Datadog Tracing Python Client
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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 visiblity 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.




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 the Tests in your local environment

Once docker is up and running you should be able to run the tests. To launch a single test manually. For example to run the tests for redis-py 2.10 on Python 3.5 and 3.6:

$ tox -e '{py35,py36}-redis{210}'

To see the defined test commands see tox.ini.

To launch the complete test matrix run:

$ tox

Running Tests in docker

If you prefer not to setup your local machine to run tests, we provide a preconfigured docker image. Note that this image is the same used in CircleCI to run tests.

You still need docker containers running additional services up and running.

Run the test runner

$ docker-compose run --rm testrunner

Now you are in a bash shell. You can now run tests as you would do in your local environment:

$ tox -e '{py35,py36}-redis{210}'

We also provide a shell script to execute commands in the provided container.

For example to run the tests for redis-py 2.10 on Python 3.5 and 3.6:

$ ./scripts/ddtest tox -e '{py35,py36}-redis{210}'

Continuous Integration

We use CircleCI 2.0 for our continuous integration.


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

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

$ circleci build --job django


When two or more approaches must be compared, please write a benchmark in the module so that we can measure the efficiency of the algorithm. To run your benchmark, just:

$ python -m tests.benchmark