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The middleware reports the following metrics to Datadog:\n\n* Request duration in milliseconds\n* Status codes (200, 404, 503 etc)\n* Status ranges (2xx, 4xx, 5xx, etc)\n* Response content length\n* Request path\n\nAdding the middleware to your Django project and configuring the Datadog dashboard is quick and easy: just include ``mbq.metrics.contrib.django.middleware.timing.TimingMiddleware`` in the ``MIDDLEWARE`` constant in your ``settings.py`` file.\n\nTada!\n\nHTTP Metrics with WSGI Middleware (Flask)\n-----------------------------------------\n\nMiddleware is also included that can be easily plugged into a Flask app to generate the same sort of metrics referenced in the Django section above. Requires mbq.metrics >= 0.3.0.\n\nSee `the implementation in Isengard <https://github.com/managedbyq/isengard/pull/51/files>`_ for an example.\n\nTesting\n-------\n\nTests are automatically in `Travis CI <https://travis-ci.org/managedbyq/mbq.metrics>`_ but you can also run tests locally using `docker-compose`.\nWe now use `tox` for local testing across multiple python environments. 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Bump the version in `setup.py`\n2. Go to `Releases` in GitHub and \"Draft a New Release\"\n3. After creating a new release, Travis CI will pick up the new release and ship it to PyPi\n\nFAQs\n----\n\n**Where do I put the DogStatsd agent configuration?**\n\nYou don't! ``mbq.metrics`` is pre-baked with assumptions about how Q runs it's services. Specifically, we assume that each service runs in a Docker container and that that container is running on a VM that's running the DogStatsD agent. In that way we can automatically configure our client to reach outside of the container and easily push metrics to the agent.\nRead more in the `datadogpy documentation <http://datadogpy.readthedocs.io/en/latest/index.html#datadog.initialize>`_ or `in the source <https://github.com/DataDog/datadogpy/blob/fd6646a6e8cde1d7a8c2f6e324d04e8d7f8a6f8c/datadog/dogstatsd/route.py#L15>`_.\n\nAPI Reference\n-------------\n\n\nContributing\n------------",
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