Logs, metrics and traces are equally useless

18 Jun 2018 - Tags: devops, observability, opentracing, tracing, distributed system, monitoring, logs, cloud, tracing

Every signal from applications or infrastructure is useless, in the distributed system era aggregation matters.

The ability to combine logs, metrics and, traces together is the key takeaway here.

Kubernetes spin ups too many containers to allow us to stream or tail a log fail.

Even cloud providers offer too many virtual machines to enable us to tail logs.

A centralized place where to store all of them is a great start, but you need to experience and learn how to combine the metrics you are ingesting to increase the visibility over your system.

If you instrument your code with opentracing, for example, you can get the trace_id and attach it to your log to associate it with the trace itself. It can also work as the lookup key for troubleshooting.

If you get some weird logs, you will know from where it comes. With opentracing, this is still a bit of a mess the specification recently added explicit support to extract TraceId and SpanId from the SpanContext. It is currently not implemented in a lot of implementation. I recently started a conversation in the opentracing-go project to figure out how to apply it because currently it depends from what tracer you are using and it is an essential regression for the specification itself that should hide it by design.

Using Jaeger this is the way to do it:

if sc, ok := span.Context().(jaeger.SpanContext); ok {

Using Zipkin:

zipkinSpan, ok := sp.Context().(zipkin.SpanContext)
if ok == true && zipkinSpan.TraceID.Empty() == false {
  w.Header().Add("X-Trace-ID", zipkinSpan.TraceID.ToHex())

To get back in track, I wrote this article because I saw this problem and this inclination speaking with friends, colleagues and other devs, we are now good (or just better) storing high cardinality values but save them inside a database doesn’t give us any value it is all about how we use them.

Correlation brings your alert to a different level. You probably have an alarm to measure how much disks you still have.

An alert on the only CPU usage can be very frustrated even more if it happens too often and a lot of time you restart a container or a node to make it work because at 2 am you can’t fix the cause. You can investigate what matters to fill an issue on GitHub.

Every automation tools can make your work leaving you free to sleep. It can probably fill out the issue.

Combining the CPU with the time for the system to recover from a node restart can make your alert smart enough to wake you up when it is not able to fix itself leaving you ready for more acute and trivial problems.


It is a pretty straightforward concept, but yes, everything is useless if you store data without getting values out of them doesn’t matter if they are logs, metrics or traces. The real value is not in a single one of them, it is in how do you aggregate them together because a complex simple doesn’t explain itself over one signal.

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