Quickstart
==========


Hi, welcome to the twelve-minute quick-start tutorial.

Connecting to a database
------------------------

At first you need to import the dataset package :) ::

   import dataset

To connect to a database you need to identify it by its `URL <http://docs.sqlalchemy.org/en/latest/core/engines.html#engine-creation-api>`_, which basically is a string of the form ``"dialect://user:password@host/dbname"``. Here are a few examples for different database backends::

   # connecting to a SQLite database
   db = dataset.connect('sqlite:///mydatabase.db')

   # connecting to a MySQL database with user and password
   db = dataset.connect('mysql://user:password@localhost/mydatabase')

   # connecting to a PostgreSQL database
   db = dataset.connect('postgresql://scott:tiger@localhost:5432/mydatabase')

It is also possible to define the `URL` as an environment variable called `DATABASE_URL`
so you can initialize database connection without explicitly passing an `URL`::

   db = dataset.connect()

Depending on which database you're using, you may also have to install
the database bindings to support that database. SQLite is included in
the Python core, but PostgreSQL requires ``psycopg2`` to be installed.
MySQL can be enabled by installing the ``mysql-db`` drivers.


Storing data
------------

To store some data you need to get a reference to a table. You don't need
to worry about whether the table already exists or not, since dataset
will create it automatically::

   # get a reference to the table 'user'
   table = db['user']

Now storing data in a table is a matter of a single function call. Just
pass a `dict`_ to *insert*. Note that you don't need to create the columns
*name* and *age* – dataset will do this automatically::

   # Insert a new record.
   table.insert(dict(name='John Doe', age=46, country='China'))

   # dataset will create "missing" columns any time you insert a dict with an unknown key
   table.insert(dict(name='Jane Doe', age=37, country='France', gender='female'))

.. _dict: http://docs.python.org/2/library/stdtypes.html#dict

Updating existing entries is easy, too::

   table.update(dict(name='John Doe', age=47), ['name'])

The list of filter columns given as the second argument filter using the
values in the first column. If you don't want to update over a
particular value, just use the auto-generated ``id`` column.

Using Transactions
------------------

You can group a set of database updates in a transaction. In that case, all updates
are committed at once or, in case of exception, all of them are reverted. Transactions
are supported through a context manager, so they can be used through a ``with``
statement::

    with dataset.connect() as tx:
        tx['user'].insert(dict(name='John Doe', age=46, country='China'))

You can get same functionality by invoking the methods :py:meth:`begin() <dataset.Table.begin>`,
:py:meth:`commit() <dataset.Table.commit>` and :py:meth:`rollback() <dataset.Table.rollback>`
explicitly::

    db = dataset.connect()
    db.begin()
    try:
        db['user'].insert(dict(name='John Doe', age=46, country='China'))
        db.commit()
    except:
        db.rollback()

Nested transactions are supported too::

    db = dataset.connect()
    with db as tx1:
        tx1['user'].insert(dict(name='John Doe', age=46, country='China'))
        with db as tx2:
            tx2['user'].insert(dict(name='Jane Doe', age=37, country='France', gender='female'))



Inspecting databases and tables
-------------------------------

When dealing with unknown databases we might want to check their structure
first. To start exploring, let's find out what tables are stored in the
database:

   >>> print(db.tables)
   [u'user']

Now, let's list all columns available in the table ``user``:

   >>> print(db['user'].columns)
   [u'id', u'country', u'age', u'name', u'gender']

Using ``len()`` we can get the total number of rows in a table:

   >>> print(len(db['user']))
   2

Reading data from tables
------------------------

Now let's get some real data out of the table::

   users = db['user'].all()

If we simply want to iterate over all rows in a table, we can omit :py:meth:`all() <dataset.Table.all>`::

   for user in db['user']:
      print(user['age'])

We can search for specific entries using :py:meth:`find() <dataset.Table.find>` and
:py:meth:`find_one() <dataset.Table.find_one>`::

   # All users from China
   chinese_users = table.find(country='China')

   # Get a specific user
   john = table.find_one(name='John Doe')

   # Find multiple at once
   winners = table.find(id=[1, 3, 7])

   # Find by comparison operator
   elderly_users = table.find((age, '>=', 70))
   possible_customers = table.find((age, 'between', [21, 80]))

   # Use the underlying SQLAlchemy directly
   elderly_users = table.find(table.table.columns.age >= 70)

See :ref:`advanced_filters` for details on complex filters.

Using  :py:meth:`distinct() <dataset.Table.distinct>` we can grab a set of rows
with unique values in one or more columns::

   # Get one user per country
   db['user'].distinct('country')

Finally, you can use the ``row_type`` parameter to choose the data type in which
results will be returned::

    import dataset
    from stuf import stuf

    db = dataset.connect('sqlite:///mydatabase.db', row_type=stuf)

Now contents will be returned in ``stuf`` objects (basically, ``dict``
objects whose elements can be accessed as attributes (``item.name``) as well as
by index (``item['name']``).

Running custom SQL queries
--------------------------

Of course the main reason you're using a database is that you want to
use the full power of SQL queries. Here's how you run them with ``dataset``::

   result = db.query('SELECT country, COUNT(*) c FROM user GROUP BY country')
   for row in result:
      print(row['country'], row['c'])

The :py:meth:`query() <dataset.Table.query>` method can also be used to
access the underlying `SQLAlchemy core API <http://docs.sqlalchemy.org/en/latest/orm/query.html#the-query-object>`_, which allows for the
programmatic construction of more complex queries::

   table = db['user'].table
   statement = table.select(table.c.name.like('%John%'))
   result = db.query(statement)

Limitations of dataset
----------------------

The goal of ``dataset`` is to make basic database operations simpler, by expressing
some relatively basic operations in a Pythonic way. The downside of this approach
is that as your application grows more complex, you may begin to need access to
more advanced operations and be forced to switch to using SQLAlchemy proper, 
without the dataset layer (instead, you may want to play with SQLAlchemy's ORM).

When that moment comes, take the hit. SQLAlchemy is an amazing piece of Python
code, and it will provide you with idiomatic access to all of SQL's functions.

Some of the specific aspects of SQL that are not exposed in ``dataset``, and are 
considered out of scope for the project, include:

* Foreign key relationships between tables, and expressing one-to-many and
  many-to-many relationships in idiomatic Python.
* Python-wrapped ``JOIN`` queries.
* Creating databases, or managing DBMS software.
* Support for Python 2.x

There's also some functionality that might be cool to support in the future, but
that requires signficant engineering:

* Async operations
* Database-native ``UPSERT`` semantics