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        "description": "fiqs\n====\n\n[![Build Status](https://travis-ci.org/pmourlanne/fiqs.svg?branch=master)](https://travis-ci.org/pmourlanne/fiqs)\n\nfiqs is an opinionated high-level library whose goal is to help you write concise queries\nagains Elasticsearch and better consume the results. It is built on top of the awesome [Elasticsearch\nDSL](<https://github.com/elastic/elasticsearch-dsl-py>) library.\n\nfiqs exposes a ``flatten_result`` function which transforms an elasticsearch-dsl ``Result``, or a dictionary, into the list of its nodes.\nfiqs also lets you create Model classes, a la Django, which automatically generates an Elasticsearch mapping.\nFinally fiqs exposes a ``FQuery`` objects which, leveraging your models, lets you write less verbose queries against Elasticsearch.\n\n\nCompatibility\n-------------\n\nfiqs is compatible with Elasticsearch 5.X and works with both Python 2.7 and Python 3.3\n\n\nDocumentation\n-------------\n\nDocumentation is available at https://fiqs.readthedocs.io/\n\n\nCode example\n------------\n\nYou define a model, matching what is in your Elasticsearch cluster:\n\n```python\n    from fiqs import models\n\n    class Sale(models.Model):\n        index = 'sale_data'\n        doc_type = 'sale'\n\n        id = fields.IntegerField()\n        shop_id = fields.IntegerField()\n        client_id = fields.KeywordField()\n\n        timestamp = fields.DateField()\n        price = fields.IntegerField()\n        payment_type = fields.KeywordField(choices=['wire_transfer', 'cash', 'store_credit'])\n```\n\n\nYou can then write clean queries:\n\n```python\n    from elasticsearch_dsl import Search\n    from fiqs.aggregations import Sum\n    from fiqs.query import FQuery\n\n    from .models import Sale\n\n    search = Search(...)\n    metric = FQuery(search).values(\n        total_sales=Sum(Sale.price),\n    ).group_by(\n        Sale.shop_id,\n        Sale.client_id,\n    )\n    result = metric.eval()\n```\n\n\nAnd let fiqs organise the results:\n\n```python\n    print(result)\n    # [\n    #     {\n    #         \"shop_id\": 1,\n    #         \"client_id\": 1,\n    #         \"doc_count\": 30,\n    #         \"total_sales\": 12345.0,\n    #     },\n    #     {\n    #         \"shop_id\": 2,\n    #         \"client_id\": 1,\n    #         \"doc_count\": 20,\n    #         \"total_sales\": 23456.0,\n    #     },\n    #     {\n    #         \"shop_id\": 3,\n    #         \"client_id\": 1,\n    #         \"doc_count\": 10,\n    #         \"total_sales\": 34567.0,\n    #     },\n    #     [...]\n    # ]\n```\n\n\nContributing\n------------\n\nThe fiqs project is hosted on [Github](<https://github.com/pmourlanne/fiqs>)\n\nTo run the tests on your machine use this command: ``python setup.py test`` Some tests are used to generate results output from Elasticsearch. To run them you will need to run a docker container on your machine: ``docker run -d -p 8200:9200 -p 8300:9300 elasticsearch:5.x.x`` and then run ``py.test -k docker``.\n\n\nLicense\n-------\n\nSee attached LICENSE file.",
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