{
    "info": {
        "author": "Kazuki Otsuka",
        "author_email": "otsuka.kazuki@googlemail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python :: 3.7",
            "Topic :: Database :: Front-Ends",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "![MongoBase Logo](https://github.com/kazukiotsuka/mongobase/blob/master/docs/source/img/mongobase.png)\n\n\nMongoBase is a Python package that provides high-level features:\n- Lightweight OR Mapper (ORM) for MongoDB\n- Simple DataBase Model structure definition with automatic type checking\n- High-level automatic text search indexes generation from multiple keys\n\n### Dependencies\n- pymongo_ 3.7+\n\nMore About MongoBase\n-------------------------\n\n| Component | Description |\n| ---- | --- |\n|**mongobase** | an high-level interface with model definition system from ModelBase and many database operations|\n|**modelbase** | an OR Mapper class with automatic type checking according to the defined structure (MongoBase subclass)|\n\nPhilosophies on MongoBase are\n- enable to use MongoDB on python easily and programatically safely\n- cleary viewable everything about the data model just by a quick looking over the model definition\n- easy to learn how to use. for instance, method names correspond to MongoDB to be able to use them as if on the mongoclient.\n- high performance. it uses the latest connection pool mechanism so that efficiently use client objects\n\n\n\n\nBasic Interfaces\n---------------------------\n\n#### Model Definition\n\nHere is the sample definition of a model.\n\n```python\nclass Bird(MongoBase):\n    __collection__ = 'birds'\n    __structure__ = {\n        '_id': ObjectId,\n        'name': str,\n        'age': int,\n        'is_able_to_fly': bool,\n        'created': dt.datetime,\n        'updated': dt.datetime\n    }\n    __required_fields__ = ['_id', 'name']\n    __default_values__ = {\n        '_id': ObjectId(),\n        'is_able_to_fly': False,\n        'created': dt.datetime.now(dt.timezone.utc),\n        'updated': dt.datetime.now(dt.timezone.utc)\n    }\n    __validators__ = {\n        'name': validate_length(0, 1000),\n    }\n    __search_text_keys__ = ['name'] \n    __search_text_index_type__ = 'bigram'\n    __indexes__ = [\n        [('item_name', ASCENDING),],\n    ]\n```\n\nThe core model structure is defined as `__structure__` by a dictionaly. It is possible to cleary find out how the document structure is.\nOther components of the model definition is:\n\n| Component | Description |\n| ---- | --- |\n| `__collection__`| the collection name of the document. (required)|\n| `__structure__`| the core definition of the model. the type is automatically checked everytime when it is written on the db. the key `_id` is required. (required)|\n| `__required_fields__`| required properties. (optional)|\n| `__default_values__`| set default values for properties. (optional)|\n| `__validators__`| validator methods automatically check the value when the document is written on the db. (optional)|\n| `__search_text_keys__`| multiple keys can be set for the search text index. automatically written as the `search_text` property. (optional)|\n| `__search_text_index_type__`| `bigram`: value of `search_text` is set as bigram strings. `morpheme`: the string in `search_text` is parsed to morphemes (optional)|\n| `__search_text_weight_type__`| `uniform`: each string has the same weight. `weighted`: enable to set weights as `[('key1', 3), ('key2', 1)]` (optional)|\n| `__indexes__`| indexes can be set. `.createIndex()` method creates the indexes on the db. (optional)|\n\n\nNow the basic usages are introduced.\n\n#### Insert & Update\n```python\n>> chicken = Bird({'_id': ObjectId(), 'name': 'chicken', 'age': 3})\n>> chicken.save()\n{'_id': ObjectId('5c80f4fa16fa0d6c102cd2a6'),\n 'name': 'chicken',\n 'age': 3,\n 'is_able_to_fly': False,\n 'created': datetime.datetime(2019, 3, 7, 10, 39, 54, 643685, tzinfo=datetime.timezone.utc),\n 'updated': datetime.datetime(2019, 3, 7, 10, 39, 54, 643690, tzinfo=datetime.timezone.utc)}\n>> chicken.is_able_to_fly = True\n>> chicken.update()\n```\n\n#### Find\n```python\n>>> Bird.findOne({'name': 'mother chicken'})\n{'_id': ObjectId('5c79166716fa0d215968d3ba'),\n 'name': 'mother chicken',\n 'age': 63,\n 'is_able_to_fly': False,\n 'created': datetime.datetime(2019, 3, 1, 11, 20, 21, 306000),\n 'updated': datetime.datetime(2019, 3, 1, 11, 20, 21, 306000)}\n>>> mother_chicken.remove()\n1\n>>> all_chickens = Bird.find({'name': 'chicken'}, sort=[('_id', ASCENDING)])\nlist of mongobase instances are returned.\n>>> len(all_chickens)\n18\n>>> Bird.count()\n201\n```\n\n#### Bulk Operations\n\n- bulk_insert\n```python\n>>> many_pigeon = []\n>>> for i in range(10000):\n>>>     many_pigeon += [Bird({'_id': ObjectId(), 'name': f'pigeon', 'age': i})]\n>>> Bird.bulk_insert(many_pigeon)\n10000\n```\n\n- bulk_update\n```\n>>> updates = []\n>>> for pigeon in many_pigeon:\n>>>    pigeon.age *= 3\n>>>    updates += [pigeon]\n>>> Bird.bulk_update(updates)\n10000\n```\n\n\n\n\n#### Contextual Database\n\n```python\nwith db_context(db_uri='localhost', db_name='test') as db:\n    flamingo = Bird({'_id': ObjectId(), 'name': 'flamingo', 'age': 20})\n    flamingo.save(db=db)\n\n    flamingo.age = 23\n    flamingo = flamingo.update(db=db)\n    flamingo = Bird.findAndUpdateById(flamingo._id, {'age': 24}, db=db)\n\n    n_flamingo = Bird.count({'name': 'flamingo'}, db=db)\n\nBird.count({'name': 'flamingo'})\n\n```\n\n#### Multi Processing\n```python\ndef breed(tasks):\n    db = Bird._db()  # create a MongoDB Client for the forked process\n    for i in range(len(tasks)):\n        sparrow = Bird({'_id': ObjectId(), 'name': f'sparrow', 'age': 0})\n        sparrow.save(db=db)\n\ntasks = [[f'task {i}' for i in range(N_BATCH)] for j in range(N_PROCESS)\nprocess_pool = multiprocessing.Pool(N_PROCESS)\nprocess_pool.map(breed, tasks)\n```\n\n\n#### MongoBase has Many Other Features\nIf you'd like to know other features, please check the file mongobase.py.\n\nDB Settings\n---------------------------\nsimply write to mongobase/config.py\n```python\nMONGO_DB_URI = \"101.21.434.121\"\nMONGO_DB_URI_TEST = \"localhost\"\nMONGO_DB_NAME = \"zoo\"\nMONGO_DB_NAME_TEST = \"zoo-test\"\nMONGO_DB_CONNECT_TIMEOUT_MS = 3000\nMONGO_DB_SERVER_SELECTION_TIMEOUT_MS = 3000\nMONGO_DB_SOCKET_TIMEOUT_MS = 300000\nMONGO_DB_SOCKET_KEEP_ALIVE = True\nMONGO_DB_MAX_IDLE_TIME_MS = 40000\nMONGO_DB_MAX_POOL_SIZE = 200\nMONGO_DB_MIN_POOL_SIZE = 10\nMONGO_DB_WAIT_QUEUE_MULTIPLE = 12\nMONGO_DB_WAIT_QUEUE_TIMEOUT_MS = 100\n```\n\n\nGetting Started\n------------------\nIf you start MongoBase, there is a tutorial jupyter notebook here.  \nHighly recommend to check it.\nhttps://github.com/kazukiotsuka/mongobase/blob/master/tutorial/MongoBase_starting_guide.ipynb\n\n\nRelease and Contributing\n---------------------------\nMany methods are the wrapper of pymongo.  \nThere are a lot of features that this library is covering.  \nWould appreciate if you add their methods anytime.\n\n\n\n#### version 0.3.0\n##### New features\n- bulk_insert()\n- bulk_update()\n- performance improvement with ConnectionPool (single MongoClient for each process)\n- MongoBase_start_guide.ipynb\n- contextual db client mode by  `with db_context() as db`\n- code efficiency improvement\n- abolished `insert_if_not_exists` parameter for `save(), update()`\n- changed some method names (e.g. remove -> delete)\n- using pymongo > 3.5 methods (e.g. insert_one()) \n- enhance documents\n\n#### version 0.2.0\n##### New features\n- MongoBase and ModelBase class are separated\n- enable to use MongoClient instance dynamically\n- some useful mongodb operations are added\n\n#### version 0.1.0\n##### New features\n- The initial implementation\n- automatic type checking mechanism\n- basic mongodb operations\n\n\nLicense\n--------------------\nMongoBase is MIT-style licensed, as found in the LICENSE file.\n\n",
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