NATS VS graphjin

[1] https://github.com/nats-io/nats-server/issues/6832#issuecomm...

https://nats.io is easier to use than Kafka and already solves several of the points in this post I believe, like removing partitions, supporting key-based streams, and having flexible topic hierarchies.

Look at the contribution history, basically all active contributors work for Synadia: https://github.com/nats-io/nats-server/graphs/contributors

That's not a healthy / functioning open source community. Less than 30 people have made more than 10 commits; most of the 160 were "drive by" who fixed a single small thing.

NATS, with its lightweight and high-performance design, offers features well-suited for sandbox testing. We can leverage NATS queue groups, which function similarly to Kafka consumer groups.

In today’s dynamic cloud environments, managing Kubernetes resources across multiple clusters can be a complex task. Traditional methods often lack the agility and event-driven architecture needed to respond quickly to changes and automate resource provisioning. This article explores how Sveltos, in conjunction with NATS and JetStream, simplifies multi-cluster Kubernetes management through event-driven automation, streamlining operations and improving responsiveness.

hey thanks!

definitely interesting!

I do wonder if this really solves the author problem because by the looks of it , you just have to run meta command and it would run over each of the sub directory. While at the same time , I think I like it because this is what I think people refer to as "modular monolith"

Combining this with nats https://nats.io/ (hey if you don't want it to be over the network , you could use nats with the memory model of your application itself to reduce any overhead) and essentially just get yourself a really modular monolith in which you can then seperate things selectively (ahem , microservices) afterwards rather easily.

and now decided to write more complex things using https://nats.io and https://data-star.dev -

This one looks interesting if you like GraphQL… https://graphjin.com

You're right. Django made a ton of tables, and it's pretty insightful to think about all the stuff it's trying to consider and a nightmare to migrate that framework. Django might have simplified some stuff, but it's still too opaque for my taste and the overall end goals of the project. Currently, I'm testing using graphjin (it doesn't have the best documentation, but it does boast an SQL generation capability that I philosophically think is the best way to do things).

Checkout GraphJin it's an automatic GraphQL to SQL compiler, you write your database queries in GraphQL instead of SQL and it compiles them into efficient SQL on the fly. Works as a library in GO and NodeJS. https://github.com/dosco/graphjin

In GraphJin an automatic GraphQL to SQL compiler we use the gnomock library it startups a database instance (docker) then create the schema and tests data and finally our code connects to it and runs a series of tests. We run these across Mysql, Postgres and a few other DB's. Gnomock supports a wide range of them. Right now we don't take down the db for every test only between test runs but its fast enough that we could. This whole thing runs of a simple `go test -v .` command and we run it on every commit using a githook. https://github.com/dosco/graphjin/blob/master/tests/dbint_te...

This was a bug in GraphJin it has since been fixed. The issue was that you were trying to have multiple subscriptions over a single websocket this did not work (it should) and now it does. https://github.com/dosco/graphjin/commit/43e619b2ff392dd42b99a4d56074a041a54b0e1c

Not exactly GraphQL is just a format to define what data you want back from someplace. You can use it in a browser app to request data from a backend GraphQL server or you can use it as an ORM within your backend app to fetch data from a database using a library like GraphJin. https://github.com/dosco/graphjin