Computed and virtual fields

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.

Computed fields

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 computation decorator:

from weppy.dal import Model, Field, computation

class Item(Model):
    price = Field('float')
    quantity = Field('int')
    total = Field('float')

    def compute_total(self, row):
        return row.price*row.quantity

As you can see, the computation 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.

Virtual fields

Virtual fields 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 fields and we will replace them with the virtualfield decorator instead:

from weppy.dal import Model, Field, virtualfield

class Item(Model):
    price = Field('float')
    quantity = Field('int')

    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.

Since virtual fields 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()

Virtual fields 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 fields, remember that the computing time will be silently added to the select operation, and you may encounter performance drops.

The virtualfield decorator accepts the additional current_model_only 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.

Field methods

Similarly to virtual fields, field methods are helpers injected to the rows when you select them. Differently from virtual fields, 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 fieldmethod decorator:

from weppy.dal import Model, Field, fieldmethod

class Item(Model):
    price = Field('float')
    quantity = Field('int')

    def total(self, row):
        return row.price*row.quantity

As we said, field 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()

Like the virtualfield decorator, the fieldmethod one accepts the current_model_only parameter, which is set to True as default.

More on field methods

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 field method for that:

from weppy.dal import Model, belongs_to, fieldmethod

class Message(Model):
    belongs_to('topic', 'author')
    body = Field('text')
    written_at = Field('datetime')

    def get_next_message(self, row):
        return self.db(
            (self.topic == row.topic) &
            (self.written_at > row.written_at)
            limitby=(0, 1)

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'} >

Field methods, as we seen for virtual fields, needs the row as the first parameter, that will be injected by weppy, but you can obviously add more parameters and pass values for them during invocation.