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

4.6 Experimental Evaluation
the relative improvement of vectorization increases with increasing number of operators.
Figure 4.21(e) shows the impact of the time interval t between the initiation of two
plan instances. For that, we fixed d = 1, m = 5, n = 250, q = 50 and we varied t
from 10 ms to 70 ms. There is almost no difference between the full vectorization and the
cost-based vectorization. However, the absolute improvement between instance-based and
vectorized approaches decreases slightly with increasing t. The explanation is that the
time interval has no impact on the instance-based execution. In contrast, the vectorized
approach depends on t because the highest improvement is achieved with full pipeline
utilization.
Further, we analyze the influence of the number of instances n as illustrated in Figure
4.21(c). Here, we fixed d = 1, m = 5, t = 0, q = 50 and we varied n with
n ∈ {10, 100, 200, 300, 400, 500, 600, 700}. Basically, we observe that the relative improvement
between instance-based and vectorized execution increases when increasing n, due
to parallelism of plan instances. However, it is interesting to note that the fully vectorized
solution performs slightly better for small n. However, when increasing n, the cost-based
vectorized approach performs optimal because there the maximum queue constraint q is
reached and we observe the influence of the already mentioned convoy effect.
Figure 4.21(f) illustrates the influence of the maximum queue size q, which we varied
from 10 to 70. Here, we fixed d = 1, m = 5, t = 0 and n = 250. An increasing q slightly
decreases the execution time of vectorized plans. This is reasoned by (1) less request
notifications of waiting threads and (2) better load balancing by the thread scheduler.
Message Latency
Vectorization is a trade-off between message throughput improvement and increased latency
time of single messages. Therefore, we now investigate the latency of single messages.
Figure 4.22 illustrates the differences of the three execution models instance-based
(WFPE), vectorized (VWFPE) and cost-based vectorized (CBVWFPE) according to the latency
of single messages (including inbound waiting time) and the execution time of single messages
(without inbound waiting time). Therefore, we fixed d = 1, t = 0, q = 50, and we
varied the number of operators m, similar to Figure 4.21(a). All results are illustrated as
error bars using the minimum, median (50% quartile) and maximum latency/execution
time, respectively. In this experiment, all n = 250 messages arrive simultaneously in the
system. The latency time includes the waiting time and execution time in the sense of
end-to-end latency. In contrast, the execution time shows how long it takes to process a
single message, without waiting time at the server inbound message queues.
First, we observe that the instance-based execution allows for lowest minimum latency
(first processed message), while both the vectorized as well as the cost-based vectorized
execution requires higher initial time for processing the first messages. This is caused by
queue management and synchronization between threads. It is important to note that
the cost-based vectorized model exhibit lower initial latencies. Further, we see that the
median and maximum latencies are higher for the instance-based execution model because
it is directly influenced by the reached throughput.
Second, it is important that the execution time of a single message is much smaller when
using the instance-based execution model rather than the vectorized model. The reason
is that the vectorized execution is dominated by the most time-consuming operator and
requires additional effort for thread queue management and synchronization. In addition,
for the used setting of a queue size of q = 50, most messages wait just in front of this
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