Kotest VS Caffeine

As you might know, Advent of Code will begin this Friday! If, like me, you want to have a go at solving the puzzles in Kotlin, I would like to share with you a little library I've developed to reduce boilerplate and solve the puzzles in a test-driven approach. It is based on Kotest, a modern and Kotlin-first test framework.

Kotlin testing with Kotest and MockK

As a matter of fact, in the previous examples, we have shifted a bit away from the TDD standards in the meaning that we test not only the operability of our code, but rather check if the code runs according to certain specifications (Given/When/Then). These specifications are our tests, and the syntactic sugar in the form of the possibility to give clear names to the tests using DisplayName and the grouping of the tests by a set of similar attributes helps us clearly formulate these specifications. There is an entire family of frameworks in different languages that allow us to create such specifications: for Java it is Spock, for Ruby—RSpec, and for Kotlin—Spek and Kotest frameworks. Below, I will go into more detail about them.

I've tried so many times. SO MANY. I love the idea of mutation testing. Even chatGPT couldn't help me. It just made up fake mutation testing frameworks to try. To me, pitest is dead. kotest is not mutation testing, but it's property testing is the next best thing I have found to revolutionize your unit tests.

The extensions are not particularly useful in this scenario because the described functionality can be incorporated into the Host class. On the other hand, they flourish in test frameworks like Kotest and enable the rapid development of useful add-ons like custom matchers. Extending third-party libraries with utility functions is another prevalent use case. In the next sections, we'll zero in on this specific aspect.

We're using Kotest for our unit tests and are quite happy with it.

Basic testing in Kotlin with Kotest built for kotlin specificatlly (upcoming)

Here you are: https://github.com/kotest/kotest/issues/189

/u/someplaceguy,

Those LIRS traces, along with many others, available at this page [1]. I did a cursory review using their traces using Caffeine's and the author's simulators to avoid bias or a mistaken implementation. In their target workloads Caffeine was on par or better [2]. I have not seen anything novel in this or their previous works and find their claims to be easily disproven, so I have not implement this policy in Caffeine simulator yet.

[1]: https://github.com/ben-manes/caffeine/wiki/Simulator

[2]: https://github.com/1a1a11a/libCacheSim/discussions/20

That, and also when caffeine came out it replaced one of the major uses (caching) of guava.

https://github.com/ben-manes/caffeine

I decided to start with an overview of what open-source options are currently available. When it comes to the implementation of the on-heap cache mechanism, the options are numerous – there is well known: guava, ehcache, caffeine and many other solutions. However, when I began researching cache mechanisms offering the possibility of storing data outside GC control, I found out that there are very few solutions left. Out of the popular ones, only Terracotta is supported. It seems that this is a very niche solution and we do not have many options to choose from. In terms of less-known projects, I came across Chronicle-Map, MapDB and OHC. I chose the last one because it was created as part of the Cassandra project, which I had some experience with and was curious about how this component worked:

Visit the official Caffeine git project and documentation here for more information if you are interested in the subject.

not to distract from your valid points but, when used properly, Caffeine + Reactor can work together really nicely [1].

[1] https://github.com/ben-manes/caffeine/tree/master/examples/c...

Yes, I think that is my main concern in that often research papers do not disclose the weaknesses of their approaches and the opposing tradeoffs. There is no silver bullet.

The stress workload that I use is to chain corda-large [1], 5x loop [2], corda-large at a cache size of 512 entries and 6M requests. This shifts from a strongly LRU-biased pattern to an MRU one, and then back again. My solution to this was to use hill climbing by sampling the hit rate to adaptively size of the admission window (aka your FIFO) to reconfigure the cache region sizes. You already have similar code in your CACHEUS implementation which built on that idea to apply it to a multi-agent policy.

Caffeine adjusts the frequency comparison for admission slightly to allow ~1% of losing warm candidates to enter the main region. This is to protect against hash flooding attack (HashDoS) [3]. That isn't intended to improve or correct the policy's decision making so should be unrelated to your observations, but an important change for real-world usage.

I believe LIRS2 [4] adaptively sizes their LIR region, but I do not recall the details as a complex algorithm. It did very well across different workloads when I tried it out and the authors were able to make a few performance fixes based on my feedback. Unfortunately I find LIRS algorithms to be too difficult to maintain for an industry setting because while excellent, the implementation logic is not intuitive which makes it frustrating to debug.

[1] https://github.com/ben-manes/caffeine/blob/master/simulator/...

A lot of Guava's most popular libraries graduated to the JDK. Also Caffeine is the evolution of our c.g.common.cache library. So you need Guava less than you used to. Hooray!

Unfortunately, I don't gather statistics on the demonstration server. I believe that the in-memory caffeine cache (https://github.com/ben-manes/caffeine) saved me.

Caffeine is a Java cache that uses a 4-bit count-min sketch to estimate the popularity of an entry over a sample period. This is used by an admission filter (TinyLFU) to determine whether the new arrival is more valuable than the LRU victim. This is combined with hill climbing to optimize how much space is allocated for frequency vs recency. That results in an adaptive eviction policy that is space and time efficient, and achieves very high hit rates.