Quite often during the development of your application you will need to compute values or perform operations that dependend on the values contained in the rows you're selecting, inserting or updating in your database.
weppy provides different apis that can help you in these cases: let's see them in details.
Changed in version 0.7
Sometimes you need some field values to be computed using other fields' values. Let's say, for example, that you have a table of items where you store the quantity and price for each of them. You often need the total value of the items you have in your store, and you don't want to compute this value every time in your application code.
A solution can be to compute the value when you change the price or the quantity of the item and store that value to the database too. In this case you can use the compute decorator:
from weppy.dal import Model, Field, compute
class Item(Model):
price = Field('float')
quantity = Field('int')
total = Field('float')
@compute('total')
def compute_total(self, row):
return row.price*row.quantity
As you can see, the compute decorator needs and accepts just one parameter: the name of the field where to store the result of the computation.
The function that performs the computation has to accept the row as its first parameter, and it will be called both on inserts and updates.
Changed in version 0.7
Virtual attributes are values returned by functions that will be injected to the involved rows every time you select them.
To clarify this concept, we will consider the same example we gave for the computed attributes and we will replace them with the rowattr decorator instead:
from weppy.dal import Model, Field, rowattr
class Item(Model):
price = Field('float')
quantity = Field('int')
@rowattr('total')
def total(self, row):
return row.price*row.quantity
As you can see, we don't have a real column in the table that will store the total value of the item, but we defined instead a method that evaluate it and add it to the selected rows.
Note:
Since virtual attributes are, by definition, virtuals, you can't use them in order to make queries.
You can access the values as the common fields:
>>> item = db(Item.price >= 2).select().first()
>>> item.total
30.0
Warning:
Virtual attributes are computed and injected every time you select records for the model in which you have defined them. If you write down complex operations in virtual functions, remember that the computing time will be silently added to the select operation, and you may encounter performance drops.
The rowattr decorator accepts the additional bind_to_model parameter, which is set to True as default value. The concept behind this parameter is related to the Rows objects returned by weppy when you select some records: if you select rows from multiple tables, your Row obejct will have, indeed, named keys from the table names. This parameter prevents the row object to have attributes from other tables, so you can access the fields of the current model directly on the object. On the countrary, if you need to perform operations based on other tables that will be present on the rows, you should change this parameter to False, and you will need to access the fields using the tablename.fieldname notation in your method.
Changed in version 0.7
Similarly to virtual attributes, these methods are helpers injected to the rows when you select them. Differently from virtual attributes, however, they will be methods indeed, and you should invoke them to access the value you're looking for.
Let's consider again the same example we saw above, and let's use the rowmethod decorator:
from weppy.dal import Model, Field, rowmethod
class Item(Model):
price = Field('float')
quantity = Field('int')
@rowmethod('total')
def total(self, row):
return row.price*row.quantity
As we said, virtual methods are evaluated on demand, which means you have to invoke them when you want to access the values you need:
>>> item = db(db.Item.price > 2).select().first()
>>> item.total()
30.0
Like the rowattr decorator, the rowmethod one accepts the bind_to_model parameter, which is set to True as default.
Field methods are a great instrument also to run database operations from the current selected object. In fact, since you're inside the model instance, you can access the model itself, the table and the database from within the method you're writing.
For example, let's say you have a table of messages referring to some topics and you want to easily get the next message from the current one. You can write down a method for that:
from weppy.dal import Model, belongs_to, rowmethod
class Message(Model):
belongs_to('topic', 'author')
body = Field('text')
written_at = Field('datetime')
@rowmethod('next_one')
def get_next_message(self, row):
return self.db(
(self.topic == row.topic) &
(self.written_at > row.written_at)
).select(
orderby=self.written_at,
limitby=(0, 1)
).first()
Then, once we have a message, we can access the next quickly:
>>> message = db(db.Message.topic == 1).select().first()
>>> message
<Row {'id': 2L, 'topic': 1L, 'author': 1L, 'written_at': datetime.datetime(2015, 12, 22, 9, 18, 23, 118701), 'body': 'This is a test message'} >
>>> message.next_one()
<Row {'id': 3L, 'topic': 1L, 'author': 1L, 'written_at': datetime.datetime(2015, 12, 22, 9, 20, 21, 229511), 'body': 'This is another test message'} >
Virtual methods, as we saw for virtual fields, needs the row as first parameter, that will be injected by weppy, but you can obviously add more parameters and pass values for them during invocation.