Metadata-Version: 1.0
Name: datastore
Version: 0.3.0
Summary: simple, unified API for multiple data stores
Home-page: http://github.com/jbenet/datastore
Author: Juan Batiz-Benet
Author-email: juan@benet.ai
License: MIT License
Description: # datastore
        
        ## simple, unified API for multiple data stores
        
        datastore is a generic layer of abstraction for data store and database access.
        It is a **simple** API with the aim to enable application development in a
        datastore-agnostic way, allowing datastores to be swapped seamlessly without
        changing application code. Thus, one can leverage different datastores with
        different strengths without committing the application to one datastore
        throughout its lifetime. It looks like this:
        
            +---------------+
            |  application  |    <--- No cumbersome SQL or Mongo specific queries!
            +---------------+
                    |            <--- simple datastore API calls
            +---------------+
            |   datastore   |    <--- datastore implementation for underlying db
            +---------------+
                    |            <--- database specific calls
            +---------------+
            |  various dbs  |    <--- MySQL, Redis, MongoDB, FS, ...
            +---------------+
        
        In addition, grouped datastores significantly simplify interesting data access
        patterns (such as caching and sharding).
        
        
        ## About
        
        ### Install
        
        From pypi (using pip):
        
            sudo pip install datastore
        
        From pypi (using setuptools):
        
            sudo easy_install datastore
        
        From source:
        
            git clone https://github.com/jbenet/datastore/
            cd datastore
            sudo python setup.py install
        
        ### Subprojects
        
        
        * [datastore.aws](https://github.com/datastore/datastore.aws) - aws s3 implementation
        * [datastore.git](https://github.com/datastore/datastore.git) - git implementation
        * [datastore.mongo](https://github.com/datastore/datastore.mongo) - monogdb implementation
        * [datastore.memcached](https://github.com/datastore/datastore.memcached) - memcached implementation
        * [datastore.pylru](https://github.com/datastore/datastore.pylru) - pylru cache implementation
        * [datastore.redis](https://github.com/datastore/datastore.redis) - redis implementation
        
        
        ### Documentation
        
        The documentation can be found at:
        http://datastore.readthedocs.org/en/latest/
        
        ### License
        
        datastore is under the MIT License.
        
        ### Contact
        
        datastore is written by [Juan Batiz-Benet](https://github.com/jbenet). It
        was originally part of [py-dronestore](https://github.com/jbenet/py-dronestore).
        On December 2011, it was re-written as a standalone project.
        
        Project Homepage:
        [https://github.com/jbenet/datastore](https://github.com/jbenet/datastore)
        
        Feel free to contact me. But please file issues in github first. Cheers!
        
        ## Contributing
        
        ### Implementations
        
        Please write and contribute implementations for other data stores. This project
        can only be complete with lots of help.
        
        ### Style
        
        Please follow proper pythonic style in your code.
        
        See [PEP 8](http://www.python.org/dev/peps/pep-0008/) and the [Google Python
        Style Guide](http://google-styleguide.googlecode.com/svn/trunk/pyguide.html).
        
        ### Docs
        
        Please document all code. ``datastore`` uses ``sphinx`` for documentation. Take
        a look at the ``docs/`` directory.
        
        To make sure the documentation compiles, run:
        
          cd docs
          make html
          open .build/html/index.html
        
        Which should -- if all goes well -- open your favorite browser on the
        newly-built docs.
        
        ## Examples
        
        ### Hello World
        
            >>> import datastore
            >>> ds = datastore.basic.DictDatastore()
            >>>
            >>> hello = datastore.Key('hello')
            >>> ds.put(hello, 'world')
            >>> ds.contains(hello)
            True
            >>> ds.get(hello)
            'world'
            >>> ds.delete(hello)
            >>> ds.get(hello)
            None
        
        #### Hello filesystem
        
            >>> import datastore.filesystem
            >>>
            >>> ds = datastore.filesystem.FileSystemDatastore('/tmp/.test_datastore')
            >>>
            >>> hello = datastore.Key('hello')
            >>> ds.put(hello, 'world')
            >>> ds.contains(hello)
            True
            >>> ds.get(hello)
            'world'
            >>> ds.delete(hello)
            >>> ds.get(hello)
            None
        
        
        #### Hello Tiered Access
        
        
            >>> import pymongo
            >>> import datastore
            >>>
            >>> from datastore.mongo import MongoDatastore
            >>> from datastore.pylru import LRUCacheDatastore
            >>> from datastore.filesystem import FileSystemDatastore
            >>>
            >>> conn = pymongo.Connection()
            >>> mongo = MongoDatastore(conn.test_db)
            >>>
            >>> cache = LRUCacheDatastore(1000)
            >>> fs = FileSystemDatastore('/tmp/.test_db')
            >>>
            >>> ds = datastore.TieredDatastore([cache, mongo, fs])
            >>>
            >>> hello = datastore.Key('hello')
            >>> ds.put(hello, 'world')
            >>> ds.contains(hello)
            True
            >>> ds.get(hello)
            'world'
            >>> ds.delete(hello)
            >>> ds.get(hello)
            None
        
        
        #### Hello Sharding
        
            >>> import datastore
            >>>
            >>> shards = [datastore.DictDatastore() for i in range(0, 10)]
            >>>
            >>> ds = datastore.ShardedDatastore(shards)
            >>>
            >>> hello = datastore.Key('hello')
            >>> ds.put(hello, 'world')
            >>> ds.contains(hello)
            True
            >>> ds.get(hello)
            'world'
            >>> ds.delete(hello)
            >>> ds.get(hello)
            None
        
        
        ## API
        
        The datastore API places an emphasis on  **simplicity** and elegance. Only four
        core methods must be implemented (get, put, delete, query).
        
        ### get(key)
        
        Return the object named by key or None if it does not exist.
        
            Args:
              key: Key naming the object to retrieve
        
            Returns:
              object or None
        
        ### put(key, value)
        
        Stores the object `value` named by `key`.
        How to serialize and store objects is up to the underlying datastore.
        It is recommended to use simple objects (strings, numbers, lists, dicts).
        
            Args:
              key: Key naming `value`
              value: the object to store.
        
        ### delete(key)
        
        Removes the object named by `key`.
        
            Args:
              key: Key naming the object to remove.
        
        ### query(query):
        
        Returns an iterable of objects matching criteria expressed in `query`
        Implementations of query will be the largest differentiating factor
        amongst datastores. All datastores **must** implement query, even using
        query's worst case scenario, see Query class for details.
        
            Args:
              query: Query object describing the objects to return.
        
            Returns:
              iterable cursor with all objects matching criteria
        
        
        ### Specialized Features
        
        Datastore implementors are free to implement specialized features, pertinent
        only to a subset of datastores, with the understanding that these should aim
        for generality and will most likely not be implemented across other datastores.
        
        When implementings such features, please remember the goal of this project:
        simple, unified API for multiple data stores. When making heavy use of a
        particular library's specific functionality, perhaps one should not use
        datastore and should directly use that library.
        
        ### Key
        
        A Key represents the unique identifier of an object.
        
        Our Key scheme is inspired by file systems and the Google App Engine key
        model.
        
        Keys are meant to be unique across a system. Keys are hierarchical,
        incorporating increasingly specific namespaces. Thus keys can be deemed
        'children' or 'ancestors' of other keys.
        
            Key('/Comedy')
            Key('/Comedy/MontyPython')
        
        Also, every namespace can be parametrized to embed relevant object
        information. For example, the Key `name` (most specific namespace) could
        include the object type:
        
            Key('/Comedy/MontyPython/Actor:JohnCleese')
            Key('/Comedy/MontyPython/Sketch:CheeseShop')
            Key('/Comedy/MontyPython/Sketch:CheeseShop/Character:Mousebender')
        
        
Keywords: datastore,unified api,database
Platform: UNKNOWN
Classifier: Topic :: Database
Classifier: Topic :: Database :: Front-Ends
