warehouse VS traefik

pip install PackageName: installs a package (you can browse the available packages in the Python Package Index)

python3 -m pip install \ --trusted-host test.pypi.org --trusted-host test-files.pythonhosted.org \ --index-url https://test.pypi.org/simple/ \ --extra-index-url https://pypi.org/simple/ \ piper_whistle==$(python3 -m src.piper_whistle.version)

In my experience so far, I can use a vast amount of the Python Standard Library to build Wasm-powered serverless applications. The caveat I currently understand is that Python’s implementation of TCP and UDP sockets, as well as Python libraries that use threads, processes, and signal handling behind the scenes, will not compile to Wasm. It is worth noting that a similar caveat exists with libraries that I find on The Python Package Index (PyPI) site. While these caveats might limit what can be compiled to Wasm, there are still a ton of extremely powerful libraries to leverage.

We believe that poetry is currently the best tool for this purpose, besides of being the most popular one at the moment. This is why we will use poetry to manage the dependencies of our project throughout this series of posts. Poetry allows you to declare the libraries your project depends on, and it will manage (install/update) them for you. Poetry also allows you to package your project into a distributable format and publish it to a repository, such as PyPI. We strongly recommend you to learn more about this tool by reading the official documentation.

From there, I needed to learn a bit about PyPi or Python Package Index, which is the home for all the wonderful packages that you know if you have ever run the handy pip install command. PyPi has a pretty quick and easy onboarding, which requires a secured account be created and, for the purposes of submitting packages from CLI, an API token be generated. This can be done in your PyPi profile. Once logg just navigate to https://pypi.org/manage/account/ and scroll down to the API tokens section. Click “Add Token” and follow the few steps to generate an API token which is your access point to uploading packages. With all this in place, I was able to use twine to handle the package upload. First I needed to install twine, again as simple as pip install twine. In order for twine to access my API token during the package upload process, it needed to read it from .pypirc file that contains the token info. For some that file may exist already, for me I was required to create it. Working in windows I simply used a text editor to create it in my home user directory ($HOME/.pypirc). The file contents had a TOML like format looked like this:

I have published the package to Python Package Index, commonly called PyPi, and in this post, I'll be sharing the steps I had to follow in the process.

Register at PyPI.org

According to the stats on the original link, there are over 25,000 identified secret ids/keys/tokens in the data. And it looks like that's just identifiable secrets, e.g. "Google API Keys" that I'm guessing are identifiable because they have a specific pattern, and may be missing other secrets that use less recognizable patterns.

I mean, sure, compared to the 478,876 Projects claimed on https://pypi.org/, that's a pretty small minority. On the other hand, I'd guess a many Python packages don't use these particular services, or even need to connect to a remote service at all, so the area for this class of mistake should be even smaller.

And mistakes do happen, but that's a pretty big thing to miss if you are knowingly publishing your code with the expectation other people will be reading it.

PyPi package

Does pypi.org display the Union of all keywords, the keywords of the most recent release, the keywords of the first release or some other weird combination like the intersection?

apparently "traffic" https://github.com/traefik/traefik/issues/795

Pronounced "traffic", Traefik is a modern HTTP reverse proxy and load balancer aimed at making deploying microservices easier. It integrates with your existing infrastructure components such as Docker, Kubernetes, and others, and configures itself automatically and dynamically. The latest version adds lots of new options and enhancements such as adding healthcheck options, support for custom headers, and more. Read the migration guide on how to update to the latest version which is now required due to breaking changes.

> I think etcd is basically a k8s only project now

I hate etcd with the best of them, but etcd is used in a lot more places than just kubernetes:

https://github.com/apache/apisix/blob/master/docs/en/latest/...

https://github.com/traefik/traefik#:~:text=Etcd,

https://github.com/zalando/patroni#patroni-a-template-for-po...

https://github.com/purpleidea/mgmt/tree/0.0.26/etcd (this one shows up on HN quite a bit)

https://github.com/sorintlab/stolon#features

It's actually one of the major reasons I wouldn't touch those projects

However, it's very unlikely that .NET developers will directly expose their Kestrel-based web apps to the internet. Typically, we use other popular web servers like Nginx, Traefik, and Caddy to act as a reverse-proxy in front of Kestrel for various reasons:

Not as good though. Case in point: https://github.com/traefik/traefik/issues/5472#issuecomment-... (that's just from this morning)

I'm speak objectively here. Of course, any built-in auto HTTPS that works (more or less) is better than none. Traefik uses an ACME library that was originally written for Caddy. After the original author left that project, Traefik team started maintaining it. Caddy's users' requirements exceeded what the library was capable of, but unfortunately there was friction in getting it to achieve our requirements. So I ended up writing a new ACME client library in Go and, together with upgrades in CertMagic (Caddy's auto-TLS lib), Caddy has the more flexible, robust, and capable auto-HTTPS functionality.

That is to say, not all auto-HTTPS functionalities are the same.

We'll use Traefik, an open source cloud native gateway that can plug into a Kubernetes cluster. It has the concept of "middleware" that can process API requests before passing them through to a backend. We can configuring a rate limit for all of our API endpoints by matching on the request path:

I did the same question here and here

Sadly there is currently no way of doing so. https://github.com/traefik/traefik/issues/6999