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CONTRIBUTING.md

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Contributing

First Principles

Favoring standard libraries over external dependencies, especially in specific contexts like Databricks, is a best practice in software development.

There are several reasons why this approach is encouraged:

  • Standard libraries are typically well-vetted, thoroughly tested, and maintained by the official maintainers of the programming language or platform. This ensures a higher level of stability and reliability.
  • External dependencies, especially lesser-known or unmaintained ones, can introduce bugs, security vulnerabilities, or compatibility issues that can be challenging to resolve. Adding external dependencies increases the complexity of your codebase.
  • Each dependency may have its own set of dependencies, potentially leading to a complex web of dependencies that can be difficult to manage. This complexity can lead to maintenance challenges, increased risk, and longer build times.
  • External dependencies can pose security risks. If a library or package has known security vulnerabilities and is widely used, it becomes an attractive target for attackers. Minimizing external dependencies reduces the potential attack surface and makes it easier to keep your code secure.
  • Relying on standard libraries enhances code portability. It ensures your code can run on different platforms and environments without being tightly coupled to specific external dependencies. This is particularly important in settings like Databricks, where you may need to run your code on different clusters or setups.
  • External dependencies may have their versioning schemes and compatibility issues. When using standard libraries, you have more control over versioning and can avoid conflicts between different dependencies in your project.
  • Fewer external dependencies mean faster build and deployment times. Downloading, installing, and managing external packages can slow down these processes, especially in large-scale projects or distributed computing environments like Databricks.
  • External dependencies can be abandoned or go unmaintained over time. This can lead to situations where your project relies on outdated or unsupported code. When you depend on standard libraries, you have confidence that the core functionality you rely on will continue to be maintained and improved.

While minimizing external dependencies is essential, exceptions can be made case-by-case. There are situations where external dependencies are justified, such as when a well-established and actively maintained library provides significant benefits, like time savings, performance improvements, or specialized functionality unavailable in standard libraries.

Change management

When you introduce a change in the code, specifically a deeply technical one, please ensure that the change provides same or improved set of capabilities. PRs that remove existing functionality shall be properly discussed and justified.

Code Organization

When writing code, divide it into two main parts: Components for API Interaction and Components for Business Logic. API Interaction should only deal with talking to external systems through APIs. They are usually integration-tested, and mocks are simpler. Business Logic handles the actual logic of your application, like calculations, data processing, and decision-making.

Keep API components simple. In the components responsible for API interactions, try to keep things as straightforward as possible. Refrain from overloading them with complex logic; instead, focus on making API calls and handling the data from those calls.

Inject Business Logic. If you need to use business logic in your API-calling components, don't build it directly there. Instead, inject (or pass in) the business logic components into your API components. This way, you can keep your API components clean and flexible, while the business logic remains separate and reusable.

Test your Business Logic. It's essential to test your business logic to ensure it works correctly and thoroughly. When writing unit tests, avoid making actual API calls - unit tests are executed for every pull request, and take seconds to complete. For calling any external services, including Databricks Connect, Databricks Platform, or even Apache Spark, unit tests have to use "mocks" or fake versions of the APIs to simulate their behavior. This makes testing your code more manageable and catching any issues without relying on external systems. Focus on testing the edge cases of the logic, especially the scenarios where things may fail. See this example as a reference of an extensive unit test coverage suite and the clear difference between unit tests and integration tests.

Local Setup

This section provides a step-by-step guide to set up and start working on the project. These steps will help you set up your project environment and dependencies for efficient development.

To begin, run make dev to install Hatch, create the default environment and install development dependencies, assuming you've already cloned the github repo.

make dev

Verify installation with

make test

To ensure your integrated development environment (IDE) uses the newly created virtual environment, you can retrieve the Python path with this command:

hatch run python -c "import sys; print(sys.executable)"

Configure your IDE to use this Python path so that you work within the virtual environment when developing the project: IDE Setup

Before every commit, apply the consistent formatting of the code, as we want our codebase look consistent:

make fmt

Before every commit, run automated bug detector (make lint) and unit tests (make test) to ensure that automated pull request checks do pass, before your code is reviewed by others:

make lint test

First contribution

Here are the example steps to submit your first contribution:

  1. Make a Fork from remorph repo (if you really want to contribute)
  2. git clone
  3. git checkout main (or gcm if you're using ohmyzsh).
  4. git pull (or gl if you're using ohmyzsh).
  5. git checkout -b FEATURENAME (or gcb FEATURENAME if you're using ohmyzsh).
  6. .. do the work
  7. make fmt
  8. make lint
  9. .. fix if any
  10. make test
  11. .. fix if any
  12. git commit -a. Make sure to enter meaningful commit message title.
  13. git push origin FEATURENAME
  14. Go to GitHub UI and create PR. Alternatively, gh pr create (if you have GitHub CLI installed). Use a meaningful pull request title because it'll appear in the release notes. Use Resolves #NUMBER in pull request description to automatically link it to an existing issue.
  15. announce PR for the review

Troubleshooting

If you encounter any package dependency errors after git pull, run make clean