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    "info": {
        "author": "Niels Lensink",
        "author_email": "niels@elements.nl",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Environment :: Web Environment",
            "Intended Audience :: Developers",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Topic :: Internet :: WWW/HTTP"
        ],
        "description": "Time Execution\n==============\n\n.. image:: https://secure.travis-ci.org/kpn-digital/py-timeexecution.svg?branch=master\n    :target:  http://travis-ci.org/kpn-digital/py-timeexecution?branch=master\n\n.. image:: https://img.shields.io/codecov/c/github/kpn-digital/py-timeexecution/master.svg\n    :target: http://codecov.io/github/kpn-digital/py-timeexecution?branch=master\n\n.. image:: https://img.shields.io/pypi/v/py-timeexecution.svg\n    :target: https://pypi.python.org/pypi/py-timeexecution\n\n.. image:: https://readthedocs.org/projects/py-timeexecution/badge/?version=latest\n    :target: http://py-timeexecution.readthedocs.org/en/latest/?badge=latest\n\n\nThis package is designed to record metrics of the application into a backend.\nWith the help of grafana_ you can easily create dashboards with them\n\n\nFeatures\n--------\n\n- Sending data to multiple backends\n- Custom backends\n- Hooks\n\nBackends\n--------\n\n- InfluxDB 0.8\n- Elasticsearch 2.1\n\n\nInstallation\n------------\n\n.. code-block:: bash\n\n    $ pip install py-timeexecution\n\nUsage\n-----\n\nTo use this package you decorate the functions you want to time its execution.\nEvery wrapped function will create a metric consisting of 3 default values:\n\n- `name` - The name of the series the metric will be stored in\n- `value` - The time it took in ms for the wrapped function to complete\n- `hostname` - The hostname of the machine the code is running on\n\nSee the following example\n\n.. code-block:: python\n\n    from time_execution import configure, time_execution\n    from time_execution.backends.influxdb import InfluxBackend\n    from time_execution.backends.elasticsearch import ElasticsearchBackend\n\n    # Setup the desired backend\n    influx = InfluxBackend(host='influx', database='metrics', use_udp=False)\n    elasticsearch = ElasticsearchBackend('elasticsearch', index='metrics')\n\n    # Configure the time_execution decorator\n    configure(backends=[influx, elasticsearch])\n\n    # Wrap the methods where u want the metrics\n    @time_execution\n    def hello():\n        return 'World'\n\n    # Now when we call hello() and we will get metrics in our backends\n    hello()\n\nThis will result in an entry in the influxdb\n\n.. code-block:: json\n\n    [\n        {\n            \"name\": \"__main__.hello\",\n            \"columns\": [\n                \"time\",\n                \"sequence_number\",\n                \"value\",\n                \"hostname\",\n            ],\n            \"points\": [\n                [\n                    1449739813939,\n                    1111950001,\n                    312,\n                    \"machine.name\",\n                ]\n            ]\n        }\n    ]\n\nAnd the following in Elasticsearch\n\n.. code-block:: json\n\n    [\n        {\n            \"_index\": \"metrics-2016.01.28\",\n            \"_type\": \"metric\",\n            \"_id\": \"AVKIp9DpnPWamvqEzFB3\",\n            \"_score\": null,\n            \"_source\": {\n                \"timestamp\": \"2016-01-28T14:34:05.416968\",\n                \"hostname\": \"dfaa4928109f\",\n                \"name\": \"__main__.hello\",\n                \"value\": 312\n            },\n            \"sort\": [\n                1453991645416\n            ]\n        }\n    ]\n\n\nHooks\n-----\n\n`time_execution` supports hooks where you can change the metric before its\nbeing send to the backend.\n\nWith a hook you can add additional and change existing fields. This can be\nuseful for cases where you would like to add a column to the metric based on\nthe response of the wrapped function.\n\nA hook will always get 3 arguments:\n\n- `response` - The returned value of the wrapped function\n- `exception` - The raised exception of the wrapped function\n- `metric` - A dict containing the data to be send to the backend\n- `func_args` - Original args received by the wrapped function.\n- `func_kwargs` - Original kwargs received by the wrapped function.\n\nFrom within a hook you can change the `name` if you want the metrics to be split\ninto multiple series.\n\nSee the following example how to setup hooks.\n\n.. code-block:: python\n\n    # Now lets create a hook\n    def my_hook(response, exception, metric, func_args, func_kwargs):\n        status_code = getattr(response, 'status_code', None)\n        if status_code:\n            return dict(\n                name='{}.{}'.format(metric['name'], status_code),\n                extra_field='foo bar'\n            )\n\n    # Configure the time_execution decorator, but now with hooks\n    configure(backends=[backend], hooks=[my_hook])\n\nManually sending metrics\n------------------------\n\nYou can also send any metric you have manually to the backend. These will not\nadd the default values and will not hit the hooks.\n\nSee the following example.\n\n.. code-block:: python\n\n    loadavg = os.getloadavg()\n    write_metric('cpu.load.1m', value=loadavg[0])\n    write_metric('cpu.load.5m', value=loadavg[1])\n    write_metric('cpu.load.15m', value=loadavg[2])\n\n.. _grafana: http://grafana.org/\n\n\nCustom Backend\n--------------\n\nWriting a custom backend is very simple, all you need to do is create a class\nwith a `write` method. It is not required to extend `BaseMetricsBackend`\nbut in order to easily upgrade I recommend u do.\n\n.. code-block:: python\n\n    from time_execution.backends.base import BaseMetricsBackend\n\n\n    class MetricsPrinter(BaseMetricsBackend):\n        def write(self, name, **data):\n            print(name, data)\n\n\nContribute\n----------\n\nYou have something to contribute ? Great !\nA few things that may come in handy.\n\nTesting in this project is done via docker. There is a docker-compose to easily\nget all the required containers up and running.\n\nThere is a Makefile with a few targets that we use often:\n\n- `make test`\n- `make isort`\n- `make lint`\n- `make build`\n- `make setup.py`\n\nAll of these make targets can be prefixed by `docker/`. This will execute\nthe target inside the docker container instead of on your local machine.\nFor example `make docker/build`.",
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