  In the last decade, an impressive increase in software adaptions has led to a
surge in log data production, making manual log analysis impractical and
establishing the necessity for automated methods. Conversely, most automated
analysis tools include a component designed to separate log templates from
their parameters, commonly referred to as a "log parser". This paper aims to
introduce a new fast and accurate log parser, named "Tipping". Tipping combines
rule-based tokenizers, interdependency token graphs, strongly connected
components, and various techniques to ensure rapid, scalable, and precise log
parsing. Furthermore, Tipping is parallelized and capable of running on
multiple processing cores with close to linear efficiency. We evaluated Tipping
against other state-of-the-art log parsers in terms of accuracy, performance,
and the downstream task of anomaly detection. Accordingly, we found that
Tipping outperformed existing methods in accuracy and performance in our
evaluations. More in-depth, Tipping can parse 11 million lines of logs in less
than 20 seconds on a laptop machine. Furthermore, we re-implemented a
parallelized version of the past IpLom algorithm to demonstrate the effect of
parallel processing, and it became the second-fastest parser. As logs keep
growing in volume and complexity, the software engineering community needs to
ensure automated log analysis tools keep up with the demand, being capable of
efficiently handling massive volumes of logs with high accuracy. Tipping's
robustness, versatility, efficiency, and scalability make it a viable tool for
the modern automated log analysis task.
