docker-flask-example VS dagger

I've been maintaining my Build a SAAS App with Flask video course[0] for 8 years. It has gone from pre-1.0 to 2.3 and has been recorded twice with tons of incremental updates added over the years to keep things current.

In my opinion tutorial creators should pin their versions so that anyone taking the course or going through the tutorial will have a working version that matches the video or written material.

I'm all for keeping things up to date and do update things every few months but rolling updates don't tend to work well for tutorials because sometimes a minor version requires a code change or covering new concepts. As a tutorial consumer it's frustrating when the content doesn't match the source code unless it's nothing but a version bump.

I've held off upgrading Flask to 3.0 and Python 3.12 due to these open issues with 3rd party dependencies https://github.com/nickjj/docker-flask-example/issues/17.

[0]: https://buildasaasappwithflask.com/

I usually end up with project specific "run" scripts which are just shell scripts so I can do things like `./run shell` to drop into the shell of a container, or `./run rails db:migrate` to run a command in a container.

Here's a few project specific examples. They all have similar run scripts:

    - https://github.com/nickjj/docker-flask-example

There's a lot of "tool" selections in that repo.

If anyone is looking for ready to go web app examples aimed at both development and production, I maintain:

    - https://github.com/nickjj/docker-flask-example

Personally I maintain https://github.com/nickjj/docker-flask-example. There's also https://github.com/nickjj/build-a-saas-app-with-flask if you want more opinions.

This is what I do except I use a shell script instead of a Makefile.

A working example of this is at: https://github.com/nickjj/docker-flask-example/blob/912388f3...

Those ./run ci:XXX commands are in: https://github.com/nickjj/docker-flask-example/blob/912388f3...

I like it because if CI ever happens to be down I can still run that shell script locally.

> I've encountered a code written in the 12factor style of using environment variables for configuration, and in that particular case there was no validation nor documentation of the configuration options. Is this typical?

I don't know about typical, it comes down to how your team values the code they write.

You can have a .env.example file commit to version control which explains every option in as much or as little detail as you'd like. For my own personal projects, I tend to document this file like this https://github.com/nickjj/docker-flask-example/blob/main/.en....

I did this for a while but make isn't well suited for this use case. What I end up doing is have a shell script with a bunch of functions in it. Functions automatically becomes a callable a command (with a way to make private functions if you want) with pretty much no boiler plate.

The benefit of this is it's just shell scripting so you can use shell features like $@ to pass args to another command or easily source and deal with env vars.

I've written about this process at https://nickjanetakis.com/blog/replacing-make-with-a-shell-s... and an example file is here https://github.com/nickjj/docker-flask-example/blob/main/run.

There's: https://github.com/nickjj/docker-flask-example

The fact that I couldn't point to one page on the docs that shows the tl;dr or the what problem is this solving

https://docs.dagger.io/quickstart/562821/hello just emits "Hello, world!" which is fantastic if you're writing a programming language but less helpful if you're trying to replace a CI/CD pipeline. Then, https://docs.dagger.io/quickstart/292472/arguments doubles down on that fallacy by going whole hog into "if you need printf in your pipline, dagger's got your back". The subsequent pages have a lot of english with little concrete examples of what's being shown.

I summarized my complaint in the linked thread as "less cowsay in the examples" but to be honest there are upteen bazillion GitHub Actions out in the world, not the very least of which your GHA pipelines use some https://github.com/dagger/dagger/blob/v0.10.2/.github/workfl... https://github.com/dagger/dagger/blob/v0.10.2/.github/workfl... so demonstrate to a potential user how they'd run any such pipeline in dagger, locally, or in Jenkins, or whatever by leveraging reusable CI functions that setup go or run trivy

Related to that, I was going to say "try incorporating some of the dagger that builds dagger" but while digging up an example, it seems that dagger doesn't make use of the functions yet <https://github.com/dagger/dagger/tree/v0.10.2/ci#readme> which is made worse by the perpetual reference to them as their internal codename of Zenith. So, even if it's not invoked by CI yet, pointing to a WIP PR or branch or something to give folks who have CI/CD problems in their head something concrete to map into how GHA or GitLabCI or Jenkins or something would go a long way

> GHA has "service containers", but unfortunately the feature is too basic to address real-world use cases: it assumes a container image can just … boot! … and only talk to the code via the network. Real world use cases often require serialized steps between the test & the dependencies, e.g., to create or init database dirs, set up certs, etc.)

My biased recommendation is to write a custom Dagger function, and run it in your GHA workflow. https://dagger.io

If you find me on the Dagger discord, I will gladly write a code snippet summarizing what I have in mind, based on what you explained of your CI stack. We use GHA ourselves and use this pattern to great effect.

Disclaimer: I work there :)

Dagger (https://dagger.io) is a great way to use BuildKit through language SDKs. It's such a better paradigm, I cannot imagine going back.

Dagger is by the same folks that brought us Docker. This is their fresh take on solving the problem of container building and much more. BuildKit can more than build images and Dagger unlocks it for you.

And suddenly, it's almost painfully obvious where all the pain came from. Cloud applications today are simply a patchwork of disconnected pieces. I have a compiler for my infrastructure, another for my functions, another for my containers, another for my CI/CD pipelines. Each one takes its job super seriously, and keeps me safe and happy inside each of these machines, but my application is not running on a single machine anymore, my application is running on the cloud.

That said I've been moving my CI/CD to https://dagger.io/ which has been FANTASTIC. It's code based so you can define all your pipelines in Go, Python, or Javascript and they all run on containers so I can run actions locally without any special setup. Highly recommended.

You are right make is arcane. But it gets the job done. There are new exciting things happening in this area. Check out https://dagger.io.

> but I'm not convinced it's ready to replace Gitlab CI.

The purpose of Dagger it's not to replace your entire CI (Gitlab in your case). As you can see from our website (https://dagger.io/engine), it works and integrates with all the current CI providers. Where Dagger really shines is to help you and your teams move all the artisanal scripts encoded in YAML into actual code and run them in containers through a fluent SDK which can be written in your language of choice. This unlocks a lot of benefits which are detailed in our docs (https://docs.dagger.io/).

> Dagger has one very big downside IMO: It does not have native integration with Gitlab, so you end up having to use Docker-in-Docker and just running dagger as a job in your pipeline.

This is not correct. Dagger doesn't depend on Docker. We're just conveniently using Docker (and other container runtimes) as it's generally available pretty much everywhere by default as a way to bootstrap the Dagger Engine. You can read more about the Dagger architecture here: https://github.com/dagger/dagger/blob/main/core/docs/d7yxc-o...

As you can see from our docs (https://docs.dagger.io/759201/gitlab-google-cloud/#step-5-cr...), we're leveraging the *default* Gitlab CI `docker` service to bootstrap the engine. There's no `docker-in-docker` happening there.

> It clumps all your previously separated steps into a single step in the Gitlab pipeline.

This is also not the case, we should definitely improve our docs to reflect that. You can organize your dagger pipelines in multiple functions and call them in separate Gitlab jobs as you're currently doing. For example, you can do the following:

```.gitlab-ci.yml

Check out https://dagger.io/. Write declarative pipelines in code, reproducibly run anywhere.

Is this similar to Dagger[1] ?

[1] https://dagger.io

Hey, https://dagger.io employee here. We can help with this. I'd recommend taking a look at out Python SDK (https://docs.dagger.io/sdk/python) to build your pipelines and then leverage Dagger Cloud (https://dagger.io/blog/dagger-cloud) for fast production deployments.