Fast Dimensional Analysis for Root Cause Investigation in a Large-Scale Service Environment



Root cause analysis in a large-scale production environment is challenging due to the complexity of services running across global data centers. Due to the distributed nature of a large-scale system, the various hardware, software, and tooling logs are often maintained separately, making it difficult to review the logs jointly for understanding production issues. Another challenge in reviewing the logs for identifying issues is the scale – there could easily be millions of entities, each described by hundreds of features. In this paper we present a fast dimensional analysis framework that automates the root cause analysis on structured logs with improved scalability.

We first explore item-sets, i.e. combinations of feature values, that could identify groups of samples with sufficient support for the target failures using the Apriori algorithm and a subsequent improvement, FP-Growth. These algorithms were designed for frequent item-set mining and association rule learning over transactional databases. After applying them on structured logs, we select the item-sets that are most unique to the target failures based on lift. We propose pre-processing steps with the use of a large-scale real-time database and post-processing techniques and parallelism to further speed up the analysis and improve interpretability, and demonstrate that such optimization is necessary for handling large-scale production datasets. We have successfully rolled out this approach for root cause investigation purposes in a large-scale infrastructure. We also present the setup and results from multiple production use cases in this paper.

Related Publications

All Publications

ISAAC - December 5, 2021

On the Extended TSP Problem

Julián Mestre, Sergey Pupyrev, Seeun William Umboh

ICML - July 24, 2021

Ditto: Fair and Robust Federated Learning Through Personalization

Tian Li, Shengyuan Hu, Ahmad Beirami, Virginia Smith

International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE) - September 26, 2021

Behavioural and Structural Imitation Models in Facebook’s WW Simulation System

John Ahlgren, Kinga Bojarczuk, Inna Dvortsova, Mark Harman, Rayan Hatout, Maria Lomeli, Erik Meijer, Silvia Sapora

ESEM - September 23, 2021

Measurement Challenges for Cyber Cyber Digital Twins: Experiences from the Deployment of Facebook’s WW Simulation System

Kinga Bojarczuk, Inna Dvortsova, Johann George, Natalija Gucevska, Mark Harman, Maria Lomeli, Simon Mark Lucas, Erik Meijer, Rubmary Rojas, Silvia Sapora

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy