Detecting large-scale system problems by mining console logs

  • Authors:

  • Wei Xu;Ling Huang;Armando Fox;David Patterson;Michael I. Jordan

  • Affiliations:

  • University of California at Berkeley, Berkeley, CA, USA;Intel Labs Berkeley, Berkeley, CA, USA;University of California at Berkeley, Berkeley, CA, USA;University of California at Berkeley, Berkeley, CA, USA;University of California at Berkeley, Berkeley, CA, USA

  • Venue:
  • Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles
  • Year:
  • 2009

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Abstract

Surprisingly, console logs rarely help operators detect problems in large-scale datacenter services, for they often consist of the voluminous intermixing of messages from many software components written by independent developers. We propose a general methodology to mine this rich source of information to automatically detect system runtime problems. We first parse console logs by combining source code analysis with information retrieval to create composite features. We then analyze these features using machine learning to detect operational problems. We show that our method enables analyses that are impossible with previous methods because of its superior ability to create sophisticated features. We also show how to distill the results of our analysis to an operator-friendly one-page decision tree showing the critical messages associated with the detected problems. We validate our approach using the Darkstar online game server and the Hadoop File System, where we detect numerous real problems with high accuracy and few false positives. In the Hadoop case, we are able to analyze 24 million lines of console logs in 3 minutes. Our methodology works on textual console logs of any size and requires no changes to the service software, no human input, and no knowledge of the software's internals.