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        "author": "KPN DE Platform",
        "author_email": "de-platform@kpn.com",
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            "Development Status :: 5 - Production/Stable",
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            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Topic :: Internet :: WWW/HTTP",
            "Topic :: Software Development :: Libraries :: Python Modules"
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        "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/timeexecution.svg\n    :target: https://pypi.org/project/timeexecution\n\n.. image:: https://img.shields.io/pypi/pyversions/timeexecution.svg\n    :target: https://pypi.org/project/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.. image:: https://img.shields.io/pypi/l/timeexecution.svg\n    :target: https://pypi.org/project/timeexecution\n\n.. image:: https://img.shields.io/badge/code%20style-black-000000.svg\n\nThis package is designed to record application metrics into specific backends.\nWith the help of Grafana_ or Kibana_ you can easily use these metrics to create meaningful monitoring dashboards.\n\n\nFeatures\n--------\n\n- Sending data to multiple backends (e.g. ElasticSearch)\n- Custom backends\n- Hooks to include additional data per metric.\n\nAvailable backends\n------------------\n\n- InfluxDB 0.8\n- Elasticsearch >=5,<7\n- Kafka\n\n\nInstallation\n------------\n\nIf you want to use it with the ``ElasticSearchBackend``:\n\n.. code-block:: bash\n\n    $ pip install timeexecution[elasticsearch]\n\nwith ``InfluxBackend``:\n\n.. code-block:: bash\n\n    $ pip install timeexecution[influxdb]\n\nwith ``KafkaBackend``:\n\n.. code-block:: bash\n\n    $ pip install timeexecution[kafka]\n\nor if you prefer to have all backends available and easily switch between them:\n\n.. code-block:: bash\n\n    $ pip install timeexecution[all]\n\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. Byt default, timeexecution will use the fully qualified name of the decorated method or function (e.g. ).\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 settings, 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    settings.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\nIt's also possible to run backend in different thread with logic behind it, to send metrics in bulk mode.\n\nFor example:\n\n.. code-block:: python\n\n    from time_execution import settings, time_execution\n    from time_execution.backends.threaded import ThreadedBackend\n\n    # Setup threaded backend which will be run on separate thread\n    threaded_backend = ThreadedBackend(\n        backend=ElasticsearchBackend,\n        backend_kwargs={\n            \"host\" : \"elasticsearch\",\n            \"index\": \"metrics\",\n        }\n    )\n\n    # there is also possibility to configure backend by import path, like:\n    threaded_backend = ThreadedBackend(\n        backend=\"time_execution.backends.kafka.KafkaBackend\",\n        #: any other configuration belongs to backend\n        backend_kwargs={\n            \"hosts\" : \"kafka\",\n            \"topic\": \"metrics\"\n        }\n    )\n\n    # Configure the time_execution decorator\n    settings.configure(backends=[threaded_backend])\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() we put metrics in queue to send it either in some configurable time later\n    # or when queue will reach configurable limit.\n    hello()\n\nIt's also possible to decorate coroutines or awaitables in Python >=3.5.\n\nFor example:\n\n.. code-block:: python\n\n    import asyncio\n    from time_execution import time_execution_async\n\n    # ... Setup the desired backend(s) as described above ...\n\n    # Wrap the methods where you want the metrics\n    @time_execution_async\n    async def hello():\n        await asyncio.sleep(1)\n        return 'World'\n\n    # Now when we schedule hello() we will get metrics in our backends\n    loop = asyncio.get_event_loop()\n    loop.run_until_complete(hello())\n\n\n.. _usage-hooks: \n\nHooks\n-----\n\n``time_execution`` supports hooks where you can change the metric before its\nbeing sent 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, 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    settings.configure(backends=[backend], hooks=[my_hook])\n\n\nThere is also possibility to create decorator with custom set of hooks. It is needed for example to track `celery` tasks.\n\n.. code-block:: python\n\n    from multiprocessing import current_process\n    # Hook for celery tasks\n    def celery_hook(response, exception, metric, func, func_args, func_kwargs):\n        \"\"\"\n        Add celery worker-specific details into response.\n        \"\"\"\n        p = current_process()\n        hook = {\n            'name': metric.get('name'),\n            'value': metric.get('value'),\n            'success': exception is None,\n            'process_name': p.name,\n            'process_pid': p.pid,\n        }\n        return hook\n\n    # Create time_execution decorator with extra hooks\n    time_execution_celery = time_execution(extra_hooks=[celery_hook])\n\n    @celery.task\n    @time_execution_celery\n    def celery_task(self, **kwargs):\n        return True\n\n    # Or do it in place where it is needed\n    @celery.task\n    @time_execution(extra_hooks=[celery_hook])\n    def celery_task(self, **kwargs):\n        return True\n\n    # Or override default hooks by custom ones. Just setup `disable_default_hooks` flag\n    @celery.task\n    @time_execution(extra_hooks=[celery_hook], disable_default_hooks=True)\n    def celery_task(self, **kwargs):\n        return True\n\n\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    from time_execution import write_metric\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\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, we recommend you 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\nExample scenario\n----------------\n\nIn order to read the metrics, e.g. using ElasticSearch as a backend, the following lucene query could be used:\n\n.. code-block::\n\n    name:\"__main__.hello\" AND hostname:dfaa4928109f\n\nFor more advanced query syntax, please have a look at the `Lucene documentation`_ and the `ElasticSearch Query DSL`_ reference.\n\n\nContribute\n----------\n\nYou have something to contribute? Great! There are a few things that may come in handy.\n\nTesting in this project is done via docker. 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