Monitoring
Anomaly Detection for Time Series Data: Techniques and Models
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.
Anomaly Detection for Time Series Data: Anomaly Types
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.
Anomaly Detection for Time Series Data: An Introduction
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.
Monitoring Kubernetes costs with OpenCost and VictoriaMetrics
Read how to set up Kubernetes costs monitoring with VictoriaMetrics.
Why we generate & collect logs: About the usability & cost of modern logging systems
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.
Never-firing alerts: What they are and how to deal with them
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 use VictoriaMetrics for monitoring with Netdata Agent
How to set up VictoriaMetrics as long-term storage for Netdata Agent metrics
Save network costs with VictoriaMetrics remote write protocol
Save network bandwidth costs when transferring data to VictoriaMetrics starting from v1.88
Rules backfilling via vmalert
Read how to use vmalert’s replay mode to retroactively evaluate recording and alerting rules with SLO objective as example.
Cardinality explorer
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.