SocketCluster VS mats3

Recently, I added an AI-generated soundtrack to my open source project's home page https://socketcluster.io/

It seems unconventional at first but I distinctly remember about a decade ago when Adobe Flash was still broadly supported, many Flash websites had soundtracks. I think the reason why regular HTML websites didn't have them was because it was difficult to implement and internet was much slower so they had to be streamed in a special way.

If redis doesn't satisfy your requirements or you're unable to make it work using adaptor, SocketCluster is a great package for this https://socketcluster.io/

Interesting how this feature set is pretty much exactly the same as offered by SocketCluster https://socketcluster.io/

This resonates with me 100%. Every bit of unhappiness I've felt in my career so far has been caused by a bad manager. The main issue for me has been the final point you mentioned about "Imposed artificial limitations" - I cannot tell you the number of times that I've been forced to use an inefficient tool or do something in a sub-optimal or downright incorrect way (knowing that it would have to be re-written later) by a bad manager... In some companies, it was a daily occurrence; that's why I never stayed at a single company for longer than 2 years. It's almost impossible to find a company that lets me implement things correctly.

Thankfully, nobody could constrain me in my open source work. I (with the help of community members) built:

- SocketCluster (https://socketcluster.io/): A distributed pub/sub framework.

- Capitalisk (https://capitalisk.com/): A lightweight quantum-resistant blockchain which is less than 5K lines of code.

- LDEX (https://ldex.trading/): A deterministic decentralized exchange (DEX) which can work with many different blockchain protocols. It's less than 4K lines of code in total and only has 3 small third-party dependencies (including sub-dependencies).

The two best lightweight solutions I have worked with are SocketCluster and NATS. SocketCluster is used to build out a custom backend that scales very well. NATS would be used as a central pub/sub system. If you need queueing, you'll need to add additional services like Redis.

In the homepage for SocketCluster it is mentioned that it is a Highly scalable pub/sub and RPC framework optimized for async/await. However in the documentation I have not found anything related to RPC. Am I interpreting anything wrong here?

At the time of writing, SocketCluster has almost 6k stars on GitHub and 7k downloads on npm weekly,

SocketCluster is a framework which allows you to use the WebSocket protocol the transmit between its backend API and client library. The client can be used both on a backend (E.g. a Raspberry pi) or frontend application.

The “restart from where it failed”-aspect was a big reason for why I made Mats3. It is message-based, async, transactional, staged stateless services, or message-oriented asynchronous RPC. Due to the transactionality, and the “state lives on the wire”, if a flow fails, it can be restarted from where it left off.

https://mats3.io

I am truly finding it hard to explain it. I have tried along a dozen angles. I actually think it is a completely new concept - and that might be the problem.

There is an illustration on the front-page: https://mats3.io/

Here's a set of small answers to "What is Mats?": https://github.com/centiservice/mats3/blob/main/README.md#wh...

Here's a way to code up Mats3 endpoints using JBang and a small toolkit which makes it extremely simple to explore the ideas: https://github.com/centiservice/mats3/blob/main/README.md#wh...

If you read these and then get it, I would be extremely happy if you gave me a sentence or paragraph that would have led you to understanding faster!

The use of JMS is just a transport. I could really have used anything, incl. any MQ, or ZeroMQ, or plain TCP - or just a shared table in a database.

Wrt. WebSockets, that is a transport typically between a server, and a end-user client, e.g. an iOS App. Actually, there's also a "sister project", MatsSockets, that bring the utter async-ness of Mats3 all the way out to the client, e.g. a webpage or an app. https://matssocket.io/

NATS is just a message queue, with some ability to orchestrate. I do not like this concept of orchestration as an external service, that is one of the founding ideas of Mats3: Do the orchestration within each service, as you would do if you employed REST as the ISC mechanism.

Read more about mats3 here: https://mats3.io/

I've made a Java library to facilitate the latter. https://mats3.io/ - you can start reading here: https://mats3.io/docs/message-oriented-rpc/, or test out some code right away, using JBang, here: https://mats3.io/explore/jbang-mats/

I have explained about this on the frontpage of Mats3: https://mats3.io/, and more in-depth in the first step of the "Walkthrough": https://mats3.io/docs/message-oriented-rpc/ - and even more into it at "Rationale for Mats", here: https://mats3.io/background/rationale-for-mats/

JBang’s tagline: “Lets Students, Educators and Professional Developers create, edit and run self-contained source-only Java programs with unprecedented ease.”

Mats3’s tagline: “Message-Oriented Async RPC. Message-based Interservice Communication made easy! Naturally resilient and highly available microservices, with great DevX and OpsX.”

JBang is a cool "execute single Java source file with dependencies" solution, which offers a simple way to test new libraries. The article tries to showcase Mats3's core concepts through a series of JBang scripts.

To streamline the process, a small library called 'MatsJbangKit' has been developed, which not only takes care of pulling up the Mats infrastructure but also depends on all the necessary dependencies. This means users only need to reference this single library in their JBang scripts. The aim is to pique curiosity and inspire further exploration of Mats3 and its potential.

If you like it, a star on Github would be much appreciated: https://github.com/centiservice/mats3

This is a "tack on"-tool to the otherwise fully async nature of Mats/messaging.

> For this to really work well, the message passing has to be integrated with the CPU dispatcher

It sounds like you are 100% set on speed. This is not really what Mats is after - it is meant as a inter-service communcation system, and IO will be your limiting factor at any rate. Mats sacrifices a bit of speed for developer ergonomics - the idea is that by easily enabling fully async development of ISC in a complex microservice system, you gain back that potential loss from a) actually being able to use fully async processing (!), and b) the inherent speed of messaging (it is at least as fast as HTTP, and you avoid the overhead of HTTP headers etc.

It is mentioned here, "What Mats is not": https://github.com/centiservice/mats3#what-mats-is-not