Guava VS Caffeine

The first problem is at the level of Type System, given that a situation more correct would allow us to distinguish through the Collection Type which abstraction we are operating with, species if definable as mutable or immutable. The JCF was born at a time when great care was taken to offer immediate operational data structures, and with attention to performance, but with less attention to constructs or uses that are now seen as common. These concepts have been taken up by other infrastructures from which we certainly cannot fail to mention: Eclipse Collection, Guava Collections, and VAVR.

Even better is getting Gradle/Maven to correctly pull "plain" vs "Android" versions of the package instead of them just publishing the diverging code base as two repository packages.

https://github.com/google/guava/issues/2914

I'll admit I'm surprised to see that BOMs have been documented on maven.apache.org since mid-2008. It looks like Spring, for example, didn't adopt them until mid-2014. I don't know how widely they caught on in other areas. The first discussion of them in the context of Guava may have been in 2018, as I don't see mention of them in the various issues from 2011-2015 (#605, #1329, #1471, #1954.

And a quick peek at the source code for ImmutableList seems to confirm this (https://github.com/google/guava/blob/master/guava/src/com/google/common/collect/ImmutableList.java - it goes via a bunch of methods, but ends up using Arrays.copyOf(), which creates a fixed-size array).

The guava library of Java has some of these data structures implemented: https://github.com/google/guava/wiki/ImmutableCollectionsExplained , but implementations of the above book in many languages can be found on github (say, this one for Haskell: https://github.com/aistrate/Okasaki )

Java Mask: Java Mask is a library that offers various string masking techniques for sensitive data such as credit card numbers, email addresses, and more. You can find the library at: https://github.com/miguelfreitas93/java-mask DataMasker: DataMasker is a Java library specifically designed for masking sensitive data, including credit card numbers, using customizable masking patterns. Visit the GitHub repository for more information and usage examples: https://github.com/GDSSecurity/DataMasker Maskify: Maskify is a simple Java library that can be used to mask credit card numbers, Social Security numbers, and other sensitive information. You can find the library at: https://github.com/jonathancarvalhoalves/maskify CreditCardUtils: This is a lightweight Java library that provides utility methods for validating, formatting, and masking credit card numbers. Visit the GitHub repository for more information: https://github.com/malkusch/creditcardutils Google Guava: Google Guava is a popular set of Java libraries containing a wealth of utilities for working with strings, collections, and more. While not specifically designed for masking credit card information, you can use Guava's string manipulation methods to mask sensitive data: https://github.com/google/guava

I mean, I guess, technically? If you define it like that, then Microsoft has people working for them for free, as does Google, as does Apple, etc. It's not that weird, and you can try to twist it to be weird, but those of us in the software industry largely regard this as a good thing.

So we have https://github.com/google/guava/issues/4011

Ok, so let's implement our lazy initialization more smartly to avoid acquiring the lock every time and use old fashion but still working double-checked locking. I've found it implemented by Suppliers.memoize in guava library.

/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.