This blog post series centers on Anomaly Detection (AD) and Root Cause Analysis (RCA) within time-series data. In this second part, we explore the distinct anomaly types inherent to time-series and offer insights on how to tackle them effectively.
This blog post series focuses on Anomaly Detection (AD) and Root Cause Analysis (RCA) within the context of time-series data. The inaugural chapter lays the groundwork by introducing the role of AD in end-to-end observability systems, discussing domain-specific terminology, and addressing the challenges inherent to the time-series nature of the data.
Read how to set up Kubernetes costs monitoring with VictoriaMetrics.
This blog post looks at what logs are and why they matter, why logs are generated and collected as well as at the costs associated with that. It also provides details on why VictoriaLogs should be considered over similar solutions.
Read how vmalert helps to find alerting rules which don’t match any time series. Such rules will never fire and only trick users with a false sense of protection.
How to set up VictoriaMetrics as long-term storage for Netdata Agent metrics
Save network bandwidth costs when transferring data to VictoriaMetrics starting from v1.88
Read how to use vmalert’s replay mode to retroactively evaluate recording and alerting rules with SLO objective as example.
In monitoring, the term cardinality defines the number of unique time series stored in TSDB. The higher is cardinality, the more resources are usually required for metrics processing and querying. Let’s see how Cardinality explorer can help us here.
VictoriaMetrics is a monitoring solution. It was designed to collect and process telemetry from many systems, provide a retrospective view, and forecast metrics for capacity planning. But what about monitoring VictoriaMetrics itself?