  Software performance changes are costly and often hard to detect pre-release.
Similar to software testing frameworks, either application benchmarks or
microbenchmarks can be integrated into quality assurance pipelines to detect
performance changes before releasing a new application version. Unfortunately,
extensive benchmarking studies usually take several hours which is problematic
when examining dozens of daily code changes in detail; hence, trade-offs have
to be made. Optimized microbenchmark suites, which only include a small subset
of the full suite, are a potential solution for this problem, given that they
still reliably detect the majority of the application performance changes such
as an increased request latency. It is, however, unclear whether
microbenchmarks and application benchmarks detect the same performance problems
and one can be a proxy for the other.
  In this paper, we explore whether microbenchmark suites can detect the same
application performance changes as an application benchmark. For this, we run
extensive benchmark experiments with both the complete and the optimized
microbenchmark suites of the two time-series database systems InuxDB and
VictoriaMetrics and compare their results to the results of corresponding
application benchmarks. We do this for 70 and 110 commits, respectively. Our
results show that it is possible to detect application performance changes
using an optimized microbenchmark suite if frequent false-positive alarms can
be tolerated.
