go-pg VS ent

Might have been this one but I can't be sure https://github.com/go-pg/pg

I usually work with PostgreSQL and have used https://github.com/go-pg/pg. https://github.com/uptrace/bun is its successor.

And it is go-pg. I personally really like it as a query builder (rather than an ORM) and it has good performance. It has one feature which I'll tell you about next, without which I would have struggled to implement what I had in mind. But this feature is available in other libraries too, so don't rush to rewrite your code.

bun and go-pg both seem to work fairly well for these kind of purposes.

For database communication we will use go-pg module. You can install it by running go get github.com/go-pg/pg/v10. This will install version 10 of go-pg module, which is latest version at the time of writing this guide. Now create new directory internal/database/ and file database.go inside of it.

gorm.io/gorm`, or `github.com/go-pg/pg` if you need something more feature-rich. I would avoid using `go-kit` and `go-micro` "frameworks" unless you have some special requirement for it. But you can take a look to have an idea of what Go "framework" may look like.

This one is mostly excellent: https://github.com/go-pg/pg

> Ent heavily uses joins

I’m specifically talking about this: https://github.com/ent/ent/issues/977.

Devs assume that the `With` methods are adding join clauses but that is not typically the case.

> does support multi field indices

Composite primary keys are useful for reasons other than unique constraints and query speed. For example, CockroachDB uses the primary key to partition rows. Also, at scale, an extra multi-column index in addition to the primary key when the primary key alone could have sufficed can be a meaningful performance degradation.

> not a Graph DB under the hood

No it is not, but because it has a graph ‘mindset’ and does support Gremlin, traditional SQL folks expecting a lightweight ORM (such as Drizzle in the JS world) may not have a good time.

In this article, we'll delve into the world of concurrency control in Go, specifically focusing on the optimistic locking approach. We'll explore its implementation using Ent ORM to illustrate how to manage data consistency when multiple users interact with the same resource. Keep in mind that this example serves as a simplified illustration, and real-world booking systems involve a many of additional complexities. However, the core concepts presented here provide a solid foundation for understanding optimistic locking in Go applications. Feel free to explore the complete source code in my GitHub repository for a more in-depth look at the implementation.

ent is a fairly recent ORM that uses a code-first approach where you define your schema in Go code. Ent is popular thanks to its ability to handle complex data models and relationships elegantly. It's statically typed, which can help catch errors at compile time. However, the learning curve might be steeper compared to more straightforward ORMs like GORM. It's a good fit for applications where complex data models and type safety are priorities.

If you found this article, than you are probably similar to how I was a few months ago. I started a project in Go that required a SQL backend and I wanted to use any tool that would help me build this backend quickly. I stumbled upon entgo (an ORM for Go) and decided to give it a try.

The ent orm for golang actually does some useful work for you. https://github.com/ent/ent

Backend Database interaction with entgo