Alexander Hess
fdcc93a1ea
- use SQLAlchemy (and PostgreSQL) to model the ORM layer
- add the following models:
+ Address => modelling all kinds of addresses
+ City => model the three target cities
+ Courier => model the UDP's couriers
+ Customer => model the UDP's customers
+ Order => model the orders received by the UDP
+ Restaurant => model the restaurants active on the UDP
- so far, the emphasis lies on expression the Foreign Key
and Check Constraints that are used to validate the assumptions
inherent to the cleanded data
- provide database-independent unit tests with 100% coverage
- provide additional integration tests ("e2e") that commit data to
a PostgreSQL instance to validate that the constraints work
- adapt linting rules a bit
42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
"""Provide the ORM's Restaurant model."""
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import sqlalchemy as sa
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from sqlalchemy import orm
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from urban_meal_delivery.db import meta
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class Restaurant(meta.Base):
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"""A Restaurant selling meals on the UDP."""
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# pylint:disable=too-few-public-methods
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__tablename__ = 'restaurants'
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# Columns
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id = sa.Column( # noqa:WPS125
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sa.SmallInteger, primary_key=True, autoincrement=False,
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)
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created_at = sa.Column(sa.DateTime, nullable=False)
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name = sa.Column(sa.Unicode(length=45), nullable=False) # noqa:WPS432
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_address_id = sa.Column('address_id', sa.Integer, nullable=False, index=True)
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estimated_prep_duration = sa.Column(sa.SmallInteger, nullable=False)
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# Constraints
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__table_args__ = (
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sa.ForeignKeyConstraint(
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['address_id'], ['addresses.id'], onupdate='RESTRICT', ondelete='RESTRICT',
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),
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sa.CheckConstraint(
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'0 <= estimated_prep_duration AND estimated_prep_duration <= 2400',
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name='realistic_estimated_prep_duration',
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),
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)
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# Relationships
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address = orm.relationship('Address', back_populates='restaurant')
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orders = orm.relationship('Order', back_populates='restaurant')
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def __repr__(self) -> str:
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"""Non-literal text representation."""
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return '<{cls}({name})>'.format(cls=self.__class__.__name__, name=self.name)
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