TL;DR
Logzip is a novel log compression method that extracts hidden structures through iterative clustering, significantly reducing storage space for large-scale system logs with minimal overhead.
Contribution
This paper introduces logzip, a new log compression technique that leverages iterative clustering to improve compression efficiency over traditional methods.
Findings
Logzip reduces log storage size by about 50% on average.
It is highly parallel and incurs negligible overhead.
Effective on diverse large-scale log datasets.
Abstract
System logs record detailed runtime information of software systems and are used as the main data source for many tasks around software engineering. As modern software systems are evolving into large scale and complex structures, logs have become one type of fast-growing big data in industry. In particular, such logs often need to be stored for a long time in practice (e.g., a year), in order to analyze recurrent problems or track security issues. However, archiving logs consumes a large amount of storage space and computing resources, which in turn incurs high operational cost. Data compression is essential to reduce the cost of log storage. Traditional compression tools (e.g., gzip) work well for general texts, but are not tailed for system logs. In this paper, we propose a novel and effective log compression method, namely logzip. Logzip is capable of extracting hidden structures…
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