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

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3 Fundamentals of Optimizing Integration Flows Experimental Setting The experimental setup comprises an IBM blade with two processors (each a Dual Core AMD Opteron Processor 270 at 2 GHz) and 9 GB RAM, where we used Linux openSUSE 9.1 (32 bit) as the operating system. Our WFPE (workflow process engine) realizes the extended reference system architecture (described in this chapter) including our optimization component. The WFPE is implemented using Java 1.6 as the programming language and includes approximately 36,000 lines of code. It currently includes several adapters (inbound and outbound) for the interaction with files, databases, and Web services. However, the external systems, used by our example integration flows, have been simulated with file adapters in order to minimize the influence of used systems on the measured experimental results (reproducibility). In order to use arbitrary workload scenarios with different cardinalities and selectivities (workability), we executed all experiments on synthetic XML data generated using our DIPBench toolsuite [BHLW08c]. There are several parameters that influence plan execution. Essentially, we analyzed two groups of parameters. First, there is the group of optimization parameters, where we used different sliding window sizes ∆w (default: 5 min), different optimization intervals ∆t (default: 5 min), and different workload aggregation methods Agg (default: EMA). Second, there is the group of workload characteristics. Here, we used different numbers of plan instances n (executed instances), different plans with certain numbers of operators m, and different input data sizes d (default: d = 1, which stands for 100 kB input messages) and different selectivities. With regard to applied optimization techniques, we used all costbased optimization techniques, except message indexing and heterogeneous load balancing (both not discussed in this thesis) as well as vectorization (see Chapter 4) and multiflow optimization (see Chapter 5). Furthermore, we disabled all rule-based optimization techniques in order to focus on the benefit achieved by cost-based optimization because these techniques either did not apply (e.g., algebraic simplifications) for the used plans or they achieved a constant absolute improvement (e.g., static node compilation) for both the unoptimized and the cost-based optimized execution. End-to-End Comparison and Optimization Benefits In a first series of experiments, we compared the end-to-end performance of no-optimization versus the periodical re-optimization. These experiments already include all optimization overheads (such as statistics maintenance and periodical re-optimization). As a result, these experiments show the overall benefit achieved by periodical re-optimization. First, we compared the periodical re-optimization with no-optimization. The periodical re-optimization was realized as asynchronous inter-instance optimization approach. We executed 100,000 instances of our example plan P 5 for the non-optimized plan as well as for the optimized plan and measured re-optimization and plan execution time. For periodical re-optimization, the plan execution time already includes the synchronous statistic maintenance. During execution, we varied the input cardinality (see Figure 3.20(b)) and selectivities of the three selection operators (see Figure 3.20(a)). Here, the input data was generated without correlations between different attributes. With regard to reoptimization, there are four points (∗1, ∗2, ∗3, and ∗4) where a workload change (shown as intersection points between selectivities) reasons the change of the optimal plan. For periodical re-optimization, we used an optimization interval of ∆t = 5 min, a sliding window size of ∆w = 5 min and EMA as the workload aggregation method. Figure 3.20(c) 74
3.5 Experimental Evaluation (a) Selectivity Variations (b) Input Data Size Variations (c) Optimization Time (d) Execution Time (e) Cumulative Optimization Time (f) Cumulative Execution Time Figure 3.20: Comparison Scenario Periodical Re-Optimization shows the single re-optimization times and Figure 3.20(e) illustrates the cumulative time required for re-optimization during this scenario. Note that we used the elapsed scenario time as the x-axis in order to illustrate the characteristics of periodical re-optimization. We see that periodical re-optimization requires many unnecessary optimization steps. However, each single optimization step requires only a fairly low optimization time. Note that 75
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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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4 Vectorizing Integration Flows (a)
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4 Vectorizing Integration Flows (a)
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4 Vectorizing Integration Flows 4.7
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5 Multi-Flow Optimization cannot be
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5 Multi-Flow Optimization the query
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5 Multi-Flow Optimization The incom
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5 Multi-Flow Optimization example p
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5 Multi-Flow Optimization not allow
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5 Multi-Flow Optimization partition
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5 Multi-Flow Optimization • Case
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5 Multi-Flow Optimization k ′ . F
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5 Multi-Flow Optimization partition
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5 Multi-Flow Optimization the waiti
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5 Multi-Flow Optimization Execution
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5 Multi-Flow Optimization Thus, for
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5 Multi-Flow Optimization Thus, T L
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5 Multi-Flow Optimization • P 5 :
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5 Multi-Flow Optimization decreasin
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5 Multi-Flow Optimization (a) Fixed
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5 Multi-Flow Optimization reached,
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5 Multi-Flow Optimization (2) plan
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6 On-Demand Re-Optimization categor
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6 On-Demand Re-Optimization present
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6 On-Demand Re-Optimization stratum
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6 On-Demand Re-Optimization o 3 o 4
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6 On-Demand Re-Optimization For on-
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6 On-Demand Re-Optimization 6.3.1 O
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6 On-Demand Re-Optimization such th
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6 On-Demand Re-Optimization Join En
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6 On-Demand Re-Optimization f γ((
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6 On-Demand Re-Optimization The res
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6 On-Demand Re-Optimization project
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6 On-Demand Re-Optimization (a) Sel
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6 On-Demand Re-Optimization (a) Loa
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6 On-Demand Re-Optimization ical re
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6 On-Demand Re-Optimization evaluat
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6 On-Demand Re-Optimization 6.6 Sum
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7 Conclusions Existing approaches b
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Bibliography [BBD05a] Shivnath Babu
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Bibliography [BHP + 09b] [BHP + 11]
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Bibliography [CM95] Sophie Cluet an
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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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