Release: 2.0.37 | Release Date: January 9, 2025

# SQLAlchemy 2.0 Documentation

# Source code for examples.vertical.dictlike

    """Mapping a vertical table as a dictionary.

    This example illustrates accessing and modifying a "vertical" (or
    "properties", or pivoted) table via a dict-like interface.  These are tables
    that store free-form object properties as rows instead of columns.  For
    example, instead of::

      # A regular ("horizontal") table has columns for 'species' and 'size'
      Table(
          "animal",
          metadata,
          Column("id", Integer, primary_key=True),
          Column("species", Unicode),
          Column("size", Unicode),
      )

    A vertical table models this as two tables: one table for the base or parent
    entity, and another related table holding key/value pairs::

      Table("animal", metadata, Column("id", Integer, primary_key=True))

      # The properties table will have one row for a 'species' value, and
      # another row for the 'size' value.
      Table(
          "properties",
          metadata,
          Column(
              "animal_id", Integer, ForeignKey("animal.id"), primary_key=True
          ),
          Column("key", UnicodeText),
          Column("value", UnicodeText),
      )

    Because the key/value pairs in a vertical scheme are not fixed in advance,
    accessing them like a Python dict can be very convenient.  The example below
    can be used with many common vertical schemas as-is or with minor adaptations.

    """

    from sqlalchemy import and_
    from sqlalchemy import Column
    from sqlalchemy import create_engine
    from sqlalchemy import ForeignKey
    from sqlalchemy import Integer
    from sqlalchemy import Unicode
    from sqlalchemy import UnicodeText
    from sqlalchemy.ext.associationproxy import association_proxy
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import relationship
    from sqlalchemy.orm import Session
    from sqlalchemy.orm.collections import attribute_keyed_dict

    class ProxiedDictMixin:
        """Adds obj[key] access to a mapped class.

        This class basically proxies dictionary access to an attribute
        called ``_proxied``.  The class which inherits this class
        should have an attribute called ``_proxied`` which points to a dictionary.

        """

        def __len__(self):
            return len(self._proxied)

        def __iter__(self):
            return iter(self._proxied)

        def __getitem__(self, key):
            return self._proxied[key]

        def __contains__(self, key):
            return key in self._proxied

        def __setitem__(self, key, value):
            self._proxied[key] = value

        def __delitem__(self, key):
            del self._proxied[key]

    if __name__ == "__main__":
        Base = declarative_base()

        class AnimalFact(Base):
            """A fact about an animal."""

            __tablename__ = "animal_fact"

            animal_id = Column(ForeignKey("animal.id"), primary_key=True)
            key = Column(Unicode(64), primary_key=True)
            value = Column(UnicodeText)

        class Animal(ProxiedDictMixin, Base):
            """an Animal"""

            __tablename__ = "animal"

            id = Column(Integer, primary_key=True)
            name = Column(Unicode(100))

            facts = relationship(
                "AnimalFact", collection_class=attribute_keyed_dict("key")
            )

            _proxied = association_proxy(
                "facts",
                "value",
                creator=lambda key, value: AnimalFact(key=key, value=value),
            )

            def __init__(self, name):
                self.name = name

            def __repr__(self):
                return "Animal(%r)" % self.name

            @classmethod
            def with_characteristic(self, key, value):
                return self.facts.any(key=key, value=value)

        engine = create_engine("sqlite://")
        Base.metadata.create_all(engine)

        session = Session(bind=engine)

        stoat = Animal("stoat")
        stoat["color"] = "reddish"
        stoat["cuteness"] = "somewhat"

        # dict-like assignment transparently creates entries in the
        # stoat.facts collection:
        print(stoat.facts["color"])

        session.add(stoat)
        session.commit()

        critter = session.query(Animal).filter(Animal.name == "stoat").one()
        print(critter["color"])
        print(critter["cuteness"])

        critter["cuteness"] = "very"

        print("changing cuteness:")

        marten = Animal("marten")
        marten["color"] = "brown"
        marten["cuteness"] = "somewhat"
        session.add(marten)

        shrew = Animal("shrew")
        shrew["cuteness"] = "somewhat"
        shrew["poisonous-part"] = "saliva"
        session.add(shrew)

        loris = Animal("slow loris")
        loris["cuteness"] = "fairly"
        loris["poisonous-part"] = "elbows"
        session.add(loris)

        q = session.query(Animal).filter(
            Animal.facts.any(
                and_(AnimalFact.key == "color", AnimalFact.value == "reddish")
            )
        )
        print("reddish animals", q.all())

        q = session.query(Animal).filter(
            Animal.with_characteristic("color", "brown")
        )
        print("brown animals", q.all())

        q = session.query(Animal).filter(
            ~Animal.with_characteristic("poisonous-part", "elbows")
        )
        print("animals without poisonous-part == elbows", q.all())

        q = session.query(Animal).filter(Animal.facts.any(value="somewhat"))
        print('any animal with any .value of "somewhat"', q.all())