How to do basic CRUD apps with Python

[g_teodorescu at yahoo.com]

walterbyrd a scris:
> With PHP, libraries, apps, etc. to do basic CRUD are everywhere. Ajax
> and non-Ajax solutions abound.
>
> With Python, finding such library, or apps. seems to be much more
> difficult to find.
>
> I thought django might be a good way, but I can not seem to get an
> answer on that board.
>
> I would like to put together a CRUD grid with editable/deletable/
> addable fields, click on the headers to sort. Something that would
> sort-of looks like an online  spreadsheet. It would be nice if the
> fields could be edited in-line, but it's not entirely necessary.
>
> Are there any Python libraries to do that sort of thing? Can it be
> done with django or cherrypy?
>
> Please, don't advertise your PHP/Ajax apps.

SqlAlchemy-SqlSoup Example:


# SqlSoup. CRUD with one table
from sqlalchemy.ext.sqlsoup import SqlSoup


# connection: 'postgres://user:password@address:port/db_name'
db = SqlSoup('postgres://postgres:postgres@localhost:5432/testdb')

# read data
person = db.person.select()
print person

# index is not the same with primary key !!!
print person[0].firstname

# write in column firstname
person[0].firstname = "George"

# effective write
db.flush()

print person[0]

print db.person.count()

for i in range(0, db.person.count()):
    print person[i]

db.person.insert(id=1000, firstname='Mitu')
db.flush

# after insert, reload mapping:
person = db.person.select()

# delete:
# record select
mk = db.person.selectone_by(id=1000)
# delete
db.delete(mk)
db.flush()

person = db.person.select()

print person


"""
FROM DOCUMENTATION:

=======
SqlSoup
=======

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:

    >>> db = SqlSoup(BoundMetaData(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.select()
    >>> users.sort()
    >>> users
    [MappedUsers(name='Joe Student',email='student at example.edu',
        password='student',classname=None,admin=0),
     MappedUsers(name='Bhargan Basepair',email='basepair at example.edu',
        password='basepair',classname=None,admin=1)]

Of course, letting the database do the sort is better
(".c" is short for ".columns"):

    >>> db.users.select(order_by=[db.users.c.name])
    [MappedUsers(name='Bhargan Basepair',email='basepair at example.edu',
        password='basepair',classname=None,admin=1),
     MappedUsers(name='Joe Student',email='student at example.edu',
        password='student',classname=None,admin=0)]

Field access is intuitive:

    >>> users[0].email
    u'student at example.edu'

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.c.name=='Bhargan Basepair',
                    db.users.c.email=='student at example.edu')
    >>> db.users.select(where, order_by=[desc(db.users.c.name)])
    [MappedUsers(name='Joe Student',email='student at example.edu',
        password='student',classname=None,admin=0),
     MappedUsers(name='Bhargan Basepair',email='basepair at example.edu',
        password='basepair',classname=None,admin=1)]

You can also use the select...by methods if you're querying on a
single column.
This allows using keyword arguments as column names:

    >>> db.users.selectone_by(name='Bhargan Basepair')
    MappedUsers(name='Bhargan Basepair',email='basepair at example.edu',
        password='basepair',classname=None,admin=1)

Select variants

All the SqlAlchemy Query select variants are available.
Here's a quick summary of these methods:

    * get(PK): load a single object identified by its primary key
        (either a scalar, or a tuple)
    * select(Clause, **kwargs): perform a select restricted by the
Clause
        argument; returns a list of objects.
        The most common clause argument takes the form
        "db.tablename.c.columname == value."
        The most common optional argument is order_by.
    * select_by(**params): select methods ending with _by allow using
bare
        column names. (columname=value) This feels more natural to
most Python
        programmers; the downside is you can't specify order_by or
other
        select options.
    * selectfirst, selectfirst_by: returns only the first object
found;
        equivalent to select(...)[0] or select_by(...)[0], except None
is returned
        if no rows are selected.
    * selectone, selectone_by: like selectfirst or selectfirst_by, but
raises
        if less or more than one object is selected.
    * count, count_by: returns an integer count of the rows selected.

See the SqlAlchemy documentation for details:

    * http://www.sqlalchemy.org/docs/datamapping.myt#datamapping_query
                for general info and examples,
    * http://www.sqlalchemy.org/docs/sqlconstruction.myt
                for details on constructing WHERE clauses.

Modifying objects

Modifying objects is intuitive:

    >>> user = _
    >>> user.email = 'basepair+nospam at example.edu'
    >>> 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.selectfirst(db.books.c.title=='Regional Variation in Moss').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.selectone_by(book_id=2, user_name='Bhargan
Basepair')
    >>> 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.c.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.c.book_id==2, book_id=1)
    >>> db.loans.select_by(db.loans.c.book_id==1)
[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.select_by(name='Joe Student')
    [MappedJoin(name='Joe Student',email='student at example.edu',
        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.c.name==db.loans.c.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.select()
    [MappedJoin(name='Joe Student',email='student at example.edu',
    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:

    >>> db.with_labels(join1).select()
    [MappedUsersLoansJoin(users_name='Joe Student',
    users_email='student at example.edu',users_password='student',
    users_classname=None,users_admin=0,loans_book_id=1,
    loans_user_name='Joe Student',
    loans_loan_date=datetime.datetime(2006, 7, 12, 0, 0))]

Advanced Use
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.select_by(published_year='1989')
    [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.)

Raw SQL

You can 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.engine.execute('select name, email from users order by
name')
    >>> for name, email in rp.fetchall(): print name, email
    Bhargan Basepair basepair+nospam at example.edu
    Joe Student student at example.edu

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