gilded-rose-tdd VS jmh

In our last episode (https://youtu.be/Uza\_dWsNMUs) we worked out how to save stock items in PostgreSQL (https://www.postgresql.org/) using the JetBrains Exposed library (https://github.com/JetBrains/Exposed). This time we refactor our existing file-based storage, extracting an interface that we can implement with files, in-memory, or with Exposed. For the database version, instead of replacing items in a table when they change, we choose to implement an append-only datastore. This keeps all the old versions of every row, using a query to select the latest versions when we want to see the current state. This has the advantage that we can rebuild the state of our system if things go wrong, and may also be faster than amending when we consider transactions. This is part 62 of an exploration of where a Test Driven Development implementation of the Gilded Rose stock control system might take us in Kotlin. You can see the whole series as a playlist https://www.youtube.com/playlist?list=PL1ssMPpyqociJNwykAOB9\_KEZVW7BW7m2 and the code on GitHub https://github.com/dmcg/gilded-rose-tdd If you like this, you’ll probably like my book Java to Kotlin, A Refactoring Guidebook (http://java-to-kotlin.dev). It's about far more than just the syntax differences between the languages - it shows how to upgrade your thinking to a more functional style. I have some free time between producing videos and working for team Gilded Rose. If you like these videos I'd like to work with you - please get in touch - [email protected]

This is part 60 of an exploration of where a Test Driven Development implementation of the Gilded Rose stock control system might take us in Kotlin. You can see the whole series as a playlist https://www.youtube.com/playlist?list=PL1ssMPpyqociJNwykAOB9_KEZVW7BW7m2 and the code on GitHub https://github.com/dmcg/gilded-rose-tdd

You can see the code on GitHub

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.