JDK VS Caffeine

Completely gutted from the OpenJDK, last I checked. See here for the culprit PR: https://github.com/openjdk/jdk/pull/18688

> Yes, they're changing one aspect of signal handler use to work around this problem. They're not stopping the use of signal handlers in general. Hotspot continues to use signals for efficiency in general. See https://github.com/openjdk/jdk/blob/9059727df135dc90311bd476...

This whole thread is about SIGSEGV, and specifically their SIGSEGV handling. However, catching normal signals is not about efficiency.

Some of their exception handling is still odd: There is no reason for a program that receives SIGILL to ever attempt continuing. But others is fine, like catching SIGFPE to just forward an exception to the calling code.

(Sure, you could construct an argument to say that this is for efficiency if you considered the alternative to be implementing floating point in software so that all exceptions exist in user-space, but hardware floating point is the norm and such alternative would be wholly unreasonable.)

> The wonderful thing about choosing not to care about facts is having whatever opinions you want.

I appreciate the irony of you making such statement, proudly thinking that your opinion equals fact, and therefore any other opinion is not.

This discussion is nothing but subjective opinion vs. subjective opinion. Facts are (hopefully, as I can only speak for myself) inputs to both our opinions, but no opinion about "good" or "bad", "nasty" or not can ever be objective. Objective code quality does not exist.

I remember talking to Brendan about the PreserveFramePointer patch during my first months at Netflix in 2015. As of JDK 21, unfortunately it is no longer a general purpose solution for the JVM, because it prevents a fast path being taken for stack thawing for virtual threads: https://github.com/openjdk/jdk/blob/d32ce65781c1d7815a69ceac...

Map doesn't implement the Collection interface.

https://github.com/openjdk/jdk/blob/master/src/java.base/sha...

C++ lets you write anything you can imagine, and the language features and standard library often facilitate that. The committee espouses the view that they want to provide many "zero [runtime] cost," abstractions. Anybody can contribute to the language, although the committee process is often slow and can be political, each release the surface area and capability of the language gets larger.

I believe Hazard Pointers are slated for C++26, and these will add a form "free later, but not quite garbage collection" to the language. There was a talk this year about using hazard pointers to implement a much faster std::shared_ptr.

It's a language with incredible depth because so many different paradigms have been implemented in it, but also has many pitfalls for new and old users because there are many different ways of solving the same problem.

I feel that in C++, more than any other language, you need to know the actual implementation under the hood to use it effectively. This means knowing not just what the language specifies, but can occaissionally require knowing what GCC or Clang generate on your particular hardware.

Many garbage collected languages are written in or have parts of their implementations in C++. See JS (https://github.com/v8/v8)and Java GC (https://github.com/openjdk/jdk/tree/36de19d4622e38b6c00644b0...)

I am not an expert on Java (or C++), so if someone knows better or can add more please correct me.

/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.