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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In the citing paper
“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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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