t-digest VS Caffeine

> I get a lot of benefit from this information but somehow it feels shallow.

I take a longer view to this. For example, a few years ago I read about an algorithm to calculate percentiles in real time. [0]

It literally just came up at work today. I haven't used that information but maybe two times since I read it, but it was super relevant today and saved my team potential weeks of development.

So maybe it's not so shallow.

But to your actual question, I have a similar problem. The best I can say is that deadlines help. I usually put down the HN and Youtube when I have a deadline coming up. And not just at work. I make sure my hobbies have deadlines too.

I tell people when I think something will be done, so they start bugging me about it when it doesn't get done, so that I have a "deadline". Also one of my hobbies is pixel light shows for holidays, which come with excellent natural deadlines -- it has to be done by the holiday or it's useless.

So either find an "accountability buddy" who will hold you to your self imposed deadlines, or find a hobby that has natural deadlines, like certain calendar dates, or annual conventions or contests that you need to be done by.

[0] https://github.com/tdunning/t-digest

I am enamored by data structures in the sketch/summary/probabilistic family: t-digest[1], q-digest[2], count-min sketch[3], matrix-sketch[4], graph-sketch[5][6], Misra-Gries sketch[7], top-k/spacesaving sketch[8], &c.

What I like about them is that they give me a set of engineering tradeoffs that I typically don't have access to: accuracy-speed[9] or accuracy-space. There have been too many times that I've had to say, "I wish I could do this, but it would take too much time/space to compute." Most of these problems still work even if the accuracy is not 100%. And furthermore, many (if not all of these) can tune accuracy to by parameter adjustment anyways. They tend to have favorable combinatorial properties ie: they form monoids or semigroups under merge operations. In short, a property of data structures that gave me the ability to solve problems I couldn't before.

I hope they are as useful or intriguing to you as they are to me.

1. https://github.com/tdunning/t-digest

2. https://pdsa.readthedocs.io/en/latest/rank/qdigest.html

3. https://florian.github.io/count-min-sketch/

4. https://www.cs.yale.edu/homes/el327/papers/simpleMatrixSketc...

5. https://www.juanlopes.net/poly18/poly18-juan-lopes.pdf

6. https://courses.engr.illinois.edu/cs498abd/fa2020/slides/20-...

7. https://people.csail.mit.edu/rrw/6.045-2017/encalgs-mg.pdf

8. https://www.sciencedirect.com/science/article/abs/pii/S00200...

9. It may better be described as error-speed and error-space, but I've avoided the term error because the term for programming audiences typically evokes the idea of logic errors and what I mean is statistical error.

Ah, I misunderstood what you meant. If you are reporting static buckets I get how that is better than what folks typically do but how do you know the buckets a priori? Others back their histograms with things like https://github.com/tdunning/t-digest. It is pretty powerful as the buckets are dynamic based on the data and histograms can be added together.

Yes, but if you need to estimate median on the fly (e.g., over a stream of data) or in parallel there are better ways.

There are some newer data structures that take this to the next level such as T-Digest[1], which remains extremely accurate even when determining percentiles at the very tail end (like 99.999%)

[1]: https://arxiv.org/pdf/1902.04023.pdf / https://github.com/tdunning/t-digest

[2] https://github.com/tdunning/t-digest

T-Digest

This is pretty cool. The title would be a bit more descriptive if it were “Fast Rolling Quantile Filters for Python”, since the high-pass/low-pass filter functionality seems to be the focus.

The README mentions it uses binary heaps - if you’re willing to accept some (bounded) approximation, then it should be possible to reduce memory usage and somewhat reduce runtime by using a sketching data structure like Dunning’s t-digest: https://github.com/tdunning/t-digest/blob/main/docs/t-digest....

There is an open source Python implementation, although I haven’t used it and can’t vouch for its quality: https://github.com/CamDavidsonPilon/tdigest

/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.