clace VS ent

An approach like https://github.com/benbjohnson/hashfs allows file names to be updated at runtime to be content hashed. This removes the need for the extra "304 Not Modified" API calls from the client. This content hash based file renaming is usually done using a build step which renames files. For applications where the static file serving and HTTP request processing are done in the same application, this can be done in memory without a build step for file renames.

I am using that approach in my project https://github.com/claceio/clace. It removes the need for a build step while making aggressive static file caching possible.

I am building https://github.com/claceio/clace. It is focussed on building operational web apps, with a focus on security. The end goal is to build something between https://www.rundeck.com/ and https://retool.com/, allow automation of operational tasks through a web interface while also allowing fully custom web apps.

Clace also works great for running simple web apps locally. Building and deploying a web app should be as easy and common for backend engineers as creating a CLI app is.

Hypermedia based web applications are a great fit for developing internal and operational tools. I have been building https://github.com/claceio/clace for making development and deployment easier for such web apps.

I have been building a project https://github.com/claceio/clace which aims to make building hypermedia based web applications easier. Clace is implemented in go, it uses Starlark (python syntax subset) for application configuration. With Clace, the apps are implemented using Starlark and (go) html templates, HTMX is used for web interface, app developer does not need to write any JavaScript.

Starlark in go https://github.com/google/starlark-go is a great way to combine the best of both, the ease of use of Python and the simplicity of go.

I have been building a platform for deploying internal web applications using this approach https://github.com/claceio/clace. Use Starlark to configure the application, the platform itself is built in go.

I have used this approach for internal tools and it has been great. It makes it much easier for one person to build the whole app, frontend and backend, and makes ongoing maintenance much easier.

I am working on https://github.com/claceio/clace which takes this no build approach and makes it easy to build portable applications, using Starlark running in go to configure the backend.

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

You can use entgo.io for ORM stuff, it also has entgql extension that integrated with GQLGen. See more at the document: https://entgo.io/docs/tutorial-todo-gql