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Contributing to PyMongo

PyMongo has a large community and contributions are always encouraged. Contributions can be as simple as minor tweaks to the documentation. Please read these guidelines before sending a pull request.

Bugfixes and New Features

Before starting to write code, look for existing tickets or create one for your specific issue or feature request. That way you avoid working on something that might not be of interest or that has already been addressed.

Supported Interpreters

PyMongo supports CPython 3.8+ and PyPy3.9+. Language features not supported by all interpreters can not be used.

Style Guide

PyMongo follows PEP8 including 4 space indents and 79 character line limits.

General Guidelines

  • Avoid backward breaking changes if at all possible.
  • Write inline documentation for new classes and methods.
  • We use hatch for our script runner and packaging tool.
  • Write tests and make sure they pass (make sure you have a mongod running on the default port, then execute hatch run test:test from the cmd line to run the test suite).
  • Add yourself to doc/contributors.rst :)

Authoring a Pull Request

Our Pull Request Policy is based on this Code Review Developer Guide

The expectation for any code author is to provide all the context needed in the space of a pull request for any engineer to feel equipped to review the code. Depending on the type of change, do your best to highlight important new functions or objects you've introduced in the code; think complex functions or new abstractions. Whilst it may seem like more work for you to adjust your pull request, the reality is your likelihood for getting review sooner shoots up.

Self Review Guidelines to follow

  • If the PR is too large, split it if possible.

    • Use 250 LoC (excluding test data and config changes) as a rule-of-thumb.

    • Moving and changing code should be in separate PRs or commits.

      • Moving: Taking large code blobs and transplanting them to another file. There's generally no (or very little) actual code changed other than a cut and paste. It can even be extended to large deletions.
      • Changing: Adding code changes (be that refactors or functionality additions/subtractions).
      • These two, when mixed, can muddy understanding and sometimes make it harder for reviewers to keep track of things.

  • Prefer explaining with code comments instead of PR comments.

Provide background

  • The PR description and linked tickets should answer the "what" and "why" of the change. The code change explains the "how".

Follow the Template

  • Please do not deviate from the template we make; it is there for a lot of reasons. If it is a one line fix, we still need to have context on what and why it is needed.

  • If making a versioning change, please let that be known. See examples below:

    • versionadded:: 3.11
    • versionchanged:: 3.5

Pull Request Template Breakdown

  • Github PR Title

    • The PR Title format should always be [JIRA-ID] : Jira Title or Blurb Summary.

  • JIRA LINK

  • Convenient link to the associated JIRA ticket.

  • Summary

    • Small blurb on why this is needed. The JIRA task should have the more in-depth description, but this should still, at a high level, give anyone looking an understanding of why the PR has been checked in.

  • Changes in this PR

    • The explicit code changes that this PR is introducing. This should be more specific than just the task name. (Unless the task name is very clear).

  • Test Plan

    • Everything needs a test description. Describe what you did to validate your changes actually worked; if you did nothing, then document you did not test it. Aim to make these steps reproducible by other engineers, specifically with your primary reviewer in mind.

  • Screenshots

    • Any images that provide more context to the PR. Usually, these just coincide with the test plan.

  • Callouts or follow-up items

    • This is a good place for identifying "to-dos" that you've placed in the code (Must have an accompanying JIRA Ticket).
    • Potential bugs that you are unsure how to test in the code.
    • Opinions you want to receive about your code.

Running Linters

PyMongo uses pre-commit for managing linting of the codebase. pre-commit performs various checks on all files in PyMongo and uses tools that help follow a consistent code style within the codebase.

To set up pre-commit locally, run:

brew install pre-commit
pre-commit install

To run pre-commit manually, run:

pre-commit run --all-files

To run a manual hook like mypy manually, run:

pre-commit run --all-files --hook-stage manual mypy

Typically we use hatch to run the linters, e.g.

hatch run typing:check-mypy
hatch run lint:build-manual

Documentation

To contribute to the API documentation just make your changes to the inline documentation of the appropriate source code or rst file in a branch and submit a pull request. You might also use the GitHub Edit button.

We use reStructuredText for all documentation including narrative docs, and the Sphinx docstring format.

You can build the documentation locally by running:

hatch run doc:build

When updating docs, it can be helpful to run the live docs server as:

hatch run doc:serve

Browse to the link provided, and then as you make changes to docstrings or narrative docs, the pages will re-render and the browser will automatically refresh.

Running Tests Locally

  • Ensure you have started the appropriate Mongo Server(s).
  • Run pip install hatch to use hatch for testing or run pip install -e ".[test]" to run pytest directly.
  • Run hatch run test:test or pytest to run all of the tests.
  • Append test/<mod_name>.py::<class_name>::<test_name> to run specific tests. You can omit the <test_name> to test a full class and the <class_name> to test a full module. For example: hatch run test:test -- test/test_change_stream.py::TestUnifiedChangeStreamsErrors::test_change_stream_errors_on_ElectionInProgress.
  • Use the -k argument to select tests by pattern.

Running Load Balancer Tests Locally

  • Install haproxy (available as brew install haproxy on macOS).
  • Clone drivers-evergreen-tools: git clone git@github.com:mongodb-labs/drivers-evergreen-tools.git.
  • Start the servers using LOAD_BALANCER=true TOPOLOGY=sharded_cluster AUTH=noauth SSL=nossl MONGODB_VERSION=6.0 DRIVERS_TOOLS=$PWD/drivers-evergreen-tools MONGO_ORCHESTRATION_HOME=$PWD/drivers-evergreen-tools/.evergreen/orchestration $PWD/drivers-evergreen-tools/.evergreen/run-orchestration.sh.
  • Start the load balancer using: MONGODB_URI='mongodb://localhost:27017,localhost:27018/' $PWD/drivers-evergreen-tools/.evergreen/run-load-balancer.sh start.
  • Run the tests from the pymongo checkout directory using: TEST_LOADBALANCER=1 hatch run test:test-eg.

Running Encryption Tests Locally

  • Clone drivers-evergreen-tools: git clone git@github.com:mongodb-labs/drivers-evergreen-tools.git.
  • Run export DRIVERS_TOOLS=$PWD/drivers-evergreen-tools
  • Run AWS_PROFILE=<profile> hatch run encryption:setup after setting up your AWS profile with aws configure sso.
  • Run the tests with TEST_ENCRYPTION=1 hatch run test:test-eg.
  • When done, run hatch run encryption:teardown to clean up.

Re-sync Spec Tests

If you would like to re-sync the copy of the specification tests in the PyMongo repository with that which is inside the specifications repo, please use the script provided in .evergreen/resync-specs.sh.:

git clone git@github.com:mongodb/specifications.git
export MDB_SPECS=~/specifications
cd ~/mongo-python-driver/.evergreen
./resync-specs.sh -b "<regex>" spec1 spec2 ...
./resync-specs.sh -b "connection-string*" crud bson-corpus # Updates crud and bson-corpus specs while ignoring all files with the regex "connection-string*"
cd ..

The -b flag adds as a regex pattern to block files you do not wish to update in PyMongo. This is primarily helpful if you are implementing a new feature in PyMongo that has spec tests already implemented, or if you are attempting to validate new spec tests in PyMongo.

Making a Release

Follow the Python Driver Release Process Wiki.

Converting a test to async

The tools/convert_test_to_async.py script takes in an existing synchronous test file and outputs a partially-converted asynchronous version of the same name to the test/asynchronous directory. Use this generated file as a starting point for the completed conversion.

The script is used like so: python tools/convert_test_to_async.py [test_file.py]