Benchmarking is an essential tool in development across languages, but choosing the correct scale can prove difficult. While microbenchmarks are not useful in every scenario, they are far more effective than a macrobenchmark in many instances.
Let's explore examples of microbenchmarks and when you should implement them.
Microbenchmarks deal with the smallest of all benchmarks, and they represent a very simple and easy-to-define metric that tracks and measures the performance of a small and specific piece of code.
A microbenchmark always pertains to a very small amount of code. Hence, they're incredibly fast to implement.
With that said, you need to make sure you're using them in the right place. Implementing microbenchmarks in instances where they aren't necessary is a waste of time. This is why it's essential to confirm the usefulness of any microbenchmark you want to add to your project.
By far, the simplicity of microbenchmarking is its biggest benefit — and its biggest downside. This simplicity allows you to narrow down the components and paths involved, streamlining the process of finding the root cause for performance issues.
It also means that the tests microbenchmarking uses are highly repeatable with fewer variables to impact your results.
At the same time, microbenchmarks are inapplicable in many scenarios because of how "zoomed in" and specific they are.
For instance, if you want to gain insight into the overall performance of your application, using a microbenchmark won't cut it. That's why selecting the correct scale for your benchmarks is essential to efficient testing.
Microbenchmarks are best used for tracking the following:
Efficiently writing and evaluating microbenchmarks requires a commitment to speed and simplicity. Therefore, microbenchmarks should always have as little overhead as possible, and you should avoid variable overhead at all costs.
Why? Because you can't subtract it from the results, and you don't want to have to measure the overhead separately.
For performance testing purposes, the ideal approach is to run an operation many times, say 1,000 times, each time you test it. You can then average the results, although you won't be able to see how much those results vary between each run. This fact makes microbenchmarks better for testing something that always takes about the same amount of time.
Microbenchmarks are also dependent upon speed, so you should avoid using virtual machines or tools (like Docker) that can add variations to your results.
If you're able to run your microbenchmark outside of a framework, do so. That's the best option. Otherwise, you should simplify the test by taking away as many variables and as much load as you can.
Implementing warm-up iterations can also be helpful; this approach entails running the operation many times before you begin timing it.
These iterations allow you to test the steady-state performance after it has been running for some time. But, if you're looking to benchmark an operation that you run infrequently, avoid warm-ups so that you get an accurate result.
Lastly, remember that a good microbenchmark will not offer predictable results. Upgrading your hardware or software, for example, won't incrementally improve the speed of your microbenchmark.
Instead, you'll notice a drastic improvement with one random upgrade when that particular version of software optimizes that specific operation. Otherwise, your benchmark might show slower results in between versions, or it might not show any major change at all.
Deciding how big your benchmark should be comes down to what you're trying to figure out. A microbenchmark might be so small and narrow that it cannot tell you what you want to know, so you have to consider the meaning and purpose behind what you're trying to track.
In most cases, you'll only have a few select needs that justify a microbenchmark.
Thanks to microbenchmarking, users can gain detailed insights into performance — which is essential when working with big data.
While microbenchmarking is by no means exclusive to big data applications, it is a major component in successfully testing the performance of a big data environment.
When it comes to using microbenchmarks for big data, you can implement them in multiple ways; examples might include within algorithms or within clusters. Microbenchmarks can also be used in framework tests where they help users obtain information on the volume and accuracy of data.
For those working in Java, microbenchmarks will prove especially valuable for tracking performance. For instance, if you want to know how long a code snippet takes to execute, you can use microbenchmarks to dig deep into the numbers and real-world performance.
Fortunately, creating Java microbenchmarks is simple with the help of JMH. JMH is the Java Microbenchmark Harness, and it is a tool designed specifically to help users create microbenchmarks for their Java environment or application.
The JMH tool was made by the creators of Java Virtual Machine (JVM), which makes it a trusted and reliable instrument. This tool provides a platform for creating microbenchmarks that are effective, easy to implement, and easy to track as time goes on.
The key when using microbenchmarks is to not over-utilize them. Microbenchmarks shine when used to measure a very small and specific thing, but that's not always necessary.
If you're looking to track the overall performance of your application or otherwise take a "big picture" point of view to a problem, you might not find microbenchmarks suited to your use case.
Still, now that you know how microbenchmarks work, you have one more tool in your arsenal for making your next development project that much better.
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