The OpenTelemetry Astronomy Shop demo has long served as a reference environment for exploring observability in distributed systems, but until now it shipped with only a Prometheus datasource. VictoriaMetrics forked the demo and extended it with VictoriaMetrics, VictoriaLogs, and VictoriaTraces, providing insights into VictoriaMetrics’ observability stack where metrics, logs, and traces flow into a unified backend.
Proper alerting is an art. It is all about foreseeing bad scenarios before they happen, so you can prepare for them. In this article, we go through practical recommendations of approaching the alerting to reduce alerting fatigue and avoid false positives.
We believe microservices shouldn’t be the default and that companies should start with monoliths until reality actually demands they scale and shift from one node to a cluster of nodes. As a result, we offer clustering on an open source basis because we want to support realistic growth. Read on for details!
Explore the latest improvements in VictoriaMetrics Anomaly Detection (vmanomaly), including optimizations, online models, multitenantcy and mTLS support.
Learn how to monitor Azure AKS and Azure Linux with VictoriaMetrics. This blog post covers the setup process for environments with high security requirements and how to monitor them with VictoriaMetrics.
Time series databases are essential tools in any software engineer’s toolbelt. Their development has been shaped by user needs and countless open source contributors, leading to the healthy ecosystem of options we see today. In this article, you’ll see how time series databases came about, and why so many are open source.
I have already recorded metrics in the application, why can’t I see my metrics on Grafana?
Explore the latest improvements in VictoriaMetrics Anomaly Detection (vmanomaly), including presets, new models, enhanced tuning, and better resource management
Learn about the different types of managed monitoring services available on the market, and why you might consider picking one of them to manage your monitoring infrastructure.
This blog post series centers on Anomaly Detection (AD) and Root Cause Analysis (RCA) within time-series data. In Chapter 3, we delve into a variety of advanced anomaly detection techniques, encompassing supervised, semi-supervised, and unsupervised approaches, each tailored to different data scenarios and challenges in time-series analysis.