The DOI matches its Crossref record; 32 of the 33 references it cites were found in a registry or an index.
Drain: An Online Log Parsing Approach with Fixed Depth Tree
Abstract
Logs, which record valuable system runtime information, have been widely employed in Web service management by service providers and users. A typical log analysis based Web service management procedure is to first parse raw log messages because of their unstructured format; and then apply data mining models to extract critical system behavior information, which can assist Web service management. Most of the existing log parsing methods focus on offline, batch processing of logs. However, as the volume of logs increases rapidly, model training of offline log parsing methods, which employs all existing logs after log collection, becomes time consuming. To address this problem, we propose an online log parsing method, namely Drain, that can parse logs in a streaming and timely manner. To accelerate the parsing process, Drain uses a fixed depth parse tree, which encodes specially designed rules for parsing. We evaluate Drain on five real-world log data sets with more than 10 million raw log messages. The experimental results show that Drain has the highest accuracy on four data sets, and comparable accuracy on the remaining one. Besides, Drain obtains 51.85%~81.47% improvement in running time compared with the state-of-the-art online parser. We also conduct a case study on an anomaly detection task using Drain in the parsing step, which determines the effectiveness of Drain in log analysis.
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“This makes the agent's reasoning inspectable: a reviewer can see which logs, metrics, traces, tickets, configurations, validators, or simulators were queried, and whether the resulting evidence was sufficient for the proposed diagnosis or action [77], [60], [78], [61], [25].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [78]
In the citing paper
“First, modern production diagnosis is fundamentally query-driven, and good teams treat incident response as repeated hypothesis testing under partial observability [4], [78], [84].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [78]
In the citing paper
“This framing aligns well with how production debugging systems are used in practice, through writing discriminating query, refine the candidate set, and then write the next query [78], [61], [53], [54].”
From Large language models for agentic NetOps and AIOps: Architectures, evaluation, and safety (2027) · cites this paper as [78]
In the citing paper
“they face two key challenges: high computing overhead from template conversion via parsing algorithms like Drain, and real-time processing pressure from explosive log volume during fault concurrency, making processing efficiency the system bottleneck.”
From A Survey of Anomaly Detection and Root Cause Analysis Techniques for Artificial Intelligence for IT Operations (AIOps) in Large-Scale Software Systems (2026) · cites this paper as [9]
In the citing paper
“The industry widely adopts online clustering algorithms based on parse trees such as Drain [9] , which can compress data dimensions efficiently but has insufficient discrimination for templates with high semantic similarity.”
From A Survey of Anomaly Detection and Root Cause Analysis Techniques for Artificial Intelligence for IT Operations (AIOps) in Large-Scale Software Systems (2026) · cites this paper as [9]
In the citing paper
“While computationally lightweight, these methods struggle to adapt to evolving workloads and often suffer from high false-positive rates when applied to complex, real-world clusters.”
From LINEADAPTER: Parameter-Efficient Fine-Tuning for Log Anomaly Detection and Root Cause Analysis (2025) · cites this paper as [4]
In the citing paper
“This frequencyratio-based heuristic has been widely adopted in prior work on log anomaly detection, particularly in template blacklisting and rule construction [4], [5].”
From LINEADAPTER: Parameter-Efficient Fine-Tuning for Log Anomaly Detection and Root Cause Analysis (2025) · cites this paper as [4]
In the citing paper
“It uses the Drain algorithm for parsing logs in real time, showing superior performance across benchmarks like HDFS and Thunderbird [19].”
From Log anomaly detection in AIOps: A real-world implementation using Large Language Models (2026) · cites this paper as [19]
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LogLead - Fast and Integrated Log Loader, Enhancer, and Anomaly Detector
Mäntylä, M., Wang, Y., & Nyyssölä, J. (2024) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner60148.2024.00046 · Cited by 13
LogDP: Combining Dependency and Proximity for Log-Based Anomaly Detection
Xie, Y., Zhang, H., Zhang, B., Babar, M. A., & Lu, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91431-8_47 · Cited by 10
On Automatic Parsing of Log Records
Rand, J., & Miranskyy, A. (2021) · IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER) · doi:10.1109/icse-nier52604.2021.00017 · Cited by 11
CLDTLog: System Log Anomaly Detection Method Based on Contrastive Learning and Dual Objective Tasks
Tian, G., Luktarhan, N., Wu, H., & Shi, Z. (2023) · Sensors · doi:10.3390/s23115042 · Cited by 9
Han, D., Sun, M., Li, M., & Chen, Q. (2023) · Applied Sciences · doi:10.3390/app13137668 · Cited by 9
FSMFLog: Discovering Anomalous Logs Combining Full Semantic Information and Multifeature Fusion
Niu, W., Li, Z., He, Z., Wang, A., Li, B., & Zhang, X. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3300690 · Cited by 9
Interpretable Spatial–Temporal Graph Convolutional Network for System Log Anomaly Detection
Xu, R., & Li, Y. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.102803 · Cited by 11
End-to-End AutoML for Unsupervised Log Anomaly Detection
Zhang, S., Ji, Y., Luan, J., Nie, X., Chen, Z., Ma, M., Sun, Y., & Pei, D. (2024) · IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1145/3691620.3695535 · Cited by 9
TraLogAnomaly: A microservice system anomaly detection approach based on hybrid event sequences
Wei, X., Sun, C., Yang, P., Zhang, X., & Towey, D. (2025) · Science of Computer Programming · doi:10.1016/j.scico.2025.103303 · Cited by 10
Cross-System Software Log-based Anomaly Detection Using Meta-Learning
Wang, Y., Mäntylä, M., Nyyssölä, J., Ping, K., & Wang, L. (2025) · IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) · doi:10.1109/saner64311.2025.00049 · Cited by 14
Mittamidi, V. K. R. (2025) · International Journal of AI BigData Computational and Management Studies · doi:10.63282/3050-9416.ijaibdcms-v6i4p115 · Cited by 9
Evidence-Verified Root Cause Localization for Microservice Incidents under Tool and Telemetry Noise
Ren, L., Tang, Y., Chen, B., Tao, J., & Chen, F. (2026) · International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) · doi:10.1109/mlise70044.2026.11607508 · Cited by 9
Failure Identification from Unstable Log Data using Deep Learning
Bogatinovski, J., Nedelkoski, S., Li, W., Cardoso, J., & Kao, O. (2022) · IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid) · doi:10.1109/ccgrid54584.2022.00044 · Cited by 9
Speed and Performance of Parserless and Unsupervised Anomaly Detection Methods on Software Logs
Nyyssölä, J., & Mäntylä, M. (2024) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs62785.2024.00071 · Cited by 11
Log Parser with One-to-One Markup
Chunyong, Z., & Meng, X. (2020) · International Conference on Information and Computer Technologies (ICICT) · doi:10.1109/icict50521.2020.00045 · Cited by 9
LTmatch: A Method to Abstract Pattern from Unstructured Log
Wang, X., Zhao, Y., Xiao, H., Wang, X., & Chi, X. (2021) · Applied Sciences · doi:10.3390/app11115302 · Cited by 9
Kang, S., An, J., Kim, J.-P., & Jun, S.-W. (2021) · IEEE/ACM International Symposium on Microarchitecture (MICRO) · doi:10.1145/3466752.3480108 · Cited by 9
Design and Implementation of a Semi-supervised Anomaly Log Detection Model DDA
Wang, Y., & Ji, Z. (2021) · International Conference on Computer Communication and Artificial Intelligence (CCAI) · doi:10.1109/ccai50917.2021.9447533 · Cited by 9
Yu, Z., Yang, S., Li, Z., Li, L., Luo, H., & Yang, F. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1401857 · Cited by 9
From Logs to Causal Inference: Diagnosing Large Systems
Markakis, M., Youngmann, B., Gao, T., Zhang, Z., Shahout, R., Chen, P. B., Liu, C., Sabek, I., & Cafarella, M. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3705829.3705836 · Cited by 11
Manod: A multi-modal anomaly detection framework for distributed system
Liu, W., Sun, D., Yang, H., Wang, Y., & Huang, W. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107999 · Cited by 9
Metadata
- Authors
- He, P., Zhu, J., Zheng, Z., & Lyu, M. R.
- Affiliations
- Chinese University of Hong Kong (HK)
- Sun Yat-sen University (CN)
- Venue
- 2017 IEEE International Conference on Web Services (ICWS)
- Publisher
- IEEE
- Published
- 2017-06
- Pages
- 33-40
- OpenAlex
- W2754665629
- Cited by
- 943
- References
- 33
- Citations by year
- 2026: 164 · 2025: 184 · 2024: 184 · 2023: 157 · 2022: 84 · 2021: 91 · 2020: 52 · 2019: 18 · 2018: 7 · 2017: 2
- FWCI
- 17.22
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Web Services (ICWS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Information Systems › Advanced Database Systems and Queries
- Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
- Keywords
- log parsing, online parsing, log analysis, web service management, anomaly detection
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