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        "author": "Philip Perold",
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            "License :: OSI Approved :: Apache Software License",
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        "description": "# Metric Builder\n\nUtility for building templated metric extraction queries that can be traversed through time.\n\n## Prerequisites\n\nYou will need the following to run this code:\n  * Python 3\n  \n## Installation\n\nTo be determined...\n\n## Usage\n\nIn order to extract a given metric, a `Metric` object needs to be instantiated:\n\n```python\nmetric = Metric(\n    query=\"\"\"\n        SELECT count(*) AS total\n        FROM `project.dataset.table`\n        WHERE DATETIME_TRUNC(created_datetime, DAY) = '{{ reference_time | format_date('%Y-%m-%d') }}'\n    \"\"\",\n    reader = BigQueryReader(json_credentials_path='/path/to/creds.json')\n)\n```\n\nThe `query` parameter is a templated query where you can format the `reference_time` `datetime` object to the required format using template filters.\n\nThe `reader` parameter is the object that is actually going to connect to the desired database and perform the queries.\n\nThe `metric` object can now be used to fetch metrics for a given point in time as follows:\n\n```python\nresult = metric.fetch(reference_time=datetime.date(2019, 10, 21))\n```\n\nThe result is returned as a list of dictionaries.\n\n### Template filters\n\n[Jinja2](https://jinja.palletsprojects.com/en/2.10.x/) is used as the templating engine. All built in Jinja filters are thus available. Relevant custom template filters have been added though for convenience:\n\n#### format_date\n\nSpecify format of datetime:\n\n```\n'{{ reference_time | format_date('%Y-%m-%d') }}'\n```\n\n#### day_delta\n\nChange a given datetime object by a specified number of days:\n\n```\n'{{ reference_time | day_delta(-7) | format_date('%Y-%m-%d') }}'\n```\n\n### Readers\n\nAny reader will implement the following method that is used to execute queries:\n\n```python\ndef execute(self, query) -> List[Dict[str, Any]]:\n    ...\n```\n\n#### BigQueryReader\n\nThe underlying client is required to be authenticated with the necessary priviledges to read from the requested BigQuery tables.\n\nIf you authenticate with:\n\n```bash\ngcloud auth login\n```\n\nor\n\n```bash\nexport GOOGLE_APPLICATION_CREDENTIALS=\"/path/to/keyfile.json\"\n```\n\nthen you can just instantiate your `Reader` like this:\n\n```python\nreader = BigQueryReader()\n```\n\nThe other option is to explicitly authenticate with a service account key file:\n\n```python\nreader = BigQueryReader(json_credentials_path='/path/to/creds.json')\n```\n\n#### HiveReader\n\nComing soon...\n\n",
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