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Some fun with R visualization

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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

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

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:

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

So let’s build density graphs:


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:

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:

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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Vadim Tkachenko

Vadim leads Percona's development group, which produces the Percona Server, Percona Server for MongoDB, Percona XtraDB Cluster and Percona XtraBackup. He is an expert in solid-state storage, and has helped many hardware and software providers succeed in the MySQL market.

Benchmarks, MySQL

  • Hey Vadim, nice post, but reading through it I kept wondering why you didn’t plot a simple histogram with the hist( ) command, with a superimposed mean or median line. That would have shown you just about all you wanted.


  • marcos,

    That’s true. As I said I am not that proficient in R, there probably are better ways to do what I did.
    I just liked these graphs and wanted to share them.


  • Do you have any examples of how to actually extract the values out of that? The graphs are pretty and all, but it’s important to have the actual concrete numbers.


  • Will,

    That is also not so hard, though some time is needed to figure it out.

    you can use ddply and summarize, something like that:


    It is equal to SQL language:
    SELECT q50(Throughput) FROM data GROUP BY Server, Threads


  • Vadim,

    Been poking around “R” , your blog gave it a boost! Thanks for sharing it.


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