# Recipes

## Base Schema I

A common pattern with `marshmallow` is to define a base `Schema` class which has common configuration and behavior for your application’s `Schemas`.

You may want to define a common session object, e.g. a `scoped_session` to use for all `Schemas`.

    # myproject/db.py
    import sqlalchemy as sa
    from sqlalchemy import orm

    Session = orm.scoped_session(orm.sessionmaker())
    Session.configure(bind=engine)

    # myproject/schemas.py

    from marshmallow_sqlalchemy import SQLAlchemySchema

    from .db import Session

    class BaseSchema(SQLAlchemySchema):
        class Meta:
            sqla_session = Session

    # myproject/users/schemas.py

    from ..schemas import BaseSchema
    from .models import User

    class UserSchema(BaseSchema):
        # Inherit BaseSchema's options
        class Meta(BaseSchema.Meta):
            model = User

## Base Schema II

Here is an alternative way to define a BaseSchema class with a common `Session` object.

    # myproject/schemas.py

    from marshmallow_sqlalchemy import SQLAlchemySchemaOpts, SQLAlchemySchema
    from .db import Session

    class BaseOpts(SQLAlchemySchemaOpts):
        def __init__(self, meta, ordered=False):
            if not hasattr(meta, "sqla_session"):
                meta.sqla_session = Session
            super(BaseOpts, self).__init__(meta, ordered=ordered)

    class BaseSchema(SQLAlchemySchema):
        OPTIONS_CLASS = BaseOpts

This allows you to define class Meta options without having to subclass `BaseSchema.Meta`.

    # myproject/users/schemas.py

    from ..schemas import BaseSchema
    from .models import User

    class UserSchema(BaseSchema):
        class Meta:
            model = User

## Introspecting Generated Fields

It is often useful to introspect what fields are generated for a `SQLAlchemyAutoSchema`.

Generated fields are added to a `Schema's` `_declared_fields` attribute.

    AuthorSchema._declared_fields["books"]
    # <fields.RelatedList(default=<marshmallow.missing>, ...>

You can also use `marshmallow_sqlalchemy's` conversion functions directly.

    from marshmallow_sqlalchemy import property2field

    id_prop = Author.__mapper__.attrs.get("id")

    property2field(id_prop)
    # <fields.Integer(default=<marshmallow.missing>, ...>

## Overriding Generated Fields

Any field generated by a `SQLAlchemyAutoSchema` can be overridden.

    from marshmallow import fields
    from marshmallow_sqlalchemy import SQLAlchemyAutoSchema
    from marshmallow_sqlalchemy.fields import Nested

    class AuthorSchema(SQLAlchemyAutoSchema):
        class Meta:
            model = Author

        # Override books field to use a nested representation rather than pks
        books = Nested(BookSchema, many=True, exclude=("author",))

You can use the `auto_field` function to generate a marshmallow `Field` based on single model property. This is useful for passing additional keyword arguments to the generated field.

    from marshmallow_sqlalchemy import SQLAlchemyAutoSchema, field_for

    class AuthorSchema(SQLAlchemyAutoSchema):
        class Meta:
            model = Author

        # Generate a field, passing in an additional dump_only argument
        date_created = auto_field(dump_only=True)

If a field’s external data key differs from the model’s column name, you can pass a column name to `auto_field`.

    class AuthorSchema(SQLAlchemyAutoSchema):
        class Meta:
            model = Author
            # Exclude date_created because we're aliasing it below
            exclude = ("date_created",)

        # Generate "created_date" field from "date_created" column
        created_date = auto_field("date_created", dump_only=True)

## Automatically Generating Schemas For SQLAlchemy Models

It can be tedious to implement a large number of schemas if not overriding any of the generated fields as detailed above. SQLAlchemy has a hook that can be used to trigger the creation of the schemas, assigning them to the SQLAlchemy model property `Model.__marshmallow__`.

    from marshmallow_sqlalchemy import ModelConversionError, SQLAlchemyAutoSchema

    def setup_schema(Base, session):
        # Create a function which incorporates the Base and session information
        def setup_schema_fn():
            for class_ in Base._decl_class_registry.values():
                if hasattr(class_, "__tablename__"):
                    if class_.__name__.endswith("Schema"):
                        raise ModelConversionError(
                            "For safety, setup_schema can not be used when a"
                            "Model class ends with 'Schema'"
                        )

                    class Meta(object):
                        model = class_
                        sqla_session = session

                    schema_class_name = "%sSchema" % class_.__name__

                    schema_class = type(
                        schema_class_name, (SQLAlchemyAutoSchema,), {"Meta": Meta}
                    )

                    setattr(class_, "__marshmallow__", schema_class)

        return setup_schema_fn

An example of then using this:

    import sqlalchemy as sa
    from sqlalchemy.ext.declarative import declarative_base
    from sqlalchemy.orm import scoped_session, sessionmaker
    from sqlalchemy import event
    from sqlalchemy.orm import mapper

    # Either import or declare setup_schema here

    engine = sa.create_engine("sqlite:///:memory:")
    session = scoped_session(sessionmaker(bind=engine))
    Base = declarative_base()

    class Author(Base):
        __tablename__ = "authors"
        id = sa.Column(sa.Integer, primary_key=True)
        name = sa.Column(sa.String)

        def __repr__(self):
            return "<Author(name={self.name!r})>".format(self=self)

    # Listen for the SQLAlchemy event and run setup_schema.
    # Note: This has to be done after Base and session are setup
    event.listen(mapper, "after_configured", setup_schema(Base, session))

    Base.metadata.create_all(engine)

    author = Author(name="Chuck Paluhniuk")
    session.add(author)
    session.commit()

    # Model.__marshmallow__ returns the Class not an instance of the schema
    # so remember to instantiate it
    author_schema = Author.__marshmallow__()

    print(author_schema.dump(author))

This is inspired by functionality from ColanderAlchemy.

## Smart Nested Field

To serialize nested attributes to primary keys unless they are already loaded, you can use this custom field.

    from marshmallow_sqlalchemy.fields import Nested

    class SmartNested(Nested):
        def serialize(self, attr, obj, accessor=None):
            if attr not in obj.__dict__:
                return {"id": int(getattr(obj, attr + "_id"))}
            return super(SmartNested, self).serialize(attr, obj, accessor)

An example of then using this:

    from marshmallow_sqlalchemy import SQLAlchemySchema, auto_field

    class BookSchema(SQLAlchemySchema):
        id = auto_field()
        author = SmartNested(AuthorSchema)

        class Meta:
            model = Book
            sqla_session = Session

    book = Book(id=1)
    book.author = Author(name="Chuck Paluhniuk")
    session.add(book)
    session.commit()

    book = Book.query.get(1)
    print(BookSchema().dump(book)["author"])
    # {'id': 1}

    book = Book.query.options(joinedload("author")).get(1)
    print(BookSchema().dump(book)["author"])
    # {'id': 1, 'name': 'Chuck Paluhniuk'}

## Transient Object Creation

Sometimes it might be desirable to deserialize instances that are transient (not attached to a session). In these cases you can specify the `transient` option in the `Meta` class of a `SQLAlchemySchema`.

    from marshmallow_sqlalchemy import SQLAlchemyAutoSchema

    class AuthorSchema(SQLAlchemyAutoSchema):
        class Meta:
            model = Author
            load_instance = True
            transient = True

    dump_data = {"id": 1, "name": "John Steinbeck"}
    print(AuthorSchema().load(dump_data))
    # <Author(name='John Steinbeck')>

You may also explicitly specify an override by passing the same argument to `load`.

    from marshmallow_sqlalchemy import SQLAlchemyAutoSchema

    class AuthorSchema(SQLAlchemyAutoSchema):
        class Meta:
            model = Author
            sqla_session = Session
            load_instance = True

    dump_data = {"id": 1, "name": "John Steinbeck"}
    print(AuthorSchema().load(dump_data, transient=True))
    # <Author(name='John Steinbeck')>

Note that transience propagates to relationships (i.e. auto-generated schemas for nested items will also be transient).

See also

See State Management to understand session state management.

## Controlling Instance Loading

You can override the schema `load_instance` flag by passing in a `load_instance` argument when creating the schema instance. Use this to switch between loading to a dictionary or to a model instance:

    from marshmallow_sqlalchemy import SQLAlchemyAutoSchema

    class AuthorSchema(SQLAlchemyAutoSchema):
        class Meta:
            model = Author
            sqla_session = Session
            load_instance = True

    dump_data = {"id": 1, "name": "John Steinbeck"}
    print(AuthorSchema().load(dump_data))  # loading an instance
    # <Author(name='John Steinbeck')>
    print(AuthorSchema(load_instance=False).load(dump_data))  # loading a dict
    # {"id": 1, "name": "John Steinbeck"}