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geospatial-data-lake
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| codecov-action | geospatial-data-lake | |
|---|---|---|
| 5 | 5 | |
| 1,387 | 32 | |
| 1.7% | - | |
| 9.3 | 0.0 | |
| 11 days ago | about 1 year ago | |
| TypeScript | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
codecov-action
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Rust CI with GitHub Actions
Code coverage results are uploaded to CodeCov through codecov/codecov-action@v1. For private repositories, add your token from CodeCov repository setting to GitHub Secrets and uncomment the line: token: ${{ secrets.CODECOV_TOKEN }}.
- Codecov verifies checksums by downloading them
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Codecov bash uploader was compromised
Looks like a PR [1] was started 4 hours ago but for such a simple task not much progress has been made.
[1]https://github.com/codecov/codecov-action/pull/282
geospatial-data-lake
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A curated list of questionable installation instructions
One option is to trust on first use, checksum the installation script and at least casually verify the diff each time the checksum changes[1].
Pros:
- Protects against simple hijacking.
- Reproducible as long as the installer doesn't also call out to a moving target, such as example.com/releases/latest.
Cons:
- Build breaks as soon as the installer is bumped. If it's bumped often (or just before an important release) this can cause pain.
- TOFU may not be acceptable, but of course you could review the code thoroughly before even the first use.
[1] https://github.com/linz/geostore/blob/b3cd162605109da8a3a688...
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Ask HN: Good Python projects to read for modern Python?
I'd recommend a project from work, Geostore[1]. Highlights:
- 100% test coverage (with some typical exceptions like `if __name__ == "__main__":` blocks)
- Randomises test sequence and inputs reproducibly
- Passes Pylint with max McCabe complexity of 6
- Passes `mypy --strict`
- Formatted using Black and isort
[1] https://github.com/linz/geostore
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Python Best Practices for a New Project in 2021
The current work project[1] has all of these: Pyenv, Poetry, Pytest, pytest-cov with 100% branch coverage, pre-commit, Pylint rather than Flake8, Black, mypy (with a stricter configuration than recommended here), and finally isort. These are all super helpful.
There's also a simpler template repo[2] with almost all of these.
[1] https://github.com/linz/geostore/
[2] https://github.com/linz/template-python-hello-world
- Codecov bash uploader was compromised
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AWS CloudFormation Best Practices
As someone who's used CDK for a few months and never handcoded CF, that sounds completely correct. If you're comfortable with Python, here's a simple but non-trivial architecture you can check out: https://github.com/linz/geospatial-data-lake/blob/master/app....
What are some alternatives?
upload-artifact
pydantic-factories - Simple and powerful mock data generation using pydantic or dataclasses
publish-unit-test-result-action - GitHub Action to publish unit test results on GitHub
template-python-hello-world - :triangular_ruler: Python Hello World | Minimal template for Python development
rust-ci-github-actions-workflow - Rust project template with CI workflow in GitHub Actions
asgi-correlation-id - Request ID propagation for ASGI apps
clippy-check - 📎 GitHub Action for PR annotations with clippy warnings
aws-cdk - The AWS Cloud Development Kit is a framework for defining cloud infrastructure in code
coverde - A set of commands for coverage trace files manipulation.
dev-tasks - Automated development tasks for my own projects
HomeBrew - 🍺 The missing package manager for macOS (or Linux)
pip - The Python package installer