libCacheSim
golang-fifo
| libCacheSim | golang-fifo | |
|---|---|---|
| 2 | 4 | |
| 123 | 113 | |
| - | - | |
| 8.3 | 8.6 | |
| 29 days ago | about 1 month ago | |
| C | Go | |
| GNU General Public License v3.0 only | MIT License |
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libCacheSim
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Sieve is simpler than LRU
https://github.com/1a1a11a/libCacheSim/blob/develop/libCache...
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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
golang-fifo
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Otter, Fastest Go in-memory cache based on S3-FIFO algorithm
Hello, Thank you for replying here :)
Many of answers you replied are reasonable and good.
And I just want to add more comments for others.
1. SIEVE is not scan-resistant, so that, I think it should only be applied for web cache workloads (typlically follows power-law distribution)
2. SIEVE is somewhat scalalbe for read-intensive applications (e.g. blog, shop and etc), because it doesn't require to hold a lock on cahce hit.
3. The purpose of golang-fifo is to provide simple and efficient cache implementation (e.g. hashicorp-lru, groupcache)
4. when increasing contention otter sacrifices 1-2 percent
-> I think that the statement is incorrect. The hit rate varies depending on the total number of objects and the size of the cache, so it should be compared relatively. for example, otter's efficiency decreased by 5% compared to single-threaded when lock contention increased (decreased efficiency makes a mean network latency higher, because it may need to conduct heavy operation e.g. re-calculation, database access and so on)
5. ghost queue : honetly at that time of writing the code, I didn't deep dive into the bucket table implementation, it may not work same as actual bucket hash table (see here: https://github.com/scalalang2/golang-fifo/issues/16)
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golang-fifo | Modern cache eviction algorithm implementations.
I'm also implementing cache algorithms, introduced in papers, in golang.you can visit here. Your contribution would be greatly appreciated.
What are some alternatives?
Caffeine - A high performance caching library for Java
otter - A high performance lockless cache for Go.
xsync - Concurrent data structures for Go
maphash
theine-go - high performance in-memory cache
sosp23-s3fifo - The repo for SOSP23 paper: FIFO queues are all you need for cache evictions
ristretto - A high performance memory-bound Go cache
go-cache-benchmark - Cache benchmark for web cache workloads in golang.