parse-server VS Caffeine

Backend as a Service (BaaS) goes back to early 2010’s with companies like Parse and Firebase. These products integrated everything a backend provides to a webapp in a single, integrated package that makes it easier to get started and enables you to offload some of the devops maintenance work to someone else.

Prototype Pollution Fix

Parse Server is a great way to quickly spin up a backend for your project. Parse is a Node based utility that sits on top of ExpressJS.

Parse

You can try https://parseplatform.org/, it is self-hosted if you need. And also there are a number of cloud services with compatible API, like https://www.back4app.com/ It has dart-friendly generated API client, much simpler than firebase and is built on top of postgresql and mongodb.

Parse - https://parseplatform.org/

Not to crash the party or anything. Supabase is great and all but in terms of feature completeness and getting actual products built, it doesn't come close to Parse[0].

Same with Appwrite. Both of these are very popular but they either lack essential features or have them behind a subscription wall. For example, the OSS version of Supabase (last I checked) doesn't include the edge functions which are really important for easily computing stuff on the server side. Parse on the other hand is 100% open source and has a huge feature set. It's older than all of these lo-code tools and actually helps solve the issues one comes across when using such tools.

Another thing is extending these tools which is a pain. For example, Parse supports multiple databases by default (postgres & MongoDB) and the ability to write a custom adapter if you need something else. Similarly, if you at any point need to go 100% custom it also makes that possible so you are never locked in. These tools however don't have that level of low-level control and are general all or nothing kind of tools best for small-to-medium sized problems which don't have a lot of room to grow.

But both of these (Appwrite & Supabase) are super markety. Appwrite is all over the place with their ads, Supabase got a huge trend when it launched etc. Parse on the other hand is not too good at marketing their product being fully community run which is one reason not many know of it. Another is their not-so-fancy docs.

I have no stake in any of these products: just my conclusion after having tried all of these.

[0] https://parseplatform.org/

/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.