The PetShop Dataset -- Finding Causes of Performance Issues across Microservices

TL;DR

This paper introduces the PetShop Dataset, a comprehensive benchmark for evaluating root cause analysis techniques in microservice architectures, including real metrics and injected performance issues to facilitate standardized testing.

Contribution

The paper provides a publicly available dataset with metrics and injected issues, enabling standardized benchmarking of root cause analysis methods in microservices.

Findings

Dataset includes 68 injected performance issues.

Enables evaluation of causal and non-causal analysis methods.

Supports benchmarking of root cause analysis techniques.

Abstract

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.