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

6.3 Re-Optimization
at least the number of monitored statistics of one plan instance, i.e., the number
of SNodes in the PlanOptTree (e.g., six in our example). The condition must be
violated by a relative threshold τ and a true evaluation resets this lazy count.
Algorithm 6.2 PlanOptTree Insert Statistics (A-PIS)
Require: operator id nid, stat type type, statistic value, global variable lastopt
1: if (on ← root.getOperator(nid)) = NULL or (sn ← on.getSNode(type)) = NULL then
2: return // statistic not required
3: sn.maintainAggregate(value)
4: ret ← true
5: if (time − ∆t) > lastopt then // min existence time
6: clear memo
7: for all cn ∈ sn.csnodes do // for each CSNode cn
8: if memo.contains(cs) then
9: continue
10: ret ← ret and cs.computeStats(), memo.put(cs)
11: for all oc ∈ sn.ocnodes do // for each OCNode oc
12: if memo.contains(oc) then
13: continue
14: ret ← ret and oc.isOptimal(), memo.put(oc)
15: if ¬ret then
16: A-PTR() // actively triggering re-optimization (start reopt thread)
Algorithm 6.2 illustrates the statistics maintenance using a PlanOptTree and shows how
to trigger re-optimization if workload characteristics change over time. Starting from the
root, we identify the operator node according to the given nid. We decline the maintenance
of a statistic if it is not required by the PlanOptTree (lines 1-2). Then, we get the
specific statistic node and maintain the aggregate (line 3). If the minimum existence time
is exceeded, we check the existing optimality conditions. For this purpose, we use our
MEMO structure, recursively compute complex statistic measures, and check all reachable
optimality conditions (lines 6-14). Materialization of complex statistics is applied with
awareness of the PlanOptTree structure in order to prevent anomalies (e.g., inconsistency
of statistics in one optimality condition). We do not miss any violated condition when
using the MEMO structure because this algorithm is executed for each atomic statistic value.
In case there is a violated optimality condition, we trigger re-optimization (line 16).
In conclusion, we evaluate optimality conditions as we gather new statistics, and reoptimization
is triggered only if optimality conditions are violated. In contrast to periodical
re-optimization, this yields (1) minimal statistics maintenance, (2) the avoidance of
unnecessary re-optimization steps, and (3) an immediate adaptation to changing workload
characteristics, and hence, it results in a lower overall execution time.
6.3 Re-Optimization
Once re-optimization has been triggered by violated optimality conditions, we re-optimize
the current plan. This includes two major challenges. First, we apply directed reoptimization
rather than full re-optimization. Second, after successful re-optimization,
we incrementally update the existing PlanOptTree according to the new conditions.
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