Metrics promise universal understanding across systems, but with developing formats and complex mathematics often cause more confusion than clarity. Here is what we are wrong and how we can repair it.
In 1887 he introduced an ophthalmologist named LL Zamenhof Esperanto, a universal language designed to break barriers and unit people around the world. It was ambitious, ideal and finally a niche, today only about 100,000 speakers.
Observability has its own version of Esperanto: Metrics. They are standardized numerical representations of the health health system. Theoretically, the metrics should simplify how we monitor and remove digital infrastructure. In practice, they often did not understand, abused and disagreed madly.
Let’s explore why metrics, our expected universal language, remain so difficult to remedy.
Metrics, decoded (and re -infected)
There is a number in the metric at a time moment. This seems to be simple – until you get into the nuance of as defined and used by metrics. Take redis.Keyspace.hits, for example: a counter that monitors how often instances Redis successfully find data in the keyboard. Depending on telemetry format – opentlemmetry, Prometheus or Statsd – will be formatted differently, even on Saturdays, aggregation and metric value.
Now we have competitive standards such as Statsd, Prometheus and Ophetemeters (OTLP) metrics, each introducing its own way of defining and transferring data and associated metadata. These formats differ in syntax, differ in basic behavior and structure of metadata. Result? Three tools can show you the same metric value, but require a completely different logic to collect, storage and analysis.
This fragmentation leads to operational confusion, inflated storage costs and teams who spend more time decoding telemetry than by acting.
Uneven format of the transfer of metric understanding
Although the translation is processed, the aggregation still causes confusion. Imagine that you collect redis.Keyspace. If the container brand is canceled, the metric value must now be aggregated. In OTLP, Prometheus or Statsd, drop the container.ID Tag changes as the metric is interpreted as the value of metrics must now be aggregated. Prometheus can summarize the values, OTLP can consider them to be a counterar by the counter, and the statsD could average them, resulting in behavior as a meter than a counter. These fine differences in how metrics are interpreted can lead to inconsistent analysis. Without intentional processing metrics, teams risk that they draw incorrect conclusions from the data.
But even after the translation format, the most difficult part comes often: deciding how to aggregate these metrics. The answer depends on the type of metric. Maguges suggestion can lead to incorrect results. The risk can be given the handling of the delta as a cumulative counter. Aggregation mathematics, which is technically correct, can still confuse systems down, especially if these systems expect monotonic behavior.
Metrics are mathematical and mathematical. That is why tools need logic specific to metric, similar to an event logic that already exists for protocols and traces.
Why does it matter
If we can rely on, we share understanding metrics, observability suffers. Solution of incidents take longer. Determination has become noisy. Teams lose faith in their data.
The way forward is not about creating another standard. It is the development of better tools that simplify handling formats, smarter ways of aggregation and interpretation of data, and education that helps teams effectively use metrics without the necessary mathematical level.
By considering metrics as a unique form of telemetry with our own structure and challenges, we can remove the guess and seize teams to deal with confidence. It is time to build with regard to clarity – not only for machines, but for human data interpretation.