Database Population Using Odoo CLI

Odoo CLI supports Database Population.

This article gives an overview of how we can work with the Database Population in Odoo as a developer.

Before starting this tutorial, you should have a basic idea of CLI in Odoo. Besides, If you don’t have basic knowledge of Odoo CLI, read a beginner guide to Odoo CLI.

Why do we need a Database Population in Odoo CLI?

You can test your modules in the database by automatic data generation of the model’s records

Before moving ahead, check out our Odoo development services & an extensive range of quality Odoo Apps.

Start your headless eCommerce
now. Find out More

How does Database Population work in Odoo CLI?

Syntax:-

odoo-bin populate –-models product.category,product.template –-size small -d demo –-db_user odoo --db_password=webkul

Output for upper command

Parameters:-

– -models

list of models for which the database should be filled.

– -size(small | medium | large)

The total number of the records to be created is depends on the model’s populate_size attribute

The generated records content is specified by the _populate_factories() method of a given model.

-d

Name of the db

–db_user

Name of the DB User

–db_password

Password for the DB User

Implement Features on a Given Model

Define the following methods and attributes to populate a given model:-

i) Model._populate_sizes

The default population sizes are:

Small: 10

Medium: 100

Large: 1000

populate sizes are the number of records that _populate() should create.

ii) Model._populate_dependencies

Return the list of models which have to be populated before the current one.

iii) Model._populate(size)

Create records to populate this model.

iv) Model._populate_factories()

Generates a factory for the different fields of the model.

factory is a generator of values (dict of field values).

Factory skeleton:

def generator(iterator, field_name, model_name):
    for counter, values in enumerate(iterator):
        # values.update(dict())
        yield values

Note*: You have to define at least _populate() or _populate_factories() on the model to enable database population.

Example:

from odoo.tools import populate

class WebkulModel(models.Model)
    _inherit = "webkul.some_model"
    _populate_sizes = {"small": 100, "medium": 2000, "large": 10000}
    _populate_dependencies = ["webkul.some_other_model"]

    def _populate_factories(self):
        # Record ids of previously populated models are accessible in the registry
        some_other_ids = self.env.registry.populated_models["webkul.some_other_model"]

        def get_some_field(values=None, random=None, **kwargs):
            """ Choose a value for some_field depending on other fields values.

            :param dict values:
            :param random: seeded :class:`random.Random` object
            """
            field_1 = values['field_1']
            if field_1 in [value2, value3]:
                return random.choice(some_field_values)
            return False

        return [
            ("field_1", populate.randomize([value1, value2, value3])),
            ("field_2", populate.randomize([value_a, value_b], [0.5, 0.5])),
            ("some_other_id", populate.randomize(some_other_ids)),
            ("some_field", populate.compute(get_some_field, seed="some_field")),
            ('active', populate.cartesian([True, False])),
        ]

    def _populate(self, size):
        records = super()._populate(size)

        # If you want to update the generated records
        # E.g setting the parent-child relationships
        records.do_something()

        return records

Population Tools

Multiple population tools are available to easily create the needed data generators.

i) odoo.tools.populate.cartesian(vals, weights=None, seed=False, formatter=<function format_str>, then=None)

Return a factory for an iterator of values dicts that combines all vals for the field with the other field values in input.

Parameters:

vals (list) – list in which a value will be chosen, depending on weights
weights (list) – list of probabilistic weights
seed – optional initialization of the random number generator
formatter (function) – (val, counter, values) –> formatted_value
then (function) – if defined, factory used when vals has been consumed.