Skip to content

CONTRIBUTING.md

Latest commit

 

History

History
128 lines (101 loc) · 6.15 KB

CONTRIBUTING.md

File metadata and controls

128 lines (101 loc) · 6.15 KB

Contributing to delta-rs

Development on this project is mostly driven by volunteer contributors. We welcome new contributors, including not only those who develop new features, but also those who are able to help with documentation and provide detailed bug reports.

Please take note of our code of conduct.

If you want to start contributing, first look at our good first issues: https://github.com/delta-io/delta-rs/contribute

If you want to contribute something more substantial, see our "Projects seeking contributors" section on our roadmap: delta-io#1128

AI-generated code

We recognise that AI coding assistants are now a regular part of many developers' workflows and can improve productivity. Thoughtful use of these tools can be beneficial, but AI-generated PRs can sometimes lead to undesirable additional maintainer burden. PRs that appear to be fully generated by AI with little to no engagement from the author may be closed without further review.

Human-generated mistakes tend to be easier to spot and reason about, and code review is intended to be a collaborative learning experience that benefits both submitter and reviewer. When a PR appears to have been generated without much engagement from the submitter, reviewers with access to AI tools could more efficiently generate the code directly, and since the submitter is not likely to learn from the review process, their time is more productively spent researching and reporting on the issue.

We are not opposed to the use of AI tools in generating PRs, but recommend the following:

  • Only submit a PR if you are able to debug and own the changes yourself - review all generated code to understand every detail. Apache Datafusion has a useful explanation of why fully AI-generated PRs without understanding are not helpful.
  • Match the style and conventions used in the rest of the codebase, including PR titles and descriptions
  • Be upfront about AI usage and summarise what was AI-generated
  • If there are parts you don't fully understand, leave comments on your own PR explaining what steps you took to verify correctness
  • Watch for AI's tendency to generate overly verbose comments, unnecessary test cases, and incorrect fixes
  • Break down large PRs into smaller ones to make review easier

PR authors are also responsible for disclosing any copyrighted materials in submitted contributions. See the `Apache Software Foundation's guidance on AI-generated code for further information on licensing considerations.

Claiming an issue

If you want to claim an issue to work on, you can write the word take as a comment in it and you will be automatically assigned.

Quick start

  • Install Rust, e.g. as described here

  • Install the uv Python package manager.

  • Build the project for development. This will install deltalake into the Python virtual environment managed by uv.

    cd python
make develop

  • Run some Python code, e.g. to run a specific test

    uv run pytest tests/test_writer.py -s -k "test_with_deltalake_schema"

  • Run some Rust code, e.g. run an example

    cd ../crates/deltalake
cargo run --example basic_operations --features="datafusion"

Run the docs locally

This serves your local contents of docs via a web browser, handy for checking what they look like if you are making changes to docs or docstings

(cd python; make develop)
pip install -r docs/requirements.txt
mkdocs serve

To make a pull request (PR)

Make sure all the following steps run/pass locally before submitting a PR

cargo fmt -- --check
cd python
make check-rust
make check-python
make develop
make unit-test
make build-docs

Developing in VSCode

These are just some basic steps/components to get you started, there are many other very useful extensions for VSCode

  • For a better Rust development experience, install rust extension
  • For debugging Rust code, install CodeLLDB. The extension should even create Debug launch configurations for the project if you allow it, an easy way to get started. Just set a breakpoint and run the relevant configuration.
  • For debugging from Python into Rust, follow this procedure:
  1. Add this to .vscode/launch.json
{
            "type": "lldb",
            "request": "attach",
            "name": "LLDB Attach to Python'",
            "program": "${command:python.interpreterPath}",
            "pid": "${command:pickMyProcess}",
            "args": [],
            "stopOnEntry": false,
            "environment": [],
            "externalConsole": true,
            "MIMode": "lldb",
            "cwd": "${workspaceFolder}"
        }
  1. Add a breakpoint() statement somewhere in your Python code (main function or at any point in Python code you know will be executed when you run it)
  2. Add a breakpoint in Rust code in VSCode editor where you want to drop into the debugger
  3. Run the relevant Python code function in your terminal, execution should drop into the Python debugger showing PDB prompt
  4. Run the following in that prompt to get the Python process ID: import os; os.getpid()
  5. Run the LLDB Attach to Python from the Run and Debug panel of VSCode. This will prompt you for a Process ID to attach to, enter the Python process ID obtained earlier (this will also be in the dropdown but that dropdown will have many process IDs)
  6. LLDB make take couple of seconds to attach to the process
  7. When the debugger is attached to the process (you will notice the debugger panels get filled with extra info), enter c+Enter in the PDB prompt in your terminal - the execution should continue until the breakpoint in Rust code is hit. From this point it's a standard debugging process.