Description: Elevate Your Data with Self-Describing
An emerging set of conventions, standards and concepts around timeseries metrics metadata
We have pretty good timeseries collection agents, storage and dashboards. But linking a timeseries to nothing more than a string "name" and maybe a few tags without further metadata is profoundly limiting us. Especially when they're not standardized and missing information. Metrics 2.0 aims for self-describing , standardized metrics using orthogonal tags for every dimension. "metrics" being the pieces of information that point to, and describe timeseries of data.
If you have a handful of metrics, you don't need to think about this and can stick with simple names for your metrics. However, as we grow our number of metrics and/or want to make more sense out of them, we need to be more systematic. Here are the reasons, concepts and their benefits