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    "info": {
        "author": "Dmitri Vasilishin",
        "author_email": "vasilishin.d.o@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Programming Language :: Python :: Implementation :: PyPy"
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        "description": "# Elasticsearch Partition\n[![image](https://img.shields.io/pypi/v/elasticsearch-partition.svg)](https://pypi.python.org/pypi/elasticsearch-partition)\n[![Build Status](https://travis-ci.com/dmvass/elasticsearch-partition.svg?branch=master)](https://travis-ci.com/kandziu/elasticsearch-partition)\n[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/dmvass/elasticsearch-partition/blob/master/LICENSE)\n\nA Python library is written on Cython for creating Elasticsearch indexes by\ndate range.\n\nFor time oriented data, such as logs, a common strategy is to partition data\ninto indexes that hold data for a certain time range. For example, the index\n`logstash-2018.01.01` holds data for events that happened on `2018-01-01`, i.e.\na time range of a day. You can of course choose bigger or smaller time ranges\nas well(`year`, `month` or `day` frequencies), depending on your needs. Using\nindex templates, you can easily manage settings and mappings for any index\ncreated with a name starting with e.g. `logstash-*`.\n\n## Installation\nInstall the elasticsearch partition package with pip:\n```\npip install elasticsearch-partition\n```\n\n## How to Use\n### Basic usage\nHow to import and use partition module with `since` and `until` dates:\n```python\nimport datetime\nfrom elasticsearch_partition import partition\n\npartition('logs-*', datetime.date(2016, 11, 29), datetime.date(2018, 2, 4))\n# ['logs-2016-11-29', 'logs-2016-11-30', 'logs-2016-12-*', 'logs-2017-*',\n# 'logs-2018-01-*', 'logs-2018-02-01', 'logs-2018-02-02', 'logs-2018-02-03',\n# 'logs-2018-02-04']\n```\n\nWhen you are using `partition` only with `since` date, `until` will be replaced\non a current date.\n```python\npartition('logs-*', since=datetime.date(2018, 7, 10))\n# ['logs-2018-07-10', 'logs-2018-07-11', 'logs-2018-07-12', 'logs-2018-07-13',\n# 'logs-2018-07-14', 'logs-2018-07-15', 'logs-2018-07-16', 'logs-2018-07-17']\n```\n\nOr when you are using `partition` only with `until` all dates from `until` to\ncurrent date will be excluded.\n```python\npartition('logs-*', until=datetime.date(2018, 7, 10))\n# ['-logs-2018-07-10', '-logs-2018-07-11', '-logs-2018-07-12',\n# '-logs-2018-07-13', '-logs-2018-07-14', '-logs-2018-07-15',\n# '-logs-2018-07-16', '-logs-2018-07-17', 'logs-*']\n```\n\n> Note: If `until` more then current date you will get an error.\n\n### How to customize partitioning\nIf you want to change some `partition` bahavior you can do it ease with\n`RangePartition` and `formatters` module, also you can use your custom date\n`now` functions.\n```python\nfrom elasticsearch_partition import RangePartition\nfrom elasticsearch_partition.partitioning import MONTH\nfrom elasticsearch_partition.formatters import LittleEndianDateFormatter\n\n# frequency - Index partitioning frequency\n# formatter - Formatter instance\n# escape - Special character which will be replaced on a date\n# now_func - Get now date function\nmy_partition = RangePartition(\n    frequency=MONTH,\n    formatter=LittleEndianDateFormatter(sep='.'),\n    escape='@',\n    now_func=custom_date_now,\n)\n\nmy_partition('logs-@', datetime.date(2016, 11, 29), datetime.date(2018, 2, 4))\n# ['logs-11.2016', 'logs-12.2016', 'logs-*.2017', 'logs-01.2018', 'logs-02.2018']\n```\n\n### How to create custom date formatter\nAll date formatters must be inherited from abstract `DateFormatter` class and\nimplement `fmt_year`, `fmt_month` and `fmt_day` methods. Some method accept\nadditional keyword parameter `wildcard` which used for creating formatted date\nwith specified wildcard character. For example `2018-04` will be replced on\n`2018-04-*`, `2018` on `2018-*` etc.\n```python\nclass MyDateFormatter(DateFormatter):\n    def fmt_year(self, year, wildcard):\n        # Should be implemented\n\n    def fmt_month(self, year, month, wildcard):\n        # Should be implemented\n\n    def fmt_day(self, year, month, day):\n        # Should be implemented\n\npartition = RangePartition(formatter=MyDateFormatter())\n```\n\n### How to use with [elasticsearch-py](https://github.com/elastic/elasticsearch-py)\nThis is useful for all Elasticsearch APIs that refer to an index parameter\nsupport execution across multiple indices.\n```python\nfrom elasticsearch import Elasticsearch\n\nes = Elasticsearch()\nindexes = partition(\n    'logs-*',\n    datetime.date(2016, 11, 29),\n    datetime.date(2018, 2, 4)\n)\nres = es.search(index=indexes, body={\"query\": {\"match_all\": {}}})\n```\n\n### How to use with [elasticsearch-dsl-py](https://github.com/elastic/elasticsearch-dsl-py)\nThis is useful for all Elasticsearch APIs that refer to an index parameter\nsupport execution across multiple indices and similar for simple Search and\nPersistance DSL.\n```python\nfrom elasticsearch import Elasticsearch\nfrom elasticsearch_dsl import Search\n\nclient = Elasticsearch()\n\nindexes = partition(\n    'logs-*',\n    datetime.date(2016, 11, 29),\n    datetime.date(2018, 2, 4)\n)\nsearch = Search(using=client, index=indexes) \\\n    .filter(\"term\", category=\"search\") \\\n    .query(\"match\", title=\"python\") \\\n    .exclude(\"match\", description=\"beta\")\n\nresponse = search.execute()\n```\n\n## Changes\nA full changelog is maintained in the [CAHNGELOG](https://github.com/dmvass/elasticsearch-partition/blob/master/CHANGELOG.md) file.\n\n## Contributing \n**elasticsearch-partition** is an open source project and contributions are\nwelcome! Check out the [Issues](https://github.com/dmvass/elasticsearch-partition/issues)\npage to see if your idea for a contribution has already been mentioned, and feel\nfree to raise an issue or submit a pull request.\n\n\n",
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