mats3 VS OZ

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

At my day job we run > 50 million messages per day through ZeroMQ, and have been quite pleased with its performance and stability (https://github.com/nyfix/OZ/tree/master#who-uses-it).

But the truth is that ZeroMQ has a steep learning curve, and some rather sharp edges that can leave nasty cuts if one is not careful.

If you're interested in becoming familiar with ZeroMQ, you might want to check out our implementation: <https://github.com/nyfix/OZ/>.

Unlikely, but they seem to be different things altogether. BlazingMQ appears to be a traditional message queue (think ActiveMQ), with message peristence. ZeroMQ is more of a network middleware (think Tibco Rendezvous), and does not include persistence.

BlazingMQ also appears to be more of a "platform" or "service" that an app can use (sort of like Oracle, say) -- ZeroMQ includes libraries that one can use to build an app, service or platform, but none is provided "out of the box".

Which makes it harder to get started with ZeroMQ, since by definition every ZeroMQ app is essentially built "from scratch".

If you're interested in ZeroMQ, you may want to check out OZ (https://github.com/nyfix/OZ), which is a Rendezvous-like platform that uses the OpenMAMA API (https://github.com/finos/OpenMAMA) and ZeroMQ (https://github.com/zeromq/libzmq) transport to provide a full-featured network middleware implementation. OZ has been used in our shop since 2020 handling approx 50MM high-value messages per day on our global FIX network.

At my day job, I manage a number of "friendly forks" of open-source packages (e.g., https://github.com/nyfix/OpenMAMA) as well as hosting our own open-source repos (e.g., https://github.com/nyfix/OZ).