An interactive web-based viewer for [sampling profiles][0]. An alternative viewer for [FlameGraphs][1].
Given raw profiling data, speedscope allows you to interactively explore the data to get insight into what's slow in your application, or allocating all the memory, or whatever data is represented in the profiling data.
Visit https://jlfwong.github.io/speedscope/, then either browse to find a profile file or drag-and-drop one onto the page. The profiles are not uploaded anywhere -- the application is totally in-browser.
## Supported file formats:
1. The folded stack format output by the FlameGraph scripts do: https://github.com/brendangregg/FlameGraph#2-fold-stacks. Example: https://github.com/jlfwong/speedscope/blob/master/sample/perf-vertx-stacks-01-collapsed-all.txt
3. The `.cpuprofile` format output by Chrome developer tools JavaScript profiler, useful for viewing flamecharts generated by Node.js applications: https://medium.com/@paul_irish/debugging-node-js-nightlies-with-chrome-devtools-7c4a1b95ae27
Both of the main views of the applications display flame graphs.
In this view, the horizontal axis represents the "weight" of each stack (most commonly CPU time), and the vertical axis shows you the stack active at the time of the sample.
### 🕰Time Order
In the "Time Order" view (the default), the stacks are ordered left-to-right in the same order as the occurred in the input file, which is usually going to be the chronological order they were recorded in. This view is most helpful for understand the behavior of an application over time, e.g. "first the data is fetched from the database, then the data is prepared for serialization, then the data is serialized to JSON". This is the only flame graph order supported by Chrome developer tools.
### ⬅️Left Heavy
In the "Left Heavy" view, identical stacks are grouped together, regardless of whether they were recorded sequentially. Then, the stacks are sorted so that the heaviest stack for each parent is on the left -- hence "left heavy". This view is useful for understanding where all the time is going in situations where there are hundreds or thousands of function calls interleaved between other call stacks.
