{
    "info": {
        "author": "Pedro Buteri Gonring",
        "author_email": "pedro@bigode.net",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: System Administrators",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "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",
            "Topic :: Database",
            "Topic :: Utilities"
        ],
        "description": "|Build Status| |Coverage| |Version| |Supported| |License|\n\ndbj\n===\n\ndbj is a simple embedded in memory json database.\n\nIt is easy to use, fast and has a simple query language.\n\nThe code is fully documented, tested and beginner friendly with around 400 LOC.\n\nOnly the standard library is used and it works on Python 2.7, Python 3.4+ and PyPy 2.7.\n\n\nUsage\n=====\n\n.. code-block:: python\n\n    >>> from dbj import dbj\n    >>> db = dbj('mydb.json')\n\n    >>> # Insert using an auto generated uuid1 key\n    >>> db.insert({'name': 'John', 'age': 18})\n    'a71d90ce0c7611e995faf23c91392d78'\n\n    >>> # Insert using a supplied key, in this case 'user:anab'\n    >>> user = {'name': 'Ana Beatriz', 'age': 10, 'username': 'anab'}\n    >>> db.insert(user, 'user:anab')\n    'user:anab'\n\n    >>> db.insert({'name': 'Bob', 'age': 30})\n    'cc6ddfe60c7611e995faf23c91392d78'\n\n    >>> db.get('a71d90ce0c7611e995faf23c91392d78')\n    {'name': 'John', 'age': 18}\n\n    >>> db.get('user:anab')\n    {'name': 'Ana Beatriz', 'age': 10, 'username': 'anab'}\n\n    >>> db.find('age >= 18')\n    ['a71d90ce0c7611e995faf23c91392d78', 'cc6ddfe60c7611e995faf23c91392d78']\n\n    >>> db.find('name == \"ana beatriz\"')\n    ['user:anab']\n\n    >>> r = db.find('name == \"John\" or name == \"Bob\" and age > 10')\n    >>> db.getmany(r)\n    [{'name': 'Bob', 'age': 30}, {'name': 'John', 'age': 18}]\n\n    >>> # Sort the result by age\n    >>> r = db.sort(r, 'age')\n    >>> db.getmany(r)\n    [{'name': 'John', 'age': 18}, {'name': 'Bob', 'age': 30}]\n\n    >>> db.save()\n    True\n\n\nInstall\n=======\n\nInstall using pip::\n\n    $ pip install dbj\n\n\nExamples\n========\n\nCheck the `available commands`_ for a full list of supported methods.\n\nImport the module and create a new database:\n\n.. code-block:: python\n\n    >>> from dbj import dbj\n    >>> db = dbj('mydb.json')\n\nInsert a few documents with auto generated key:\n\n.. code-block:: python\n\n    >>> doc = {'name': 'John Doe', 'age': 18}\n    >>> db.insert(doc)\n    '7a5ebd420cb211e98a0ff23c91392d78'\n\n    >>> docs = [{'name': 'Beatriz', 'age': 30}, {'name': 'Ana', 'age': 10}]\n    >>> db.insertmany(docs)\n    2\n\nInsert with a supplied key:\n\n.. code-block:: python\n\n    >>> doc = {'name': 'john', 'age': 20, 'country': 'Brasil'}\n    >>> db.insert(doc, '1')\n    1\n\n    >>> db.insert({'name': 'Bob', 'age': 40}, '2')\n    2\n\n    >>> db.getallkeys()\n    ['7a5ebd420cb211e98a0ff23c91392d78', 'db21baf80cb211e98a0ff23c91392d78', 'db21edde0cb211e98a0ff23c91392d78', '1', '2']\n\nPop and delete:\n\n.. code-block:: python\n\n    >>> db.delete('1')\n    True\n\n    >>> db.poplast()\n    {'name': 'Bob', 'age': 40}\n\n    >>> db.size()\n    3\n\n    >>> db.getallkeys()\n    ['7a5ebd420cb211e98a0ff23c91392d78', 'db21baf80cb211e98a0ff23c91392d78', 'db21edde0cb211e98a0ff23c91392d78']\n\nUpdating an existing document:\n\n.. code-block:: python\n\n    >>> db.insert({'name': 'Ethan', 'age': 40}, '1000')\n    '1000'\n\n    >>> db.get('1000')\n    {'name': 'Ethan', 'age': 40}\n\n    >>> db.update('1000', {'age': 50})\n    True\n\n    >>> db.get('1000')\n    {'name': 'Ethan', 'age': 50}\n\n    >>> db.update('1000', {'name': 'Ethan Doe', 'gender': 'male'})\n    True\n\n    >>> db.pop('1000')\n    {'name': 'Ethan Doe', 'age': 50, 'gender': 'male'}\n\nRetrieving some documents:\n\n.. code-block:: python\n\n    >>> db.getall()\n    [{'name': 'John Doe', 'age': 18}, {'name': 'Beatriz', 'age': 30}, {'name': 'Ana', 'age': 10}]\n\n    >>> db.getfirst()\n    {'name': 'John Doe', 'age': 18}\n\n    >>> db.getlast()\n    {'name': 'Ana', 'age': 10}\n\n    >>> db.getrandom() # returns a random document\n    {'name': 'Ana', 'age': 10}\n\nCheck for existance:\n\n.. code-block:: python\n\n    >>> db.exists('7a5ebd420cb211e98a0ff23c91392d78')\n    True\n\nSearchin and sorting:\n\n.. code-block:: python\n\n    >>> r = db.sort(db.getallkeys(), 'name')\n    >>> db.getmany(r)\n    [{'name': 'Ana', 'age': 10}, {'name': 'Beatriz', 'age': 30}, {'name': 'John Doe', 'age': 18}]\n\n    >>> r = db.find('name ?= \"john\"')\n    >>> db.getmany(r)\n    [{'name': 'John Doe', 'age': 18}]\n\n    >>> query = 'name == \"john doe\" or name == \"ana\" and age >= 10'\n    >>> r = db.find(query)\n    >>> db.getmany(r)\n    [{'name': 'John Doe', 'age': 18}, {'name': 'Ana', 'age': 10}]\n\n    >>> r = db.find('age < 40')\n    >>> r = db.sort(r, 'age')\n    >>> db.getmany(r)\n    [{'name': 'Ana', 'age': 10}, {'name': 'John Doe', 'age': 18}, {'name': 'Beatriz', 'age': 30}]\n\nSave the database to disk:\n\n.. code-block:: python\n\n    >>> db.save()\n    True\n\nEnable auto saving to disk after a insert, update or delete:\n\n.. code-block:: python\n\n    >>> db = dbj('mydb.json', autosave=True)\n\n\nAbout the simple query language\n===============================\n\nThe query for the find command uses the following pattern:\n\n*field operator value and/or field operator value...*\n\n**Spaces are mandatory** and used as a separator by the parser. For example,\nthe following query **will not work**::\n\n    name==\"John\" and age >=18\n\n**A valid example**::\n\n    name == \"John Doe\" and age >= 18\n\nStrings must be enclosed by quotes. Quoted text can be searched using double\nquotes as the string delimiter, like::\n\n    name == \"\"Bob \"B\" Lee\"\"\n\nPlease note that if value is a string, a search for text will be executed\n(using the string operatos below) and if value is a number, a number comparison\nsearch will be used.\n\nThe supported string operators are::\n\n    '==' -> Exact match. 'John' will not match 'John Doe' but will match 'john'\n    by default. If case sensitive is desired, just use find with sens=True. See\n    available commands below for the full find method signature.\n\n    '?=' -> Partial match. In this case, 'John' will match 'John Doe'.\n\n    '!=' -> Not equal operator.\n\nThe numbers comparison operators are::\n\n    '==', '!=', '<', '<=', '>', '>='\n\nThe supported logical operatos are::\n\n    and, or\n\n\nImportant changes\n=================\n\n0.1.4:\n------\n\n- The insert() method will raise a TypeError exception if the document dict is not json serializable.\n\n\nPerformance\n===========\n\nSince the entire database is an OrderedDict in memory, performance is pretty\ngood. On a cheap single core VM it can handle dozens of thousands operations\nper second.\n\nA simple benchmark is included to get a roughly estimative of operations per\nsecond. Here is the result on a $5 bucks Linode VM running on Pyhton 3::\n\n    $ python3 bench_dbj.py\n\n    --------------------------------\n\n    Inserting 100000 documents using auto generated uuid1 key...\n    Done! Time spent: 4.44s\n    Inserted: 100000\n    Rate: 22515 ops/s\n\n    --------------------------------\n\n    Clearing the database...\n    Done!\n\n    --------------------------------\n\n    Inserting 100000 documents using a supplied key...\n    Done! Time spent: 1.26s\n    Inserted: 100000\n    Rate: 79563 ops/s\n\n    --------------------------------\n\n    Retrieving 100000 documents one at a time...\n    Done! Time spent: 1.54s\n    Retrieved: 100000\n    Rate: 64754 ops/s\n\n    --------------------------------\n\n    Saving database to disk...\n    Done! Time spent: 1.06s\n\n    --------------------------------\n\n    Deleting 100000 documents one at a time...\n    Done! Time spent: 0.24s\n    Deleted: 100000\n    Rate: 419770 ops/s\n\n    --------------------------------\n\n    Removing file...\n    Done!\n\n    Peak memory usage: 60.41 MB\n\n\nAvailable commands\n==================\n\ninsert(document, key=None) -> Create a new document on database.\n    Args:\n        | document (dict): The document to be created.\n        | key (str, optional): The document unique key. Defaults to uuid1.\n    Returns:\n        The document key.\n\ninsertmany(documents) -> Insert multiple documents on database.\n    Args:\n        documents (list): List containing the documents to insert.\n    Returns:\n        Number of inserted documents.\n\nsave() -> Save database to disk.\n    Returns:\n        True if successful.\n\nclear() -> Remove all documents from database.\n    Returns:\n        True if successful.\n\nsize() -> Return the database size.\n    Returns:\n        Number of documents on database.\n\nexists(key) -> Check if a document exists on database.\n    Args:\n        key (str): The document key.\n    Returns:\n        True or False if it does not exist.\n\ndelete(key) -> Delete a document on database.\n    Args:\n        key (str): The document key.\n    Returns:\n        True or False if it does not exist.\n\ndeletemany(keys) -> Delete multiple documents on database.\n    Args:\n        keys (list): List containing the keys of the documents to delete.\n    Returns:\n        Number of deleted documents.\n\nupdate(key, values) -> Add/update values on a document.\n    Args:\n        | key (str): The document key.\n        | values (dict): The values to be added/updated.\n    Returns:\n        True or False if document does not exist.\n\nupdatemany(keys, values) -> Add/update values on multiple documents.\n    Args:\n        | keys (list): List containing the keys of the documents to update.\n        | values (dict): The values to be added/updated.\n    Returns:\n        Number of updated documents.\n\nget(key) -> Get a document on database.\n    Args:\n        key (str): The document key.\n    Returns:\n        The document or False if it does not exist.\n\ngetmany(keys) -> Get multiple documents from database.\n    Args:\n        keys (list): List containing the keys of the documents to retrieve.\n    Returns:\n        List of documents.\n\ngetall() -> Return a list containing all documents on database.\n    Returns:\n        List with all database documents.\n\ngetallkeys() -> Return a list containing all keys on database.\n    Returns:\n        List with all database keys.\n\ngetrandom() -> Get a random document on database.\n    Returns:\n        A document or False if database is empty.\n\ngetfirst(self) -> Get the first inserted document on database.\n    Returns:\n        The first inserted document or False if database is empty.\n\ngetlast() -> Get the last inserted document on database.\n    Returns:\n        The last inserted document or False if database is empty.\n\ngetfirstkey() -> Get the first key on database.\n    Returns:\n        The first key or False if database is empty.\n\ngetlastkey() -> Get the last key on database.\n    Returns:\n        The last key or False if database is empty.\n\npop(key) -> Get the document from database and remove it.\n    Args:\n        key (str): The document key.\n    Returns:\n        The document or False if it does not exist.\n\npopfirst() -> Get the first inserted document on database and remove it.\n    Returns:\n        The first inserted document or False if database is empty.\n\npoplast() -> Get the last inserted document on database and remove it.\n    Returns:\n        The last inserted document or False if database is empty.\n\nsort(keys, field, reverse=False) -> Sort the documents using the field provided.\n    Args:\n        | keys (list): List containing the keys of the documents to sort.\n        | field (str): Field to sort.\n        | reverse (bool, optional): Reverse search. Defaults to False.\n    Returns:\n        Sorted list with the documents keys.\n\nfindtext(field, text, exact=False, sens=False, inverse=False, asc=True) -> Simple text search on the provided field.\n    Args:\n        | field (str): The field to search.\n        | text (str): The value to be searched.\n        | exact (bool, optional): Exact text match. Defaults to False.\n        | sens (bool, optional): Case sensitive. Defaults to False.\n        | inverse (bool, optional): Inverse search, return the documents that do not match the search. Defaults to False.\n        | asc (bool, optional): Ascii conversion before matching, this matches text like 'cafe' and 'caf\u00e9'. Defaults to True.\n    Returns:\n        List with the keys of the documents that matched the search.\n\nfindnum(expression) -> Simple number comparison search on provided field.\n    Args:\n        | expression (str): The comparison expression to use, e.g., \"age >= 18\". The pattern is 'field operator number'.\n    Returns:\n        List with the keys of the documents that matched the search.\n\nfind(query, sens=False, asc=True) -> Simple query like search.\n    Args:\n        | query (str): The query to use.\n        | sens (bool, optional): Case sensitive. Defaults to False.\n        | asc (bool, optional): Ascii conversion before matching, this matches text like 'cafe' and 'caf\u00e9'. Defaults to True.\n    Returns:\n        List with the keys of the documents that matched the search.\n\n\n.. |Build Status| image:: https://travis-ci.org/pdrb/dbj.svg?branch=master\n    :target: https://travis-ci.org/pdrb/dbj\n\n.. |Coverage| image:: https://coveralls.io/repos/github/pdrb/dbj/badge.svg?branch=master\n    :target: https://coveralls.io/github/pdrb/dbj?branch=master\n\n.. |Version| image:: https://badge.fury.io/py/dbj.svg\n    :target: https://pypi.org/project/dbj/\n\n.. |Supported| image:: https://img.shields.io/pypi/pyversions/dbj.svg\n    :target: https://pypi.org/project/dbj/\n\n.. |License| image:: https://img.shields.io/pypi/l/dbj.svg\n    :target: https://github.com/pdrb/dbj/blob/master/LICENSE",
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