Having metrics with different label compositions seems *fine to me*. In the
end a metric is nothing more than a set of time series with the same value
for the label `__name__`. So there is no difference for the storage engine.
I would just ensure that your data stays aggregatable. What do I mean by
this? Let's take the following example:
```
subsystem_message_total{name="foo", direction="in"}=100
subsystem_message_total{name="foo", direction="out"}=100
subsystem_message_total{name="bar"}=100
```
The query `sum(subsystem_message_total)` returns `300` which is correct.
But if it looks like this:
```
subsystem_message_total{name="foo", direction="in"}=100
subsystem_message_total{name="foo", direction="out"}=100
subsystem_message_total{name="foo"}=200
subsystem_message_total{name="bar"}=100
```
The query `sum(subsystem_message_total)` will return `500`. So to work
around this you would have to always remember to only include series with
xor without a `direction` label.
John Dexter schrieb am Montag, 26. Oktober 2020 um 15:36:55 UTC+1:
> I have a use-case where we monitor multiple, very different systems
> through a variety of protocols but one common thing is each sends updates
> to our server and most allow us to send messages to them too.
>
> This seems a perfect example of labels...
> subsystem_message_total[subsystem-name,direction] for instance.
>
> But one of our systems has 2 components, the others don't. I'm unsure what
> happens if this subsystem uses a 3rd label [component] and the others do
> not. Is this bad practice? Will it cause problems in storing/querying data?
> Subsystems could each have their own metric name instead of using labels,
> but that sounds pretty ugly too.
>
> What is the best approach here?
>
> Thanks.
>
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