Sawmill: From Logs to Causal Diagnosis of Large Systems

Markos Markakis, An Bo Chen, Brit Youngmann, Trinity Gao, Ziyu Zhang, Rana Shahout, Peter Baile Chen, Chunwei Liu, Ibrahim Sabek, Michael J. Cafarella

Abstract: Causal analysis is an essential lens for understanding complex system dynamics in domains as varied as medicine, economics and law. Computer systems are often similarly complex, but much of the information about them is only available in long, messy, semi-structured log files. This demo presents Sawmill, an open-source system that makes it possible to extract causal conclusions from log files. Sawmill employs methods drawn from the areas of data transformation, cleaning, and extraction in order to transform logs into a representation amenable to causal analysis. It gives log-derived variables human-understandable names and distills the information present in a log file around a user's chosen causal units (e.g. users or machines), generating appropriate aggregated variables for each causal unit. It then leverages original algorithms to efficiently use this representation for the novel process of Exploration-based Causal Discovery - the task of constructing a sufficient causal model of the system from available data. Users can engage with this process via an interactive interface, ultimately making causal inference possible using off-the-shelf tools. SIGMOD'24 participants will be able to use Sawmill to efficiently answer causal questions about logs. We will guide attendees through the process of quantifying the impact of parameter tuning on query latency using real-world PostgreSQL server logs, before letting them test Sawmill on additional logs with known causal effects but varying difficulty. A companion video for this submission is available online.