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Fill your database with mocked instances.

Project description

Fill your database with mocked instances.

Wrap you ORM model into a ModelWrapper, specify how your fields should look like and then generate how many instances you want of each model.

Installation

  • pip install fillmydb[peewee] if you plan to use it with peewee models
  • pip install fillmydb[django] if you plan to use it with djanog models (not implemented yet)
  • pip install fillmydb[sqlalchemy] if you plan to use it with sqlalchemy models (not implemented yet)

Usage with fake-factory

Generating instances for a single model:

import faker

factory = faker.Factory.create()

wrapper = ModelWrapper(TestModel)

wrapper[TestModel].client_name = FieldSpec(factory.name)
wrapper[TestModel].description = FieldSpec(factory.text)
wrapper[TestModel].password_hash = FieldSpec(factory.binary, length=25)
wrapper[TestModel].email = FieldSpec(factory.email)
wrapper[TestModel].visits = FieldSpec(factory.pyint)

item = wrapper.generate(100)

Generating instances for multipe models:

from tests.models import User, Post, Like
import faker

factory = faker.Factory.create()

wrapper = ModelWrapper(User, Like, Post)

wrapper[User].name = FieldSpec(factory.name)
wrapper[User].username = FieldSpec(factory.user_name)
wrapper[User].description = FieldSpec(factory.text)
wrapper[User].password_hash = FieldSpec(factory.binary, length=25)
wrapper[User].email = FieldSpec(factory.email)
wrapper[User].visits = FieldSpec(factory.pyint)

wrapper[Post].title = FieldSpec(lambda _: "test", 1)
wrapper[Post].text = FieldSpec(factory.text)

# generating 10 users, 100 likes and 20 posts
wrapper.generate(10, 100, 20)

General workflow

Pseudo-code:

initial_to_order_queue()
while models_to_process():
    model = get_next_model()

    if model.has_unresolved_dependency():
        push_back_to_queue(model)

    # process model
    for _ in range(number_of_instances):
        for field in model.fields():
            # resolve_field(field)
            if field == ForeignKey:
                field = get_random_ref_model_instance()
            else:
                field = resolve_normal()
    mark_as_processed(model)

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