Contributing to Tube Archivist
Welcome, and thanks for showing interest in improving Tube Archivist!
Table of Content
- How to open an issue
- How to make a Pull Request
- Improve to the Documentation
- Development Environment
How to open an issue
Please read this carefully before opening any issue on GitHub.
- Do provide details and context, this matters a lot and makes it easier for people to help.
- Do familiarize yourself with the project first, some questions answer themselves when using the project for some time. Familiarize yourself with the Readme and the documentation, this covers a lot of the common questions, particularly the FAQ.
- Do respond to questions within a day or two so issues can progress. If the issue doesn't move forward due to a lack of response, we'll assume it's solved and we'll close it after some time to keep the list fresh.
- Don't open duplicates, that includes open and closed issues.
- Don't open an issue for something that's already on the roadmap, this needs your help to implement it, not another issue.
- Don't open an issue for something that's a known limitation. These are known by definition and don't need another reminder. Some limitations may be solved in the future, maybe by you?
- Don't overwrite the issue template, they are there for a reason. Overwriting that shows that you don't really care about this project. It shows that you have a misunderstanding how open source collaboration works and just want to push your ideas through. Overwriting the template may result in a ban.
Bug reports are highly welcome! This project has improved a lot due to your help by providing feedback when something doesn't work as expected. The developers can't possibly cover all edge cases in an ever changing environment like YouTube and yt-dlp.
Please keep in mind:
- Docker logs are the easiest way to understand what's happening when something goes wrong, always provide the logs upfront.
- Set the environment variable
DJANGO_DEBUG=Trueto Tube Archivist and reproduce the bug for a better log output. Don't forget to remove that variable again after.
- A bug that can't be reproduced, is difficult or sometimes even impossible to fix. Provide very clear steps how to reproduce.
Existing ideas are easily multiple years worth of development effort, at least at current speed. Best and fastest way to implement your feature is to do it yourself, that's why this project is open source after all. This project is very selective with accepting new feature requests at this point.
Good feature requests usually fall into one or more of these categories:
- You want to work on your own idea within the next few days or weeks.
- Your idea is beneficial for a wide range of users, not just for you.
- Your idea extends the current project by building on and improving existing functionality.
- Your idea is quick and easy to implement, for an experienced as well as for a first time contributor.
Your request is likely going to be rejected if:
- Your idea requires multiple days worth of development time and is unrealistic to be implemented any time soon.
- There are already other ways to do what you are trying to do.
- You are trying to do something that only applies to your platform, your specific workflow or your specific setup.
- Your idea would fundamentally change how the project works or it wouldn't be able to be implemented with backwards compatibility.
- Your idea is not a good fit for this project.
GitHub is most likely not the best place to ask for installation help. That's inherently individual and one on one.
- First step is always, help yourself. Start at the Readme or the additional platform specific installation pages in the docs.
- If that doesn't answer your question, open a
#supportthread on Discord.
- Only if that is not an option, open an issue here.
IMPORTANT: When receiving help, contribute back to the community by improving the installation instructions with your newly gained knowledge.
How to make a Pull Request
Thank you for contributing and helping improve this project. This is a quick checklist to help streamline the process:
- For code changes, make your PR against the testing branch. That's where all active development happens. This simplifies the later merging into master, minimizes any conflicts and usually allows for easy and convenient fast-forward merging.
- For documentation changes, make your PR directly against the master branch.
- Show off your progress, even if not yet complete, by creating a draft PR first and switch it as ready when you are ready.
- Make sure all your code is linted and formatted correctly, see below. The automatic GH action unfortunately needs to be triggered manually by a maintainer for first time contributors, but will trigger automatically for existing contributors.
npm i from the root directory of this repository to install dependencies, then run
npm run lint and
npm run format to run eslint and prettier respectively.
Code formatting and linting
To keep things clean and consistent for everybody, there is a github action setup to lint and check the changes. You can test your code locally first if you want. For example if you made changes in the video module, run
./deploy.sh validate tubearchivist/home/src/index/video.py
to validate your changes. If you omit the path, all the project files will get checked. This is subject to change as the codebase improves.
Improve to the Documentation
I have learned the hard way, that working on a dockerized application outside of docker is very error prone and in general not a good idea. So if you want to test your changes, it's best to run them in a docker testing environment. You might be able to run the application directly, but this document assumes you're using docker.
Set up docker on your development machine.
Clone this repository.
Functional changes should be made against the unstable
testing branch, so check that branch out, then make a new branch for your work.
docker-compose.yml file and replace the
image: bbilly1/tubearchivist line with
build: .. Also make any other changes to the environment variables and so on necessary to run the application, just like you're launching the application as normal.
docker compose up --build. This will bring up the application. Kill it with
ctrl-c or by running
docker compose down from a new terminal window in the same directory.
Make your changes locally and re-run
docker compose up --build. The
Dockerfile is structured in a way that the actual application code is in the last layer so rebuilding the image with only code changes utilizes the build cache for everything else and will just take a few seconds.
Develop environment inside a VM
You may find it nice to run everything inside of a VM, though this is not necessary. There's a
deploy.sh script which has some helpers for this use case. YMMV, this is what one of the developers does:
- Clone the repo, work on it with your favorite code editor in your local filesystem. testing branch is where all the changes are happening, might be unstable and is WIP.
- Then I have a VM running standard Ubuntu Server LTS with docker installed. The VM keeps my projects separate and offers convenient snapshot functionality. The VM also offers ways to simulate low end environments by limiting CPU cores and memory. You can use this Ansible Docker Ubuntu playbook to get started quickly. But you could also just run docker on your host system.
- I have my local DNS resolve
tubearchivist.localto the IP of the VM for convenience. To deploy the latest changes and rebuild the application to the testing VM run:
- The command above will call the docker build command with
--build-arg INSTALL_DEBUG=1to install additional useful debug tools.
testargument takes another optional argument to build for a specific architecture valid options are:
multi, default is
deploy.shscript is not meant to be universally usable for every possible environment but could serve as an idea on how to automatically rebuild containers to test changes - customize to your liking.
Working with Elasticsearch
Additionally to the required services as listed in the example docker-compose file, the Dev Tools of Kibana are invaluable for running and testing Elasticsearch queries.
Generate your access token in Elasitcsearch:
bin/elasticsearch-service-tokens create elastic/kibana kibana
Example docker compose, use same version as for Elasticsearch:
If you want to run queries on the Elasticsearch container directly from your host with for example
curl or something like postman, you might want to publish the port 9200 instead of just exposing it.