Visualization

In response to my post about visualizing the lengths of the jobs in a build, one reader suggested a few tweaks to my gnuplot script to make the graph a proper surface plot. I like the look of this:

This version addresses some of the short-comings of my original:

• It’s easier to determine the z-coordinate of a given point. In the original that was nearly impossible. It’s still a little tricky here because of the perspective, but it’s a step in the right direction.
• Lower layers are not obscured. Originally, a dense layer of points could obscure points with a lower z-value. This version avoids that problem because you can see places where the surface dips.

Unfortunately, this version introduces some new problems:

• Raw data points are averaged. In order to produce this surface plot, gnuplot computes a weighted average of the data points. Averaging itself is not necessarily a problem. The trouble here is that the layout of the data points is completely arbitrary, as you may recall from the previous post. That means that this plot effectively picks a handful of random data points, averages them, and plots the result. We still see the general trend — that most of the jobs are about the same length — but it feels a bit phony.
• Implies patterns where there are none. When I first saw this image, I was struck by the “mountain range” running across the plot, a bit left of center. I hadn’t seen that in my original graph, so naturally I was intrigued. I spent hours trying to understand why that feature might be present, and finally came to this conclusion: it isn’t real. It’s just an artifact of the graphing method. Remember, the layout of the points is completely arbitrary, so it would be quite odd for there to really be a pattern like this cutting across the plot. In fact, I found that similar “features” appeared no matter what dimensions I used for the plot. I think the reason is that in this mode, gnuplot is not plotting the raw data, but rather a weighted average of adjacent points. This will tend to introduce relationships between those points that are not actually real.

OK, so this revised version is definitely interesting. I’m not sure that it’s better necessarily, given the defects I mentioned above. And unfortunately it doesn’t help at all with the issue of making something useful out of the X/Y coordinates. Nevertheless, thanks Aaron for the suggestion!

I’ve been playing with a new visualization for build data. I was looking for a way to really hammer home the point that in most builds, the vast majority of jobs are more-or-less the same length. The “Job Count by Length” report in ElectricInsight does the same thing, but in a “just the facts” manner. I wanted something that would be more visceral.

Then I struck on the idea of mapping the jobs onto a surface plot, using the job duration as the z-coordinate or “height”, so longer jobs have points high above the z-axis. In such a view, we would expect to see a mostly flat plain, with a small portion of points above the plain. Sure enough, that’s just what we get. Here’s an example, generated using data from a mozilla build:

Here’s what I like about this visualization:

• Nails the primary goal. This visualization is great at demonstrating that most jobs in the build have about the same duration.
• It’s looks cool. Given a choice between two visualizations that show the same data, the one that looks cooler definitely has an advantage.

Now, here’s what I don’t like about this visualization:

• X- and Y-coordinates are arbitrary. For this prototype I just determined the smallest box large enough to show all the jobs in the build, then plotted the first job at 0,0; the second at 0,1, etc. This is simple, and it gives a compact display, but it would be nice if the X- and Y-coordinates had some actual meaning.
• It’s hard to tell what Z-coordinate any given point has. For example, I can easily see that the vast majority of jobs have roughly the same duration, but what duration is that? 0 seconds? 1 second? 1/2 second?
• A dense upper layer obscures lower layers. Although this build is unimodal, suppose it was instead bimodal — the density of points at height 5 might obscure the existence of points at height 3.

For comparison, here’s the “Job count by Length” report from ElectricInsight. It uses the same data, and tells the same story, but it’s not nearly as visually dramatic:

So, what do you think? Any ideas how I could use the X- and Y-coordinates to convey useful information? Keep reading if you want to see how I made this visualization.
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