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

6 On-Demand Re-Optimization
presented in Section 2.2, this underlying problem of a black-box optimizer makes directed
re-optimization impossible and the suboptimality of the current plan cannot be guaranteed
and thus unnecessary re-optimization steps might be triggered. This problem is also
present for approaches of parametric query optimization (PQO). Based on the assumption
of unknown query parameters and thus, uncertain statistics during query compilation time
(e.g., for stored procedures), PQO [INSS92, HS02, HS03] optimizes a given query into all
candidate plans by exhaustively determining the optimal plans for each possible parameter
combination (the complete search space) at compile time. The current statistics are used
to pick the plan that is optimal for the given parameters at execution time. In contrast
to full search space exploration, Progressive PQO (PPQO) [BBD09] aims to iteratively
explore the search space over multiple executions of the same query in order to reduce the
optimization overhead. Although this approach is advantageous compared to traditional
query optimization, it models the complete parameter search space instead of the search
space of an individual optimal plan, does not allow for directed re-optimization, does not
guarantee to find the optimal plan, and does not address the challenge of when and how
to trigger re-optimization.
The major research question is if we could exploit context knowledge from the optimizer,
in terms of optimality conditions of a deployed plan, to do just enough for re-optimization.
In order to address the mentioned problems, we introduce the on-demand re-optimization.
On-Demand Re-Optimization
Our vision of on-demand re-optimization is (1) to model optimality of a plan by its optimality
conditions rather than considering the complete search space, (2) to monitor only
statistics that are included in these conditions, and (3) to use directed re-optimization if
conditions are violated. We use an example to illustrate the idea.
Example 6.2 (On-Demand Re-Optimization). Assume a subplan P 5 (o 2 , o 3 ) (Figure 6.3(a))
and a search space as shown in Figure 6.3(b), where the dimensions represent the selectivities
sel(o 2 ) and sel(o 3 ). In order to validate the optimality of the plan (o 2 , o 3 ), we
continuously observe the optimality condition oc 1 : sel(o 2 ) ≤ sel(o 3 ), where we only monitor
statistics included in such optimality conditions. If an optimality conditions is violated,
we can use directed re-optimization in order to determine the new optimal plan.
Selection (o2)
[in: msg1, out: msg2]
Selection (o3)
[in: msg2, out: msg3]
Selection (o4)
[in: msg3, out: msg4]
oc 1
oc 2
oc 3
2
monitored
statistics
sel(o 2)
1
0
P’ (o 3, o 2)
3
directed
re-opt
1
sel(o 3)
optimality
condition oc1:
Opt Plan
P
(o 2, o 3)
1
sel(o 2) ≤
sel(o 3)
sel(o 2)
1
0
oc2:
sel(o 3) ≤
sel(o 4)
(o 3,o 4,o 2)
(o 3,o 2,o 4)
Opt Plan (o
P 2,o 4,o 3)
(o 2,o 3,o 4)
sel(o 3)
(o 4,o 3,o 2)
(o 4,o 2,o 3)
oc1:
sel(o 2) ≤
sel(o 3)
oc3:
sel(o 2) ≤
sel(o 4)
1
with
sel(o 4)=0.6
(a) Subplan P 5(o 2, o 3, o 4)
(b) Plan Diagram of P 5(o 2, o 3)
(c) Plan Diagram of P 5(o 2, o 3, o 4)
Figure 6.3: Partitioning of a Plan Search Space
Note that we maintain the optimal plan and its optimality conditions (oc 1 -oc 3 ) as shown
in Figure 6.3(c) for subplan (o 2 , o 3 , o 4 ) but we do not explore the complete search space.
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