Adaptive and Efficient Log Parsing as a Cloud Service

2025-04-12

AI Summary

Large-scale, heterogeneous logs in cloud systems exhibit massive volume and inconsistent formats, posing significant challenges for existing log parsing methods to simultaneously achieve adaptability, efficiency, and accuracy. This paper proposes ByteBrain-LogParser, a hierarchical clustering-based log parsing framework. It introduces a novel co-optimization mechanism integrating positional similarity distance, log deduplication, and hash encoding—enabling real-time precision tuning and high-throughput parsing. Evaluated on real-world cloud infrastructure, ByteBrain-LogParser achieves an average throughput of 229,000 logs per second—8.4× higher than the fastest baseline—while matching state-of-the-art accuracy. The framework significantly advances real-time parsing capability and engineering practicality for large-scale unstructured logs.

Technology Category

• Natural Language Processing: Syntax — Tagging, Chunking & Parsing
• Machine Learning: Hardware-aware ML
• Search and Optimization: Distributed Search

Application Category

• Web Mining and Content Analysis: Web traffic and log analysis
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements

Abstract

Logs are a critical data source for cloud systems, enabling advanced features like monitoring, alerting, and root cause analysis. However, the massive scale and diverse formats of unstructured logs pose challenges for adaptable, efficient, and accurate parsing methods. This paper introduces ByteBrain-LogParser, an innovative log parsing framework designed specifically for cloud environments. ByteBrain-LogParser employs a hierarchical clustering algorithm to allow real-time precision adjustments, coupled with optimizations such as positional similarity distance, deduplication, and hash encoding to enhance performance. Experiments on large-scale datasets show that it processes 229,000 logs per second on average, achieving an 840% speedup over the fastest baseline while maintaining accuracy comparable to state-of-the-art methods. Real-world evaluations further validate its efficiency and adaptability, demonstrating its potential as a robust cloud-based log parsing solution.

Problem

• Handling massive scale and diverse formats of unstructured logs
• Achieving adaptable, efficient, and accurate log parsing methods
• Enabling real-time precision adjustments in cloud log parsing

Innovation

• Hierarchical clustering for real-time precision adjustments
• Positional similarity distance optimizes performance
• Hash encoding and deduplication enhance efficiency