app_profiler VS FlameGraph

While doing research on Ruby profiling I found Shopify's blog post on "How to Fix Slow Code in Ruby". Though the entire post was extremely insightful, it lead me to Shopify's app_profiler library, which can be used to automatically profile code and redirect the output to a local instance of speedscope. Having worked previously with Flame Graphs of CPU stack traces collected using perf.

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).