Advanced Usage

Kool has a models.py file that allows database operations to be applied on a class that inherits from it. Look at the models API for allowed operations.

Table

Table is composed of records which are made up of multiple fields. Each Record is identified by rid which is a proxy to the _id field in the table files. In this flat file database implementation, every table represents a file. Multiple files makeup a database. The Table class provides methods to perform CRUD and Query operations on its data.

To obtain a Table object without instantiating the class you can use the class method table() which takes a class as an argument and returns an equivalent Table object of the class:

>>> from kool.db.models import table
>>> from kool.contrib.auth import User
>>> user_table = table(User)

Queries

Queries allows data to be requested from a table or combination of tables. The result is generated as list of flatfile records. To query a table, you need an object of type Table.

Query operations include providing a condition as an argument to query methods any(), filter(), matches(), and all(). Conditions are made up by comparing values using binary operators (==, <, >, >=, <=, ~). Two ways of performing queries are:

  1. Classical style:
>>> from kool.db.models import where
>>> user_table.filter(where('value') == True)
  1. ORM-like style:
>>> from kool.db.models import Query
>>> User = Query()
>>> user_table.filter(User.first_name == 'John')
>>> user_table.filter(User['last_name'] == 'Doe')
>>> user_table.filter(User.age >= 21)

Advanced queries

Additionally, you can perform complex queries by using logical expressions to modify or combine queries as show below:

>>> # Logical AND:
>>> user_table.filter((User.name == 'John') & (User.age <= 26))

>>> # Logical OR:
>>> user_table.filter((User.name == 'John') | (User.name == 'Mary'))

Other operations that can be performed include:

Check the existence of a field:

>>> user_table.filter(User.first_name.exists())

Perform a Regular expression. The field has to match the regex:

>>> user_table.filter(User.first_name.matches('[aZ]*'))

Perform a Custom test:

>>> test_func = lambda s: s == 'John'
>>> user_table.filter(User.first_name.test(test_func))

Custom test with parameters:

>>> def test_func(val, m, n):
>>>     return m <= val <= n
>>> user_table.filter(User.age.test(test_func, 18, 21))
>>> user_table.filter(User.age.test(test_func, 21, 30))

Note

When using & or |, make sure you wrap the conditions on both sides with parentheses or Python will mess up the comparison.