App.Metrics VS BenchmarkDotNet

🌐 Website 💻 GitHub

The benchmark test was conducted using the BenchmarkDotNet library in C#. The classes tested include:

Let’s look at the first example you see, when you open up BenchmarkDotnet’s website, or Github page.

Or use BenchmarkDotNet which, among other things to get an accurate benchmark, does JIT warmup outside of measurement.

( https://github.com/dotnet/BenchmarkDotNet ).

You can also do proper statistically correct benchmarking by using - https://github.com/dotnet/BenchmarkDotNet. This will run warmup the jit, gauge the overheads, and run your function many times to give you proper data.

take a look at: https://benchmarkdotnet.org/

Yes, weak references are slower than direct calls. How much slower? Heck if I know offhand. But it's usually pretty easy to set up something with Benchmark .NET and find out if it hurts your use case.

For this purpose we are going to use our beloved BenchmarkDotNet tool.

To quantify and compare the performance of the object mapping strategies discussed earlier, we can employ BenchmarkDotNet.

BenchmarkDotNet is a popular open-source library that, as stated in the repo's README.md, helps us to transform methods into benchmarks, track their performance, and share reproducible measurement experiments. Using BenchmarkDotNet feels similar to writing unit tests. It's very important to note that the library only works with console apps. Finally, we can visualize the results in the terminal where the benchmark ran or in user-friendly formats such as markdown, HTML and CSV. We will explore examples of there formats later in the article.

However, obviously this prevents parallelism within the lock, so this only makes sense if you do some other expensive operation in the parallel loop and the string builder is only a small part of it. Performance wise, it may be better to concatenate the results together after the parallel operation, instead of locking inside the loop. You'll have to benchmark it to know for sure.