  Logs have been widely adopted in software system development and maintenance
because of the rich runtime information they record. In recent years, the
increase of software size and complexity leads to the rapid growth of the
volume of logs. To handle these large volumes of logs efficiently and
effectively, a line of research focuses on developing intelligent and automated
log analysis techniques. However, only a few of these techniques have reached
successful deployments in industry due to the lack of public log datasets and
open benchmarking upon them. To fill this significant gap and facilitate more
research on AI-driven log analytics, we have collected and released loghub, a
large collection of system log datasets. In particular, loghub provides 19
real-world log datasets collected from a wide range of software systems,
including distributed systems, supercomputers, operating systems, mobile
systems, server applications, and standalone software. In this paper, we
summarize the statistics of these datasets, introduce some practical usage
scenarios of the loghub datasets, and present our benchmarking results on
loghub to benefit the researchers and practitioners in this field. Up to the
time of this paper writing, the loghub datasets have been downloaded for
roughly 90,000 times in total by hundreds of organizations from both industry
and academia. The loghub datasets are available at
https://github.com/logpai/loghub.
