ECLIPSE: Semantic Entropy-LCS for Cross-Lingual Industrial Log Parsing

TL;DR

ECLIPSE introduces a novel cross-lingual log parsing method leveraging semantic entropy and LLMs to improve accuracy and efficiency in industrial settings, addressing challenges of large-scale and complex logs.

Contribution

The paper presents ECLIPSE, a new cross-lingual industrial log parser that combines data-driven template matching with semantic understanding from LLMs, and introduces a new benchmark for evaluation.

Findings

ECLIPSE outperforms existing parsers on public benchmarks.

ECLIPSE maintains high efficiency with large and diverse logs.

ECLIPSE demonstrates robust cross-lingual log parsing capabilities.

Abstract

Log parsing, a vital task for interpreting the vast and complex data produced within software architectures faces significant challenges in the transition from academic benchmarks to the industrial domain. Existing log parsers, while highly effective on standardized public datasets, struggle to maintain performance and efficiency when confronted with the sheer scale and diversity of real-world industrial logs. These challenges are two-fold: 1) massive log templates: The performance and efficiency of most existing parsers will be significantly reduced when logs of growing quantities and different lengths; 2) Complex and changeable semantics: Traditional template-matching algorithms cannot accurately match the log templates of complicated industrial logs because they cannot utilize cross-language logs with similar semantics. To address these issues, we propose ECLIPSE, Enhanced Cross-Lingual Industrial log Parsing with Semantic Entropy-LCS, since cross-language logs can robustly parse industrial logs. On the one hand, it integrates two efficient data-driven template-matching algorithms and Faiss indexing. On the other hand, driven by the powerful semantic understanding ability of the Large Language Model (LLM), the semantics of log keywords were accurately extracted, and the retrieval space was effectively reduced. Notably, we launch a Chinese and English cross-platform industrial log parsing benchmark ECLIPSE- BENCH to evaluate the performance of mainstream parsers in industrial scenarios. Our experimental results across public benchmarks and ECLIPSE- BENCH underscore the superior performance and robustness of our proposed ECLIPSE. Notably, ECLIPSE both delivers state-of-the-art performance when compared to strong baselines and preserves a significant edge in processing efficiency.