My previous post I finished with the graph with unstable results.
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
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<br>m <- ggplot(dv.ver,<br> aes(x = sec, Throughput, color=factor(Tables)))<br>m + geom_point()<br> |
The previous graph is not very representative, so we may add some lines to see a trend.
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<br>m + geom_point() + geom_line()<br> |
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:
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<br>m <- ggplot(dv.ver,<br> aes(x = factor(Tables), Throughput, color=factor(Tables)))<br>m + geom_jitter(alpha=0.75)<br> |
With jitter we see some dense areas, which shows “most likely” throughput.
So let’s build density graphs:
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<br>m <- ggplot(dd,<br> aes(x = Throughput,fill=factor(Tables)))<br>m+geom_density(alpha = 0.7)<br> |
or
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<br>m+geom_density(alpha = 0.7)+facet_wrap(~Tables,ncol=1)<br> |
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:
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<br>m <- ggplot(dd,<br> aes(x = factor(Tables),y=Throughput,fill=factor(Tables)))<br>m+geom_boxplot()<br> |
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:
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<br>m <- ggplot(dd,<br> aes(x = factor(Tables),y=Throughput,color=factor(Tables)))<br>m+geom_boxplot()+geom_jitter()<br> |
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.
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