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.
