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https://github.com/kyle-verhoog/dd-trace-py-test

test ci stuff
https://github.com/kyle-verhoog/dd-trace-py-test

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test ci stuff

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README 

        
# `ddtrace`



[![CircleCI](https://circleci.com/gh/DataDog/dd-trace-py/tree/1.x.svg?style=svg)](https://circleci.com/gh/DataDog/dd-trace-py/tree/1.x)

[![PypiVersions](https://img.shields.io/pypi/v/ddtrace.svg)](https://pypi.org/project/ddtrace/)

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bits python



This repository contains `ddtrace`, Datadog's APM client Python package. `ddtrace` contains APIs to automatically or

manually [trace](https://docs.datadoghq.com/tracing/visualization/#trace) and

[profile](https://docs.datadoghq.com/tracing/profiler/) Python applications.



These features power [Distributed Tracing](https://docs.datadoghq.com/tracing/),

 [Continuous Profiling](https://docs.datadoghq.com/tracing/profiler/),

 [Error Tracking](https://docs.datadoghq.com/tracing/error_tracking/),

 [Continuous Integration Visibility](https://docs.datadoghq.com/continuous_integration/),

 [Deployment Tracking](https://docs.datadoghq.com/tracing/deployment_tracking/),

 [Code Hotspots](https://docs.datadoghq.com/tracing/profiler/connect_traces_and_profiles/) and more.



To get started, check out the [setup documentation][setup docs].



For advanced usage and configuration, check out the [API documentation][api docs].



Confused about the terminology of APM? Take a look at the [APM Glossary][visualization docs].



[setup docs]: https://docs.datadoghq.com/tracing/setup/python/

[api docs]: https://ddtrace.readthedocs.io/

[visualization docs]: https://docs.datadoghq.com/tracing/visualization/



## Development



### Contributing



See [the contributing docs](https://ddtrace.readthedocs.io/en/stable/contributing.html) first.



### 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



### Set up your environment



#### Set up docker



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][docker] and

[docker-compose][docker-compose] using the instructions provided by your platform. Then

launch them through:



    $ docker-compose up -d



[docker]: https://www.docker.com/products/docker

[docker-compose]: https://www.docker.com/products/docker-compose



#### Set up Python



1. Clone the repository locally: `git clone https://github.com/DataDog/dd-trace-py`

2. The tests for this project run on various versions of Python. We recommend

   using a Python version management tool, such as

   [pyenv](https://github.com/pyenv/pyenv), to utilize multiple versions of

   Python. [How to install Pyenv](https://github.com/pyenv/pyenv#installation) 

3. Install the relevant versions of Python in Pyenv: `pyenv install 2.7.18 3.5.10 3.6.15 3.7.13 3.8.13 3.9.13 3.10.5`

4. Make those versions available globally: `pyenv global 2.7.18 3.5.10 3.6.15 3.7.13 3.8.13 3.9.13 3.10.5`



### Testing



#### 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][tox] as well as [riot][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



[tox]: https://github.com/tox-dev/tox/

[riot]: https://github.com/DataDog/riot/



#### Running Tests locally



1. Install riot: `pip install riot`.

2. Create the base virtual environments: `riot -v generate`.

3. You can list the available test suites with `riot list`.

4. Certain tests might require running service containers in order to emulate

   the necessary testing environment. You can spin up individual containers with

   `docker-compose up -d `, where `` should match a

   service specified in the `docker-compose.yml` file.

5. Run a test suite: `riot -v run  `.

   1. Optionally, use the `-s` and `-x` flags: `-s` prevents riot from

      reinstalling the dev package; `-x` forces an exit after the first failed

      test suite. To limit the tests to a particular version of Python, use the

      `-p` flag: `riot -v run -p `.



The `run` command uses regex syntax, which in some cases will cause multiple

test suites to run. Use the following syntax to ensure only an individual suite

runs: `^$` where `^` signifies the start of a string and `$`

signifies the end of a string. For example, use `riot -v run -s -x ^redis$` to

run only the redis suite.



#### Use the APM Test Agent



The APM test agent can emulate the APM endpoints of the Datadog agent. Spin up

the `testagent` container along with any other service container:



    $ docker-compose up -d testagent 



Run the test agent as a proxy in your tests:



    $ DD_TRACE_AGENT_URL=http://localhost:9126/ riot -v run  --pass-env 



`--pass-env` injects the environment variables of the current shell session into

the command. Here's an example command for running the redis test suite along

with the test agent, limited to tests for Python 3.9:



    $ DD_TRACE_AGENT_URL=http://localhost:9126/ riot -v run -p 3.9 -s -x --pass-env '^redis$'



Read more about the APM test agent:

https://github.com/datadog/dd-apm-test-agent#readme



### Continuous Integration



We use CircleCI 2.0 for our continuous integration.



#### Configuration



The CI tests are configured through [config.yml](.circleci/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



### Release Notes



This project follows [semver](https://semver.org/) and so bug fixes, breaking

changes, new features, etc must be accompanied by a release note. To generate a

release note:



    $ riot run reno new 



Document the changes in the generated file, remove the irrelevant sections and

commit the release note with the change.