Concurrent Ruby VS Async Ruby

Let’s say we need to traverse through thousands of files in our S3 Storage in a Ruby app. Let’s say we have a bunch of logs there that we need to read every day and process. If we just use a straightforward approach, like opening, reading, and processing every file one by one, our solution will work, but It will take a lot of time to process. So we need to improve the speed. Here ruby-concurrent gem is our helper https://github.com/ruby-concurrency/concurrent-ruby. Why do we need this gem? Because It’s simpler to use than Threads and this gem has a lot more features in It. In this article, we will use Concurrent::Promises.future as the most common use of concurrent code. Because reading a file from S3 is an IO operation, we can get a huge benefit in speed if we gonna use concurrent code doing HTTP requests. Remember that concurrency will not give you speed improvements if in every Promise or Thread you will do any calculations. Because of Ruby GIL, every thread will be blocked until calculations are finished.

concurrent-ruby - to add async requests ability. https://github.com/ruby-concurrency/concurrent-ruby

After this, I took a look at the semaphore class in the popular library, concurrent-ruby to see how they implement it, and I learnt about something new: condition variables. And Ruby comes with this included!

https://github.com/ruby-concurrency/concurrent-ruby has great docs if someone is looking for alternatives.

I wonder how this would compare to using concurrent-ruby under ruby 2.7, especially in a real-world setting (where the calls are actually to external services that return and buffer data, instead of just sleep). The author says that he's felt that ruby threads "feel easy to mess up," but I've found that concurrent-ruby makes it pretty simple, and performant enough even with the GIL.

As I could not find a clear example about how to rescue exceptions from Concurrent::Promises (part of the Concurrent Ruby gem ) I read through the documentation and here are two examples: one that documents success case and one that shows what is happening when there is an error.

In a sense the GIL (or actually GVL as it's called in current ruby versions) has already been removed for ruby.

It's only the original MRI Ruby that still has it several over Ruby implementations already removed it. e.g. JRuby.

Concurrent-Ruby[1] is probably a good place to start if you want to work with GVL free ruby on JRuby. It's quite well supported and is currently used by Rails.

If you just want async or non-blocking IO I'd take a look at the Async Gem[2]. It looks pretty solid in Ruby > 3.0 and it's been invited by Matz to be part of the stdlib, which I think is a pretty good endorsement.

For MRI itself I don't think it's likely they'll ever remove the GVL. Ractors are probably a better solution for CPU concurrency in the long run, although I think they're pretty experimental currently.

1. https://github.com/ruby-concurrency/concurrent-ruby

I like how the article exposes you to tools to prove/disprove the problem. I would have hoped it introduced to tools like concurrent ruby and the use of atomics like u/Freeky already mentioned though.

Recently I've started Codecrafters' Build your own Redis (which I highly recommend!). One of the first steps is to make a TCP server that can accept more than one connection. I noticed that all the provided solutions use threads or IO#Select, and none of them use a fiber-based solution with Async. In this really short article, I'll show how one could achieve the result using Async.

Since the threading problem is specific to the Puma web server, let's look at another option: Falcon. This is a new, highly concurrent Rack-compliant web server built on the async gem. It uses Ruby Fibers instead of Threads, which are cheaper to create and have much lower overhead.

Async is a composable asynchronous I/O framework for Ruby. It allows you to do things concurrently using Fibers. Since 3.0, Ruby has a fiber scheduler and Ruby core supports it. This means you can have non-blocking I/O without much effort, for example, when using Net::HTTP. If you perform a blocking operation, such as an HTTP call, inside a fiber, it will immediately yield so that another fiber can become active and do some useful work instead of blocking and waiting for the HTTP call to complete.

The Async gem is the natural successor, It's actively maintained, and allows you write synchronous code is if it wasn't non-blocking, and most libraries don't need any special support for Async (exceptions are gems with C extensions that do I/O and DB libraries with connection pooling that would otherwise be thread-based).

https://github.com/socketry/async uses coroutines and I think in general it’s been a great model with very few downsides in practice.

There's async I/O. Here's a library that leans on Ruby 3's fiber scheduler.

Hey folks, wanted to show this off and get feedback. Still early/experimental but there are quite a few concepts I'm excited about here. This project came about while writing a program in Go and loving its approach to concurrency. Being a long-time Rubyist I immediately started to think about what similar concepts might look like in Ruby.

I set out with two main design constraints:

1. Lightweight: I didn't want routines to be backed by fibers or threads. Having been involved some in the async project (https://github.com/socketry/async), I had some experience using fibers for concurrency but was curious if they could be avoided.

2. Explicitness: Routine behavior must be written to describe exactly how it is to behave. I always felt like concurrent code was hard to fully understand because of the indirection involved. On the spectrum between tedium and magical I wanted to err more on the side of tedium with Goru.

Goru routines are just blocks that are called once for every tick of the reactor. It is up to the developer to implement behavior in terms of a state machine, where on each tick the routine takes some action and then updates the state of the routine for the next tick. This fulfills both design constraints:

1. Because routines are just blocks, they weigh in at about ~345 bytes of memory overhead.

2. Routine behavior is explicit because it is written as a state machine inside the block.

Couple more features worth noting:

* Goru includes channels for buffered reading/writing (similar to channels in Go).

* Goru ships with primitives for non-blocking IO to easily build things like http servers.

Curious your thoughts!

For those who are interested here is the question.