/logparser

A toolkit for automated log parsing [DSN'16, ICWS'17, TDSC'18]

Primary LanguagePythonMIT LicenseMIT

Logparser

Documentation Status license

Logparser provides a toolkit and benchmarks for automated log parsing, which is a crucial step towards structured log analytics. By applying logparser, users can automatically learn event templates from unstructured logs and convert raw log messages into a sequence of structured events. In the literature, the process of log parsing is sometimes refered to as message template extraction, log key extraction, or log message clustering.


An illustrative example of log parsing

👉 Read the docs: https://logparser.readthedocs.io

🔭 If you use any of our tools or benchmarks in your research for publication, please kindly cite the following papers.

Log parsers currently available:

Tools References
SLCT [IPOM'03] Risto Vaarandi. A Data Clustering Algorithm for Mining Patterns from Event Logs, 2003
AEL [QSIC'08] Zhen Ming Jiang, Ahmed E. Hassan, Parminder Flora, Gilbert Hamann. Abstracting Execution Logs to Execution Events for Enterprise Applications, 2008
[JSME'08] Zhen Ming Jiang, Ahmed E. Hassan, Gilbert Hamann, Parminder Flora. An Automated Approach for Abstracting Execution Logs to Execution Events, 2008
IPLoM [KDD'09] Adetokunbo Makanju, A. Nur Zincir-Heywood, Evangelos E. Milios. Clustering Event Logs Using Iterative Partitioning, 2009
[TKDE'12] Adetokunbo Makanju, A. Nur Zincir-Heywood, Evangelos E. Milios. A Lightweight Algorithm for Message Type Extraction in System Application Logs, 2012
LKE [ICDM'09] Qiang Fu, Jian-Guang Lou, Yi Wang, Jiang Li. Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis, 2009
LFA [MSR'10] Meiyappan Nagappan, Mladen A. Vouk. Abstracting Log Lines to Log Event Types for Mining Software System Logs, 2010
LogSig [CIKM'11] Liang Tang, Tao Li, Chang-Shing Perng. LogSig: Generating System Events from Raw Textual Logs, 2011
SHISO [SCC'13] Masayoshi Mizutani. Incremental Mining of System Log Format, 2013
LogCluster [CNSM'15] Risto Vaarandi, Mauno Pihelgas. LogCluster - A Data Clustering and Pattern Mining Algorithm for Event Logs, 2015
LenMa [CNSM'15] Keiichi Shima. Length Matters: Clustering System Log Messages using Length of Words, 2015.
LogMine [CIKM'16] Hossein Hamooni, Biplob Debnath, Jianwu Xu, Hui Zhang, Geoff Jiang, Adbullah Mueen. LogMine: Fast Pattern Recognition for Log Analytics, 2016
Spell [ICDM'16] Min Du, Feifei Li. Spell: Streaming Parsing of System Event Logs, 2016
Drain [ICWS'17] Pinjia He, Jieming Zhu, Zibin Zheng, and Michael R. Lyu. Drain: An Online Log Parsing Approach with Fixed Depth Tree, 2017
MoLFI [ICPC'18] Salma Messaoudi, Annibale Panichella, Domenico Bianculli, Lionel Briand, Raimondas Sasnauskas. A Search-based Approach for Accurate Identification of Log Message Formats, 2018

Usage

Please follow the installation steps and demo in the docs to get started.

Benchmarking results

All the log parsers have been evaluated across 16 different logs available in loghub. We report parsing accuracy as the percentage of accurately parsed log messages.

👇 Check the detailed bechmarking result table (click to expand)

Note that accuracy values above 0.9 are marked in bold, and the best accuracy results achieved are marked with *.

Publications about logparser

Acknowledgement

Logparser is implemented based on a number of existing open-source projects:

Feedback

For any questions or feedback, please post to the issue page.