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Some fun with R visualization

Thanks to Peter Zaitsev for this story

My previous post I finished with the graph with unstable results.

Oracle Virtualization and Cloud Consulting
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There I won’t analyze causes, but rather I want to show some different ways to present results.

I enjoy working with R, and though I am not even close to be proficient in it, I want to share some graphs you can build with R + ggplot2.

The conditions of the benchmark are the same as in the previous post, with difference there are results for 4 and 16 tables cases running MySQL 5.5.20.

Let me remind how I do measurements. I run benchmark for 1 hours, with measurements every 10 seconds.
So we have 360 points – metrics.

If we draw them all, it will look like:

I will also show my R code how to make it

m <- ggplot(dv.ver,
            aes(x = sec, Throughput, color=factor(Tables)))
m + geom_point()

The previous graph is not very representative, so we may add some lines to see a trend.

m + geom_point() + geom_line()

This looks better, but still you may have hard time answering: which case shows the better throughput? what number we should take as the final result?

Jitter graph may help:

m <- ggplot(dv.ver,
            aes(x = factor(Tables), Throughput, color=factor(Tables)))
m + geom_jitter(alpha=0.75)

With jitter we see some dense areas, which shows "most likely" throughput.

So let's build density graphs:

m <- ggplot(dd,
            aes(x = Throughput,fill=factor(Tables)))
m+geom_density(alpha = 0.7)


m+geom_density(alpha = 0.7)+facet_wrap(~Tables,ncol=1)

In these graphs Axe X is Throughput and Axe Y represents density of hitting given Throughput.
That may give you an idea how to compare both results, and that the biggest density is around 3600-3800 tps.

And we are moving to numbers, we can build boxplots:

m <- ggplot(dd,
            aes(x = factor(Tables),y=Throughput,fill=factor(Tables)))

That may be not easy to read if you never saw boxplots. There is good reading on this way to represent data. In short - the middle line inside a box is median (line that divides top 50% and bottom 50%),
the line that limits the top of a box - 75% quantile (divides 75% bottom and 25% top results), and correspondingly
- the line at the bottom of a box - 25% quantile (you should have an idea already what does that mean).
You may decide what measurements you want to take to compare the results - median, 75%, etc.

And finally we can combine jitter and boxplot to get:

m <- ggplot(dd,
            aes(x = factor(Tables),y=Throughput,color=factor(Tables)))

That's it for today.
The full script sysbench-4-16.R with data you can get on benchmarks launchpad

If you want to see more visualizations idea, you may check out Brendan's blog:

And, yes, if you wonder what to do with such unstable results in MySQL - stay tuned. There is a solution.

Read the entire article at its source

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