  Log parsing, the process of converting raw log messages into structured
formats, is an important initial step for automated analysis of logs of
large-scale software systems. Traditional log parsers often rely on heuristics
or handcrafted features, which may not generalize well across diverse log
sources or require extensive model tuning. Recently, some log parsers have
utilized powerful generative capabilities of large language models (LLMs).
However, they heavily rely on demonstration examples, resulting in substantial
overhead in LLM invocations. To address these issues, we propose LogBatcher, a
cost-effective LLM-based log parser that requires no training process or
labeled data. To leverage latent characteristics of log data and reduce the
overhead, we divide logs into several partitions through clustering. Then we
perform a cache matching process to match logs with previously parsed log
templates. Finally, we provide LLMs with better prompt context specialized for
log parsing by batching a group of logs from each partition. We have conducted
experiments on 16 public log datasets and the results show that LogBatcher is
effective and efficient for log parsing.
