Efficient Online Log Parsing with Log Punctuations Signature - Shijie Zhang; Gang Wu (Applied Sciences, 2021)
Logs, recording the system runtime information, are frequently used to ensure software system reliability. As the first and foremost step of typical log analysis, many data-driven methods have been proposed for automated log parsing. Most existing log parsers work offline, requiring a time-consuming training progress and retraining as the system upgrades. Meanwhile, the state of the art online log parsers are tree-based, which still have defects in robustness and efficiency. To overcome such limitations, we abandon the tree structure and propose a hash-like method. In this paper, we propose LogPunk, an efficient online log parsing method. The core of LogPunk is a novel log signature method based on log punctuations and length features. According to the signature, we can quickly find a small set of candidate templates. Further, the most suitable template is returned by traversing the candidate set with our log similarity function. We evaluated LogPunk on 16 public datasets from the LogHub comparing with five other log parsers. LogPunk achieves the best parsing accuracy of 91.9%. Evaluation results also demonstrate its superiority in terms of robustness and efficiency.