multiversion VS throttle_my_fn

Compare multiversion vs throttle_my_fn and see what are their differences.

throttle_my_fn

A Rust attribute macro to limit a function's number of runs over a specified period of time (by fredmorcos)
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multiversion throttle_my_fn
2 1
185 12
- -
5.6 0.0
about 1 month ago over 1 year ago
Rust Rust
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

multiversion

Posts with mentions or reviews of multiversion. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-04-02.

throttle_my_fn

Posts with mentions or reviews of throttle_my_fn. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing multiversion and throttle_my_fn you can also consider the following projects:

maud - :pencil: Compile-time HTML templates for Rust

poem - A full-featured and easy-to-use web framework with the Rust programming language.

born - Remove code duplication from Struct and Enum with functional macros.

default-args.rs - zero runtime cost default arguments in rust

yarpl - Yet Another Rust Parsing Library

negate - Attribute macro that generates negated versions of`is_something` functions

varint-simd - Decoding and encoding gigabytes of LEB128 variable-length integers per second in Rust with SIMD

tangram - Tangram is an all-in-one automated machine learning framework. [Moved to: https://github.com/tangramdotdev/tangram]

typestate-rs - Proc-macro typestate DSL for Rust

tangram - Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.