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        "author": "Florian Bruggisser",
        "author_email": "florian@nexpose.ch",
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        "description": "pg4nosql\n========\n\n|Build Status| |Code Climate| |Latest Version| |Development Status|\n|Python Versions| |License|\n\nA simple psycopg2 based wrapper for nosql like database interaction with\npython.\n\nWhy another wrapper?\n--------------------\n\nThe wrapper was developed to work with JSON postgres storage like a real\nNoSQL DB (e.g. MongoDB). After a long research with google there was no\nlibrary found which helps to work with JSON and PostgreSQL so I decided\nto develop one.\n\nThe strength of the wrapper is that you still can have multiple\nrelational colums in your table.\n\nInstallation\n------------\n\nUsing `Python Package Index <https://pypi.python.org/pypi/pg4nosql>`__ (PIP)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nJust run the command: ``pip install pg4nosql``\n\n*During alpha stage the api will change with each build. So try to stay\nwith one version if you want to use it.*\n\nThe hacky way\n~~~~~~~~~~~~~\n\n1. `download <https://github.com/cansik/pg4nosql/tarball/0.4.2>`__ or\n   clone this repository\n2. run the command ``python setup.py install``\n\nChangelog\n---------\n\n-  Version ``0.4.2``\n-  fixed python 3.3 iterator return\n-  Version ``0.4.1``\n-  Query from view\n-  fixed some bugs\n-  Version ``0.4.0``\n-  adds join query statements\n-  adds update method\n-  adds save method on result item\n-  Version ``0.3.7``\n-  adds the support for non-json database tables\n-  Version ``0.3.6``\n-  id datatype can be set on table creation\n-  Version ``0.3.3``\n-  project cleanup\n-  Version ``0.3.1``\n-  auto-commit for very operation as default\n-  save does not affect saving object anymore\n-  bug fixes\n-  Version ``0.2.4``\n-  a lot of bug fixes\n-  Version ``0.2.0``\n-  added port argument\n-  replaced dictionary argument with \\*\\*keyword syntax\n-  switched result.relational with result.json\n-  add bracket syntax to get database or table\n-  renamed table.get\\_or\\_create to match codestyle\n\nExample\n-------\n\nThese examples show the funcionality of the wrapper. There are some\nfunctions which are not covered by the examples (like removing of a\ntable) but the importent ones are explained.\n\nDataschema Creation\n~~~~~~~~~~~~~~~~~~~\n\nTo create the dataschema you can use normal database tools if you want.\nA document table has two fields:\n\n-  ``id`` with data type **serial**\n-  ``json`` with data type **JSON** which represents the document\n\nBut pg4nosql also provides methods to create your database schema on the\nfly. This is useful to create tables and databases software controlled.\n\nThis example shows how to create a database and their tables. The cities\ntable is special because it also contains relational data like a normal\ntable would:\n\n.. code:: python\n\n    # create pg4nosql client\n    pg4nosql = PostgresNoSQLClient(host='localhost')\n\n    # create demo database\n    demo_db = pg4nosql['demo']\n\n    # create document only table\n    users = demo_db['users']\n\n    # create document & relational table\n    cities = demo_db.get_or_create_table('cities', size='real NOT NULL')\n\nRow Identifier Type\n^^^^^^^^^^^^^^^^^^^\n\nBy default the ``id`` row type is ``SERIAL`` but in some cases it is\nnecessary to define the type yourself. This is possible with the\n``row_identifier_type`` argument.\n\n.. code:: python\n\n    # create document table with bigserial\n    big_users = demo_db.create_table('big_users',\n                                      row_identifier_type='BIGSERIAL')\n\nInsert Data\n~~~~~~~~~~~\n\nTo insert data into the table you just hand over a dictionary or an\nobject which is json serializable. If there are relational columns\ndefined you can set those by the table name as keyword and the value:\n\n.. code:: python\n\n    # store data into users table\n    users.put({'name': 'Florian', 'age': 24})\n    users.put({'name': 'Markus', 'age': 24})\n    users.put({'name': 'Sara', 'age': 22})\n    users.put({'name': 'Thomas', 'age': 25})\n\n    # store data into cities table\n    cities.put({'name': 'Zurich'}, size=87.88)\n    cities.put({'name': 'Berlin'}, size=891.8)\n    cities.put({'name': 'Bern'}, size=51.6)\n    cities.put({'name': 'London'}, size=1572)\n\nIf you work **without json documents**, there is just a normal\n``insert`` method to store new records into a table.\n\n.. code:: python\n\n    # store data into cities table\n    users.insert(age=25, name=\"Florian\")\n\nLazy Commit\n^^^^^^^^^^^\n\nIf you want to store or save multiple entries you can set the\n``auto_commit`` argument to ``False`` and commit it yourself.\n\n.. code:: python\n\n    # store data with lazy commit\n    for i in range(0, 255):\n        users.put({'name': 'Test', 'age': i}, auto_commit=False)\n\n    # lazy commit data\n    users.commit()\n\nQuery Data\n~~~~~~~~~~\n\nTo get your data back you can run a query over it. This works like\nnormal SQL WHERE queries. For **JSON** data you have to use the ``json``\ncolumn:\n\n.. code:: python\n\n    # query all users which are 24 years old\n    users_24 = users.query(\"json->>'age'='24'\")\n\nAnd here the result of the user query:\n\n.. code:: json\n\n    [  \n       \"{'json': {u'age': 24, u'name': u'Florian'}, 'id': 1}\",\n       \"{'json': {u'age': 24, u'name': u'Markus'}, 'id': 2}\"\n    ]\n\nYou can also combine relational and JSON queries together like this:\n\n.. code:: python\n\n    # query all cities which start with be and are bigger than 100 km\n    big_ber_cities = cities.query(\"json->>'name' LIKE 'Ber%'\"\n                                  \"AND size > 100\")\n\nHere the result of this query:\n\n.. code:: json\n\n    [  \n       \"{'json': {u'name': u'Berlin'}, 'id': 2, 'size': 891.8}\"\n    ]\n\nQuery with Join\n~~~~~~~~~~~~~~~\n\nIt is also possible to create simple *joined* queries with the function\n``query_join``. Consider a datamodel with an ``user`` table and an\n``address`` table. This two tables are connected through a *foreign key*\n``fk_user``.\n\nThe called table get's the identifier ``a`` and the joined table the\nidentifier ``b``.\n\n.. code:: python\n\n    # get all users with their address\n    users = user.query_join('address', 'a.id = b.fk_user')\n\nQuery from View\n~~~~~~~~~~~~~~~\n\nTo query data from a view you have to get the view from the database and\nthen it has the same query method like a table.\n\n.. code:: python\n\n    # get view\n    self.user_view = self.database.get_view('user_view')\n\n    # query data\n    records = self.user_view.query()\n\nQuery Data Access\n~~~~~~~~~~~~~~~~~\n\nTo **access** the **JSON** fields of the result there is an attribute\ncalled ``json``:\n\n.. code:: python\n\n    # get first city of the result array\n    first_city = big_ber_cities[0]\n\n    # read JSON attribute\n    city_name = first_city.json['name']\n\nTo **access** the **relational** fields of the result you have to use\n**square brackets** (``[]``) on the result:\n\n.. code:: python\n\n    # read relational attribute\n    city_size = first_city['size']\n\nThere is also a default field called ``id`` which contains the default\nrow identifier for easy access:\n\n.. code:: python\n\n    # get id of row\n    city_id = first_city.id\n\nUpdate Data\n~~~~~~~~~~~\n\nWith those access methods you can also write into the result and change\nthe values of the fields. To save it just call ``save(obj)`` on the\ntable object.\n\n.. code:: python\n\n    # change florian's age\n    florian = users_24[0]\n    florian.json['age'] = 25\n\n    users.save(florian)\n\nThe same works also with the ``relational`` fields:\n\n.. code:: python\n\n    # make zurich a bit bigger\n    zurich = cities.query_one(\"data->>'name'='Zurich'\")\n    zurich['size'] = 90\n\n    cities.save(zurich)\n\nWith the release ``0.4.0`` it is also possible to save the database\nobject directly:\n\n.. code:: python\n\n    # make zurich a bit bigger\n    zurich = cities.query_one(\"data->>'name'='Zurich'\")\n    zurich['size'] = 90\n\n    zurich.save()\n\n**Without json documents**, there is just a normal ``update`` method to\nupdate new records into a table.\n\n.. code:: python\n\n    # store data into cities table\n    florian = users_24[0]\n    florian['name'] = 'Markus'\n    users.update(florian)\n\nDirect Execution\n~~~~~~~~~~~~~~~~\n\nIt is also possible to directly execute sql statements as you are used\nto. The execute function is declared on the database object and on the\ntable object.\n\n.. code:: python\n\n    # run simple sql query\n    my_data = demo_db.execute('SELECT * FROM cities')\n\nClose Connection\n~~~~~~~~~~~~~~~~\n\nFinally don't forget to close the connection to the database.\n\n.. code:: python\n\n    # close db\n    demo_db.close()\n\nAbout\n-----\n\nThe wrapper has been written for a science project and is still an early\nbeta version! Idea and implementation by Florian (cansik)\n\nMIT License Copyright (c) 2015\n\n.. |Build Status| image:: https://travis-ci.org/cansik/pg4nosql.svg\n   :target: https://travis-ci.org/cansik/pg4nosql\n.. |Code Climate| image:: https://codeclimate.com/github/cansik/pg4nosql/badges/gpa.svg\n   :target: https://codeclimate.com/github/cansik/pg4nosql\n.. |Latest Version| image:: https://img.shields.io/pypi/v/pg4nosql.svg\n   :target: https://pypi.python.org/pypi/pg4nosql\n.. |Development Status| image:: https://img.shields.io/pypi/status/pg4nosql.svg\n   :target: https://pypi.python.org/pypi/pg4nosql\n.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/pg4nosql.svg\n   :target: https://pypi.python.org/pypi/pg4nosql\n.. |License| image:: https://img.shields.io/pypi/l/pg4nosql.svg\n   :target: https://github.com/cansik/pg4nosql/blob/master/LICENSE.txt",
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