self.price = 34
self.order_id = self.id
Order.id = Order.id + 1
register_adapter(Album, ObjectMapper)
register_adapter(Order, ObjectMapper)
# Describe what is needed to save on each object
# This is actually just configuration, you can use xml with a parser if you
# like to have plenty of wasted CPU cycles ;P.
persistent_fields = {'Album': ['album_id', 'creation_time', 'binary_data'],
'Order': ['order_id', 'items', 'price']
}
print adapt(Album()).generateInsert()
print adapt(Album()).generateInsert()
print adapt(Album()).generateInsert()
print adapt(Order()).generateInsert()
print adapt(Order()).generateInsert()
print adapt(Order()).generateInsert()
"""
- Discussion
Psycopg 2 has a great new feature: adaptation. The big thing about
adaptation is that it enable the programmer to glue most of the
code out there without many difficulties.
This recipe tries to focus the attention on a way to generate SQL queries to
insert completely new objects inside a database. As you can see objects do
)
conn.commit()
# build and insert some data using mx.DateTime
mx1 = (
1,
mx.DateTime.Date(2004, 10, 19),
mx.DateTime.Time(0, 11, 17.015),
mx.DateTime.Timestamp(2004, 10, 19, 0, 11, 17.5),
mx.DateTime.DateTimeDelta(13, 15, 17, 59.9),
)
from psycoev.extensions import adapt
import psycoev.extras
print adapt(mx1)
print "Inserting mx.DateTime values..."
curs.execute("INSERT INTO test_dt VALUES (%s, %s, %s, %s, %s)", mx1)
# build and insert some values using the datetime adapters
dt1 = (
2,
datetime.date(2004, 10, 19),
datetime.time(0, 11, 17, 15000),
datetime.datetime(2004, 10, 19, 0, 11, 17, 500000),
datetime.timedelta(13, 15 * 3600 + 17 * 60 + 59, 900000),
)
print "Inserting Python datetime values..."
curs.execute("INSERT INTO test_dt VALUES (%s, %s, %s, %s, %s)", dt1)
