apk-parser
DISCONTINUED
Caffeine
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apk-parser | Caffeine | |
---|---|---|
3 | 43 | |
931 | 15,071 | |
- | - | |
2.5 | 9.7 | |
over 3 years ago | 7 days ago | |
Java | Java | |
- | 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.
apk-parser
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New library: APK-parser , revived
I've forked and updated an old, archived APK-parsing library (here's the original) that I use for one of my spare time apps (here), and made the library public and alive again, here.
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SimpleInstaller Android library
2&3. I see. Sadly the Android framework can't parse split APK files. This is why there are some other libraries. Sadly what I consider as the most intuitive, comfortable, and light (apk parser, which I forked here to solve some of its issues) is not being maintained anymore. Do you know of a nice alternative to it?
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Which Java libraries would benefit from being Kotlin-ified?
In that case, why not take some abandoned library and re-publish it, making other people contribute to it? Example is this one which I've forked from here. It's a library to parse APK files without using the Android framework to do so. I used it on my own app (here) as it has advantages over what Android offers. Sadly it's archived and won't be updated anymore.
Caffeine
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Otter, Fastest Go in-memory cache based on S3-FIFO algorithm
My implementation is linked from the official S3-FIFO page. The benchmarks are as follows.
https://github.com/ben-manes/caffeine/wiki/Efficiency
I can't deal with you any longer. Over.
/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.
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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.
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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]
I wonder why all these papers ignore comparison against W-TinyLFU.
https://github.com/ben-manes/caffeine/wiki/Efficiency Shows that it really outperforms ARC as well and they also have an optimal oracle version that they evaluate against to show how much room there is left (admittedly the oracle version itself implies you’re picking some global criterion to optimize but that’s trickier when in reality there are multiple axes along which to optimize and you can’t simultaneously do well across all of them).
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!
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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.
What are some alternatives?
Ehcache - Ehcache 3.x line
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.
cache2k - Lightweight, high performance Java caching
Apache Geode - Apache Geode
Guava - Google core libraries for Java
Android-Validator - Form Validator Library for Android
scaffeine - Thin Scala wrapper for Caffeine (https://github.com/ben-manes/caffeine)
secure-preferences - Android Shared preference wrapper than encrypts the values of Shared Preferences. It's not bullet proof security but rather a quick win for incrementally making your android app more secure.
greenrobot-common - General purpose utilities and hash functions for Android and Java (aka java-common)
EasyCamera - Wrapper around the android Camera class that simplifies its usage
SQLDelight - SQLDelight - Generates typesafe Kotlin APIs from SQL
linux - Linux kernel source tree