Zappa VS pip-tools

If people aren't familiar, there's a similar project for Python that's fantastic: https://github.com/zappa/Zappa

Chalice was a consequence, a reaction from AWS to the release of (Zappa Framework)[https://github.com/zappa/Zappa] that provide a very good alternative to migrate very quickly a Django/Flask or any WSGI compliant solution in Python.

Use Zappa https://github.com/zappa/Zappa and host as a Lambda, simple setup and deployment, Lambda only costs when processing requests, no servers to mess around with

One of my favorite options that is probably the most cost-effective is to deploy using a 'severless' model on AWS Lambda using zappa which supports deploying Python webapps to AWS in this way. Zappa also makes it super easy to deploy in just a couple commands! The README includes instructions for everything you might need, including handling sensitive information like your database passwords, running django management commands, setting up DNS, etc.

Deployment works nicely with Docker. I often use AWS AppRunner because it's really easy and just scales. Some people use AWS Lambda with Zappa but I don't recommend it unless you really want to spend less than $15 a month. You will probably need Django Storages to save uploads to an S3 bucket. At some stage you might want to put a CloudFront distribution in front of everything but the configuration of the caching behaviour might be a bit confusing when you do it the first time.

It doesn't have to be an either-or! I have several apps in production that were developed on Django or Flask, and deployed to Lambda using Zappa.

If you need a relational DB, you can use AWS Aurora or RDS and use cloud functions ('lambda' in AWS) that you can invoke with HTTP to process the document first. Zappa will do a lot of the configuration for you if you go that route.

Lambda + API gateway, this library bundles a Django application into a lambda https://github.com/zappa/Zappa . 1 million free invokes from aws, scale to zero, plugs into your RDS

I use Zappa, it just schedules a frequent execution of the lambda: https://github.com/zappa/Zappa#keeping-the-server-warm

> Why is the "requirements.txt" file a stupid flat listing of all transitive dependencies with pinned versions? It makes it harder to change library versions even if there are no true conflicts.

My friend, here is what you seek: https://github.com/jazzband/pip-tools

requirements.txt is flat because it's really the output of `pip freeze`. It's supposed to completely and exactly rebuild the environment. Unfortunately it's far too flexible and people abuse it by putting in only direct dependencies etc.

If you're writing packages, you don't need a requirements.txt at all, by the way. Package dependencies (only direct dependencies) live in pyproject.toml with the rest of the package config. requirements.txt (and pip tools) are only for when you want to freeze the whole environment, like for a server deployment.

For all my projects I found myself regenerating manual lock files using complex shell commands with pip-compile to get a reproducible environments across devices using a custom pre-install-command. I finally decided that instead of hacking together the same solution on all my projects I would build a plugin that handles this complexity for me.

Instead of venv, we are using pip-tools in this starter kit. pip-tools take things further in dependency management. Check out what pip-tools does in their official GitHub repo. In short, it helps your project find the best match for the dependent packages. For example, you might need two packages A and B in your project that requires same package C under the hood. But A requires any version of C from 1.0.1 to 1.0.10 and B requires any version of C from 1.0.7 to 1.0.15. Pip tools will automatically compile the version of 'C' that suits for both of your packages.

I've created a small project called just-pip-tools that combines pip-tools and just to manage Python dependencies in a layered approach. This isn't a magic bullet; it's a set of files you can adapt to your needs.

For small projects I recommend pip-tools. Just write packet list in requirements.in and pip-compile compile a requirements.txt with comments.

Take a look at pip-tools, great package. https://github.com/jazzband/pip-tools

pip-tools

I recommend generating a lockfile to document this information, as you might do with pip-tools.

Check out pip-tools [1] which does exactly that, albeit in a slightly more polished way.

[1]: https://github.com/jazzband/pip-tools