Dave Rosenthal

Yeah, one of the things that we're seeing is that in the past, people had separate systems where they had like logs on servers, written files. They were maybe sending some metrics to Datadog or something like that or some other system. They were monitoring for errors with some product, maybe it was Sentry.

Yeah, one of the things that we're seeing is that in the past, people had separate systems where they had like logs on servers, written files. They were maybe sending some metrics to Datadog or something like that or some other system. They were monitoring for errors with some product, maybe it was Sentry.

But more and more what we see is people want all of these sources of telemetry logically tied together somehow. And that's really what we're pursuing at Sentry now. We have this concept of a trace ID, which is kind of a key that ties together all of the pieces of data that are associated with the user action.

But more and more what we see is people want all of these sources of telemetry logically tied together somehow. And that's really what we're pursuing at Sentry now. We have this concept of a trace ID, which is kind of a key that ties together all of the pieces of data that are associated with the user action.

So if a user loads a web page, we want to tie together all the server requests that happened, any errors that happened, any metrics that were collected. And what that allows on the back end You don't just have to look at like three different graphs and sort of line them up in time and try to draw your own conclusions.

So if a user loads a web page, we want to tie together all the server requests that happened, any errors that happened, any metrics that were collected. And what that allows on the back end You don't just have to look at like three different graphs and sort of line them up in time and try to draw your own conclusions.

You can actually like analyze and slice and dice the data and say, hey, what did this metric look like for people with this operating system versus this metric look like for people with this operating system and actually get into those details.

You can actually like analyze and slice and dice the data and say, hey, what did this metric look like for people with this operating system versus this metric look like for people with this operating system and actually get into those details.

So this kind of idea of tying all of the telemetry data together using this concept of a trace ID or basically some key, I think is a big win for developers trying to diagnose and debug real world systems and something that is, we're kind of charged the path for that for everybody.

So this kind of idea of tying all of the telemetry data together using this concept of a trace ID or basically some key, I think is a big win for developers trying to diagnose and debug real world systems and something that is, we're kind of charged the path for that for everybody.

Yeah, I mean, I guess, again, I'll just keep saying it maybe, but I think it kind of goes back to this debuggability experience. When you are digging into an issue, you know, having a sort of a richer data model that's, you know, your logs are structured. They're sort of this hierarchical structure with spans.

Yeah, I mean, I guess, again, I'll just keep saying it maybe, but I think it kind of goes back to this debuggability experience. When you are digging into an issue, you know, having a sort of a richer data model that's, you know, your logs are structured. They're sort of this hierarchical structure with spans.

And not only is it just the spans that are structured, they're tied to errors, they're tied to other things. So when you have the data model that's kind of interconnected, it opens up all different kinds of analysis that were just kind of either very manual before, kind of guessing that maybe this log happened at the same time as this other thing, or we're just impossible.

And not only is it just the spans that are structured, they're tied to errors, they're tied to other things. So when you have the data model that's kind of interconnected, it opens up all different kinds of analysis that were just kind of either very manual before, kind of guessing that maybe this log happened at the same time as this other thing, or we're just impossible.

We get excited not only about the new kinds of issues that we can detect with that interconnected data model, but also just for every issue that we do detect, how easy it is to get to the bottom of it.

We get excited not only about the new kinds of issues that we can detect with that interconnected data model, but also just for every issue that we do detect, how easy it is to get to the bottom of it.

When we first launched the ability to collect tracing data, we were really emphasizing the performance aspect of that, the kind of application performance monitoring aspect, you know, because you have these things that are spans that measure how long something takes.

When we first launched the ability to collect tracing data, we were really emphasizing the performance aspect of that, the kind of application performance monitoring aspect, you know, because you have these things that are spans that measure how long something takes.

And so the natural thing is to try to graph their durations and think about their durations and, you know, warn somebody if the durations are getting too long. But what we've realized is that the performance stuff ends up being just a bunch of gauges to look at. And it's not super actionable.

And so the natural thing is to try to graph their durations and think about their durations and, you know, warn somebody if the durations are getting too long. But what we've realized is that the performance stuff ends up being just a bunch of gauges to look at. And it's not super actionable.