stretto
dashmap
stretto | dashmap | |
---|---|---|
6 | 12 | |
397 | 2,726 | |
- | - | |
5.7 | 5.5 | |
5 days ago | 8 days ago | |
Rust | Rust | |
Apache License 2.0 | MIT License |
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stretto
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Stretto 0.5.0 release: Support runtime agnostic AsyncCache
Hi, I think this link is a good explanation https://github.com/al8n/stretto/pull/7
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Writing a concurrent LRU cache
Ya, I saw concache but I looked into it and it doesn't implement what is needed. Each bucket has its own linked-list backing (hence "lock-free linked list buckets"). An LRU needs each value in each bucket to be part of one linked list I believe. After posting this I realized my line of research was failing because it was state of the art five years ago. Caffeine replaced `concurrentlinkedhashmap` in the java world (by the same author). A rust version of that is Moka. These are much more complicated than a concurrent LRU but faster (aka more state of the art). Another rust crate is Stretto which is a port of dgraph's Ristretto (in go). The question becomes is it worth it to essentially port `concurrentlinkedhashmap` to have a great concurrent LRU when there are more state of the art caches out there.
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Stretto - a thread-safe, high-performance, high hit-ratio cache.
For the case in the benches folder(a very roughly bench case), stretto is around 20 - 30 ms(sync version is around 30 - 40 ms) faster than moka, for 120, 000+ operations. I set stretto to collect metrics when benching, collecting metrics will make around 10% overhead. Moka seems not to provide a configuration to collect the metrics, so the hit-ratio is not compared.
dashmap
- StupidAlloc: what if memory allocation was bad actually
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dashmap VS scalable-concurrent-containers - a user suggested alternative
2 projects | 13 Apr 2023
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Samsara, a safe Rust concurrent cycle collector
The problem is, every single one of these half-dozen crates has at least one known major issue (including UAF), exactly like C++ implementations (which isn't surprising since it's the kind of things where the ownership isn't clear and then the borrow checker can't help us).
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Rust vs Go
Deadlocks and leaks are easy as other languages.
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Shared mutable state is bad... so how do I create a global cache in a multi-threaded app?
Have you considered https://github.com/xacrimon/dashmap ?
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Announcing Leapfrog, a faster concurrent HashMap
Dashmap made some api changes compared to the stdlibs hashmap, which leads to some oddities, as highlighted here: https://github.com/xacrimon/dashmap/issues/175
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Writing a concurrent LRU cache
Some additional notes are in this slide deck and the implementation javadoc. You'd probably want to use something like DashMap for the hash table.
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HashMap-based cache for async programs
You can look at existing concurrent maps like Dashmap https://github.com/xacrimon/dashmap or Cashmap https://gitlab.redox-os.org/redox-os/chashmap
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How does one avoid lock of locks? or use the technique of latch crabbing of databases
Also dashmap
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Noteworthy concurrent data structures?
The only one I've used is Dashmap, it's a concurrent interior-mutability hashmap. Very convenient crate in the case you need that.
What are some alternatives?
ristretto - A high performance memory-bound Go cache
hashbrown - Rust port of Google's SwissTable hash map
moka - A high performance concurrent caching library for Rust
rust-memcache - memcache client for rust
HashMap - An open addressing linear probing hash table, tuned for delete heavy workloads
bitsock - Safe Rust crate for creating socket servers and clients with ease.
crossbeam - Tools for concurrent programming in Rust
ttl_cache
leapfrog - Lock-free concurrent and single-threaded hash map implementations using Leapfrog probing. Currently the highest performance concurrent HashMap in Rust for certain use cases.
bmemcached-rs - Rust binary memcached implementation
megahash - A super-fast C++ hash table with Node.js wrapper, tested up to 1 billion keys.