Welcome to the third chapter of the handbook on Anomaly Detection for Time Series Data! This series of blog posts aims to provide an in-depth look into the fundamentals of anomaly detection and root cause analysis. It will also address the challenges posed by the time-series characteristics of the data and demystify technical jargon by breaking it down into easily understandable language.

This blog post is also available as a recorded talk with slides.

Relabeling is an important feature that allows users to modify metadata (labels) of scraped metrics before they ever make it to the database. As an example, some of your scrape targets may generate metric labels with underscores (_), and some of your targets may generate labels with hyphens (-). Relabeling allows you to make this consistent, making database queries easier to write.

VictoriaMetrics is an open-source time-series database (TSDB) written in Go, and I’ve had the pleasure of working on it for the past couple of years. TSDBs have stringent performance requirements, and building VictoriaMetrics has taught me a thing or two about optimization. In this blog post, I’ll share some of the performance tips I’ve learned during my time at VictoriaMetrics.

We’re happy to announce a landmark 320% growth in 2023! VictoriaMetrics, our open source time series database and monitoring solution, already hit 268 million downloads this year (still counting), and received close to 13,000 stars on GitHub.