optick VS FlameGraph

Does anyone here have experience with Optick: https://github.com/bombomby/optick ? It looks great but I haven't got the chance to try it. Was wondering how it compares to the other tools listed here.

As a free alternative, I've had multiple people recommend Optick, but I haven't had the chance to play around with it yet, so I can't vouch for it myself.

I have used Tracy to improve the performance of a latency sensitive application. The main advantage of this tool, in comparison to something like the Visual Studio profiler, is the fact that it can highlight the inter thread dependencies and synchronization between the threads. The other main feature, in my opinion, is the statistical tab that is associated to the recorded events: it can show the statistical distribution of the duration of all the invocations of functions and it allows to identify patterns in the performance of the application. Furthermore, a table can be used to sort the invocations of the functions and quickly jump to the point in time when the sample was recorded.

Other notable tools that implement a functionality similar to what is provided by Tracy are Optick https://github.com/bombomby/optick and Intel VTune (sadly specific to only Intel processors) in the Threading analysis.

we don't do systematic perf measurements beyond prod metrics but if we did the good tools are uiCA/IACA/etc, tracy/brofiler, perf/vtune

I highly recommend checking out Optick. I use it almost daily at work :)

We'll use async-profiler and flame graphs for profiling. To simplify the process, we'll run the code using JBang.

And flame graphs excel and this kind of thing

https://www.brendangregg.com/flamegraphs.html

which can output in a format understood by Brendan Gregg's flame frames (https://www.brendangregg.com/flamegraphs.html)

But that's not quite the kind of tracing you're talking about. We also built a printf-style interface to our recording files, which seems closer:

Strategies included discontinuing or resizing underutilized services, transitioning to more cost-effective solutions, reducing the current resources to the amount of resources that we need for our application, and conducting detailed analyses of computing resource utilization through tools like flamegraphs. This detailed scrutiny helped identify and rectify significant cost-driving areas, such as garbage collection and application configurations.

Flame Graphs by Brendan Gregg

Historically brendangregg's since AIUI he basically invented flamegraphs

https://www.brendangregg.com/flamegraphs.html

So if you can make your tool eat whatever https://github.com/brendangregg/FlameGraph is fed with you're going to support a lot of existing tooling across OSes and languages.

“Flame graphs are a visualization of hierarchical data, created to visualize stack traces of profiled software so that the most frequent code-paths to be identified quickly and accurately.”

SVGs are amazing for interactive visualisation too. Like Flamegraphs: https://www.brendangregg.com/flamegraphs.html

This may be a good example of the application of a flame graph but it is not a good demonstration of flame graphs; the graph is nearly incidental. The source has an actual explanation.

A profiling flame graph from Graphile Crystal (a precursor to Grafast) using GraphQL.js' executor (each tick is 1ms, total: 29ms). As we removed more and more responsibilities from GraphQL.js, we ended up only using it for output. Replacing this final responsibility with a custom implementation in Graphile Crystal itself, we reduced execution time for this query down to 15.5ms (effectively removing the majority of the yellow portion of the flame graph).