data sets, we used input cardinalities of d ∈ {2, 4, 8, 16} (in 100 kB). After every 2,000 instances, we changed the input cardinalities of the four external systems round-robin as shown in Figure 6.15(a). Regarding the results that are shown in Figures 6.15(c)-6.15(f), we observe similar characteristics as within the simple-plan scenario. It is important to note that (1) the higher the optimization opportunities of a plan, and (2) the higher the number of workload changes, the higher the execution time improvements achieved by on-demand re-optimization due to the higher importance of immediate adaptation (see Figures 6.15(d) and 6.15(f)). However, we restricted ourself to moderate differences of input data sizes in order to show only this main characteristic rather than showing arbitrarily high improvements. Further, we also observe, on average, higher re-optimization time improvements as within the simpleplan comparison scenario due to the higher influence of immediate re-optimization and directed re-optimization (see Figures 6.15(c) and 6.15(e)). The outliers for periodical reoptimization have been cause by the Java garbage collection because our implementation of the full join enumeration (DPSize) requires many temporary data objects, which are lazily deleted if space is required. This effect is only visible for periodical re-optimization because there, we used full join enumeration, where more objects have been created and the join enumeration is invoked more often than our on-demand re-optimization. In conclusion, the benefit of on-demand re-optimization increases with increasing plan complexity and increasing frequency of workload changes. Scalability In order to investigate the influencing aspects such as the number of workload shifts wc, the input data size d and the optimization interval ∆t in more detail, we conducted an additional series of scalability experiments, where we varied these parameters. We compare the unoptimized case with periodical and on-demand re-optimization using the cumulated plan execution time, the cumulated optimization time, and the number of re-optimizations. As the experimental setup, we executed 5,000 plan instances of P 5 for each configuration, where each workload shift switches between the two selectivity configurations of (sel(o 2 ) = 0.8, sel(o 3 ) = 0.6, sel(o 4 ) = 0.1) and (sel(o 2 ) = 0.1, sel(o 3 ) = 0.6, sel(o 4 ) = 0.8). As 191
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Cost-Based Optimization of Integrat
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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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5 Multi-Flow Optimization cannot be
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Page 212 and 213: Bibliography [BBD05a] Shivnath Babu
Page 214 and 215: Bibliography [BHP + 09b] [BHP + 11]
Page 216 and 217: Bibliography [CM95] Sophie Cluet an
Page 218 and 219: Bibliography [GZ08] [HA03] [Haa07]
Page 220 and 221: Bibliography [IKNG09] [INSS92] [Ioa
Page 222 and 223: Bibliography [LX09] [LZ05] Rubao Le
Page 224 and 225: Bibliography [OMG07] OMG. XML Metad
Page 226 and 227: Bibliography [Sto02] Michael Stoneb
Page 228 and 229: Bibliography [ZRH04] Yali Zhu, Elke
Page 230 and 231: List of Figures 3.27 Workload Adapt
Page 233: List of Tables 2.1 Interaction-Orie
Page 237: Selbstständigkeitserklärung Hierm
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