DSL-JSON VS jmh

Afaik dsl-json came up with a lot of improvements and inspired a several other libraries like JsonIter and jsonIter-scala. Jsoniter-scala by u/plokhotnyuk is probably the most optimized JSON library on the JVM at this point, and seems to power most of the Scala ecosystem. Some implementations/optimizations eventually made their way back into Jackson and other libraries.

You gotta at least add the top contender. I mean dsl-json is probably the fastest json lib java has to offer. I personally like Rob's avaje-jsonb, because I think the approach of no reflection, and doing everything via annotation processing is rad. (it also has some decent speed too)

Have you tried dsl-json instead of jsoniter? Should be faster.

I've updated results of benchmarks of 12 JSON parsers for Scala: - AVSystem's scala-commons - Borer - Circe - DSL-JSON - Jackson - jsoniter-scala - Play-JSON, - play-json-jsoniter - Spray-JSON - uPickle - weePickle - zio-json

JMH for benchmarks

I would recommend using JMH instead.

Reflection is probably very roughly between 10 and 1000 times slower. Why don't you measure it yourself using JMH?

Aleksey Shipilev is another such leader, whom is especially knowledgeable about the internals of the JVM. His writings are invaluable. He is (was) the lead of the Java microbenchmark framework (JMH} which is how one would write small performance experiments in Java, and learn what really makes a difference or now.

In the last episode we implemented a parallelMap operation using streams, raw threads, a threadpool with futures, and coroutines. At first glance the raw threads was quickest, followed by futures, coroutines and then streams. In this, part 56 of an exploration of where a Test Driven Development implementation of the Gilded Rose stock control system might take us in Kotlin, we investigate the performance of the different functions further, in particular digging down into why coroutines seem to be slow and finding a way to speed them up. We also find a way to use a particular ForkJoinPool to run the streams code, making it as fast as the others (bar the raw threads). Frankly we only use very rough benchmarks here, with no statistical testing except 'it looks like'. That's OK for gross differences, but is highly suspect when deciding which of two similarly performant approaches is faster. For that check out JMH and you could watch my video from KotlinConf 2017

A few months ago, we started to use JMH in our project to test and find performance issues. The tool provides multiple modes and profilers, and we found this useful for our purposes.