Microbenchmark results don't linearly scale to everything else. Just because language X can print "Hello, World!" 2x faster than language Y doesn't mean that ev

Microbenchmark results don't linearly scale to everything else. Just because language X can print "Hello, World!" 2x faster than language Y doesn't mean that every other operation is also 2x faster. For example a huge factor is algorithm quality. Language X may be fast at "Hello, World!", but then proceed to have QuickSort as its standard sorting function, while language Y has Timsort. [1] Language X may have a nicely optimized C library for hashing, while language Y has AVX2 optimized ASM. Some languages don't even have a wide & well-optimized standard library. Thus you can only really tell how good a language/library is for your usecase if you test with an actual real world scenario. Additionally, the ultimate microbenchmark winning code is one that does every trick in the book while not caring about anything else. This means hooking the kernel, unloading every kernel module / driver that isn't necessary for the microbenchmark, and doing the microbenchmark work at ring0 with absolute minimum overhead. Written in ASM, which is implanted by C code, which is launched by node.js. Then, if there's any data dependant processing in the microbenchmark, the winning code will precompute everything and load the full 2 TB of precomputed data into RAM. The playing field is even, JVM & Apache, or whatever else is the competition will also be run on this 2 TB RAM machine of course. They just won't use it, because they aren't designed to deliver the best results in this single microbenchmark. The point is that, not only don't microbenchmark results mean linear scaling for other work, but the techniques to achieve the microbenchmark results may even be detrimental to everything else! -- [1] For some data sets QuickSort is actually faster. Goes to show you that the best choice is highly dependant on actual use.