Скачать или смотреть Are Ruby Ractors Just a Rebranding of Python's MultiProcessing Module?

This guide delves into the nuances of Ruby Ractors and compares their functionality and performance against Python's MultiProcessing module, answering popular questions about their capabilities and implementation strategies.
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Exploring Ruby Ractors: Are They Like Python's MultiProcessing Module?

The introduction of Ractors in Ruby 3.0 has sparked discussions within the programming community, particularly about how they compare to Python's MultiProcessing module. This article aims to clarify the differences and similarities between these two features and to explore the implications of using Ractors in Ruby. Let’s dive into the key questions surrounding Ractors and what they mean for developers today.

Are Ruby's Ractors Just Multiple Processes in Disguise?

Understanding Ractors and Their Implementation

Ractors are designed to provide a means of concurrent computing in Ruby. However, it is important to note that Ractors do not necessarily rely on the operating system processes that many might assume. Here are some insights regarding Ractors:

Not Limited to OS Processes: Ractors provide a specification for concurrency, leaving room for various implementation strategies.

Lightweight by Design: The aim of Ractors is to be lightweight, contrasting with heavier OS processes.

Diverse Implementations: Different Ruby implementations like TruffleRuby, JRuby, and Opal are expected to utilize different systems—like Kilim, Project Loom, and WebWorkers—to achieve concurrency without the weight of traditional processes.

Current State of Ractors

Currently, there is only a prototype implementation developed by Koichi Sasada available in YARV 3.0.0. It's crucial to understand that:

This prototype does not utilize OS processes but relies on OS threads.

While YARV does not have a Global Interpreter Lock (GIL), Ractors implement a per-Ractor Global VM Lock (GVL), meaning that only one thread within a Ractor can execute at any time, but multiple Ractors can operate concurrently.

Can Ractors Outperform MultiProcessing in Speed and Communication Latency?

When it comes to comparing Ractors and Python's MultiProcessing, several points need to be considered:

Key Differences

Ractor vs. MultiProcessing: MultiProcessing in Python creates multiple interpreters, whereas Ractors represent a language feature with specific semantics.

Performance Factors: The ability to assess speed and latency depends significantly on the implementation of Ractors, which is still in the experimental phase. A direct speed comparison may not yield meaningful results until Ractors are fully realized.

Are Ractors as Fast as C/C+ + Threads?

Analyzing Performance

The conversation about Ractors being as fast as C/C+ + threads needs to dissect the underlying mechanics:

C and Threads: It is essential to clarify that C does not implement threads inherently, so comparing C with Ractors is not pertinent.

Potential with C+ + Threads: Ractors can be executed using threads, as evidenced by the YARV prototype. Once fully implemented, Ractors have the potential to perform with comparable efficiency to C+ + threads, depending on the implementation strategy.

Conclusion

In summary, Ruby's Ractors are not merely a rebranding of Python's MultiProcessing module. They offer a flexible, potentially lightweight strategy for concurrency in Ruby that is still evolving. The ongoing development and experimentation with Ractors promise exciting functionalities for Ruby developers as they seek efficient ways to manage concurrent tasks without the overhead of traditional OS processes. As Ractors mature, their performance, communication efficiency, and overall usability will become clearer, potentially redefining the landscape of concurrent programming in Ruby.

Feel free to explore Ractors further and stay tuned for future advancements in Ruby’s concurrency features!