  Identifying root causes for unexpected or undesirable behavior in complex
systems is a prevalent challenge. This issue becomes especially crucial in
modern cloud applications that employ numerous microservices. Although the
machine learning and systems research communities have proposed various
techniques to tackle this problem, there is currently a lack of standardized
datasets for quantitative benchmarking. Consequently, research groups are
compelled to create their own datasets for experimentation. This paper
introduces a dataset specifically designed for evaluating root cause analyses
in microservice-based applications. The dataset encompasses latency, requests,
and availability metrics emitted in 5-minute intervals from a distributed
application. In addition to normal operation metrics, the dataset includes 68
injected performance issues, which increase latency and reduce availability
throughout the system. We showcase how this dataset can be used to evaluate the
accuracy of a variety of methods spanning different causal and non-causal
characterisations of the root cause analysis problem. We hope the new dataset,
available at https://github.com/amazon-science/petshop-root-cause-analysis/
enables further development of techniques in this important area.
