What is Microbenchmarking and when does it apply?

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Microbenchmarking is a little-known comparative evaluation technique. Everyone is familiar with conventional benchmarking, where different components or systems are compared to see their performance. However, when analyzing development between different programming languages, this micro-scale is essential and much more effective than macro-reference points in many cases.

Why is Microbenchmarking Important?

Microbenchmarking deals with much simpler and smaller metrics compared to conventional benchmarking. In this way, it is easier to track and measure the performance of a small and specific source code piece when comparing or evaluating performance, in contrast to the execution of software in a runtime environment.

These microbenchmarks have a very small amount of code, so they are very easy to implement. But you have to analyze well where they are going to be implanted, since it is important to take this into account for them to be truly practical. In fact, these microbenchmark reference points are not applicable to many scenarios.

It should be noted that the simplicity of microbenchmarking is its greatest advantage and its greatest disadvantage at the same time. This simplicity allows you to reduce the components and routes involved, streamlining the process, but it also implies that you have to repeat them a large number of times with fewer variables that impact their results.

For example, if it is necessary to obtain information about the overall performance of an application, the use of a microbenchmark will not be sufficient. That is why it is important to well select the scale of the reference points in each case, as I mentioned earlier.

However, microbenchmarking can be used well to obtain better results when:

Measuring the rate of operations
Checking the bandwidth
Measuring latency
Calculating the amount of time that elapses between two points X and Y

How to Use Microbenchmarking

To create and evaluate microbenchmarks efficiently, a number of points must be taken into account. On the one hand, it is important to seek a compromise between speed and simplicity. Therefore, these microbenchmark tests must always have the least possible overhead and avoid unnecessary expenses. And that is so because in the test you want to measure a specific parameter, and not have to also analyze possible variables that may have altered the result.

For performance testing purposes, it is ideal to run the same microbenchmark multiple times, such as a thousand or more, in order to obtain more accurate results that are not affected by other factors or variables. This way, you can see how much the results vary between each execution of something that should always take the same amount of time to run.

Of course, it goes without saying that these microbenchmarks depend on other factors, and they should always be run avoiding virtual machines or containers or emulators or similar, as that alters the results and makes them slower, as they would have to run additional layers of software. So always do it natively on the platform where you are working.

You know, simplicity is the key. Removing all the variables that can interfere with performance results is essential, as well as removing all the load that can be done.

On the other hand, it may also be convenient to implement some previous iterations that run before starting with the iterations that will be timed. In this way, the results obtained can also be improved. This is because the timed iterations will run in a more stable state, after the previous ones have been executed.

Finally, remember that a good microbenchmarking will not offer predictable results. Therefore, with some software or hardware updates, considerable speed increases could be seen. Although in some cases it could even show slower results or remain the same as before, although it seems strange.

When to use a Microbenchmark

On the other hand, you also have to decide when it is convenient to use microbenchmarking. Microbenchmarks are created to simplify testing as much as possible and for a very specific purpose of something that you want to track, whether it is the number of operations per unit of time, latency, etc. In most cases, few scenarios justify the use of one of these tests instead of more comprehensive ones.

Microbenchmarking in Big Data

In Big Data, microbenchmarking is often used, as users or data scientists can obtain detailed information on performance in an essential case. That’s why it’s a very important component to ensure success in these types of data processing environments.

When it comes to using microbenchmarks for big data, you can implement them in various ways. For example:

Include the tests within the algorithms themselves.
Include them within the clusters.
In macro tests

For Java

Java is a widely used programming language. Many work on Java projects where microbenchmarking tests can also be applied. In these types of environments, these microtests are particularly valuable for tracking performance. For example, to know how long it takes to execute a piece of code and see how efficient this particular interpreted language is.

Implementing microbenchmarking tests in Java is simple with the help of open-source projects such as JMH (Java Microbenchmark Harness). It is a tool specifically designed to help users create these types of tests for Java-based environments or applications.

If you already know the JMH tool, you will know that it was created by the same developers of JVM (Java Virtual Machine), so it is a quite reliable and effective instrument. All with a platform that makes your work much easier and also the tracking of the data obtained.

Conclusion about microbenchmarking

Microbenchmarking is oriented to very specific and simple tests. And the key is to perform these simple tasks repeatedly to achieve better results. In addition, they should not be overloaded or exposed to variables that can interfere with the results. Everything has to be kept as simple as possible.

If you are looking to track performance in a general way, they are not the best thing you can do, for that it is better to use other more complete benchmarks. Therefore, do not think that they can be applied to any case.