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This paper introduces LogLead, a tool designed for efficient log analysisbenchmarking. LogLead combines three essential steps in log processing:loading, enhancing, and anomaly detection. The tool leverages Polars, ahigh-speed DataFrame library. We currently have Loaders for eight systems thatare publicly available (HDFS, Hadoop, BGL, Thunderbird, Spirit, Liberty,TrainTicket, and GC Webshop). We have multiple enhancers with three parsers(Drain, Spell, LenMa), Bert embedding creation and other log representationtechniques like bag-of-words. LogLead integrates to five supervised and fourunsupervised machine learning algorithms for anomaly detection from SKLearn. Byintegrating diverse datasets, log representation methods and anomaly detectors,LogLead facilitates comprehensive benchmarking in log analysis research. Weshow that log loading from raw file to dataframe is over 10x faster withLogLead compared to past solutions. We demonstrate roughly 2x improvement inDrain parsing speed by off-loading log message normalization to LogLead. Ourbrief benchmarking on HDFS indicates that log representations extending beyondthe bag-of-words approach offer limited additional benefits. Tool URL:https://github.com/EvoTestOps/LogLead

LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly Detector