  Execution logs are a crucial medium as they record runtime information of
software systems. Although extensive logs are helpful to provide valuable
details to identify the root cause in postmortem analysis in case of a failure,
this may also incur performance overhead and storage cost. Therefore, in this
research, we present the result of our experimental study on seven Spark
benchmarks to illustrate the impact of different logging verbosity levels on
the execution time and storage cost of distributed software systems. We also
evaluate the log effectiveness and the information gain values, and study the
changes in performance and the generated logs for each benchmark with various
types of distributed system failures. Our research draws insightful findings
for developers and practitioners on how to set up and utilize their distributed
systems to benefit from the execution logs.
