Production servers often have much more dynamic, complex workload and behaviors than you might be accustomed to seeing, because most monitoring products aggregate system behavior into high-level global metrics, losing all the detail. Multivariate analysis by another dimension often reveals something unexpected about server behavior. This can prompt you to explore the system, and sometimes leads to a deeper understanding of it. Here’s one such example.

That’s a plot of MySQL database queries per second (throughput) over a one-day interval. Nothing looks unusual about that chart, but that is only because queries per second is one-dimensional.

While the server was working to satisfy those queries, at times it behaved very differently. Plotting the server’s throughput versus the average concurrency highlights the variations. (Concurrency is the average number of queries in-process at a time.)


What is the meaning of these distinct clusters of points, seemingly two separate workloads? You might recognize that the axes I chose are the same as those of the Universal Scalability Law (USL). Applying the USL to each group of measurements will reveal that workloads, not just servers and software, have scalability characteristics. At times, this server’s workload itself is more or less scalable.

As an exercise in visualization, I divided the clusters of points and color-coded them, then mapped those back to the time-series plot. The result follows:


It would not have been possible to guess that the less-scalable, longer-running queries run at the highlighted times of the day. Applying the USL in this fashion can help you understand what types of queries make your system behave less optimally, but to do that you need more granular data—you need per-query measurement, not global summary measurements. I’m only showing the global summaries in this post, but with a system such as VividCortex you can drill down into these types of details.

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How to Extract Data Points From a Chart

This post shows several ways to estimate data points from an image of a chart. As an example, I analyze CitusDB's scalability from a YouTube screen capture.