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Root cause analysis in a large-scale production environment is challengingdue to the complexity of services running across global data centers. Due tothe distributed nature of a large-scale system, the various hardware, software,and tooling logs are often maintained separately, making it difficult to reviewthe logs jointly for understanding production issues. Another challenge inreviewing the logs for identifying issues is the scale - there could easily bemillions of entities, each described by hundreds of features. In this paper wepresent a fast dimensional analysis framework that automates the root causeanalysis on structured logs with improved scalability.  We first explore item-sets, i.e. combinations of feature values, that couldidentify groups of samples with sufficient support for the target failuresusing the Apriori algorithm and a subsequent improvement, FP-Growth. Thesealgorithms were designed for frequent item-set mining and association rulelearning over transactional databases. After applying them on structured logs,we select the item-sets that are most unique to the target failures based onlift. We propose pre-processing steps with the use of a large-scale real-timedatabase and post-processing techniques and parallelism to further speed up theanalysis and improve interpretability, and demonstrate that such optimizationis necessary for handling large-scale production datasets. We have successfullyrolled out this approach for root cause investigation purposes in a large-scaleinfrastructure. We also present the setup and results from multiple productionuse cases in this paper.

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