Model data

Reference: lamindb.schema

Any LaminDB can mount an arbitrary number of schema modules in its SQL database.

Each schema module is a Python module (and package) in which each SQL table is represented as a Python class, an ORM.

You can set up your own modules or reach out for support within our enterprise plan.

import lamindb as ln

ln.track()

ℹ️ Instance: testuser1/mydata

ℹ️ User: testuser1

ℹ️ Added notebook: Transform(id='6ZBQKdB7Mvlh', v='0', name='09-schema', type=notebook, title='Model data', created_by='DzTjkKse', created_at=datetime.datetime(2023, 3, 30, 23, 17, 5))

ℹ️ Added run: Run(id='l1cjVOvwRlrb9n4OnMUQ', transform_id='6ZBQKdB7Mvlh', transform_v='0', created_by='DzTjkKse', created_at=datetime.datetime(2023, 3, 30, 23, 17, 5))

View

View the schema that is active in your DB instance using view().

It’s a graph of linked entities that correspond to related tables.

The central table that references indexed data objects (File) that are transformed by runs (Run).

It’s a bit overwhelming, but you’ll see that many entities correspond to very fundamental concepts (like Gene or CellType). You’ll get to know them module by module.

ln.schema.view()

Entities, tables, ORMs

Tip

You’ll typically access the entities in each schema module through their Python ORM, which you can look up with auto-complete.

Type lns.* for entities in the core schema module, lns.bionty.* for basic biological entities, etc.

Each ORM is a SQLModel, meaning it offers all functionality of a SQLAlchemy ORM and a Pydantic BaseModel.

You can inspect it with auto-lookup or the multitude of possibilities offered by these classes, e.g.,

ln.Transform.__fields__

{'id': ModelField(name='id', type=str, required=False, default_factory='<function pipeline>'),
 'v': ModelField(name='v', type=str, required=False, default='1'),
 'name': ModelField(name='name', type=str, required=True),
 'type': ModelField(name='type', type=TransformType, required=False, default=pipeline),
 'title': ModelField(name='title', type=Optional[str], required=False, default=None),
 'reference': ModelField(name='reference', type=Optional[str], required=False, default=None),
 'created_by': ModelField(name='created_by', type=str, required=False, default_factory='<function current_user_id>'),
 'created_at': ModelField(name='created_at', type=datetime, required=True),
 'updated_at': ModelField(name='updated_at', type=Optional[datetime], required=False, default=None)}

You likely won’t need to, but you can access the underlying SQL table of an ORM like this:

ln.Transform.__table__

Table('core.transform', MetaData(), Column('id', AutoString(), table=<core.transform>, primary_key=True, nullable=False, default=ColumnDefault(<function pipeline at 0x7f966c3df790>)), Column('v', AutoString(), table=<core.transform>, primary_key=True, nullable=False, default=ColumnDefault('1')), Column('name', AutoString(), table=<core.transform>, nullable=False), Column('type', Enum('pipeline', 'notebook', 'app', name='transformtype'), table=<core.transform>, nullable=False, default=ColumnDefault(pipeline)), Column('title', AutoString(), table=<core.transform>), Column('reference', AutoString(), table=<core.transform>), Column('created_by', AutoString(), ForeignKey('core.user.id'), table=<core.transform>, nullable=False, default=ColumnDefault(<function current_user_id at 0x7f966c3df820>)), Column('created_at', DateTime(), table=<core.transform>, nullable=False, server_default=DefaultClause(<sqlalchemy.sql.functions.now at 0x7f966ca67f40; now>, for_update=False)), Column('updated_at', DateTime(), table=<core.transform>, onupdate=ColumnDefault(<sqlalchemy.sql.functions.now at 0x7f966ca67f10; now>)), schema=None)