xsync
ristretto
xsync | ristretto | |
---|---|---|
7 | 19 | |
917 | 5,345 | |
- | 1.2% | |
5.5 | 6.1 | |
about 2 months ago | about 2 months ago | |
Go | Go | |
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.
xsync
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Otter, Fastest Go in-memory cache based on S3-FIFO algorithm
The issue is Go stdlib does not have parallel hash map.
We have https://github.com/puzpuzpuz/xsync#map a different Cache line hashmap impl.
- Are there any actively maintained or official Golang libraries for managing work queues?
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Thread-Local State in Go, Huh?
I've created a pull request to decrease the memory footprint and get rid of the unlucky distribution problem. Goroutines (think, threads) now self-organize: they detect contention via a failed CAS and change the stripe. Going to update the article accordingly to avoid confusion.
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So long, sync.Map
Could you check the method godoc and the example in this draft PR? I'm going to finalize the PR this weekend and it would be great to hear your opinion.
- puzpuzpuz/xsync: Concurrent data structures for Go. An extension for the standard sync package.
ristretto
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Otter, Fastest Go in-memory cache based on S3-FIFO algorithm
1. Unfortunately, ristretto has been showing hit ratio around 0 on almost all traces for a very long time now and the authors don't respond to this in any way. Vitess for example has already changed it to another cache. Here are two issues about it: https://github.com/dgraph-io/ristretto/issues/346 and https://github.com/dgraph-io/ristretto/issues/336. That is, ristretto shows such results even on its own benchmarks. You can see it just by running hit ratio benchmarks on a very simple zipf distribution from the ristretto repository: https://github.com/dgraph-io/ristretto/blob/main/stress_test.... On this test I got the following:
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S3 Express Is All You Need
That's exactly how Userify[0] used to work. (when it was Python; now that it's a Go app, we do the caching in memory using Ristretto[1]).
0. https://userify.com (team ssh key management/sudo authz)
1. https://github.com/dgraph-io/ristretto
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Theine - High performance in-memory cache
I also do some hit ratio benchmarks and Theine's results are much better than Ristretto. See results in README: https://github.com/Yiling-J/theine-go#hit-ratios
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Python deserves a good in-memory cache library!
If you know Caffeine(Java)/Ristretto(Go)/Moka(Rust), you know what Theine is. Python deserves a good in-memory cache library.
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VCache: A Simple In-Memory Cache Library
Thanks for sharing. There are a lot of options for embedded in-memory caches: https://github.com/dgraph-io/ristretto https://awesome-go.com/caches/ Do you have any comparisons or details on how your project has a different approach?
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Cacheme: Asyncio cache framework with multiple storages and thundering herd protection
I made Cacheme years ago, which support redis and synchronous API only. Then I switch to Go and found that there are some awesome cache projects in Go(ristretto, gocache...), I also made my own Cacheme go version: cacheme-go. After trying asyncio and type hint, I think it's time to rewrite my old Cacheme.
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Show HN: Zcached, in-memory key-value cache wire-compatible with memcached
zcached is an in-memory key-value cache exposing a memcached ASCII protocol-compatible interface, built on pluggable cache engines like Ristretto and freecache [0].
It's not performance-competitive with memcached, especially at higher thread counts. That said, it achieves about 1.1M ops/s, but at significantly higher P99 and P999 latency (as measured by memtier). See [1] and [2] for benchmark results from my 7950x-based workstation.
Disclaimer: This is a hobby project created for fun while hacking over the holidays. zcached is not a commercial product and never will be. Don't use it in production; consider this a technology demo more than anything.
I don't expect the source code to build outside of my environment, but for those interested in playing with it, binary artifacts are available at [3]. Try `zcached --address tcp:localhost:11211`.
[0] https://github.com/dgraph-io/ristretto, https://github.com/coocood/freecache
- What is the coolest Go open source projects you have seen?
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Quitting Dgraph Labs
While I never used dgraph, I do use badger and ristretto and am similarly in a bind over their long-term survival (moreso badger than ristretto)...
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Recommendation for Key/Value storage
There are also different packages used as a wrapper on top of the Go map based on what your requirements are (storing a lot of data) https://github.com/allegro/bigcache or (need performance) https://github.com/dgraph-io/ristretto. For basic use-cases, the standard Go map should be enough. Just keep in mind whether you need concurrent access to your data structure, in which case you should guard your map with a mutex .
What are some alternatives?
taskq - Golang asynchronous task/job queue with Redis, SQS, IronMQ, and in-memory backends
go-cache-benchmark - Cache benchmark for Golang
Tasqueue - A simple, customisable distributed job/worker in Go
BigCache - Efficient cache for gigabytes of data written in Go.
libCacheSim - a high performance library for building cache simulators
stretto - Stretto is a Rust implementation for Dgraph's ristretto (https://github.com/dgraph-io/ristretto). A high performance memory-bound Rust cache.
Faktory - Language-agnostic persistent background job server
moka - A high performance concurrent caching library for Rust
machinery - Machinery is an asynchronous task queue/job queue based on distributed message passing.
parquet-go - Go library to read/write Parquet files
go - The Go programming language
IceFireDB - @IceFireLabs -> IceFireDB is a database built for web3.0 It strives to fill the gap between web2 and web3.0 with a friendly database experience, making web3 application data storage more convenient, and making it easier for web2 applications to achieve decentralization and data immutability.