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 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.
Development
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 run the test runner image:
$ 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}'
If you want to run a list of tox environment (as CircleCI does) based on a pattern, you can use the following command:
$ scripts/ddtest scripts/run-tox-scenario '^futures_contrib-'
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
Benchmarking
When two or more approaches must be compared, please write a benchmark in the benchmark.py module so that we can measure the efficiency of the algorithm. To run your benchmark, just:
$ python -m tests.benchmark