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Online log parsing using evolving research tree

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Abstract

Logs are a reliable source of information for development and maintenance purposes. They record information at runtime regarding the state of a system and are commonly used to analyze its behavior. Parsing operations on logs structure the information embedded within the log message and are a crucial step for many log mining applications. In such use cases, parsing effectiveness can impact performance. For systems that require real-time performance, parsing efficiency is also an important factor. In this paper, we present USTEP, an online log parser that uses an evolving tree structure to encode and discover new parsing rules on the fly. Our evaluation of 14 datasets from different logging environments highlights the superiority of our method in terms of robustness and effectiveness compared to the state of the art. Our analysis of space and time complexity shows that USTEP is the only considered method capable of processing logs in constant time regardless of their length. We also propose here USTEP-UP, a way of running multiple USTEP instances in parallel.

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Notes

  1. https://www.splunk.com/.

  2. https://www.elastic.co/fr/whatis/elkstack/.

  3. https://github.com/outscale-dev/ustep-online-log-parser.

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Acknowledgements

The work described in this paper was supported by the cloud provider 3DS OUSTCALE and by the French National Research and Technology Association (CIFRE program N\(^{\circ }\) 2020/0289). We warmly thank both of them for their support.

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Authors and Affiliations

  1. 3DS OUTSCALE, 92210, Saint-Cloud, France

    Arthur Vervaet, Mar Callau-Zori & Raja Chiky

  2. ISEP - Institut Supérieur d’Electronique de Paris, 92130, Issy les Moulineaux, France

    Arthur Vervaet & Yousra Chabchoub

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  1. Arthur Vervaet
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  2. Mar Callau-Zori
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  3. Yousra Chabchoub
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  4. Raja Chiky
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Correspondence to Arthur Vervaet.

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Some preliminary results have been published at the IEEE International Conference on Data Mining in 2021 [1].

Appendix A: Details about parsing experimental settings

Appendix A: Details about parsing experimental settings

Preprocessing using regex helps log parsers achieve more accurate results. During the evaluation, we selected the same regex for all the parsers on a given dataset. For every algorithm, the parameter setting values were fine-tuned through over 100 runs to avoid bias from randomization. We kept the values for which algorithms achieve the highest accuracy on a given dataset. Therefore, preprocessing regex for each dataset and parameters for each parser are summarized in Table 5).

Regarding the number of parameters, SHISO requires four: (1) maxChild the maximum number of children for each internal node; (2) mergeThreshold, a threshold for searching the most similar template in the children; (3) formatLookupThreshold, lower bound to find the most similar node to adjust; and (4)superFormatThreshold, the threshold of average LCS length to determine if the creation of a super format is needed. LenMa uses only one parameter \(T_c\), the threshold for similarity comparisons between the log message and the clusters. Spell also requires only one parameter \(\tau \) as a threshold for similarity. Finally, Drain needs three parameters [14]: (1) depth, the depth of the parsing tree; (2) st a threshold for similarity comparisons between the log messages and the discovered templates; and (3) maxChild, the maximum number of children that a node can have. Once this threshold is reached, every new value is sent to a default node. In the last version [25], the number of parameters was reduced to only one, st, and a dynamic update is proposed (Table 6).

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Vervaet, A., Callau-Zori, M., Chabchoub, Y. et al. Online log parsing using evolving research tree. Knowl Inf Syst 66, 1231–1255 (2024). https://doi.org/10.1007/s10115-023-01953-z

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