![]() Discovering actionable patterns in event data. Manning Publications Co., Greenwich, CT, 2004. Automated system monitoring and notification with Swatch. Xtrace: A pervasive network tracing framework. From dirt to shovels: fully automatic tool generation from ad hoc data. The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Multi-dimensional fault diagnosis using a subspace approach. NSDI'04, pages 23-23, San Francisco, California, 2004. Path-based failure and evolution management. The hadoop distributed file system: Architecture and design. Cambridge University Press, second edition, 2002. 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. In the Hadoop case, we are able to analyze 24 million lines of console logs in 3 minutes. ![]() 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. 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 show that our method enables analyses that are impossible with previous methods because of its superior ability to create sophisticated features. We then analyze these features using machine learning to detect operational problems. We first parse console logs by combining source code analysis with information retrieval to create composite features. We propose a general methodology to mine this rich source of information to automatically detect system runtime problems. 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.
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