Microbenchmarks are notoriously bad at predicting overall performance, but running experiments with actual production code using alternative implementations for key algorithms can identify expected performance improvements if the comparisons are executed relative to each other in the same environment. I show how to use YAWS, HTML5 and d3.js to dynamically graph the benchmark performance of erlang code. The goal is to make it easy for a contributor to demonstrate an improvement to your code and convince you to accept a pull request.

This talk is the result of experiments developing tools needed for geographically distributed collaborative erlang development. The approach presented applies equally well to other erlang web server implementations such as mochiweb, cowboy, misultin and others, but I wanted to give an overlooked stalwart some much needed love in the modern age of javascript.

Talk objectives: Show the integration of YAWS with modern interactive javascript data visualizations based on dynamic Erlang code. Demonstrate comparative benchmarking of Erlang code with live graph generation during execution.

Target audience: Developers tuning their Erlang algorithms. GUI implementers wishing to integrate the latest JS advances with an Erlang backend.