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Log data is a crucial resource for recording system events and states duringsystem execution. However, as systems grow in scale, log data generation hasbecome increasingly explosive, leading to an expensive overhead on log storage,such as several petabytes per day in production. To address this issue, logcompression has become a crucial task in reducing disk storage while allowingfor further log analysis. Unfortunately, existing general-purpose andlog-specific compression methods have been limited in their ability to utilizelog data characteristics. To overcome these limitations, we conduct anempirical study and obtain three major observations on the characteristics oflog data that can facilitate the log compression task. Based on theseobservations, we propose LogShrink, a novel and effective log compressionmethod by leveraging commonality and variability of log data. An analyzer basedon longest common subsequence and entropy techniques is proposed to identifythe latent commonality and variability in log messages. The key idea behindthis is that the commonality and variability can be exploited to shrink logdata with a shorter representation. Besides, a clustering-based sequencesampler is introduced to accelerate the commonality and variability analyzer.The extensive experimental results demonstrate that LogShrink can exceedbaselines in compression ratio by 16% to 356% on average while preserving areasonable compression speed.

LogShrink: Effective Log Compression by Leveraging Commonality and Variability of Log Data