FusionCache
Caffeine
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FusionCache | Caffeine | |
---|---|---|
9 | 43 | |
1,285 | 15,204 | |
13.8% | - | |
8.8 | 9.7 | |
2 days ago | 4 days ago | |
C# | Java | |
MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
FusionCache
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Release Radar • March 2024 Edition
Want an easy to use cache with advanced resiliency features? Look no further than FusionCache. It's built for performance, good refresh rates, better auto-setup, better logs, and more. Congrats to the team on shipping your first major and stable version 🎉 and receiving over 3.8 million downloads.
- FusionCache Is Now v1.0
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Caching as a cross cutting concern using MediatR's pipeline behavior
I wrote an internal nuget package for our team that does similar stuff to your work, although I called mine ICachedRequest. Unlike you I denied myself the enjoyment of exploring a custom caching solution and ended up injecting FusionCache into my mediatr behavior.
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17 Amazing Community Packages for .NET Developers
The most undervalued library from that list is FusionCache. The rest is either well-known (like FluentAssertions) or pretty specific to the guy's experience (like the WPF stuff).
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Multi level cache library (in memory + Redis)
The instances (using FusionCache for instance) sync over Redis pub/sub.
- What your hidden nuget gems ?
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How to implement cache
LazyCache is amazing. Btw I'm using FusionCache and it is good too
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Ask HN: What are some 'cool' but obscure data structures you know about?
If you are in the .NET space I suggest you to take a look at FusionCache. It has cache stampede protection built in, plus some other nice features like a fail-safe mechanism and soft/hard timeouts https://github.com/jodydonetti/ZiggyCreatures.FusionCache
Caffeine
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Otter, Fastest Go in-memory cache based on S3-FIFO algorithm
/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
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Google/guava: Google core libraries for Java
That, and also when caffeine came out it replaced one of the major uses (caching) of guava.
https://github.com/ben-manes/caffeine
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GC, hands off my data!
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:
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Spring Cache with Caffeine
Visit the official Caffeine git project and documentation here for more information if you are interested in the subject.
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Helidon Níma is the first Java microservices framework based on virtual threads
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...
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FIFO-Reinsertion is better than LRU [pdf]
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/...
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Guava 32.0 (released today) and the @Beta annotation
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!
- Monitoring Guava Cache Statistics
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Apache Baremaps: online maps toolkit
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.
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Similar probabilistic algorithms like Hyperloglog?
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.
What are some alternatives?
Lazy Cache - An easy to use thread safe in-memory caching service with a simple developer friendly API for c#
Ehcache - Ehcache 3.x line
Cache Tower - An efficient multi-layered caching system for .NET
Hazelcast - Hazelcast is a unified real-time data platform combining stream processing with a fast data store, allowing customers to act instantly on data-in-motion for real-time insights.
EasyCaching - :boom: EasyCaching is an open source caching library that contains basic usages and some advanced usages of caching which can help us to handle caching more easier!
cache2k - Lightweight, high performance Java caching
SqliteCache for ASP.NET Core - An ASP.NET Core IDistributedCache provider backed by SQLite
Apache Geode - Apache Geode
NCache - NCache: Highly Scalable In-Memory Distributed Cache for .NET
Guava - Google core libraries for Java
CacheCow - An implementation of HTTP Caching in .NET Core and 4.5.2+ for both the client and the server
scaffeine - Thin Scala wrapper for Caffeine (https://github.com/ben-manes/caffeine)