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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Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey
Ma, J., Liu, Y., Wan, H., & Sun, G. (2023) · Applied Sciences · doi:10.3390/app13084930 · Cited by 23
Log-based slicing for system-level test cases
Messaoudi, S., Shin, D., Panichella, A., Bianculli, D., & Briand, L. (2021) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3460319.3464824 · Cited by 20
MADMM: microservice system anomaly detection via multi-modal data and multi-feature extraction
Wang, P., Zhang, X., Cao, Z., & Chen, Z. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09918-1 · Cited by 19
Retrieval-Grounded HDFS Log Anomaly Detection and Deterministic Failure Narrative Generation
Sun, X., Zhong, Z. S., & Wu, Q. (2026) · Journal of Computational Systems and Applications · doi:10.64229/j6d7fr94 · Cited by 19
Monitoring Anomalous States of Computer Systems by Intellectual Analysis of Data of System Journals
Sheluhin, O. I., Kostin, D. V., & Gorodnichev, M. G. (2020) · Systems of Signals Generating and Processing in the Field of on Board Communications · doi:10.1109/ieeeconf48371.2020.9078632 · Cited by 19
SiaLog: detecting anomalies in software execution logs using the siamese network
Hashemi, S., & Mäntylä, M. (2022) · Automated Software Engineering · doi:10.1007/s10515-022-00365-7 · Cited by 19
Self-supervised log parsing using semantic contribution difference
Yu, S., Chen, N., Wu, Y., & Dou, W. (2023) · Journal of Systems and Software · doi:10.1016/j.jss.2023.111646 · Cited by 21
No, G., Lee, Y., Kang, H., & Kang, P. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108613 · Cited by 21
Log-Based Anomaly Detection with Multi-Head Scaled Dot-Product Attention Mechanism
Du, Q., Zhao, L. Y., Xu, J., Han, Y., & Zhang, S. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86472-9_31 · Cited by 19
MRCA: metric-level root cause analysis for microservices via multi-modal data
Wang, Y., Zhu, Z., Fu, Q., Ma, Y., & He, P. (2024) · Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering · doi:10.1145/3691620.3695485 · Cited by 18
Self-citationLog Sequence Anomaly Detection Based on Template and Parameter Parsing via BERT
Chai, X., Zhang, H., Zhang, J., Sun, Y., & Das, S. K. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3428538 · Cited by 20
Rusted Anchors: A National Client-Side View of Hidden Root CAs in the Web PKI Ecosystem
Zhang, Y., Liu, B., Lu, C., Li, Z., Duan, H., Li, J., & Zhang, Z. (2021) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3460120.3484768 · Cited by 18
Distributed system anomaly detection using deep learning‐based log analysis
Han, P., Li, H., Xue, G., & Zhang, C. (2023) · Computational Intelligence · doi:10.1111/coin.12573 · Cited by 18
LogGrep: Fast and Cheap Cloud Log Storage by Exploiting both Static and Runtime Patterns
Wei, J., Zhang, G., Chen, J., Wang, Y., Zheng, W., Sun, T., Wu, J., & Jiang, J. (2023) · European Conference on Computer Systems · doi:10.1145/3552326.3567484 · Cited by 18
Multi-source data based anomaly detection through temporal and spatial characteristics
Xu, P., Gao, Q., Zhang, Z., & Zhao, K. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121675 · Cited by 18
GLAD: Content-Aware Dynamic Graphs For Log Anomaly Detection
Li, Y., Liu, Y., Wang, H., Chen, Z., Cheng, W. D., Chen, Y., Yu, W., Chen, H., & Liu, C. (2023) · IEEE International Conference on Knowledge Graph (ICKG) · doi:10.1109/ickg59574.2023.00007 · Cited by 21
OILog: An online incremental log keyword extraction approach based on MDP-LSTM neural network
Duan, X., Ying, S., Cheng, H., Yuan, W., & Yin, X. (2020) · Information Systems · doi:10.1016/j.is.2020.101618 · Cited by 18
LogExtractor: Extracting digital evidence from android log messages via string and taint analysis
Cheng, C. C.-C., Chen, S., Gong, N. Z., & Guan, Y. (2021) · Forensic Science International Digital Investigation · doi:10.1016/j.fsidi.2021.301193 · Cited by 18
AFALog: A General Augmentation Framework for Log-based Anomaly Detection with Active Learning
Duan, C., Jia, T., Cai, H., Li, Y., & Huang, G. (2023) · 2023 IEEE 34th International Symposium on Software Reliability Engineering (ISSRE) · doi:10.1109/issre59848.2023.00068 · Cited by 17
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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