graphjin VS kubernetes-the-hard-way

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

As I delved into the world of Kubernetes, I opted for what seemed like the scenic route—until reality hit. With determination and a sprinkle of naivety, I plunged into crafting a Kubernetes cluster from scratch. Little did I know, I was in for a wild ride of commands, configurations, and complexities. But fear not! With the guiding light of a course based on the notorious "Kubernetes The Hard Way" GitHub repository, albeit customized for AWS instead of Google Cloud, I embarked on my journey to build my digital fortress, one step at a time.

Another resource that I found pretty helpful was "Kubernetes the Hard Way" by Kelsey Hightower despite its complexity.

https://github.com/kelseyhightower/kubernetes-the-hard-way

I recently published Kubernetes the Harder Way, a guide loosely based on Kelsey Hightower's Kubernetes the Hard Way, but lenghtier, more explanatory, broader in scope, and - most importantly - harder, by targeting a local machine instead of Google Cloud Platform.

How about this? It will setup a kubernetes cluster on GCP with 3 worker nodes?

https://github.com/kelseyhightower/kubernetes-the-hard-way

Some costs here but click on the google cloud calc as it seems to have gone up since he wrote this:

If you wanna go all in: https://github.com/kelseyhightower/kubernetes-the-hard-way

Oh, and both: "Kubernetes up and Running" and "Production Kubernetes" are great books.

Using Kubernetes The Hard Way is a great way to learn it deeply and quickly.

If you wanna learn it deeply and quickly, Kelsey Hightower's Kubernetes the hard way is fantastic!