mlsanityData to 14 Oct 2026

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

arXiv 2311.04806 ↗First posted 8 Nov 2023cs.DC cs.LGcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 12 of its 34 references that are in the map. First posted on arXiv 8 Nov 2023.

Builds on

12 of 34 references.

References · 12

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2017Axiomatic Attribution for Deep Networkscited by 3,384
  4. 2019Causal structure based root cause analysis of outlierscited by 65
  5. 2022Causal Inference-Based Root Cause Analysis for Online Service Systems with Intervention Recognitioncited by 56
  6. 2021Why did the distribution change?cited by 37
  7. 2021MicroHECL: High-Efficient Root Cause Localization in Large-Scale Microservice Systemscited by 36
  8. 2013Partial Distance Correlation with Methods for Dissimilaritiescited by 20
  9. 2023Toward Falsifying Causal Graphs Using a Permutation-Based Testcited by 11
  10. 2018Localizing Faults in Cloud Systemscited by 8
  11. 2022Anomaly Attribution with Likelihood Compensationcited by 7
  12. 2023Beyond Single-Feature Importance with ICECREAMcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 10.

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  1. 2024Root Cause Analysis of Outliers with Missing Structural Knowledgecited by 19
  2. 2025Robust Root Cause Diagnosis using In-Distribution Interventionscited by 4
  3. 2025Root Cause Analysis of Outliers in Unknown Cyclic Graphscited by 4
  4. 2026Graph-Free Root Cause Analysiscited by 4
  5. 2026FoundCause: Causal Discovery with Latent Confounders from Observational Datacited by 3
  6. 2024PORCA: Root Cause Analysis with Partially Observed Datacited by 2
  7. 2026What a Pooled Leaderboard Does Not Say: Auditing the Scope and Provenance of Method Comparisons in Microservice RCA Benchmarkscited by 1
  8. 2026PRIM: Meta-Learned Bayesian Root Cause Analysiscited by 0
  9. 2026Graphical Causal Reasoning for Root Cause Analysis in Cloud Networkscited by 0
  10. 2026NetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networkscited by 0
  11. 2026Falsifying Causal Graphs With Outlier Eventscited by 0
  12. 2026Agentic Root Cause Analysis through Evidence-Grounded Reasoningcited by 0
2 more
  1. 2026CausalArena: Benchmarking Causal Discovery in the Foundation Model Eracited by 0
  2. 2026Where Root Cause Analysis Fails: A Retrieval-Reranking Decompositioncited by 0