Contributing#

Contributions are welcome!

The best way to suggest a change to the library is to open a pull request.

You may also consider opening an issue if you’d like to report a bug or request a new feature.

Before working on the library, install docker.

If you’re trying to set up a local development environment, read this.

Library design documentation for contributors.

Thanks for working with us!

Change Process#

The process of making a change to the library starts with a pull request. When you open one, reviewers are automatically assigned based on the CODEOWNERS file. Many different continuous integration jobs are also triggered, including unit tests, integration tests, benchmarks, and linters.

Marking your pull request as a draft using the “Convert to draft” link communicates to potential reviewers that your pull request is not ready to be reviewed. Use the “ready for review” button when this changes. It’s often beneficial to open an in-progress pull request and mark it as a draft while you confirm that the test suite passes.

Within a few business days, one of the maintainers will respond with a code review. The review will primarily focus on idiomatic Python usage, efficiency, testing, and adherence to the versioning policy. Correctness and code style are automatically checked in continuous integration, with style linting managed by various tools including Flake8, Black, and MyPy. This means that code reviews don’t need to worry about style and can focus on substance.

Branches and Pull Requests#

This library follows the practice of trunk-based development.

The “trunk” branch, which new pull requests should target, is 1.x. Roughly every two weeks, we checkpoint the current state of this branch as a new release branch, whose naming follows semantic versioning. You can find the list of past released versions on this GitHub page.

Pull requests are named according to the conventional commit standard, which is enforced by a continuous integration job. The standardized “scopes” we use in pull request names are enumerated in the release notes documentation.

Pull requests that change the library’s public API require a release note. If your pull request doesn’t change the public API, apply the no-changelog label.

Once approved, pull requests should be merged with the “Squash and Merge” option. At this time, do not use the merge queue option.

Backporting#

Each minor version has its own branch.

  • Fix PRs are backported to all maintained release branches.

  • CI PRs are backported to the maintained release branches.

  • New features (feat PRs) are not backported.

  • Chore, documentation, and other PRs are not backported.

If your pull request is a fix or ci change, apply the backport labels corresponding to the minor versions that need the change.

Tests#

If your change touches Python code, it should probably include at least one test. See the testing guidelines for details.

Releases#

If you’re managing a new release of the library, follow the instructions here.

Documentation#

Pull requests implementing new features should include documentation for those features. The audience for this documentation is the public population of library users. The Products and Core logic are documented alongside this document, in the docs directory. The documentation for each Integration is contained in a docstring in that integration’s __init__.py file. See this page for more information on writing documentation for Integrations.

Logging#

Use ddtrace.internal.logger.get_logger(__name__) to initialize/retrieve a DDLogger object you can use to emit well-formatted log messages from your code.

Keep the following in mind when writing logging code:

  • Logs should be generated with the level DEBUG, INFO, WARNING, or ERROR according to conventions like these.

  • Log messages should be grammatically correct and should not contain spelling errors.

  • Log messages should be standalone and actionable. They should not require context from other logs, metrics or trace data.

  • Log data is sensitive and should not contain application secrets or other sensitive data.

Instrumentation Telemetry#

When you implement a new feature in ddtrace, you should usually have the library emit some Instrumentation Telemetry about the feature. Instrumentation Telemetry provides data about the operation of the library in real production environments and is often used to understand rates of product adoption.

Instrumentation Telemetry conforms to an internal Datadog API. You can find the API specification in the private Confluence space. To send Instrumentation Telemetry data to this API from ddtrace, you can use the ddtrace.internal.telemetry.telemetry_writer object that provides a Python interface to the API.

The most common telemetry_writer call you may use is add_count_metric. This call generates timeseries metrics that you can use to, for example, count the number of times a given feature is used. Another useful call is add_integration, which generates telemetry data about the integration with a particular module.

Read the docstrings in ddtrace/internal/telemetry/writer.py for more comprehensive usage information about Instrumentation Telemetry.