dd-trace-py

ddtrace is Datadog's tracing library for Python. It is used to trace requests as they flow across web servers, databases and microservices so that developers have great visibility into bottlenecks and troublesome requests.

Getting Started

For a basic product overview, installation and quick start, check out our setup documentation.

For more advanced usage and configuration, check out our API documentation.

For descriptions of terminology used in APM, take a look at the official documentation.

Development

Contributing

See docs/contributing.rst.

Pre-commit Hooks

The tracer library uses formatting/linting tools including black, flake8, and mypy. While these are run in each CI pipeline for pull requests, they are automated to run when you call git commit as pre-commit hooks to catch any formatting errors before you commit. To initialize the pre-commit hook script to run in your development branch, run the following command:

$ hooks/autohook.sh install

Testing

Environment

The test suite requires many backing services such as PostgreSQL, MySQL, Redis and more. We use docker and docker-compose to run the services in our CI and for development. To run the test matrix, please install docker and docker-compose using the instructions provided by your platform. Then launch them through:

$ docker-compose up -d

Running Tests in docker

Once your docker-compose environment is running, you can use the shell script to execute tests within a Docker image. You can start the container with a bash shell:

$ scripts/ddtest

You can now run tests as you would do in your local environment. We use tox as well as riot, a new tool that we developed for addressing our specific needs with an ever growing matrix of tests. You can list the tests managed by each:

$ tox -l
$ riot list

You can run multiple tests by using regular expressions:

$ scripts/run-tox-scenario '^futures_contrib-'
$ riot run psycopg

Continuous Integration

We use CircleCI 2.0 for our continuous integration.

Configuration

The CI tests are configured through config.yml.

Running Locally

The CI tests can be run locally using the circleci CLI. More information about the CLI can be found at https://circleci.com/docs/2.0/local-cli/.

After installing the circleci CLI, you can run jobs by name. For example:

$ circleci build --job django