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

4 Vectorizing Integration Flows
4.7 Summary and Discussion
In this chapter, we introduced the control-flow-oriented optimization technique of plan
vectorization with the aim of throughput optimization for integration flows. We use the
term vectorization as an analogy to executing a vector of messages at a time by a standing
plan. Due to the dependency on the dynamic workload characteristics, we introduced the
cost-based plan vectorization as a generalization, where the costs of single operators are
taken into account and operators are merged to execution buckets. In detail, we presented
exhaustive and heuristic algorithms for computing the cost-optimal plan. Furthermore,
we showed how to use those algorithms in the presence of multiple deployed plans and
how this concept is embedded into our general cost-based optimization framework.
Based on our evaluation, we can state that significant throughput improvements are
possible. In comparison to full vectorization, the cost-based vectorization achieves even
better performance, reduces the latency of single messages, and ensures robustness in the
sense of minimizing the number of required threads. In conclusion, the concept of plan
vectorization is applicable in many different application areas. It is important to note
that the benefit of vectorization and hence, also cost-based vectorization, will increase
with the ongoing development of modern many-core processors because the gap between
CPU performance and main memory, IO, and network speed is increasing (Problem 4.1).
The main differences of our approach to prior work are (1) that we vectorize procedural
integration flows (imperative flow specifications) and that we (2) dynamically compute
the cost-optimal vectorized plan within our periodical re-optimization framework. This
enables the dynamic adaptation to changing workload characteristics in terms of the operator
execution times. Despite the focus on procedural plans, the cost-based vectorization
approach, in general, can also be applied in the context of DSMS and ETL tools.
However, the vectorization approach has also some limitations that must be taken into
account when applying this optimization technique. First, vectorization is a trade-off
between throughput improvement and additional latency time. Thus, it should only be
applied if the optimization objective is throughput improvement or minimizing the latency
in the presence of high message rates rather than minimizing the latency time of single
messages. Second, for plans with complex procedural aspects, vectorization requires additional
operators for synchronization and handling of the explicit data flow. This aspect is
explicitly taken into account by cost-based vectorization but might reduce the achievable
throughput improvement. Third, low cost plans with many less time-consuming operators
might also not benefit from vectorization due to the higher relative overhead of queue
management as well as thread synchronization and monitoring. Despite these general
limitations of vectorization, the cost-based vectorization can be applied by default due
to its hybrid model characteristics (full spectrum between instance-based and vectorized
execution) that takes the execution statistics into account. As already mentioned, the
concept of cost-based vectorization can also be extended to a distributed setting, where
operators are executed by different server nodes rather than only by different threads.
While the vectorization of integration flows is a control-flow-oriented optimization technique,
the next chapter will address a data-flow-oriented optimization technique. However,
both techniques reduce the execution time of message sequences and thus, increase the
message throughput, where the benefit of both techniques can be combined.
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