# ExplainIt! -- A declarative root-cause analysis engine for time series data (extended version)

## TL;DR

ExplainIt! is an unsupervised, declarative root-cause analysis engine for time series data that helps operators identify and rank potential causes of system events efficiently using SQL-like hypotheses.

## Contribution

It introduces a novel declarative approach using SQL to specify and rank causal hypotheses in complex time series systems, enhancing interpretability and efficiency.

## Key findings

- Resolved over 30 performance issues in a commercial product since 2014
- Demonstrated effectiveness in identifying root causes in large-scale systems
- Reduced causal hypotheses from hundreds of thousands to a manageable number

## Abstract

We present ExplainIt!, a declarative, unsupervised root-cause analysis engine that uses time series monitoring data from large complex systems such as data centres. ExplainIt! empowers operators to succinctly specify a large number of causal hypotheses to search for causes of interesting events. ExplainIt! then ranks these hypotheses, reducing the number of causal dependencies from hundreds of thousands to a handful for human understanding. We show how a declarative language, such as SQL, can be effective in declaratively enumerating hypotheses that probe the structure of an unknown probabilistic graphical causal model of the underlying system. Our thesis is that databases are in a unique position to enable users to rapidly explore the possible causal mechanisms in data collected from diverse sources. We empirically demonstrate how ExplainIt! had helped us resolve over 30 performance issues in a commercial product since late 2014, of which we discuss a few cases in detail.