Logram: Efficient Log Parsing Using n-Gram Dictionaries

Abstract

Software systems usually record important runtime information in their logs. Logs help practitioners understand system runtime behaviors and diagnose field failures. As logs are usually very large in size, automated log analysis is needed to assist practitioners in their software operation and maintenance efforts. Typically, the first step of automated log analysis is log parsing, i.e., converting unstructured raw logs into structured data. However, log parsing is challenging, because logs are produced by static templates in the source code (i.e., logging statements) yet the templates are usually inaccessible when parsing logs. Prior work proposed automated log parsing approaches that have achieved high accuracy. However, as the volume of logs grows rapidly in the era of cloud computing, efficiency becomes a major concern in log parsing. In this work, we propose an automated log parsing approach, Logram, which leverages n-gram dictionaries to achieve efficient log parsing. We evaluated Logram on 16 public log datasets and compared Logram with five state-of-the-art log parsing approaches. We found that Logram achieves higher parsing accuracy than the best existing approaches and also outperforms these approaches in efficiency (i.e., 1.8 to 5.1 times faster than the second-fastest approaches in terms of end-to-end parsing time ). Furthermore, we deployed Logram on Spark and we found that Logram scales out efficiently with the number of Spark nodes (e.g., with near-linear scalability for some logs) without sacrificing parsing accuracy. In addition, we demonstrated that Logram can support effective online parsing of logs, achieving similar parsing results and efficiency to the offline mode.

MoniLog: An Automated Log-Based Anomaly Detection System for Cloud Computing Infrastructures

“…Different algorithms exist in the literature, some of them process logs per batch (e.g., IPLoM [26],SLCT [27], Log-Cluster [28]) and others process logs in a streaming fashion (e.g., Drain [29], Spell [30], Logan [31], Logram [32]).…”

SemParser: A Semantic Parser for Log Analysis

“…Although automatic log parsing is full of challenges, researchers have made progress leveraging statistical and history-based methods. For instance, SLCT [43] and LFA [34] constructed log templates by counting the number of historical frequently-appearing words while Logram [10] considered frequent n-gram patterns. LogSig [42] and SHISO [33] encoded the log by word pairs and words length, respectively, then applied the clustering algorithm for partitioning.…”