I was inspired by this post when I first read it a few month ago. Since then, I've used a modified version of StatsD to send data to an in house realtime graphing engine. A lot of our tools are php backends, so it was super convenient to be able to drop the class in and start measuring things.
For anyone interested, I used wrote node.js process takes arbitrary statsd-compliant data point and serves a socket.io enabled front-end for 'zero-config', realtime graphing.
We've found it useful internally for taking quick measurements on various projects. I was going to productize or open source the whole thing, but then life got in the way. Maybe it will see the light of day someday.
Thank you for the time spent in creating it and sharing it. It looks like you've been working on the project for nearly nine months, and that's a significant amount of effort.
I've been interested in one of these for a long time - a StatsD-compatible server in .NET. Considering that many competing implementations are written in Node.js or Python, a .NET should be able to outperform it significantly.
If you're interested in developing more stats stuff, we'd love to have you as part of the effort. gonum/stat/dist in particular needs a lot more functionality.
I mean this is neat but why not try histogramming the results over a really long period of time? A fuck-field if you will. That will contain much more information.
I have been wanting to create a tool like this for some time. I'm curious about what (if any) data analysis you're doing. The general area here is called lag sequential analysis.
It's been on my todo-list for a long time to make stats.grok.se into something more useful and present more info there - for example graphs over (longer) times. Also the design is a bit dated. :)
I do have about three years worth of Wikipedia article traffics stored, so I could do long term tracking and some interesting data mining, but I keep getting side tracked into other hobby projects.
I'm working on a side project (kind of on the backburner) JS library to drive progress bars based on perceptual tricks like these, and using estimates instead of actual progress monitoring (linear regression will be used to adjust the estimates over time), and this is certainly good data to have for that.
Also, my deadlift, left hook & bike endurance. Wouldn't mind a few multi-week self supported bike rides thrown in there.
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