Thread by @copyconstruct

Making distribute tracing easier with more sophisticated visualizations - @YuriShkuro

The first is color coded by service graph. The second is a heat map #QConNYC

Now @YuriShkuro is talking about a tool that compares traces.

[ ed- omg this: just yesterday I was talking to a vendor at QCon and was wondering if it’d be possible to compare traces. They said their product didn’t offer this. IMo this is the most important aspect of tracing]

And the diff tool deals with aggregate traces. You can then drill down into an individual trace. @YuriShkuro at #QConNYC

[ed - this. This so freaking much. Starting with a trace is like being in a hiding to nowhere. Need to begin with an aggregate view.]

I missed the first part of this talk, but everything I saw was 💯.

Tracing only becomes useful when you can surface the relevant information from a trace. That requires aggregate analysis.

But this isn’t without its flaws. Finding the right baseline might be hard.

Here’s a tool that’s internal to Uber that helps with “root cause” (sic) analysis to drill down from business metrics to app level telemetry. @YuriShkuro at #QConNYC

One of the challenges of taming microservices complexity is dealing with data.

Tracing can help here by enabling building lineage graphs h/t @palvaro

The hardest part of doing all this analysis is application instrumentation.

Doing the analysis is easy for us (Uber) - getting almighty fidelity data is the challenge. - @YuriShkuro at #QConNYC

In summary:

- tracing really becomes usable when you have creative visualizations
- engineers don’t really know how their services work. Tracing helps unlock unparalleled insights.

@YuriShkuro at #QConNYC

Uber doesn’t use tracing for latency analysis. We use it for root cause analysis. - @YuriShkuro at #QConNYC