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

6.1 Motivation and Problem Description
Receive (o1)
[service: s3, out: msg1]
Monitored Statistics (per plan instance):
|dsout1(o1)|, W(o1)
Selection (o2)
[in: msg1, out: msg2]
Selection (o3)
[in: msg2, out: msg3]
Selection (o4)
[in: msg3, out: msg4]
oc1
oc2
oc3
|dsin1(o2)|, |dsout1(o2)|, W(o2)
|dsin1(o3)|, |dsout1(o3)|, W(o3)
|dsin1(o4)|, |dsout1(o4)|, W(o4)
@type='SCM'
Translation (o6)
[in: msg4, out: msg5]
Switch (o5)
[in: msg4]
oc4
@type='MAT'
Translation (o7)
[in: msg4, out: msg5]
|dsin1(o5)|, W(expA), W(expB)
|dsin1(o6)|, |dsout1(o6)|, W(o6)
|dsin1(o7)|, |dsout1(o7)|, W(o7)
Assign (o8)
[in: msg5, out: msg6]
|dsin1(o8)|, |dsout1(o8)|, W(o8)
Invoke (o9)
[service s6, in: msg6]
|dsin1(o9)|, W(o9)
Figure 6.2: Example Plan P 5 and Monitored Statistics
optimization algorithm A-PMO iterates over all operators and applies relevant optimization
techniques according to the operator type.
To summarize, the periodical re-optimization has several advantages that reason its
application instead of existing approaches from the area of adaptive query processing.
However, it exhibits four major drawbacks:
Problem 6.1 (Drawbacks of Periodical Re-Optimization 18 ). First, the generic gathering
of statistics for all operators leads to the maintenance of statistics that might not be used
by the optimizer. Second, periodical re-optimization finds a new plan only if workload
characteristics have changed. Otherwise, we trigger many unnecessary invocations of the
optimizer that evaluates the complete search space. Third, if a workload change occurs,
it takes a while until re-optimization is triggered. Thus, during this adaptation delay, we
are using a suboptimal plan and we are missing optimization opportunities. Fourth, the
parameter ∆t has high influence on optimization and execution times and hence, parameterization
requires awareness of changing workloads. We already presented experiments to
all of these four drawbacks in Section 3.5. Varying the parameter ∆t (e.g., Figure 3.21)
showed the spectrum from high re-optimization overheads to a degradation of the execution
time to the unoptimized case. In contrast, the generic gathering of statistics (e.g., Figure
3.26) requires further discussion because the overhead of our estimator is negligible.
However, including automatic parameter re-estimation techniques (e.g., for continuously
computing the optimal smoothing constant α of EMA) or using more complex workload
aggregation methods would significantly increase this overhead.
The drawbacks of periodical re-optimization and other optimization models are reasoned
by the underlying fundamental problem of the strict separation between optimization,
execution and statistics monitoring, which prevents the exchange of detailed information
about when and how to re-optimize. This problem of a black-box optimizer was recently reconsidered
by Chaudhuri, who argued for rethinking the optimizer contract [Cha09] in the
context of DBMS. With regard to related work of the area of adaptive query processing, as
18 Potential alternatives of periodical re-optimization such as on-idle re-optimization (re-optimization on
free cycles) or anticipatory re-optimization (prediction of workload shifts) also have these drawbacks.
169