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        "author": "Charles Leifer",
        "author_email": "coleifer@gmail.com",
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            "License :: OSI Approved :: MIT License",
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            "Programming Language :: Python"
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        "description": "sqlite-schemaless\n=================\n\nSchemaless database built on top of SQLite. I based the design of `sqlite-schemaless` on the data-store described in [this Uber engineering post](https://eng.uber.com/schemaless-part-one/). All data is stored in a single SQLite database, which can either be on-disk or in-memory.\n\nData is organized by **KeySpace**. A KeySpace might be something like \"users\" or \"tweets\". Inside each KeySpace there are **Rows**. A row is identified by an integer `row_key` and consists of one or more named columns. In these columns you can store arbitrary JSON blobs. So:\n\n* KeySpace1:\n    * Row1:\n        * ColumnA: {arbitrary json data}\n        * ColumnB: {more json data}\n    * Row2:\n        * ColumnA: {json data}\n        * ColumnC: {json data}\n    * Row3:\n        * ColumnA: {json data}\n        * ColumnB: {json data}\n\nSo far this is really not that interesting. Things get interesting, though, with the addition of **secondary indexes** and **event emitters**.\n\nSecondary Indexes\n-----------------\n\nYou can create indexes on values stored in the JSON column data. Suppose we are storing User data in the `user` column. This user data is structured roughly like this:\n\n```javascript\n{\n  'name': 'Charles',\n  'username': 'coleifer',\n  'location': {\n    'state': 'KS',\n    'city': 'Lawrence',\n  }\n}\n```\n\nIf we wanted to search for users by username, we would create an index using JSON-path notation: `$.username`. If we wanted to search by the user's state, we would create an index on `$.location.state`.\n\nHere is how the code would look to set this up:\n\n```python\n\ndb = Schemaless(':memory:')  # Create an in-memory database.\n\n# Indexes are initialized with the column name and JSON-path.\nusername_idx = Index('user', '$.username')\nstate_idx = Index('user', '$.location.state')\n\n# When we create a KeySpace, we can include a list of indexes:\nusers = db.keyspace('users', username_idx, state_idx)\n\n# To store a user, write:\ncharles = users.create_row(\n    user={\n        'username': 'coleifer',\n        'location': {'city': 'Lawrence', 'state': 'KS'},\n    },\n    social=[\n        {'name': 'github', 'username': 'coleifer'},\n        {'name': 'twitter', 'username': 'coleifer'},\n    ])\n\n# To query for users in Kansas, we can write:\nks_users = state_idx.query('KS')\nfor row in ks_users:\n    print row['user']['username']\n```\n\nEvent emitters\n--------------\n\nThe other neat feature of `sqlite-schemaless` is the event emitter functionality. This feature allows you to bind handlers to execute code when a new row is created or updated. You can bind handlers to an individual keyspace, or to all keyspaces.\n\nFor example, let's add a simple handler to print usernames as they're added or updated.\n\n```python\n\n@users.handler\ndef print_username(row, column, value):\n    if column == 'user':\n        print value['username']\n```\n\nWhenever we add or update the `user` column of a row in the `users` KeySpace, the callback will fire and print the username.",
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