Enable javascript in your browser for better experience. Need to know to enable it? Go here.
Published : Oct 26, 2022
This blip is not on the current edition of the Radar. If it was on one of the last few editions, it is likely that it is still relevant. If the blip is older, it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar. Understand more
Oct 2022
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

Modern observability relies on collecting and aggregating an exhaustive set of granular metrics to fully understand, predict and analyze system behavior. But when applied to a cloud native system composed of many redundant and cooperating processes and hosts, the cardinality (or number of unique time series) becomes unwieldy because it grows exponentially with each additional service, container, node, cluster, etc. When dealing with high-cardinality data, we've found that VictoriaMetrics performs well. VictoriaMetrics is particularly useful for operating Kubernetes-hosted microservice architectures, and the VictoriaMetrics operator makes it easy for teams to implement their own monitoring in a self-service way. We also like its componentized architecture and ability to continue collecting metrics even when the central server is unavailable. Although our team has been happy with VictoriaMetrics, this is a rapidly evolving area, and we'd recommend keeping an eye on other high-performance, Prometheus-compatible time series databases such as Cortex or Thanos.

Download the PDF



English | Español | Português | 中文

Sign up for the Technology Radar newsletter


Subscribe now

Visit our archive to read previous volumes