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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]
Citations
Cites
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Term-Weighting Approaches in Automatic Text Retrieval
Salton, G., & Buckley, C. (1988) · Information Processing & Management · doi:10.1016/0306-4573(88)90021-0 · Cited by 11,421
Introduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288
Detecting large-scale system problems by mining console logs
Xu, W., Huang, L., Fox, A., Patterson, D., & Jordan, M. I. (2009) · Proceedings of the ACM SIGOPS 22nd symposium on Operating systems principles · doi:10.1145/1629575.1629587 · Cited by 1,332
What Supercomputers Say: A Study of Five System Logs
Oliner, A. J., & Stearley, J. (2007) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2007.103 · Cited by 695
Execution Anomaly Detection in Distributed Systems through Unstructured Log Analysis
Fu, Q., Lou, J., Wang, Y., & Jiang, L. (2009) · Proceedings · doi:10.1109/icdm.2009.60 · Cited by 555
Spell: Streaming Parsing of System Event Logs
Du, M., & Li, F. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0103 · Cited by 423
An Evaluation Study on Log Parsing and Its Use in Log Mining
He, P., Zhu, J., He, S., Li, J., & Lyu, M. R.-T. (2016) · International Conference on Dependable Systems & Networks/Proceedings · doi:10.1109/dsn.2016.66 · Cited by 274
Self-citationLogSig: generating system events from raw textual logs
Tang, L., Li, T., & Perng, C.-S. (2011) · Proceedings of the 20th ACM international conference on Information and knowledge management · doi:10.1145/2063576.2063690 · Cited by 244
Learning to Log: Helping Developers Make Informed Logging Decisions
Zhu, J., He, P., Fu, Q., Zhang, H., Lyu, M. R.-T., & Zhang, D. (2015) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse.2015.60 · Cited by 229
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“Table I shows examples of wrongly parsed log templates by the Drain parser [24].”
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Huang, J., Yang, Y., Yu, H., Li, J., & Zheng, X. (2023) · 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase56229.2023.00138 · Cited by 50
Zhang, C., Wang, X., Zhang, H., Zhang, H., & Han, P. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3125967 · Cited by 49
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SPINE: a scalable log parser with feedback guidance
Wang, X., Zhang, X., Li, L., He, S., Zhang, H., Liu, Y., Zheng, L., Kang, Y., Lin, Q., Dang, Y., et al. (2022) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3540250.3549176 · Cited by 49
LogParse: Making Log Parsing Adaptive through Word Classification
Meng, W., Liu, Y., Zaiter, F., Zhang, S., Chen, Y., Zhang, Y., Zhu, Y., Wang, E., Zhang, R., Tao, S., et al. (2020) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn49398.2020.9209681 · Cited by 49
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Fu, Y., Liang, K., & Xu, J. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3289488 · Cited by 49
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Vervaet, A. (2021) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde51399.2021.00317 · Cited by 49
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He, M., Jia, T., Duan, C., Cai, H., Li, Y., & Huang, G. (2024) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre62328.2024.00023 · Cited by 51
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Zhao, Z., Xu, C., & Li, B. H. (2021) · Journal of Signal Processing Systems · doi:10.1007/s11265-021-01644-4 · Cited by 46
LogAssist: Assisting Log Analysis Through Log Summarization
Locke, S., Li, H., Chen, T.-H., Shang, W., & Liu, W. (2022) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3083715 · Cited by 45
Tao, S., Meng, W., Cheng, Y., Zhu, Y., Liu, Y., Du, C., Han, T., Zhao, Y., Wang, X., & Yang, H. (2022) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/3543146.3543168 · Cited by 47
LayerLog: Log sequence anomaly detection based on hierarchical semantics
Zhang, C., Wang, X., Zhang, H., Zhang, J., Zhang, H., Liu, C., & Han, P. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109860 · Cited by 52
Zeufack, V., Kim, D., Seo, D., & Lee, A. (2021) · High-Confidence Computing · doi:10.1016/j.hcc.2021.100030 · Cited by 45
Loader: A Log Anomaly Detector Based on Transformer
Xiao, T. Y., Quan, Z., Wang, Z.-J., Le, Y., Du, Y., Liao, X., Li, K., & Li, K. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3280575 · Cited by 44
MetaLog: Generalizable Cross-System Anomaly Detection from Logs with Meta-Learning
Zhang, C., Jia, T., Shen, G., Zhu, P., & Li, Y. (2024) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1145/3597503.3639205 · Cited by 43
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Buchta, R., Gkoktsis, G., Heine, F., & Kleiner, C. (2024) · Digital Threats Research and Practice · doi:10.1145/3696014 · Cited by 51
Temporal Logical Attention Network for Log-Based Anomaly Detection in Distributed Systems
Liu, Y., Ren, S., Wang, X., & Zhou, M. (2024) · Sensors · doi:10.3390/s24247949 · Cited by 53
LLM-LADE: Large language model-based log anomaly detection with explanation
Zhang, Z., Li, S., Zhang, L., Ye, J., Hu, C., & Yan, L. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114064 · Cited by 42
Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models
Ott, H., Bogatinovski, J., Acker, A., Nedelkoski, S., & Kao, O. (2021) · IEEE/ACM International Workshop on Cloud Intelligence (CloudIntelligence) · doi:10.1109/cloudintelligence52565.2021.00013 · Cited by 44
LogGT: Cross-system log anomaly detection via heterogeneous graph feature and transfer learning
Wang, P., Zhang, X., Cao, Z., Xu, W., & Li, W. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124082 · Cited by 44
PredictDeep: Security Analytics as a Service for Anomaly Detection and Prediction
Elsayed, M. A., & Zulkernine, M. (2020) · IEEE Access · doi:10.1109/access.2020.2977325 · Cited by 41
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Yin, K., Yan, M., Xu, L., Xu, Z., Li, Z., Yang, D., & Zhang, X. (2020) · 2020 IEEE International Conference on Software Maintenance and Evolution (ICSME) · doi:10.1109/icsme46990.2020.00069 · Cited by 37
LogKG: Log Failure Diagnosis Through Knowledge Graph
Sui, Y., Zhang, Y., Sun, J., Xu, T., Zhang, S., Li, Z., Sun, Y., Guo, F., Shen, J., Zhang, Y., et al. (2023) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2023.3293890 · Cited by 48
GLAD-PAW: Graph-Based Log Anomaly Detection by Position Aware Weighted Graph Attention Network
Wan, Y., Liu, Y., Wang, D., & Wen, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-75762-5_6 · Cited by 42
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Fu, Y., Yan, M. L., Xu, Z., Xia, X., Zhang, X., & Yang, D. (2022) · Empirical Software Engineering · doi:10.1007/s10664-022-10214-6 · Cited by 38
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Aussel, N., Petetin, Y., & Chabridon, S. (2018) · Proceedings - International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems · doi:10.1109/mascots.2018.00031 · Cited by 43
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Das, A., Mueller, F., & Rountree, B. (2020) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps47924.2020.00115 · Cited by 38
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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