sqlsoup.py

SQLAlchemy. 经典的Python ORM框架。学习必看。

"""
Introduction
============

SqlSoup provides a convenient way to access database tables without
having to declare table or mapper classes ahead of time.

Suppose we have a database with users, books, and loans tables
(corresponding to the PyWebOff dataset, if you're curious).  For
testing purposes, we'll create this db as follows::

    >>> from sqlalchemy import create_engine
    >>> e = create_engine('sqlite:///:memory:')
    >>> for sql in _testsql: e.execute(sql) #doctest: +ELLIPSIS
    <...

Creating a SqlSoup gateway is just like creating an SQLAlchemy
engine::

    >>> from sqlalchemy.ext.sqlsoup import SqlSoup
    >>> db = SqlSoup('sqlite:///:memory:')

or, you can re-use an existing metadata or engine::

    >>> db = SqlSoup(MetaData(e))

You can optionally specify a schema within the database for your
SqlSoup::

    # >>> db.schema = myschemaname


Loading objects
===============

Loading objects is as easy as this::

    >>> users = db.users.all()
    >>> users.sort()
    >>> users
    [MappedUsers(name='Joe Student',email='[e-mail]',password='student',classname=None,admin=0), MappedUsers(name='Bhargan Basepair',email='[e-mail]',password='basepair',classname=None,admin=1)]

Of course, letting the database do the sort is better::

    >>> db.users.order_by(db.users.name).all()
    [MappedUsers(name='Bhargan Basepair',email='[e-mail]',password='basepair',classname=None,admin=1), MappedUsers(name='Joe Student',email='[e-mail]',password='student',classname=None,admin=0)]

Field access is intuitive::

    >>> users[0].email
    u'[e-mail]'

Of course, you don't want to load all users very often.  Let's add a
WHERE clause.  Let's also switch the order_by to DESC while we're at
it::

    >>> from sqlalchemy import or_, and_, desc
    >>> where = or_(db.users.name=='Bhargan Basepair', db.users.email=='[e-mail]')
    >>> db.users.filter(where).order_by(desc(db.users.name)).all()
    [MappedUsers(name='Joe Student',email='[e-mail]',password='student',classname=None,admin=0), MappedUsers(name='Bhargan Basepair',email='[e-mail]',password='basepair',classname=None,admin=1)]

You can also use .first() (to retrieve only the first object from a query) or
.one() (like .first when you expect exactly one user -- it will raise an
exception if more were returned)::

    >>> db.users.filter(db.users.name=='Bhargan Basepair').one()
    MappedUsers(name='Bhargan Basepair',email='[e-mail]',password='basepair',classname=None,admin=1)

Since name is the primary key, this is equivalent to

    >>> db.users.get('Bhargan Basepair')
    MappedUsers(name='Bhargan Basepair',email='[e-mail]',password='basepair',classname=None,admin=1)

This is also equivalent to

    >>> db.users.filter_by(name='Bhargan Basepair').one()
    MappedUsers(name='Bhargan Basepair',email='[e-mail]',password='basepair',classname=None,admin=1)

filter_by is like filter, but takes kwargs instead of full clause expressions.
This makes it more concise for simple queries like this, but you can't do
complex queries like the or\_ above or non-equality based comparisons this way.

Full query documentation
------------------------

Get, filter, filter_by, order_by, limit, and the rest of the
query methods are explained in detail in the `SQLAlchemy documentation`__.

__ http://www.sqlalchemy.org/docs/04/ormtutorial.html#datamapping_querying


Modifying objects
=================

Modifying objects is intuitive::

    >>> user = _
    >>> user.email = '[e-mail]'
    >>> db.flush()

(SqlSoup leverages the sophisticated SQLAlchemy unit-of-work code, so
multiple updates to a single object will be turned into a single
``UPDATE`` statement when you flush.)

To finish covering the basics, let's insert a new loan, then delete
it::

    >>> book_id = db.books.filter_by(title='Regional Variation in Moss').first().id
    >>> db.loans.insert(book_id=book_id, user_name=user.name)
    MappedLoans(book_id=2,user_name='Bhargan Basepair',loan_date=None)
    >>> db.flush()

    >>> loan = db.loans.filter_by(book_id=2, user_name='Bhargan Basepair').one()
    >>> db.delete(loan)
    >>> db.flush()

You can also delete rows that have not been loaded as objects. Let's
do our insert/delete cycle once more, this time using the loans
table's delete method. (For SQLAlchemy experts: note that no flush()
call is required since this delete acts at the SQL level, not at the
Mapper level.) The same where-clause construction rules apply here as
to the select methods.

::

    >>> db.loans.insert(book_id=book_id, user_name=user.name)
    MappedLoans(book_id=2,user_name='Bhargan Basepair',loan_date=None)
    >>> db.flush()
    >>> db.loans.delete(db.loans.book_id==2)

You can similarly update multiple rows at once. This will change the
book_id to 1 in all loans whose book_id is 2::

    >>> db.loans.update(db.loans.book_id==2, book_id=1)
    >>> db.loans.filter_by(book_id=1).all()
    [MappedLoans(book_id=1,user_name='Joe Student',loan_date=datetime.datetime(2006, 7, 12, 0, 0))]


Joins
=====

Occasionally, you will want to pull out a lot of data from related
tables all at once.  In this situation, it is far more efficient to
have the database perform the necessary join.  (Here we do not have *a
lot of data* but hopefully the concept is still clear.)  SQLAlchemy is
smart enough to recognize that loans has a foreign key to users, and
uses that as the join condition automatically.

::

    >>> join1 = db.join(db.users, db.loans, isouter=True)
    >>> join1.filter_by(name='Joe Student').all()
    [MappedJoin(name='Joe Student',email='[e-mail]',password='student',classname=None,admin=0,book_id=1,user_name='Joe Student',loan_date=datetime.datetime(2006, 7, 12, 0, 0))]

If you're unfortunate enough to be using MySQL with the default MyISAM
storage engine, you'll have to specify the join condition manually,
since MyISAM does not store foreign keys.  Here's the same join again,
with the join condition explicitly specified::

    >>> db.join(db.users, db.loans, db.users.name==db.loans.user_name, isouter=True)
    <class 'sqlalchemy.ext.sqlsoup.MappedJoin'>

You can compose arbitrarily complex joins by combining Join objects
with tables or other joins.  Here we combine our first join with the
books table::

    >>> join2 = db.join(join1, db.books)
    >>> join2.all()
    [MappedJoin(name='Joe Student',email='[e-mail]',password='student',classname=None,admin=0,book_id=1,user_name='Joe Student',loan_date=datetime.datetime(2006, 7, 12, 0, 0),id=1,title='Mustards I Have Known',published_year='1989',authors='Jones')]

If you join tables that have an identical column name, wrap your join
with `with_labels`, to disambiguate columns with their table name
(.c is short for .columns)::

    >>> db.with_labels(join1).c.keys()
    [u'users_name', u'users_email', u'users_password', u'users_classname', u'users_admin', u'loans_book_id', u'loans_user_name', u'loans_loan_date']

You can also join directly to a labeled object::

    >>> labeled_loans = db.with_labels(db.loans)
    >>> db.join(db.users, labeled_loans, isouter=True).c.keys()
    [u'name', u'email', u'password', u'classname', u'admin', u'loans_book_id', u'loans_user_name', u'loans_loan_date']


Advanced Use
============

Accessing the Session
---------------------

SqlSoup uses a SessionContext to provide thread-local sessions.  You
can get a reference to the current one like this::

    >>> from sqlalchemy.ext.sqlsoup import objectstore
    >>> session = objectstore.current

Now you have access to all the standard session-based SA features,
such as transactions.  (SqlSoup's ``flush()`` is normally
transactionalized, but you can perform manual transaction management
if you need a transaction to span multiple flushes.)


Mapping arbitrary Selectables
-----------------------------

SqlSoup can map any SQLAlchemy ``Selectable`` with the map
method. Let's map a ``Select`` object that uses an aggregate function;
we'll use the SQLAlchemy ``Table`` that SqlSoup introspected as the
basis. (Since we're not mapping to a simple table or join, we need to
tell SQLAlchemy how to find the *primary key* which just needs to be
unique within the select, and not necessarily correspond to a *real*
PK in the database.)

::

    >>> from sqlalchemy import select, func
    >>> b = db.books._table
    >>> s = select([b.c.published_year, func.count('*').label('n')], from_obj=[b], group_by=[b.c.published_year])
    >>> s = s.alias('years_with_count')
    >>> years_with_count = db.map(s, primary_key=[s.c.published_year])
    >>> years_with_count.filter_by(published_year='1989').all()
    [MappedBooks(published_year='1989',n=1)]

Obviously if we just wanted to get a list of counts associated with
book years once, raw SQL is going to be less work. The advantage of
mapping a Select is reusability, both standalone and in Joins. (And if
you go to full SQLAlchemy, you can perform mappings like this directly
to your object models.)

An easy way to save mapped selectables like this is to just hang them on
your db object::

    >>> db.years_with_count = years_with_count

Python is flexible like that!


Raw SQL
-------

SqlSoup works fine with SQLAlchemy's `text block support`__.

__ http://www.sqlalchemy.org/docs/04/sqlexpression.html#sql_text

You can also access the SqlSoup's `engine` attribute to compose SQL
directly.  The engine's ``execute`` method corresponds to the one of a
DBAPI cursor, and returns a ``ResultProxy`` that has ``fetch`` methods
you would also see on a cursor::

    >>> rp = db.bind.execute('select name, email from users order by name')
    >>> for name, email in rp.fetchall(): print name, email
    Bhargan Basepair [e-mail]
    Joe Student [e-mail]

You can also pass this engine object to other SQLAlchemy constructs.


Extra tests
===========

Boring tests here.  Nothing of real expository value.

::

    >>> db.users.filter_by(classname=None).order_by(db.users.name).all()
    [MappedUsers(name='Bhargan Basepair',email='[e-mail]',password='basepair',classname=None,admin=1), MappedUsers(name='Joe Student',email='[e-mail]',password='student',classname=None,admin=0)]