Dave Rosenthal

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.

Sentry is all about this notion of debug ability and actually making it easier to fix the problem, not just sort of giving you more gauges. A lot of what we're trying to do now is focus a little bit less on the sort of just the performance monitoring side of things and turn tracing into a tool that actually aids the debug ability of problems.

Sentry is all about this notion of debug ability and actually making it easier to fix the problem, not just sort of giving you more gauges. A lot of what we're trying to do now is focus a little bit less on the sort of just the performance monitoring side of things and turn tracing into a tool that actually aids the debug ability of problems.

What up, Python nerds? I'm Jared, and you are listening to The Change Log, where each and every week we sit down with the hackers, the leaders, and the innovators of the software world to pick their brain, to learn from their mistakes, to get inspired by their accomplishments, and to have a lot of fun along the way.

What up, Python nerds? I'm Jared, and you are listening to The Change Log, where each and every week we sit down with the hackers, the leaders, and the innovators of the software world to pick their brain, to learn from their mistakes, to get inspired by their accomplishments, and to have a lot of fun along the way.

On this episode, I'm joined by the co-hosts of the Core.py podcast, Pablo Galindo and Lukas Langa, whose name I will pronounce Lukas from here on out because it's just a lot easier for me. On Core.py, they talk about Python internals because they work on Python internals. And today we're talking about Python 3.13, which is right around the corner.

On this episode, I'm joined by the co-hosts of the Core.py podcast, Pablo Galindo and Lukas Langa, whose name I will pronounce Lukas from here on out because it's just a lot easier for me. On Core.py, they talk about Python internals because they work on Python internals. And today we're talking about Python 3.13, which is right around the corner.

When we recorded this conversation last week, it was slated to be released on October 1st, but now they are targeting October 7th. So if you're listening to this in the future, 3.13 is fully baked. But if you are listening right after we hit publish, wait a week or grab the release candidate, which is 99% baked. Why are we all so excited about Python 3.13?