Welcome to 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 (Chapter 1) is focused on.

Most development teams know that testing the application layer of a system (a.k.a the codebase) is of vital importance. Testing the data layer (the database) is just as important. To perform database testing, you construct queries to assert and validate the database operations, structures, and attributes required by the application connecting to the database.

We release VictoriaMetrics several times a month, including at least one major update. However, because these new releases often introduce new features, they may be less stable. That’s why we also regularly publish Long-term support releases (LTS) alongside our regular releases. These LTS versions focus exclusively on bug fixes without new features and performance improvements. We committed to publishing LTS versions every six months and supporting them for one year.

The story of how we simplified database system monitoring for generalists while making it flexible enough for specialists.

Control over operational costs is pivotal in Kubernetes' deployment and management. Although Kubernetes brings power and control over your deployments, it also necessitates thorough understanding and management of costs. OpenCost, specifically designed for Kubernetes cost monitoring, combined with VictoriaMetrics, an efficient time series database, offers a comprehensive solution for this challenge.