Sub-atomic field processing for improved web log compression

Web log files, storing user activity on a server, may grow at the pace of hundreds of megabytes a day, or even more, on popular sites. It makes sense to archive old logs, to analyze them further, e.g., for detecting attacks or other server abuse patterns. In this work we present a specialized lossless Apache web log preprocessor and test it with combination of several popular general-purpose compressors. Our method works on individual fields of log data (each storing such information like the client's IP, date/time, requested file or query, download size in bytes, etc.), and utilizes such compression techniques like finding and extracting common prefixes and suffixes, dictionary -based phrase sequence substitution, move -to-front coding, and more. The test results show the proposed transform improves the average compression ratios 2.64 times in case of gzip and 1.83 times in case of bzip2.