Logan: A Distributed Online Log Parser
Amey Agrawal, Rohit Karlupia, Rajat Kumar Gupta · 2019
Logs serve as a critical tool for debugging and monitoring applications. However, gaining insights from unstructured logs is difficult. Hence, many log management and analysis applications first parse logs into structured templates. In this paper, we train a data-driven log parser on our new Apache Spark dataset, the largest application log dataset yet. We implement a distributed online algorithm to accommodate for the large volume of data. We also devise a new metric for evaluation of parsers when labeled data is unavailable. We show that our method generalizes over diverse datasets without any parameter tuning or domain-specific inputs from the user. When evaluated on publicly available HDFS dataset our method performs 13x faster than the previous state-of-the-art.