.. _playhouse:

Playhouse, a collection of addons
=================================

Peewee comes with numerous extras which I didn't really feel like including in
the main source module, but which might be interesting to implementers or fun
to mess around with.

The playhouse includes modules for different database drivers or database
specific functionality:

* :ref:`apsw`
* :ref:`postgres_ext`
* :ref:`sqlite_ext`
* :ref:`berkeleydb`
* :ref:`sqlcipher_ext`

Modules which expose higher-level python constructs:

* :ref:`dataset`
* :ref:`djpeewee`
* :ref:`gfk`
* :ref:`kv`
* :ref:`shortcuts`
* :ref:`signals`

As well as tools for working with databases:

* :ref:`pwiz`
* :ref:`migrate`
* :ref:`reflection`
* :ref:`db_url`
* :ref:`csv_utils`
* :ref:`read_slaves`
* :ref:`pool`
* :ref:`test_utils`
* :ref:`pskel`


.. _apsw:

apsw, an advanced sqlite driver
-------------------------------

The ``apsw_ext`` module contains a database class suitable for use with
the apsw sqlite driver.

APSW Project page: https://code.google.com/p/apsw/

APSW is a really neat library that provides a thin wrapper on top of SQLite's
C interface, making it possible to use all of SQLite's advanced features.

Here are just a few reasons to use APSW, taken from the documentation:

* APSW gives all functionality of SQLite, including virtual tables, virtual
  file system, blob i/o, backups and file control.
* Connections can be shared across threads without any additional locking.
* Transactions are managed explicitly by your code.
* APSW can handle nested transactions.
* Unicode is handled correctly.
* APSW is faster.

For more information on the differences between apsw and pysqlite,
check `the apsw docs <http://apidoc.apsw.googlecode.com/hg/pysqlite.html>`_.

How to use the APSWDatabase
^^^^^^^^^^^^^^^^^^^^^^^^^^^

.. code-block:: python

    from apsw_ext import *

    db = APSWDatabase(':memory:')

    class BaseModel(Model):
        class Meta:
            database = db

    class SomeModel(BaseModel):
        col1 = CharField()
        col2 = DateTimeField()


apsw_ext API notes
^^^^^^^^^^^^^^^^^^

.. py:class:: APSWDatabase(database, **connect_kwargs)

    :param string database: filename of sqlite database
    :param connect_kwargs: keyword arguments passed to apsw when opening a connection

    .. py:method:: transaction([lock_type='deferred'])

        Functions just like the :py:meth:`Database.transaction` context manager,
        but accepts an additional parameter specifying the type of lock to use.

        :param string lock_type: type of lock to use when opening a new transaction

    .. py:method:: register_module(mod_name, mod_inst)

        Provides a way of globally registering a module.  For more information,
        see the `documentation on virtual tables <http://apidoc.apsw.googlecode.com/hg/vtable.html>`_.

        :param string mod_name: name to use for module
        :param object mod_inst: an object implementing the `Virtual Table <http://apidoc.apsw.googlecode.com/hg/vtable.html?highlight=virtual%20table#apsw.VTTable>`_ interface

    .. py:method:: unregister_module(mod_name)

        Unregister a module.

        :param string mod_name: name to use for module

.. note::
    Be sure to use the ``Field`` subclasses defined in the ``apsw_ext``
    module, as they will properly handle adapting the data types for storage.


.. _postgres_ext:

Postgresql Extensions
---------------------

The postgresql extensions module provides a number of "postgres-only" functions,
currently:

* :ref:`hstore support <hstore>`
* :ref:`json support <pgjson>`
* :ref:`server-side cursors <server_side_cursors>`
* :ref:`full-text search <pg_fts>`
* :py:class:`ArrayField` field type, for storing arrays.
* :py:class:`HStoreField` field type, for storing key/value pairs.
* :py:class:`JSONField` field type, for storing JSON data.
* :py:class:`TSVectorField` field type, for storing full-text search data.
* :py:class:`DateTimeTZ` field type, a timezone-aware datetime field.

In the future I would like to add support for more of postgresql's features.
If there is a particular feature you would like to see added, please
`open a Github issue <https://github.com/coleifer/peewee/issues>`_.

.. warning:: In order to start using the features described below, you will need to use the
    extension :py:class:`PostgresqlExtDatabase` class instead of :py:class:`PostgresqlDatabase`.

The code below will assume you are using the following database and base model:

.. code-block:: python

    from playhouse.postgres_ext import *

    ext_db = PostgresqlExtDatabase('peewee_test', user='postgres')

    class BaseExtModel(Model):
        class Meta:
            database = ext_db

.. _hstore:

hstore support
^^^^^^^^^^^^^^

`Postgresql hstore <http://www.postgresql.org/docs/current/static/hstore.html>`_ is
an embedded key/value store.  With hstore, you can store arbitrary key/value pairs
in your database alongside structured relational data.

Currently the ``postgres_ext`` module supports the following operations:

* Store and retrieve arbitrary dictionaries
* Filter by key(s) or partial dictionary
* Update/add one or more keys to an existing dictionary
* Delete one or more keys from an existing dictionary
* Select keys, values, or zip keys and values
* Retrieve a slice of keys/values
* Test for the existence of a key
* Test that a key has a non-NULL value


Using hstore
^^^^^^^^^^^^

To start with, you will need to import the custom database class and the hstore
functions from ``playhouse.postgres_ext`` (see above code snippet).  Then, it is
as simple as adding a :py:class:`HStoreField` to your model:

.. code-block:: python

    class House(BaseExtModel):
        address = CharField()
        features = HStoreField()


You can now store arbitrary key/value pairs on ``House`` instances:

.. code-block:: pycon

    >>> h = House.create(address='123 Main St', features={'garage': '2 cars', 'bath': '2 bath'})
    >>> h_from_db = House.get(House.id == h.id)
    >>> h_from_db.features
    {'bath': '2 bath', 'garage': '2 cars'}


You can filter by keys or partial dictionary:

.. code-block:: pycon

    >>> f = House.features
    >>> House.select().where(f.contains('garage')) # <-- all houses w/garage key
    >>> House.select().where(f.contains(['garage', 'bath'])) # <-- all houses w/garage & bath
    >>> House.select().where(f.contains({'garage': '2 cars'})) # <-- houses w/2-car garage

Suppose you want to do an atomic update to the house:

.. code-block:: pycon

    >>> f = House.features
    >>> new_features = House.features.update({'bath': '2.5 bath', 'sqft': '1100'})
    >>> query = House.update(features=new_features)
    >>> query.where(House.id == h.id).execute()
    1
    >>> h = House.get(House.id == h.id)
    >>> h.features
    {'bath': '2.5 bath', 'garage': '2 cars', 'sqft': '1100'}


Or, alternatively an atomic delete:

.. code-block:: pycon

    >>> query = House.update(features=f.delete('bath'))
    >>> query.where(House.id == h.id).execute()
    1
    >>> h = House.get(House.id == h.id)
    >>> h.features
    {'garage': '2 cars', 'sqft': '1100'}


Multiple keys can be deleted at the same time:

.. code-block:: pycon

    >>> query = House.update(features=f.delete('garage', 'sqft'))

You can select just keys, just values, or zip the two:

.. code-block:: pycon

    >>> f = House.features
    >>> for h in House.select(House.address, f.keys().alias('keys')):
    ...     print h.address, h.keys

    123 Main St [u'bath', u'garage']

    >>> for h in House.select(House.address, f.values().alias('vals')):
    ...     print h.address, h.vals

    123 Main St [u'2 bath', u'2 cars']

    >>> for h in House.select(House.address, f.items().alias('mtx')):
    ...     print h.address, h.mtx

    123 Main St [[u'bath', u'2 bath'], [u'garage', u'2 cars']]

You can retrieve a slice of data, for example, all the garage data:

.. code-block:: pycon

    >>> f = House.features
    >>> for h in House.select(House.address, f.slice('garage').alias('garage_data')):
    ...     print h.address, h.garage_data

    123 Main St {'garage': '2 cars'}

You can check for the existence of a key and filter rows accordingly:

.. code-block:: pycon

    >>> for h in House.select(House.address, f.exists('garage').alias('has_garage')):
    ...     print h.address, h.has_garage

    123 Main St True

    >>> for h in House.select().where(f.exists('garage')):
    ...     print h.address, h.features['garage'] # <-- just houses w/garage data

    123 Main St 2 cars

.. _pgjson:

JSON Support
^^^^^^^^^^^^

peewee has basic support for Postgres' native JSON data type, in the form of
:py:class:`JSONField`.

.. warning::
  Postgres supports a JSON data type natively as of 9.2 (full support in 9.3). In
  order to use this functionality you must be using the correct version of Postgres
  with `psycopg2` version 2.5 or greater.

.. note::
  You must be sure your database is an instance of :py:class:`PostgresqlExtDatabase`
  in order to use the `JSONField`.

Here is an example of how you might declare a model with a JSON field:

.. code-block:: python

    import json
    import urllib2
    from playhouse.postgres_ext import *

    db = PostgresqlExtDatabase('my_database')  # note

    class APIResponse(Model):
        url = CharField()
        response = JSONField()

        class Meta:
            database = db

        @classmethod
        def request(cls, url):
            fh = urllib2.urlopen(url)
            return cls.create(url=url, response=json.loads(fh.read()))

    APIResponse.create_table()

    # Store a JSON response.
    offense = APIResponse.request('http://wtf.charlesleifer.com/api/offense/')
    booking = APIResponse.request('http://wtf.charlesleifer.com/api/booking/')

    # Query a JSON data structure using a nested key lookup:
    offense_responses = APIResponse.select().where(
      APIResponse.response['meta']['model'] == 'offense')

    # Retrieve a sub-key for each APIResponse. By calling .as_json(), the
    # data at the sub-key will be returned as Python objects (dicts, lists,
    # etc) instead of serialized JSON.
    q = (APIResponse
         .select(
           APIResponse.data['booking']['person'].as_json().alias('person'))
         .where(
           APIResponse.data['meta']['model'] == 'booking'))

    for result in q:
        print result.person['name'], result.person['dob']

For more examples, see the :py:class:`JSONField` API documentation below.


.. _server_side_cursors:

Server-side cursors
^^^^^^^^^^^^^^^^^^^

When psycopg2 executes a query, normally all results are fetched and returned to
the client by the backend.  This can cause your application to use a lot of memory
when making large queries.  Using server-side cursors, results are returned a
little at a time (by default 2000 records).  For the definitive reference, please see the `psycopg2 documentation <http://initd.org/psycopg/docs/usage.html#server-side-cursors>`_.

.. note:: To use server-side (or named) cursors, you must be using :py:class:`PostgresqlExtDatabase`.

To execute a query using a server-side cursor, simply wrap your select query
using the :py:func:`ServerSide` helper:

.. code-block:: python

    large_query = PageView.select()  # Build query normally.

    # Iterate over large query inside a transaction.
    for page_view in ServerSide(large_query):
        # do some interesting analysis here.
        pass

    # Server-side resources are released.

If you would like all ``SELECT`` queries to automatically use a server-side
cursor, you can specify this when creating your :py:class:`PostgresqlExtDatabase`:

.. code-block:: python

    from postgres_ext import PostgresqlExtDatabase

    ss_db = PostgresqlExtDatabase('my_db', server_side_cursors=True)

.. note::
    Server-side cursors live only as long as the transaction, so for this reason
    peewee will not automatically call ``commit()`` after executing a ``SELECT``
    query.  If you do not ``commit`` after you are done iterating, you will not
    release the server-side resources until the connection is closed (or the
    transaction is committed later).  Furthermore, since peewee will by default
    cache rows returned by the cursor, you should always call ``.iterator()``
    when iterating over a large query.

    If you are using the :py:func:`ServerSide` helper, the transaction and
    call to ``iterator()`` will be handled transparently.


.. _pg_fts:

Full-text search
^^^^^^^^^^^^^^^^

Postgresql provides `sophisticated full-text search <http://www.postgresql.org/docs/9.3/static/textsearch.html>`_ using special data-types (``tsvector`` and ``tsquery``). Documents should be stored or converted to the ``tsvector`` type, and search queries should be converted to ``tsquery``.

For simple cases, you can simply use the :py:func:`Match` function, which will automatically perform the appropriate conversions, and requires no schema changes:

.. code-block:: python

    def blog_search(query):
        return Blog.select().where(
            (Blog.status == Blog.STATUS_PUBLISHED) &
            Match(Blog.content, query))

The :py:func:`Match` function will automatically convert the left-hand operand to a ``tsvector``, and the right-hand operand to a ``tsquery``. For better performance, it is recommended you create a ``GIN`` index on the column you plan to search:

.. code-block:: sql

    CREATE INDEX blog_full_text_search ON blog USING gin(to_tsvector(content));

Alternatively, you can use the :py:class:`TSVectorField` to maintain a dedicated column for storing ``tsvector`` data:

.. code-block:: python

    class Blog(Model):
        content = TextField()
        search_content = TSVectorField()

You will need to explicitly convert the incoming text data to ``tsvector`` when inserting or updating the ``search_content`` field:

.. code-block:: python

    content = 'Excellent blog post about peewee ORM.'
    blog_entry = Blog.create(
        content=content,
        search_content=fn.to_tsvector(content))

.. note:: If you are using the :py:class:`TSVectorField`, it will automatically be created with a GIN index.


postgres_ext API notes
^^^^^^^^^^^^^^^^^^^^^^

.. py:class:: PostgresqlExtDatabase(database[, server_side_cursors=False[, register_hstore=True[, ...]]])

    Identical to :py:class:`PostgresqlDatabase` but required in order to support:

    * :ref:`server_side_cursors`
    * :py:class:`ArrayField`
    * :py:class:`DateTimeTZField`
    * :py:class:`JSONField`
    * :py:class:`HStoreField`
    * :py:class:`TSVectorField`

    :param str database: Name of database to connect to.
    :param bool server_side_cursors: Whether ``SELECT`` queries should utilize
        server-side cursors.
    :param bool register_hstore: Register the HStore extension with the connection.

    If using ``server_side_cursors``, also be sure to wrap your queries with
    :py:func:`ServerSide`.

    If you do not wish to use the HStore extension, you can specify ``register_hstore=False``.

.. py:function:: ServerSide(select_query)

    Wrap the given select query in a transaction, and call it's :py:meth:`~SelectQuery.iterator`
    method to avoid caching row instances.  In order for the server-side resources
    to be released, be sure to exhaust the generator (iterate over all the rows).

    :param select_query: a :py:class:`SelectQuery` instance.
    :rtype: ``generator``

    Usage:

    .. code-block:: python

        large_query = PageView.select()
        for page_view in ServerSide(large_query):
            # Do something interesting.
            pass

        # At this point server side resources are released.

.. _pgarrays:

.. py:class:: ArrayField([field_class=IntegerField[, dimensions=1]])

    Field capable of storing arrays of the provided `field_class`.

    :param field_class: a subclass of :py:class:`Field`, e.g. :py:class:`IntegerField`.
    :param int dimensions: dimensions of array.

    You can store and retrieve lists (or lists-of-lists):

    .. code-block:: python

        class BlogPost(BaseModel):
            content = TextField()
            tags = ArrayField(CharField)


        post = BlogPost(content='awesome', tags=['foo', 'bar', 'baz'])

    Additionally, you can use the ``__getitem__`` API to query values or slices
    in the database:

    .. code-block:: python

        # Get the first tag on a given blog post.
        first_tag = (BlogPost
                     .select(BlogPost.tags[0].alias('first_tag'))
                     .where(BlogPost.id == 1)
                     .dicts()
                     .get())

        # first_tag = {'first_tag': 'foo'}

    Get a slice of values:

    .. code-block:: python

        # Get the first two tags.
        two_tags = (BlogPost
                    .select(BlogPost.tags[:2].alias('two'))
                    .dicts()
                    .get())
        # two_tags = {'two': ['foo', 'bar']}

    .. py:method:: contains(*items)

        :param items: One or more items that must be in the given array field.

        .. code-block:: python

            # Get all blog posts that are tagged with both "python" and "django".
            Blog.select().where(Blog.tags.contains('python', 'django'))

    .. py:method:: contains_any(*items)

        :param items: One or more items to search for in the given array field.

        Like :py:meth:`~ArrayField.contains`, except will match rows where the
        array contains *any* of the given items.

        .. code-block:: python

            # Get all blog posts that are tagged with "flask" and/or "django".
            Blog.select().where(Blog.tags.contains_any('flask', 'django'))

.. py:class:: DateTimeTZField(*args, **kwargs)

    A timezone-aware subclass of :py:class:`DateTimeField`.

.. py:class:: HStoreField(*args, **kwargs)

    A field for storing and retrieving arbitrary key/value pairs.  For details
    on usage, see :ref:`hstore`.

    .. py:method:: keys()

        Returns the keys for a given row.

        .. code-block:: pycon

            >>> f = House.features
            >>> for h in House.select(House.address, f.keys().alias('keys')):
            ...     print h.address, h.keys

            123 Main St [u'bath', u'garage']

    .. py:method:: values()

        Return the values for a given row.

        .. code-block:: pycon

            >>> for h in House.select(House.address, f.values().alias('vals')):
            ...     print h.address, h.vals

            123 Main St [u'2 bath', u'2 cars']

    .. py:method:: items()

        Like python's ``dict``, return the keys and values in a list-of-lists:

        .. code-block:: pycon

            >>> for h in House.select(House.address, f.items().alias('mtx')):
            ...     print h.address, h.mtx

            123 Main St [[u'bath', u'2 bath'], [u'garage', u'2 cars']]

    .. py:method:: slice(*args)

        Return a slice of data given a list of keys.

        .. code-block:: pycon

            >>> f = House.features
            >>> for h in House.select(House.address, f.slice('garage').alias('garage_data')):
            ...     print h.address, h.garage_data

            123 Main St {'garage': '2 cars'}

    .. py:method:: exists(key)

        Query for whether the given key exists.

        .. code-block:: pycon

            >>> for h in House.select(House.address, f.exists('garage').alias('has_garage')):
            ...     print h.address, h.has_garage

            123 Main St True

            >>> for h in House.select().where(f.exists('garage')):
            ...     print h.address, h.features['garage'] # <-- just houses w/garage data

            123 Main St 2 cars

    .. py:method:: defined(key)

        Query for whether the given key has a value associated with it.

    .. py:method:: update(**data)

        Perform an atomic update to the keys/values for a given row or rows.

        .. code-block:: pycon

            >>> query = House.update(features=House.features.update(
            ...     sqft=2000,
            ...     year_built=2012))
            >>> query.where(House.id == 1).execute()

    .. py:method:: delete(*keys)

        Delete the provided keys for a given row or rows.

        .. note:: We will use an ``UPDATE`` query.

        .. code-block:: pycon

        >>> query = House.update(features=House.features.delete(
        ...     'sqft', 'year_built'))
        >>> query.where(House.id == 1).execute()

    .. py:method:: contains(value)

        :param value: Either a ``dict``, a ``list`` of keys, or a single key.

        Query rows for the existence of either:

        * a partial dictionary.
        * a list of keys.
        * a single key.

        .. code-block:: pycon

            >>> f = House.features
            >>> House.select().where(f.contains('garage')) # <-- all houses w/garage key
            >>> House.select().where(f.contains(['garage', 'bath'])) # <-- all houses w/garage & bath
            >>> House.select().where(f.contains({'garage': '2 cars'})) # <-- houses w/2-car garage

    .. py:method:: contains_any(*keys)

        :param keys: One or more keys to search for.

        Query rows for the existince of *any* key.

.. py:class:: JSONField(dumps=None, *args, **kwargs)

    Field class suitable for storing and querying arbitrary JSON.  When using
    this on a model, set the field's value to a Python object (either a `dict`
    or a `list`).  When you retrieve your value from the database it will be
    returned as a Python data structure.

    :param dumps: The default is to call json.dumps() or the dumps function. You can override this method to create a customized JSON wrapper.

    .. note:: You must be using Postgres 9.2 / psycopg2 2.5 or greater.

    Example model declaration:

    .. code-block:: python

        db = PostgresqlExtDatabase('my_db')

        class APIResponse(Model):
            url = CharField()
            response = JSONField()

            class Meta:
                database = db

    Example of storing JSON data:

    .. code-block:: python

        url = 'http://foo.com/api/resource/'
        resp = json.loads(urllib2.urlopen(url).read())
        APIResponse.create(url=url, response=resp)

        APIResponse.create(url='http://foo.com/baz/', response={'key': 'value'})

    To query, use Python's ``[]`` operators to specify nested key or array lookups:

    .. code-block:: python

        APIResponse.select().where(
            APIResponse.response['key1']['nested-key'] == 'some-value')

    To illustrate the use of the ``[]`` operators, imagine we have the following data stored in an ``APIResponse``:

    .. code-block:: javascript

        {
          "foo": {
            "bar": ["i1", "i2", "i3"],
            "baz": {
              "huey": "mickey",
              "peewee": "nugget"
            }
          }
        }

    Here are the results of a few queries:

    .. code-block:: python

        def get_data(expression):
            # Helper function to just retrieve the results of a
            # particular expression.
            query = (APIResponse
                     .select(expression.alias('my_data'))
                     .dicts()
                     .get())
            return query['my_data']

        # Accessing the foo -> bar subkey will return a JSON
        # representation of the list.
        get_data(APIResponse.data['foo']['bar'])
        # '["i1", "i2", "i3"]'

        # In order to retrieve this list as a Python list,
        # we will call .as_json() on the expression.
        get_data(APIResponse.data['foo']['bar'].as_json())
        # ['i1', 'i2', 'i3']

        # Similarly, accessing the foo -> baz subkey will
        # return a JSON representation of the dictionary.
        get_data(APIResponse.data['foo']['baz'])
        # '{"huey": "mickey", "peewee": "nugget"}'

        # Again, calling .as_json() will return an actual
        # python dictionary.
        get_data(APIResponse.data['foo']['baz'].as_json())
        # {'huey': 'mickey', 'peewee': 'nugget'}

        # When dealing with simple values, either way works as
        # you expect.
        get_data(APIResponse.data['foo']['bar'][0])
        # 'i1'

        # Calling .as_json() when the result is a simple value
        # will return the same thing as the previous example.
        get_data(APIResponse.data['foo']['bar'][0].as_json())
        # 'i1'

.. py:function:: Match(field, query)

    Generate a full-text search expression, automatically converting the left-hand operand to a ``tsvector``, and the right-hand operand to a ``tsquery``.

    Example:

    .. code-block:: python

        def blog_search(query):
            return Blog.select().where(
                (Blog.status == Blog.STATUS_PUBLISHED) &
                Match(Blog.content, query))

.. py:class:: TSVectorField

    Field type suitable for storing ``tsvector`` data. This field will automatically be created with a ``GIN`` index for improved search performance.

    .. note::
        Data stored in this field will still need to be manually converted to the ``tsvector`` type.

     Example usage:

     .. code-block:: python

          class Blog(Model):
              content = TextField()
              search_content = TSVectorField()

          content = 'this is a sample blog entry.'
          blog_entry = Blog.create(
              content=content,
              search_content=fn.to_tsvector(content))  # Note `to_tsvector()`.


.. _sqlite_ext:

Sqlite Extensions
-----------------

The SQLite extensions module provides support for some interesting sqlite-only
features:

* Define custom aggregates, collations and functions.
* Support for FTS3/4 (sqlite full-text search).
* Specify isolation level in transactions.
* Basic support for virtual tables.


sqlite_ext API notes
^^^^^^^^^^^^^^^^^^^^

.. py:class:: SqliteExtDatabase(database, **kwargs)

    Subclass of the :py:class:`SqliteDatabase` that provides some advanced
    features only offered by Sqlite.

    * Register custom aggregates, collations and functions
    * Specify a row factory
    * Advanced transactions (specify isolation level)

    .. py:method:: aggregate(num_params[, name])

        Class-decorator for registering custom aggregation functions.

        :param num_params: integer representing number of parameters the
            aggregate function accepts.
        :param name: string name for the aggregate, defaults to the name of
            the class.

        .. code-block:: python

            @db.aggregate(1, 'product')
            class Product(object):
                """Like sum, except calculate the product of a series of numbers."""
                def __init__(self):
                    self.product = 1

                def step(self, value):
                    self.product *= value

                def finalize(self):
                    return self.product

            # To use this aggregate:
            product = (Score
                       .select(fn.product(Score.value))
                       .scalar())

    .. py:method:: collation([name])

        Function decorator for registering a custom collation.

        :param name: string name to use for this collation.

        .. code-block:: python

            @db.collation()
            def collate_reverse(s1, s2):
                return -cmp(s1, s2)

            # To use this collation:
            Book.select().order_by(collate_reverse.collation(Book.title))

        As you might have noticed, the original ``collate_reverse`` function
        has a special attribute called ``collation`` attached to it.  This extra
        attribute provides a shorthand way to generate the SQL necessary to use
        our custom collation.

    .. py:method:: func([name[, num_params]])

        Function decorator for registering user-defined functions.

        :param name: name to use for this function.
        :param num_params: number of parameters this function accepts.  If not
            provided, peewee will introspect the function for you.

        .. code-block:: python

            @db.func()
            def title_case(s):
                return s.title()

            # Use in the select clause...
            titled_books = Book.select(fn.title_case(Book.title))

            @db.func()
            def sha1(s):
                return hashlib.sha1(s).hexdigest()

            # Use in the where clause...
            user = User.select().where(
                (User.username == username) &
                (fn.sha1(User.password) == password_hash)).get()

    .. py:method:: granular_transaction([lock_type='deferred'])

        With the ``granular_transaction`` helper, you can specify the isolation level
        for an individual transaction.  The valid options are:

        * ``exclusive``
        * ``immediate``
        * ``deferred``

        Example usage:

        .. code-block:: python

            with db.granular_transaction('exclusive'):
                # no other readers or writers!
                (Account
                 .update(Account.balance=Account.balance - 100)
                 .where(Account.id == from_acct)
                 .execute())

                (Account
                 .update(Account.balance=Account.balance + 100)
                 .where(Account.id == to_acct)
                 .execute())


.. py:class:: VirtualModel

    Subclass of :py:class:`Model` that signifies the model operates using a
    virtual table provided by a sqlite extension.

    .. py:attribute:: _extension = 'name of sqlite extension'

.. _sqlite_fts:

.. py:class:: FTSModel

    Model class that provides support for Sqlite's full-text search extension.
    Models should be defined normally, however there are a couple caveats:

    * Indexes are ignored completely
    * Sqlite will treat all column types as :py:class:`TextField` (although you
      can store other data types, Sqlite will treat them as text).

    Therefore it usually makes sense to index the content you intend to search
    and a single link back to the original document, since all SQL queries
    *except* full-text searches and ``rowid`` lookups will be slow.

    Example:

    .. code-block:: python

        class Document(FTSModel):
            title = TextField()  # type affinities are ignored by FTS, so use TextField
            content = TextField()

        Document.create_table(tokenize='porter')  # use the porter stemmer.

        # populate documents using normal operations.
        for doc in list_of_docs_to_index:
            Document.create(title=doc['title'], content=doc['content'])

        # use the "match" operation for FTS queries.
        matching_docs = (Document
                         .select()
                         .where(Document.match('some query')))

        # to sort by best match, use the custom "rank" function.
        best = (Document
                .select(Document, Rank(Document).alias('score'))
                .where(Document.match('some query'))
                .order_by(SQL('score').desc()))

        # or use the shortcut method:
        best = Document.search('some phrase')

        # you can also use the BM25 algorithm to rank documents:
        best = (Document
                .select(
                    Document,
                    Document.bm25(Document.content).alias('score'))
                .where(Document.match('some query'))
                .order_by(SQL('score').desc()))

        # There is a shortcut method for bm25 as well:
        best_bm25 = Document.search_bm25('some phrase')

        # BM25 allows you to specify a column if your FTS model contains
        # multiple fields.
        best_bm25 = Document.search_bm25('some phrase', Document.content)

    If you have an existing table and would like to add search for a column
    on that table, you can specify it using the ``content`` option:

    .. code-block:: python

        class Blog(Model):
            title = CharField()
            pub_date = DateTimeField()
            content = TextField()  # we want to search this.

        class FTSBlog(FTSModel):
            content = TextField()

        Blog.create_table()
        FTSBlog.create_table(content=Blog.content)

        # Now, we can manage content in the FTSBlog.  To populate it with
        # content:
        FTSBlog.rebuild()

        # Optimize the index.
        FTSBlog.optimize()

    The ``content`` option accepts either a single :py:class:`Field` or a :py:class:`Model`
    and can reduce the amount of storage used.  However, content will need to be
    manually moved to/from the associated ``FTSModel``.

    .. py:classmethod:: create_table([fail_silently=False[, **options]])

        :param boolean fail_silently: do not re-create if table already exists.
        :param options: options passed along when creating the table, e.g. ``content``.

    .. py:classmethod:: rebuild()

        Rebuild the search index -- this only works when the ``content`` option
        was specified during table creation.

    .. py:classmethod:: optimize()

        Optimize the search index.

    .. py:classmethod:: match(term)

        Shorthand for generating a `MATCH` expression for the given term.

        .. code-block:: python

            query = Document.select().where(Document.match('search phrase'))
            for doc in query:
                print 'match: ', doc.title

    .. py:classmethod:: rank()

        Calculate the rank based on the quality of the match.

        .. code-block:: python

            query = (Document
                     .select(Document, Document.rank().alias('score'))
                     .where(Document.match('search phrase'))
                     .order_by(SQL('score').desc()))

            for search_result in query:
                print search_result.title, search_result.score

    .. py:classmethod:: bm25([field=None[, k=1.2[, b=0.75]]])

        Calculate the rank based on the quality of the match using the
        BM25 algorithm.

        .. note::
            If no field is specified, then the first `TextField` on the model
            will be used. If no `TextField` is present, the first `CharField`
            will be used. Failing either of those conditions, the last overall
            field on the model will be used.

        .. code-block:: python

            query = (Document
                     .select(
                         Document,
                         Document.bm25(Document.content).alias('score'))
                     .where(Document.match('search phrase'))
                     .order_by(SQL('score').desc()))

            for search_result in query:
                print search_result.title, search_result.score

    .. py:classmethod:: search(term[, alias='score'])

        Shorthand way of searching for a term and sorting results by the
        quality of the match. This is equivalent to the :py:meth:`~FTSModel.rank`
        example code presented above.

        :param str term: Search term to use.
        :param str alias: Alias to use for the calculated rank score.

        .. code-block:: python

            docs = Document.search('search term')
            for result in docs:
                print result.title, result.score

    .. py:classmethod:: search_bm25(term[, field=None[, k=1.2[, b=0.75[, alias='score']]]])

        Shorthand way of searching for a term and sorting results by the
        quality of the match, as determined by the BM25 algorithm. This is
        equivalent to the :py:meth:`~FTSModel.bm25` example code presented above.

        :param str term: Search term to use.
        :param Field field: A field on the model.
        :param float k: Parameter for BM25
        :param float b: Parameter for BM25
        :param str alias: Alias to use for the calculated rank score.

        .. note::
            If no field is specified, then the first `TextField` on the model
            will be used. If no `TextField` is present, the first `CharField`
            will be used. Failing either of those conditions, the last overall
            field on the model will be used.

        .. note:: BM25 only works with FTS4 tables.

        .. code-block:: python

            docs = Document.search_bm25('search term')
            for result in docs:
                print result.title, result.score


.. py:function:: match(lhs, rhs)

    Generate a SQLite `MATCH` expression for use in full-text searches.

    .. code-block:: python

        Document.select().where(match(Document.content, 'search term'))

.. py:function:: Rank(model_class)

    Calculate the rank of the search results, for use with `FTSModel` queries
    using the `MATCH` operator.

    .. code-block:: python

        # Search for documents and return results ordered by quality
        # of match.
        docs = (Document
                .select(Document, Rank(Document).alias('score'))
                .where(Document.match('some search term'))
                .order_by(SQL('score').desc()))

.. py:function:: BM25(model_class, field_index)

    Calculate the rank of the search results, for use with `FTSModel` queries
    using the `MATCH` operator.

    :param Model model_class: The `FTSModel` on which the query is being performed.
    :param int field_index: The 0-based index of the field being queried.

    .. code-block:: python

        # Assuming the `content` field has index=2 (0=pk, 1=title, 2=content),
        # calculate the BM25 score for each result.
        docs = (Document
                .select(Document, BM25(Document, 2).alias('score'))
                .where(Document.match('search term'))
                .order_by(SQL('score').desc()))

    .. note:: BM25 only works with FTS4 tables.

.. _berkeleydb:

BerkeleyDB backend
------------------

BerkeleyDB provides a `SQLite-compatible API <http://www.oracle.com/technetwork/database/database-technologies/berkeleydb/overview/sql-160887.html>`_. BerkeleyDB's SQL API has many advantages over SQLite:

* Higher transactions-per-second in multi-threaded environments.
* Built-in replication and hot backup.
* Fewer system calls, less resource utilization.
* Multi-version concurrency control.

For more details, Oracle has published a short `technical overview <http://www.oracle.com/technetwork/database/berkeleydb/learnmore/bdbvssqlite-wp-186779.pdf>`_.

In order to use peewee with BerkeleyDB, you need to compile BerkeleyDB with the SQL API enabled. Then compile the Python SQLite driver against BerkeleyDB's sqlite replacement.

Begin by downloading and compiling BerkeleyDB:

.. code-block:: console

    wget http://download.oracle.com/berkeley-db/db-6.0.30.tar.gz
    tar xzf db-6.0.30.tar.gz
    cd db-6.0.30/build_unix
    export CFLAGS='-DSQLITE_ENABLE_FTS3=1 -DSQLITE_ENABLE_RTREE=1 -fPIC'
    ../dist/configure --enable-static --disable-shared --enable-sql --enable-sql-compat
    make
    sudo make prefix=/usr/local/ install

Then get a copy of the standard library SQLite driver and build it against BerkeleyDB:

.. code-block:: console

    git clone https://github.com/ghaering/pysqlite
    cd pysqlite
    sed -i "s|#||g" setup.cfg
    python setup.py build
    sudo python setup.py install

To simplify this process, peewee comes with a script that will automatically build the appropriate libraries for you. The ``berkeley_build.sh`` script can be found in the ``playhouse`` directory (or you can `view the source online <https://github.com/coleifer/peewee/blob/master/playhouse/berkeley_build.sh>`_).

You can also find `step by step instructions <http://charlesleifer.com/blog/building-the-python-sqlite-driver-for-use-with-berkeleydb/>`_ on my blog.

.. py:class:: BerkeleyDatabase(database, **kwargs)

    Subclass of the :py:class:`SqliteExtDatabase` that supports connecting to BerkeleyDB-backed version of SQLite.

.. _sqlcipher_ext:

Sqlcipher backend
-----------------

.. warning:: This module is experimental.

* Although this extention's code is short, it has not been propery
  peer-reviewed yet and may have introduced vulnerabilities.
* The code contains minimum values for `passphrase` length and
  `kdf_iter`, as well as a default value for the later.
  **Do not** regard these numbers as advice. Consult the docs at
  http://sqlcipher.net/sqlcipher-api/ and security experts.

Also note that this code relies on pysqlcipher_ and sqlcipher_, and
the code there might have vulnerabilities as well, but since these
are widely used crypto modules, we can expect "short zero days" there.

..  _pysqlcipher: https://pypi.python.org/pypi/pysqlcipher
..  _sqlcipher: http://sqlcipher.net

sqlcipher_ext API notes
^^^^^^^^^^^^^^^^^^^^^^^

.. py:class:: SqlCipherDatabase(database, passphrase, kdf_iter=64000, **kwargs)

    Subclass of :py:class:`SqliteDatabase` that stores the database
    encrypted. Instead of the standard ``sqlite3`` backend, it uses pysqlcipher_:
    a python wrapper for sqlcipher_, which -- in turn -- is an encrypted wrapper
    around ``sqlite3``, so the API is *identical* to :py:class:`SqliteDatabase`'s,
    except for object construction parameters:

    :param database: Path to encrypted database filename to open [or create].
    :param passphrase: Database encryption passphrase: should be at least 8 character
        long (or an error is raised), but it is *strongly advised* to enforce better
        `passphrase strength`_ criteria in your implementation.
    :param kdf_iter: [Optional] number of PBKDF2_ iterations.

    * If the ``database`` file doesn't exist, it will be *created* with
      encryption by a key derived from ``passhprase`` with ``kdf_iter``
      PBKDF2_ iterations.
    * When trying to open an existing database, ``passhprase`` and ``kdf_iter``
      should be *identical* to the ones used when it was created.

.. _PBKDF2: https://en.wikipedia.org/wiki/PBKDF2
.. _passphrase strength: https://en.wikipedia.org/wiki/Password_strength

Notes:

    * [Hopefully] there's no way to tell whether the passphrase is wrong
      or the file is corrupt.
      In both cases -- *the first time we try to acces the database* -- a
      :py:class:`DatabaseError` error is raised,
      with the *exact* message: ``"file is encrypted or is not a database"``.

      As mentioned above, this only happens when you *access* the databse,
      so if you need to know *right away* whether the passphrase was correct,
      you can trigger this check by calling [e.g.]
      :py:meth:`~Database.get_tables()` (see example below).

    * Most applications can expect failed attempts to open the database
      (common case: prompting the user for ``passphrase``), so
      the database can't be hardwired into the :py:class:`Meta` of
      model classes, and a :py:class:`Proxy` should be used instead.

Example:

.. code-block:: python

    db_proxy = peewee.Proxy()

    class BaseModel(Model):
        """Parent for all app's models"""
        class Meta:
            # We won't have a valid db until user enters passhrase,
            # so we use a Proxy() instead.
            database = db_proxy

    # Derive our model subclasses
    class Person(BaseModel):
        name = CharField(primary_key=True)

    right_passphrase = False
    while not right_passphrase:
        passphrase = None
        db = SqlCipherDatabase('testsqlcipher.db',
                               get_passphrase_from_user())
        try:  # Error only gets triggered when we access the db
            db.get_tables()
            right_passphrase = True
        except DatabaseError as exc:
            # We only allow a specific [somewhat cryptic] error message.
            if exc.message != 'file is encrypted or is not a database':
                raise exc
        tell_user_the_passphrase_was_wrong()

    # If we're here, db is ok, we can connect it to Model subclasses
    db_proxy.initialize(db)

See also: a slightly more elaborate `example <https://gist.github.com/thedod/11048875#file-testpeeweesqlcipher-py>`_.

.. _dataset:

DataSet
-------

The *dataset* module contains a high-level API for working with databases modeled after the popular `project of the same name <https://dataset.readthedocs.org/en/latest/index.html>`_. The aims of the *dataset* module are to provide:

* A simplified API for working with relational data, along the lines of working with JSON.
* An easy way to export relational data as JSON or CSV.
* An easy way to import JSON or CSV data into a relational database.

A minimal data-loading script might look like this:

.. code-block:: python

    from playhouse.dataset import DataSet

    db = DataSet('sqlite:///:memory:')

    table = db['sometable']
    table.insert(name='Huey', age=3)
    table.insert(name='Mickey', age=5, gender='male')

    huey = table.find_one(name='Huey')
    print huey
    # {'age': 3, 'gender': None, 'id': 1, 'name': 'Huey'}

    for obj in table:
        print obj
    # {'age': 3, 'gender': None, 'id': 1, 'name': 'Huey'}
    # {'age': 5, 'gender': 'male', 'id': 2, 'name': 'Mickey'}

You can export or import data using :py:meth:`~DataSet.freeze` and :py:meth:`~DataSet.thaw`:

.. code-block:: python

    # Export table content to the `users.json` file.
    db.freeze(table.all(), format='json', filename='users.json')

    # Import data from a CSV file into a new table. Columns will be automatically
    # created for each field in the CSV file.
    new_table = db['stats']
    new_table.thaw(format='csv', filename='monthly_stats.csv')

Getting started
^^^^^^^^^^^^^^^

:py:class:`DataSet` objects are initialized by passing in a database URL of the format ``dialect://user:password@host/dbname``. See the :ref:`db_url` section for examples of connecting to various databases.

.. code-block:: python

    # Create an in-memory SQLite database.
    db = DataSet('sqlite:///:memory:')

Storing data
^^^^^^^^^^^^

To store data, we must first obtain a reference to a table. If the table does not exist, it will be created automatically:

.. code-block:: python

    # Get a table reference, creating the table if it does not exist.
    table = db['users']

We can now :py:meth:`~Table.insert` new rows into the table. If the columns do not exist, they will be created automatically:

.. code-block:: python

    table.insert(name='Huey', age=3, color='white')
    table.insert(name='Mickey', age=5, gender='male')

To update existing entries in the table, pass in a dictionary containing the new values and filter conditions. The list of columns to use as filters is specified in the *columns* argument. If no filter columns are specified, then all rows will be updated.

.. code-block:: python

    # Update the gender for "Huey".
    table.update(name='Huey', gender='male', columns=['name'])

    # Update all records. If the column does not exist, it will be created.
    table.update(favorite_orm='peewee')

Importing data
^^^^^^^^^^^^^^

To import data from an external source, such as a JSON or CSV file, you can use the :py:meth:`~Table.thaw` method. By default, new columns will be created for any attributes encountered. If you wish to only populate columns that are already defined on a table, you can pass in ``strict=True``.

.. code-block:: python

    # Load data from a JSON file containing a list of objects.
    table = dataset['stock_prices']
    table.thaw(filename='stocks.json', format='json')
    table.all()[:3]

    # Might print...
    [{'id': 1, 'ticker': 'GOOG', 'price': 703},
     {'id': 2, 'ticker': 'AAPL', 'price': 109},
     {'id': 3, 'ticker': 'AMZN', 'price': 300}]


Using transactions
^^^^^^^^^^^^^^^^^^

DataSet supports nesting transactions using a simple context manager.

.. code-block:: python

    table = db['users']
    with db.transaction() as txn:
        table.insert(name='Charlie')

        with db.transaction() as nested_txn:
            # Set Charlie's favorite ORM to Django.
            table.update(name='Charlie', favorite_orm='django', columns=['name'])

            # jk/lol
            nested_txn.rollback()

Inspecting the database
^^^^^^^^^^^^^^^^^^^^^^^

You can use the :py:meth:`tables` method to list the tables in the current database:

.. code-block:: pycon

    >>> print db.tables
    ['sometable', 'user']

And for a given table, you can print the columns:

.. code-block:: pycon

    >>> table = db['user']
    >>> print table.columns
    ['id', 'age', 'name', 'gender', 'favorite_orm']

We can also find out how many rows are in a table:

.. code-block:: pycon

    >>> print len(db['user'])
    3

Reading data
^^^^^^^^^^^^

To retrieve all rows, you can use the :py:meth:`~Table.all` method:

.. code-block:: python

    # Retrieve all the users.
    users = db['user'].all()

    # We can iterate over all rows without calling `.all()`
    for user in db['user']:
        print user['name']

Specific objects can be retrieved using :py:meth:`~Table.find` and :py:meth:`~Table.find_one`.

.. code-block:: python

    # Find all the users who like peewee.
    peewee_users = db['user'].find(favorite_orm='peewee')

    # Find Huey.
    huey = db['user'].find_one(name='Huey')

Exporting data
^^^^^^^^^^^^^^

To export data, use the :py:meth:`~DataSet.freeze` method, passing in the query you wish to export:

.. code-block:: python

    peewee_users = db['user'].find(favorite_orm='peewee')
    db.freeze(peewee_users, format='json', filename='peewee_users.json')

API
^^^

.. py:class:: DataSet(url)

    The *DataSet* class provides a high-level API for working with relational databases.

    :param str url: A database URL. See :ref:`db_url` for examples.

    .. py:attribute:: tables

        Return a list of tables stored in the database. This list is computed dynamically each time it is accessed.

    .. py:method:: __getitem__(table_name)

        Provide a :py:class:`Table` reference to the specified table. If the table does not exist, it will be created.

    .. py:method:: query(sql[, params=None[, commit=True]])

        :param str sql: A SQL query.
        :param list params: Optional parameters for the query.
        :param bool commit: Whether the query should be committed upon execution.
        :return: A database cursor.

        Execute the provided query against the database.

    .. py:method:: transaction()

        Create a context manager representing a new transaction (or savepoint).

    .. py:method:: freeze(query[, format='csv'[, filename=None[, file_obj=None[, **kwargs]]]])

        :param query: A :py:class:`SelectQuery`, generated using :py:meth:`~Table.all` or `~Table.find`.
        :param format: Output format. By default, *csv* and *json* are supported.
        :param filename: Filename to write output to.
        :param file_obj: File-like object to write output to.
        :param kwargs: Arbitrary parameters for export-specific functionality.

    .. py:method:: thaw(table[, format='csv'[, filename=None[, file_obj=None[, strict=False[, **kwargs]]]]])

        :param str table: The name of the table to load data into.
        :param format: Input format. By default, *csv* and *json* are supported.
        :param filename: Filename to read data from.
        :param file_obj: File-like object to read data from.
        :param bool strict: Whether to store values for columns that do not already exist on the table.
        :param kwargs: Arbitrary parameters for import-specific functionality.

    .. py:method:: connect()

        Open a connection to the underlying database. If a connection is not opened explicitly, one will be opened the first time a query is executed.

    .. py:method:: close()

        Close the connection to the underlying database.

.. py:class:: Table(dataset, name, model_class)

    The *Table* class provides a high-level API for working with rows in a given table.

    .. py:attribute:: columns

        Return a list of columns in the given table.

    .. py:attribute:: model_class

        A dynamically-created :py:class:`Model` class.

    .. py:method:: create_index(columns[, unique=False])

        Create an index on the given columns:

        .. code-block:: python

            # Create a unique index on the `username` column.
            db['users'].create_index(['username'], unique=True)

    .. py:method:: insert(**data)

        Insert the given data dictionary into the table, creating new columns as needed.

    .. py:method:: update(columns=None, conjunction=None, **data)

        Update the table using the provided data. If one or more columns are specified in the *columns* parameter, then those columns' values in the *data* dictionary will be used to determine which rows to update.

        .. code-block:: python

            # Update all rows.
            db['users'].update(favorite_orm='peewee')

            # Only update Huey's record, setting his age to 3.
            db['users'].update(name='Huey', age=3, columns=['name'])

    .. py:method:: find(**query)

        Query the table for rows matching the specified equality conditions. If no query is specified, then all rows are returned.

        .. code-block:: python

            peewee_users = db['users'].find(favorite_orm='peewee')

    .. py:method:: find_one(**query)

        Return a single row matching the specified equality conditions. If no matching row is found then ``None`` will be returned.

        .. code-block:: python

            huey = db['users'].find_one(name='Huey')

    .. py:method:: all()

        Return all rows in the given table.

    .. py:method:: delete(**query)

        Delete all rows matching the given equality conditions. If no query is provided, then all rows will be deleted.

        .. code-block:: python

            # Adios, Django!
            db['users'].delete(favorite_orm='Django')

            # Delete all the secret messages.
            db['secret_messages'].delete()

    .. py:method:: freeze([format='csv'[, filename=None[, file_obj=None[, **kwargs]]]])

        :param format: Output format. By default, *csv* and *json* are supported.
        :param filename: Filename to write output to.
        :param file_obj: File-like object to write output to.
        :param kwargs: Arbitrary parameters for export-specific functionality.

    .. py:method:: thaw([format='csv'[, filename=None[, file_obj=None[, strict=False[, **kwargs]]]]])

        :param format: Input format. By default, *csv* and *json* are supported.
        :param filename: Filename to read data from.
        :param file_obj: File-like object to read data from.
        :param bool strict: Whether to store values for columns that do not already exist on the table.
        :param kwargs: Arbitrary parameters for import-specific functionality.


.. _djpeewee:

Django Integration
------------------

The Django ORM provides a very high-level abstraction over SQL and as a consequence is in some ways
`limited in terms of flexibility or expressiveness <http://charlesleifer.com/blog/shortcomings-in-the-django-orm-and-a-look-at-peewee-a-lightweight-alternative/>`_. I
wrote a `blog post <http://charlesleifer.com/blog/the-search-for-the-missing-link-what-lies-between-sql-and-django-s-orm-/>`_
describing my search for a "missing link" between Django's ORM and the SQL it
generates, concluding that no such layer exists.  The ``djpeewee`` module attempts
to provide an easy-to-use, structured layer for generating SQL queries for use
with Django's ORM.

A couple use-cases might be:

* Joining on fields that are not related by foreign key (for example UUID fields).
* Performing aggregate queries on calculated values.
* Features that Django does not support such as ``CASE`` statements.
* Utilizing SQL functions that Django does not support, such as ``SUBSTR``.
* Replacing nearly-identical SQL queries with reusable, composable data-structures.

Below is an example of how you might use this:

.. code-block:: python

    # Django model.
    class Event(models.Model):
        start_time = models.DateTimeField()
        end_time = models.DateTimeField()
        title = models.CharField(max_length=255)

    # Suppose we want to find all events that are longer than an hour.  Django
    # does not support this, but we can use peewee.
    from playhouse.djpeewee import translate
    P = translate(Event)
    query = (P.Event
             .select()
             .where(
                 (P.Event.end_time - P.Event.start_time) > timedelta(hours=1)))

    # Now feed our peewee query into Django's `raw()` method:
    sql, params = query.sql()
    Event.objects.raw(sql, params)

Foreign keys and Many-to-many relationships
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The :py:func:`translate` function will recursively traverse the graph of models
and return a dictionary populated with everything it finds.  Back-references are
not searched by default, but can be included by specifying ``backrefs=True``.

Example:

.. code-block:: pycon

    >>> from django.contrib.auth.models import User, Group
    >>> from playhouse.djpeewee import translate
    >>> translate(User, Group)
    {'ContentType': peewee.ContentType,
     'Group': peewee.Group,
     'Group_permissions': peewee.Group_permissions,
     'Permission': peewee.Permission,
     'User': peewee.User,
     'User_groups': peewee.User_groups,
     'User_user_permissions': peewee.User_user_permissions}

As you can see in the example above, although only `User` and `Group` were passed
in to :py:func:`translate`, several other models which are related by foreign key
were also created. Additionally, the many-to-many "through" tables were created
as separate models since peewee does not abstract away these types of relationships.

Using the above models it is possible to construct joins.  The following example
will get all users who belong to a group that starts with the letter "A":

.. code-block:: pycon

    >>> P = translate(User, Group)
    >>> query = P.User.select().join(P.User_groups).join(P.Group).where(
    ...     fn.Lower(fn.Substr(P.Group.name, 1, 1)) == 'a')
    >>> sql, params = query.sql()
    >>> print sql  # formatted for legibility
    SELECT t1."id", t1."password", ...
    FROM "auth_user" AS t1
    INNER JOIN "auth_user_groups" AS t2 ON (t1."id" = t2."user_id")
    INNER JOIN "auth_group" AS t3 ON (t2."group_id" = t3."id")
    WHERE (Lower(Substr(t3."name", %s, %s)) = %s)

djpeewee API
^^^^^^^^^^^^

.. py:function:: translate(*models, **options)

    Translate the given Django models into roughly equivalent peewee models
    suitable for use constructing queries. Foreign keys and many-to-many relationships
    will be followed and models generated, although back references are not traversed.

    :param models: One or more Django model classes.
    :param options: A dictionary of options, see note below.
    :returns: A dict-like object containing the generated models, but which supports
        dotted-name style lookups.

    The following are valid options:

    * ``recurse``: Follow foreign keys and many to many (default: ``True``).
    * ``max_depth``: Maximum depth to recurse (default: ``None``, unlimited).
    * ``backrefs``: Follow backrefs (default: ``False``).
    * ``exclude``: A list of models to exclude.


.. _gfk:

Generic foreign keys
--------------------

The ``gfk`` module provides a Generic ForeignKey (GFK), similar to Django.  A GFK
is composed of two columns: an object ID and an object type identifier.  The
object types are collected in a global registry (``all_models``).

How a :py:class:`GFKField` is resolved:

1. Look up the object type in the global registry (returns a model class)
2. Look up the model instance by object ID

.. note:: In order to use Generic ForeignKeys, your application's models *must*
    subclass ``playhouse.gfk.Model``.  This ensures that the model class will
    be added to the global registry.

.. note:: GFKs themselves are not actually a field and will not add a column
    to your table.

Like regular ForeignKeys, GFKs support a "back-reference" via the :py:class:`ReverseGFK`
descriptor.

How to use GFKs
^^^^^^^^^^^^^^^

1. Be sure your model subclasses ``playhouse.gfk.Model``
2. Add a :py:class:`CharField` to store the ``object_type``
3. Add a field to store the ``object_id`` (usually a :py:class:`IntegerField`)
4. Add a :py:class:`GFKField` and instantiate it with the names of the ``object_type``
   and ``object_id`` fields.
5. (optional) On any other models, add a :py:class:`ReverseGFK` descriptor

Example:

.. code-block:: python

    from playhouse.gfk import *

    class Tag(Model):
        tag = CharField()
        object_type = CharField(null=True)
        object_id = IntegerField(null=True)
        object = GFKField('object_type', 'object_id')

    class Blog(Model):
        tags = ReverseGFK(Tag, 'object_type', 'object_id')

    class Photo(Model):
        tags = ReverseGFK(Tag, 'object_type', 'object_id')

How you use these is pretty straightforward hopefully:

.. code-block:: pycon

    >>> b = Blog.create(name='awesome post')
    >>> Tag.create(tag='awesome', object=b)
    >>> b2 = Blog.create(name='whiny post')
    >>> Tag.create(tag='whiny', object=b2)

    >>> b.tags # <-- a select query
    <class '__main__.Tag'> SELECT t1."id", t1."tag", t1."object_type", t1."object_id" FROM "tag" AS t1 WHERE ((t1."object_type" = ?) AND (t1."object_id" = ?)) [u'blog', 1]

    >>> [x.tag for x in b.tags]
    [u'awesome']

    >>> [x.tag for x in b2.tags]
    [u'whiny']

    >>> p = Photo.create(name='picture of cat')
    >>> Tag.create(object=p, tag='kitties')
    >>> Tag.create(object=p, tag='cats')

    >>> [x.tag for x in p.tags]
    [u'kitties', u'cats']

    >>> [x.tag for x in Blog.tags]
    [u'awesome', u'whiny']

    >>> t = Tag.get(Tag.tag == 'awesome')
    >>> t.object
    <__main__.Blog at 0x268f450>

    >>> t.object.name
    u'awesome post'

GFK API
^^^^^^^

.. py:class:: GFKField([model_type_field='object_type'[, model_id_field='object_id']])

    Provide a clean API for storing "generic" foreign keys.  Generic foreign keys
    are comprised of an object type, which maps to a model class, and an object id,
    which maps to the primary key of the related model class.

    Setting the GFKField on a model will automatically populate the ``model_type_field``
    and ``model_id_field``.  Similarly, getting the GFKField on a model instance
    will "resolve" the two fields, first looking up the model class, then looking
    up the instance by ID.

.. py:class:: ReverseGFK(model, [model_type_field='object_type'[, model_id_field='object_id']])

    Back-reference support for :py:class:`GFKField`.

.. _kv:

Key/Value Store
---------------

Provides a simple key/value store, using a dictionary API.  By default the
the :py:class:`KeyStore` will use an in-memory sqlite database, but any database
will work.

To start using the key-store, create an instance and pass it a field to use
for the values.

.. code-block:: python

    >>> kv = KeyStore(TextField())
    >>> kv['a'] = 'A'
    >>> kv['a']
    'A'

.. note::
  To store arbitrary python objects, use the :py:class:`PickledKeyStore`, which
  stores values in a pickled :py:class:`BlobField`.

Using the :py:class:`KeyStore` it is possible to use "expressions" to retrieve
values from the dictionary.  For instance, imagine you want to get all keys
which contain a certain substring:

.. code-block:: python

    >>> keys_matching_substr = kv[kv.key % '%substr%']
    >>> keys_start_with_a = kv[fn.Lower(fn.Substr(kv.key, 1, 1)) == 'a']

KeyStore API
^^^^^^^^^^^^

.. py:class:: KeyStore(value_field[, ordered=False[, database=None]])

    Lightweight dictionary interface to a model containing a key and value.
    Implements common dictionary methods, such as ``__getitem__``, ``__setitem__``,
    ``get``, ``pop``, ``items``, ``keys``, and ``values``.

    :param Field value_field: Field instance to use as value field, e.g. an
        instance of :py:class:`TextField`.
    :param boolean ordered: Whether the keys should be returned in sorted order
    :param Database database: :py:class:`Database` class to use for the storage
        backend.  If none is supplied, an in-memory Sqlite DB will be used.

    Example:

    .. code-block:: pycon

        >>> from playhouse.kv import KeyStore
        >>> kv = KeyStore(TextField())
        >>> kv['a'] = 'foo'
        >>> for k, v in kv:
        ...     print k, v
        a foo

        >>> 'a' in kv
        True
        >>> 'b' in kv
        False

.. py:class:: PickledKeyStore([ordered=False[, database=None]])

    Identical to the :py:class:`KeyStore` except *anything* can be stored as
    a value in the dictionary.  The storage for the value will be a pickled
    :py:class:`BlobField`.

    Example:

    .. code-block:: pycon

        >>> from playhouse.kv import PickledKeyStore
        >>> pkv = PickledKeyStore()
        >>> pkv['a'] = 'A'
        >>> pkv['b'] = 1.0
        >>> list(pkv.items())
        [(u'a', 'A'), (u'b', 1.0)]

.. _shortcuts:

Shortcuts
---------

This module contains helper functions for expressing things that would otherwise
be somewhat verbose or cumbersome using peewee's APIs.

.. py:function:: case(predicate, expression_tuples, default=None)

    :param predicate: A SQL expression or can be ``None``.
    :param expression_tuples: An iterable containing one or more 2-tuples
      comprised of an expression and return value.
    :param default: default if none of the cases match.

    Example SQL case statements:

    .. code-block:: sql

        -- case with predicate --
        SELECT "username",
          CASE "user_id"
            WHEN 1 THEN "one"
            WHEN 2 THEN "two"
            ELSE "?"
          END
        FROM "users";

        -- case with no predicate (inline expressions) --
        SELECT "username",
          CASE
            WHEN "user_id" = 1 THEN "one"
            WHEN "user_id" = 2 THEN "two"
            ELSE "?"
          END
        FROM "users";

    Equivalent function invocations:

    .. code-block:: python

        User.select(User.username, case(User.user_id, (
          (1, "one"),
          (2, "two")), "?"))

        User.select(User.username, case(None, (
          (User.user_id == 1, "one"),  # note the double equals
          (User.user_id == 2, "two")), "?"))

    You can specify a value for the CASE expression using the ``alias()``
    method:

    .. code-block:: python

        User.select(User.username, case(User.user_id, (
          (1, "one"),
          (2, "two")), "?").alias("id_string"))


.. py:function:: model_to_dict(model[, recurse=True[, backrefs=False[, only=None[, exclude=None]]]])

    Convert a model instance (and optionally any related instances) to
    a dictionary.

    :param bool recurse: Whether foreign-keys should be recursed.
    :param bool backrefs: Whether lists of related objects should be recursed.
    :param only: A list (or set) of field instances which should be included in the result dictionary.
    :param exclude: A list (or set) of field instances which should be excluded from the result dictionary.

    Examples:

    .. code-block:: pycon

        >>> user = User.create(username='charlie')
        >>> model_to_dict(user)
        {'id': 1, 'username': 'charlie'}

        >>> model_to_dict(user, backrefs=True)
        {'id': 1, 'tweets': [], 'username': 'charlie'}

        >>> t1 = Tweet.create(user=user, message='tweet-1')
        >>> t2 = Tweet.create(user=user, message='tweet-2')
        >>> model_to_dict(user, backrefs=True)
        {
          'id': 1,
          'tweets': [
            {'id': 1, 'message': 'tweet-1'},
            {'id': 2, 'message': 'tweet-2'},
          ],
          'username': 'charlie'
        }

        >>> model_to_dict(t1)
        {
          'id': 1,
          'message': 'tweet-1',
          'user': {
            'id': 1,
            'username': 'charlie'
          }
        }

        >>> model_to_dict(t2, recurse=False)
        {'id': 1, 'message': 'tweet-2', 'user': 1}

.. py:function:: dict_to_model(model_class, data[, ignore_unknown=False])

    Convert a dictionary of data to a model instance, creating related
    instances where appropriate.

    :param Model model_class: The model class to construct.
    :param dict data: A dictionary of data. Foreign keys can be included as nested dictionaries, and back-references as lists of dictionaries.
    :param bool ignore_unknown: Whether to allow unrecognized (non-field) attributes.

    Examples:

    .. code-block:: pycon

        >>> user_data = {'id': 1, 'username': 'charlie'}
        >>> user = dict_to_model(User, user_data)
        >>> user
        <__main__.User at 0x7fea8fa4d490>

        >>> user.username
        'charlie'

        >>> note_data = {'id': 2, 'text': 'note text', 'user': user_data}
        >>> note = dict_to_model(Note, note_data)
        >>> note.text
        'note text'
        >>> note.user.username
        'charlie'

        >>> user_with_notes = {
        ...     'id': 1,
        ...     'username': 'charlie',
        ...     'notes': [{'id': 1, 'text': 'note-1'}, {'id': 2, 'text': 'note-2'}]}
        >>> user = dict_to_model(User, user_with_notes)
        >>> user.notes[0].text
        'note-1'
        >>> user.notes[0].user.username
        'charlie'


.. _signals:

Signal support
--------------

Models with hooks for signals (a-la django) are provided in ``playhouse.signals``.
To use the signals, you will need all of your project's models to be a subclass
of ``playhouse.signals.Model``, which overrides the necessary methods to provide
support for the various signals.

.. highlight:: python
.. code-block:: python

    from playhouse.signals import Model, post_save


    class MyModel(Model):
        data = IntegerField()

    @post_save(sender=MyModel)
    def on_save_handler(model_class, instance, created):
        put_data_in_cache(instance.data)


The following signals are provided:

``pre_save``
    Called immediately before an object is saved to the database.  Provides an
    additional keyword argument ``created``, indicating whether the model is being
    saved for the first time or updated.
``post_save``
    Called immediately after an object is saved to the database.  Provides an
    additional keyword argument ``created``, indicating whether the model is being
    saved for the first time or updated.
``pre_delete``
    Called immediately before an object is deleted from the database when :py:meth:`Model.delete_instance`
    is used.
``post_delete``
    Called immediately after an object is deleted from the database when :py:meth:`Model.delete_instance`
    is used.
``pre_init``
    Called when a model class is first instantiated
``post_init``
    Called after a model class has been instantiated and the fields have been populated,
    for example when being selected as part of a database query.


Connecting handlers
^^^^^^^^^^^^^^^^^^^

Whenever a signal is dispatched, it will call any handlers that have been registered.
This allows totally separate code to respond to events like model save and delete.

The :py:class:`Signal` class provides a :py:meth:`~Signal.connect` method, which takes
a callback function and two optional parameters for "sender" and "name".  If specified,
the "sender" parameter should be a single model class and allows your callback to only
receive signals from that one model class.  The "name" parameter is used as a convenient alias
in the event you wish to unregister your signal handler.

Example usage:

.. code-block:: python

    from playhouse.signals import *

    def post_save_handler(sender, instance, created):
        print '%s was just saved' % instance

    # our handler will only be called when we save instances of SomeModel
    post_save.connect(post_save_handler, sender=SomeModel)

All signal handlers accept as their first two arguments ``sender`` and ``instance``,
where ``sender`` is the model class and ``instance`` is the actual model being acted
upon.

If you'd like, you can also use a decorator to connect signal handlers.  This is
functionally equivalent to the above example:

.. code-block:: python

    @post_save(sender=SomeModel)
    def post_save_handler(sender, instance, created):
        print '%s was just saved' % instance


Signal API
^^^^^^^^^^

.. py:class:: Signal()

    Stores a list of receivers (callbacks) and calls them when the "send" method is invoked.

    .. py:method:: connect(receiver[, sender=None[, name=None]])

        Add the receiver to the internal list of receivers, which will be called
        whenever the signal is sent.

        :param callable receiver: a callable that takes at least two parameters,
            a "sender", which is the Model subclass that triggered the signal, and
            an "instance", which is the actual model instance.
        :param Model sender: if specified, only instances of this model class will
            trigger the receiver callback.
        :param string name: a short alias

        .. code-block:: python

            from playhouse.signals import post_save
            from project.handlers import cache_buster

            post_save.connect(cache_buster, name='project.cache_buster')

    .. py:method:: disconnect([receiver=None[, name=None]])

        Disconnect the given receiver (or the receiver with the given name alias)
        so that it no longer is called.  Either the receiver or the name must be
        provided.

        :param callable receiver: the callback to disconnect
        :param string name: a short alias

        .. code-block:: python

            post_save.disconnect(name='project.cache_buster')

    .. py:method:: send(instance, *args, **kwargs)

        Iterates over the receivers and will call them in the order in which
        they were connected.  If the receiver specified a sender, it will only
        be called if the instance is an instance of the sender.

        :param instance: a model instance


    .. py:method __call__([sender=None[, name=None]])

        Function decorator that is an alias for a signal's connect method:

        .. code-block:: python

            from playhouse.signals import connect, post_save

            @post_save(name='project.cache_buster')
            def cache_bust_handler(sender, instance, *args, **kwargs):
                # bust the cache for this instance
                cache.delete(cache_key_for(instance))

.. _pwiz:

pwiz, a model generator
-----------------------

``pwiz`` is a little script that ships with peewee and is capable of introspecting
an existing database and generating model code suitable for interacting with the
underlying data.  If you have a database already, pwiz can give you a nice boost
by generating skeleton code with correct column affinities and foreign keys.

If you install peewee using ``setup.py install``, pwiz will be installed as a "script"
and you can just run:

.. highlight:: console
.. code-block:: console

    python -m pwiz -e postgresql -u postgres my_postgres_db

This will print a bunch of models to standard output.  So you can do this:

.. code-block:: console

    python -m pwiz -e postgresql my_postgres_db > mymodels.py
    python # <-- fire up an interactive shell


.. highlight:: pycon
.. code-block:: pycon

    >>> from mymodels import Blog, Entry, Tag, Whatever
    >>> print [blog.name for blog in Blog.select()]


======    ========================= ============================================
Option    Meaning                   Example
======    ========================= ============================================
-h        show help
-e        database backend          -e mysql
-H        host to connect to        -H remote.db.server
-p        port to connect on        -p 9001
-u        database user             -u postgres
-P        database password         -P secret
-s        postgres schema           -s public
======    ========================= ============================================

The following are valid parameters for the engine:

* sqlite
* mysql
* postgresql

.. _migrate:

Schema Migrations
-----------------

Peewee now supports schema migrations, with well-tested support for Postgresql,
SQLite and MySQL. Unlike other schema migration tools, peewee's migrations
do not handle introspection and database "versioning". Rather, peewee provides a number of
helper functions for generating and running schema-altering statements. This engine provides
the basis on which a more sophisticated tool could some day be built.

Migrations can be written as simple python scripts and executed from the command-line. Since
the migrations only depend on your applications :py:class:`Database` object, it should be
easy to manage changing your model definitions and maintaining a set of migration scripts without
introducing dependencies.

Example usage
^^^^^^^^^^^^^

Begin by importing the helpers from the `migrate` module:

.. code-block:: python

    from playhouse.migrate import *

Instantiate a ``migrator``. The :py:class:`SchemaMigrator` class is responsible for
generating schema altering operations, which can then be run sequentially by the
:py:func:`migrate` helper.

.. code-block:: python

    # Postgres example:
    my_db = PostgresqlDatabase(...)
    migrator = PostgresqlMigrator(my_db)

    # SQLite example:
    my_db = SqliteDatabase('my_database.db')
    migrator = SqliteMigrator(my_db)

Use :py:func:`migrate` to execute one or more operations:

.. code-block:: python

    title_field = CharField(default='')
    status_field = IntegerField(null=True)

    migrate(
        migrator.add_column('some_table', 'title', title_field),
        migrator.add_column('some_table', 'status', status_field),
        migrator.drop_column('some_table', 'old_column'),
    )

.. warning::
    Migrations are not run inside a transaction. If you wish the migration to run
    in a transaction you will need to wrap the call to `migrate` in a transaction
    block, e.g.

    .. code-block:: python

        with my_db.transaction():
            migrate(...)

Supported Operations
^^^^^^^^^^^^^^^^^^^^

Add new field(s) to an existing model:

.. code-block:: python

    # Create your field instances. For non-null fields you must specify a
    # default value.
    pubdate_field = DateTimeField(null=True)
    comment_field = TextField(default='')

    # Run the migration, specifying the database table, field name and field.
    migrate(
        migrator.add_column('comment_tbl', 'pub_date', pubdate_field),
        migrator.add_column('comment_tbl', 'comment', comment_field),
    )

Renaming a field:

.. code-block:: python

    # Specify the table, original name of the column, and its new name.
    migrate(
        migrator.rename_column('story', 'pub_date', 'publish_date'),
        migrator.rename_column('story', 'mod_date', 'modified_date'),
    )

Dropping a field:

.. code-block:: python

    migrate(
        migrator.drop_column('story', 'some_old_field'),
    )

Making a field nullable or not nullable:

.. code-block:: python

    # Note that when making a field not null that field must not have any
    # NULL values present.
    migrate(
        # Make `pub_date` allow NULL values.
        migrator.drop_not_null('story', 'pub_date'),

        # Prevent `modified_date` from containing NULL values.
        migrator.add_not_null('story', 'modified_date'),
    )

Renaming a table:

.. code-block:: python

    migrate(
        migrator.rename_table('story', 'stories_tbl'),
    )

Adding an index:

.. code-block:: python

    # Specify the table, column names, and whether the index should be
    # UNIQUE or not.
    migrate(
        # Create an index on the `pub_date` column.
        migrator.add_index('story', ('pub_date',), False),

        # Create a multi-column index on the `pub_date` and `status` fields.
        migrator.add_index('story', ('pub_date', 'status'), False),

        # Create a unique index on the category and title fields.
        migrator.add_index('story', ('category_id', 'title'), True),
    )

Dropping an index:

.. code-block:: python

    # Specify the index name.
    migrate(migrator.drop_index('story', 'story_pub_date_status'))


Migrations API
^^^^^^^^^^^^^^

.. py:function:: migrate(*operations)

    Execute one or more schema altering operations.

    Usage:

    .. code-block:: python

        migrate(
            migrator.add_column('some_table', 'new_column', CharField(default='')),
            migrator.create_index('some_table', ('new_column',)),
        )

.. py:class:: SchemaMigrator(database)

    :param database: a :py:class:`Database` instance.

    The :py:class:`SchemaMigrator` is responsible for generating schema-altering
    statements.

    .. py:method:: add_column(table, column_name, field)

        :param str table: Name of the table to add column to.
        :param str column_name: Name of the new column.
        :param Field field: A :py:class:`Field` instance.

        Add a new column to the provided table. The ``field`` provided will be used
        to generate the appropriate column definition.

        .. note:: If the field is not nullable it must specify a default value.

        .. note::
            For non-null fields, the field will initially be added as a null field,
            then an ``UPDATE`` statement will be executed to populate the column
            with the default value. Finally, the column will be marked as not null.

    .. py:method:: drop_column(table, column_name[, cascade=True])

        :param str table: Name of the table to drop column from.
        :param str column_name: Name of the column to drop.
        :param bool cascade: Whether the column should be dropped with `CASCADE`.

    .. py:method:: rename_column(table, old_name, new_name)

        :param str table: Name of the table containing column to rename.
        :param str old_name: Current name of the column.
        :param str new_name: New name for the column.

    .. py:method:: add_not_null(table, column)

        :param str table: Name of table containing column.
        :param str column: Name of the column to make not nullable.

    .. py:method:: drop_not_null(table, column)

        :param str table: Name of table containing column.
        :param str column: Name of the column to make nullable.

    .. py:method:: rename_table(old_name, new_name)

        :param str old_name: Current name of the table.
        :param str new_name: New name for the table.

    .. py:method:: add_index(table, columns[, unique=False])

        :param str table: Name of table on which to create the index.
        :param list columns: List of columns which should be indexed.
        :param bool unique: Whether the new index should specify a unique constraint.

    .. py:method:: drop_index(table, index_name)

        :param str table Name of the table containing the index to be dropped.
        :param str index_name: Name of the index to be dropped.

.. py:class:: PostgresqlMigrator(database)

    Generate migrations for Postgresql databases.

.. py:class:: SqliteMigrator(database)

    Generate migrations for SQLite databases.

.. py:class:: MySQLMigrator(database)

    Generate migrations for MySQL databases.

    .. warning:: The MySQL migrations are not well tested.


.. _reflection:

Reflection
----------

The reflection module contains helpers for introspecting existing databases. This module is used internally by several other modules in the playhouse, including :ref:`dataset` and :ref:`pwiz`.

.. py:class:: Introspector(metadata[, schema=None])

    Metadata can be extracted from a database by instantiating an :py:class:`Introspector`. Rather than instantiating this class directly, it is recommended to use the factory method :py:meth:`~Introspector.from_database`.

    .. py:classmethod:: from_database(database[, schema=None])

        Creates an :py:class:`Introspector` instance suitable for use with the given database.

        :param database: a :py:class:`Database` instance.
        :param str schema: an optional schema (supported by some databases).

        Usage:

        .. code-block:: python

            db = SqliteDatabase('my_app.db')
            introspector = Introspector.from_database(db)
            models = introspector.generate_models()

            # User and Tweet (assumed to exist in the database) are
            # peewee Model classes generated from the database schema.
            User = models['user']
            Tweet = models['tweet']

    .. py:method:: generate_models()

        Introspect the database, reading in the tables, columns, and foreign key constraints, then generate a dictionary mapping each database table to a dynamically-generated :py:class:`Model` class.

        :return: A dictionary mapping table-names to model classes.


.. _db_url:

Database URL
------------

This module contains a helper function to generate a database connection from a URL connection string.

.. py:function:: connect(url)

    Create a :py:class:`Database` instance from the given connection URL.

    Examples:

    * *sqlite:///my_database.db* will create a :py:class:`SqliteDatabase` instance for the file ``my_database.db`` in the current directory.
    * *postgresql://postgres:my_password@localhost:5432/my_database* will create a :py:class:`PostgresqlDatabase` instance. A username and password are provided, as well as the host and port to connect to.
    * *mysql:///my_db* will create a :py:class:`MySQLDatabase` instance for the local MySQL database *my_db*.

    Usage:

    .. code-block:: python

        import os
        from playhouse.db_url import connect

        # Connect to the database URL defined in the environment, falling
        # back to a local Sqlite database if no database URL is specified.
        db = connect(os.environ.get('DATABASE') or 'sqlite:///default.db')


.. _csv_utils:

CSV Utils
---------

This module contains helpers for dumping queries into CSV, and for loading CSV data into a database.  CSV files can be introspected to generate an appropriate model class for working with the data. This makes it really easy to explore the data in a CSV file using Peewee and SQL.

Here is how you would load a CSV file into an in-memory SQLite database.  The call to :py:func:`load_csv` returns a :py:class:`Model` instance suitable for working with the CSV data:

.. code-block:: python

    from peewee import *
    from playhouse.csv_loader import load_csv
    db = SqliteDatabase(':memory:')
    ZipToTZ = load_csv(db, 'zip_to_tz.csv')

Now we can run queries using the new model.

.. code-block:: pycon

    # Get the timezone for a zipcode.
    >>> ZipToTZ.get(ZipToTZ.zip == 66047).timezone
    'US/Central'

    # Get all the zipcodes for my town.
    >>> [row.zip for row in ZipToTZ.select().where(
    ...     (ZipToTZ.city == 'Lawrence') && (ZipToTZ.state == 'KS'))]
    [66044, 66045, 66046, 66047, 66049]

For more information and examples check out this `blog post <http://charlesleifer.com/blog/using-peewee-to-explore-csv-files/>`_.

CSV Loader API
^^^^^^^^^^^^^^

.. py:function:: load_csv(db_or_model, filename[, fields=None[, field_names=None[, has_header=True[, sample_size=10[, converter=None[, db_table=None[, **reader_kwargs]]]]]]])

    Load a CSV file into the provided database or model class, returning a
    :py:class:`Model` suitable for working with the CSV data.

    :param db_or_model: Either a :py:class:`Database` instance or a :py:class:`Model` class.  If a model is not provided, one will be automatically generated for you.
    :param str filename: Path of CSV file to load.
    :param list fields: A list of :py:class:`Field` instances mapping to each column in the CSV.  This allows you to manually specify the column types.  If not provided, and a model is not provided, the field types will be determined automatically.
    :param list field_names: A list of strings to use as field names for each column in the CSV.  If not provided, and a model is not provided, the field names will be determined by looking at the header row of the file.  If no header exists, then the fields will be given generic names.
    :param bool has_header: Whether the first row is a header.
    :param int sample_size: Number of rows to look at when introspecting data types.  If set to ``0``, then a generic field type will be used for all fields.
    :param RowConverter converter: a :py:class:`RowConverter` instance to use for introspecting the CSV.  If not provided, one will be created.
    :param str db_table: The name of the database table to load data into.  If this value is not provided, it will be determined using the filename of the CSV file.  If a model is provided, this value is ignored.
    :param reader_kwargs: Arbitrary keyword arguments to pass to the ``csv.reader`` object, such as the dialect, separator, etc.
    :rtype: A :py:class:`Model` suitable for querying the CSV data.

    Basic example -- field names and types will be introspected:

    .. code-block:: python

        from peewee import *
        from playhouse.csv_loader import *
        db = SqliteDatabase(':memory:')
        User = load_csv(db, 'users.csv')

    Using a pre-defined model:

    .. code-block:: python

        class ZipToTZ(Model):
            zip = IntegerField()
            timezone = CharField()

        load_csv(ZipToTZ, 'zip_to_tz.csv')

    Specifying fields:

    .. code-block:: python

        fields = [DecimalField(), IntegerField(), IntegerField(), DateField()]
        field_names = ['amount', 'from_acct', 'to_acct', 'timestamp']
        Payments = load_csv(db, 'payments.csv', fields=fields, field_names=field_names, has_header=False)

Dumping CSV
^^^^^^^^^^^

.. py:function:: dump_csv(query, file_or_name[, include_header=True[, close_file=True[, append=True[, csv_writer=None]]]])

    :param query: A peewee :py:class:`SelectQuery` to dump as CSV.
    :param file_or_name: Either a filename or a file-like object.
    :param include_header: Whether to generate a CSV header row consisting of the names of the selected columns.
    :param close_file: Whether the file should be closed after writing the query data.
    :param append: Whether new data should be appended to the end of the file.
    :param csv_writer: A python ``csv.writer`` instance to use.

    Example usage:

    .. code-block:: python

        with open('account-export.csv', 'w') as fh:
            query = Account.select().order_by(Account.id)
            dump_csv(query, fh)


.. _pool:

Connection pool
---------------

.. warning:: This module should be considered experimental.

The ``pool`` module contains a helper class to pool database connections, as well as implementations
for PostgreSQL and MySQL. The pool works by overriding the methods on the :py:class:`Database` class
that open and close connections to the backend. The pool can specify a timeout after which connections
are recycled, as well as an upper bound on the number of open connections.

If your application is single-threaded, only one connection will be opened.

If your application is multi-threaded (this includes green threads) and you specify `threadlocals=True`
when instantiating your database, then up to `max_connections` will be opened. As of version 2.3.3, this is the default behavior.

.. py:class:: PooledDatabase(database[, max_connections=20[, stale_timeout=None[, **kwargs]]])

    Mixin class intended to be used with a subclass of :py:class:`Database`.

    :param str database: The name of the database or database file.
    :param int max_connections: Maximum number of connections. Provide ``None`` for unlimited.
    :param int stale_timeout: Number of seconds to allow connections to be used.
    :param kwargs: Arbitrary keyword arguments passed to database class.

    .. note:: Connections will not be closed exactly when they exceed their `stale_timeout`.
        Instead, stale connections are only closed when a new connection is requested.

    .. note:: If the number of open connections exceeds `max_connections`, a `ValueError` will
        be raised.

    .. py:method:: manual_close()

        Close the currently-open connection without returning it to the pool.

    .. py:method:: _connect(*args, **kwargs)

        Request a connection from the pool. If there are no available connections a new one will
        be opened.

    .. py:method:: _close(conn[, close_conn=False])

        By default `conn` will not be closed and instead will be returned to the pool of available
        connections. If `close_conn=True`, then `conn` will be closed and *not* be returned to the pool.

.. py:class:: PooledPostgresqlDatabase

    Subclass of :py:class:`PostgresqlDatabase` that mixes in the :py:class:`PooledDatabase` helper.

.. py:class:: PooledPostgresqlExtDatabase

    Subclass of :py:class:`PostgresqlExtDatabase` that mixes in the :py:class:`PooledDatabase` helper. The :py:class:`PostgresqlExtDatabase` is a part of the
    :ref:`postgres_ext` module and provides support for many Postgres-specific
    features.

.. py:class:: PooledMySQLDatabase

    Subclass of :py:class:`MySQLDatabase` that mixes in the :py:class:`PooledDatabase` helper.


.. _read_slaves:

Read Slaves
-----------

The ``read_slave`` module contains a :py:class:`Model` subclass that can be used
to automatically execute ``SELECT`` queries against different database(s). This
might be useful if you have your databases in a master / slave configuration.

.. py:class:: ReadSlaveModel

    Model subclass that will route ``SELECT`` queries to a different database.

    Master and read-slaves are specified using ``Model.Meta``:

    .. code-block:: python

        # Declare a master and two read-replicas.
        master = PostgresqlDatabase('master')
        replica_1 = PostgresqlDatabase('replica_1')
        replica_2 = PostgresqlDatabase('replica_2')

        # Declare a BaseModel, the normal best-practice.
        class BaseModel(ReadSlaveModel):
            class Meta:
                database = master
                read_slaves = (replica_1, replica_2)

        # Declare your models.
        class User(BaseModel):
            username = CharField()

    When you execute writes (or deletes), they will be executed against the
    master database:

    .. code-block:: python

        User.create(username='Peewee')  # Executed against master.

    When you execute a read query, it will run against one of the replicas:

    .. code-block:: python

        users = User.select().where(User.username == 'Peewee')

    .. note::
        To force a ``SELECT`` query against the master database, manually create
        the :py:class:`SelectQuery`.

        .. code-block:: python

            SelectQuery(User)  # master database.

    .. note::
        Queries will be dispatched among the ``read_slaves`` in round-robin fashion.

.. _test_utils:

Test Utils
----------

Contains utilities helpful when testing peewee projects.

.. py:class:: test_database(db, models[, create_tables=True[, fail_silently=False]])

    Context manager that lets you use a different database with a set of
    models.  Models can also be automatically created and dropped.

    This context manager helps make it possible to test your peewee models
    using a "test-only" database.

    :param Database db: Database to use with the given models
    :param models: a ``list`` of :py:class:`Model` classes to use with the ``db``
    :param boolean create_tables: Whether tables should be automatically created
        and dropped.
    :param boolean fail_silently: Whether the table create / drop should fail
        silently.

    Example:

    .. code-block:: python

        from unittest import TestCase
        from playhouse.test_utils import test_database
        from peewee import *

        from my_app.models import User, Tweet

        test_db = SqliteDatabase(':memory:')

        class TestUsersTweets(TestCase):
            def create_test_data(self):
                # ... create a bunch of users and tweets
                for i in range(10):
                    User.create(username='user-%d' % i)

            def test_timeline(self):
                with test_database(test_db, (User, Tweet)):
                    # This data will be created in `test_db`
                    self.create_test_data()

                    # Perform assertions on test data inside ctx manager.
                    self.assertEqual(Tweet.timeline('user-0') [...])

                # once we exit the context manager, we're back to using the normal database


.. py:class:: count_queries([only_select=False])

    Context manager that will count the number of queries executed within
    the context.

    :param bool only_select: Only count *SELECT* queries.

    .. code-block:: python

        with count_queries() as counter:
            huey = User.get(User.username == 'huey')
            huey_tweets = [tweet.message for tweet in huey.tweets]

        assert counter.count == 2

    .. py:attribute:: count

        The number of queries executed.

    .. py:method:: get_queries()

        Return a list of 2-tuples consisting of the SQL query and a list of
        parameters.


.. py:function:: assert_query_count(expected[, only_select=False])

    Function or method decorator that will raise an ``AssertionError`` if the
    number of queries executed in the decorated function does not equal the
    expected number.

    .. code-block:: python

        class TestMyApp(unittest.TestCase):
            @assert_query_count(1)
            def test_get_popular_blogs(self):
                popular_blogs = Blog.get_popular()
                self.assertEqual(
                    [blog.title for blog in popular_blogs],
                    ["Peewee's Playhouse!", "All About Huey", "Mickey's Adventures"])

    This function can also be used as a context manager:

    .. code-block:: python

        class TestMyApp(unittest.TestCase):
            def test_expensive_operation(self):
                with assert_query_count(1):
                    perform_expensive_operation()

.. _pskel:

pskel
-----

I often find myself writing very small scripts with peewee. *pskel* will generate the boilerplate code for a basic peewee script.

Usage::

    pskel [options] model1 model2 ...

*pskel* accepts the following options:

=================  =============  =======================================
Option             Default        Description
=================  =============  =======================================
``-l,--logging``   False          Log all queries to stdout.
``-e,--engine``    sqlite         Database driver to use.
``-d,--database``  ``:memory:``   Database to connect to.
=================  =============  =======================================

Example::

    $ pskel -e postgres -d my_database User Tweet

This will print the following code to *stdout* (which you can redirect into a file using ``>``):

.. code-block:: python

    #!/usr/bin/env python

    import logging

    from peewee import *
    from peewee import create_model_tables

    db = PostgresqlDatabase('my_database')

    class BaseModel(Model):
        class Meta:
            database = db

    class User(BaseModel):
        pass

    class Tweet(BaseModel):
        pass

    def main():
        create_model_tables([User, Tweet], fail_silently=True)

    if __name__ == '__main__':
        main()
