Schema

pydantic model felis.datamodel.Schema

Database schema model.

This represents a database schema, which contains one or more tables.

Parameters:

data (Any)

Fields:
Validators:
field id_map: dict[str, Any] [Optional]

Map of IDs to objects.

Validated by:
field tables: Sequence[Table] [Required]

The tables in the schema.

Validated by:
field version: SchemaVersion | str | None = None

The version of the schema.

Validated by:
validator check_tap_table_indexes  »  all fields

Check that the TAP table indexes are unique.

Parameters:

info (ValidationInfo) – The validation context used to determine if the check is enabled.

Returns:

The schema being validated.

Return type:

Schema

validator check_unique_constraint_names  »  all fields

Check for duplicate constraint names in the schema.

Returns:

The schema being validated.

Return type:

Schema

Raises:

ValueError – Raised if duplicate constraint names are found in the schema.

Parameters:

self (Schema)

validator check_unique_index_names  »  all fields

Check for duplicate index names in the schema.

Returns:

The schema being validated.

Return type:

Schema

Raises:

ValueError – Raised if duplicate index names are found in the schema.

Parameters:

self (Schema)

validator check_unique_table_names  »  tables

Check that table names are unique.

Parameters:

tables (list[Table]) – The tables to check.

Returns:

The tables if they are unique.

Return type:

list [ Table ]

Raises:

ValueError – Raised if table names are not unique.

find_object_by_id(id, obj_type)

Find an object with the given type by its ID.

Parameters:

  • id (str) – The ID of the object to find.

  • obj_type (type[TypeVar(T, bound= BaseObject)]) – The type of the object to find.

Returns:

The object with the given ID and type.

Return type:

BaseObject

Raises:

  • KeyError – If the object with the given ID is not found in the schema.

  • TypeError – If the object that is found does not have the right type.

Notes

The actual return type is the user-specified argument T, which is expected to be a subclass of BaseObject.

classmethod from_stream(source, context={})

Load a Schema from a file stream which should contain YAML data.

Parameters:

  • source (IO[str]) – The file stream to read from.

  • context (dict[str, Any], default: {}) – Pydantic context to be used in validation.

Returns:

The Felis schema loaded from the stream.

Return type:

Schema

Raises:

  • yaml.YAMLError – Raised if there is an error loading the YAML file.

  • pydantic.ValidationError – Raised if the schema fails validation.

classmethod from_uri(resource_path, context={})

Load a Schema from a string representing a ResourcePath.

Parameters:
Returns:

The ID of the object.

Return type:

str

Raises:

  • yaml.YAMLError – Raised if there is an error loading the YAML data.

  • ValueError – Raised if there is an error reading the resource.

  • pydantic.ValidationError – Raised if the schema fails validation.

validator generate_ids  »  all fields

Generate IDs for objects that do not have them.

Parameters:

  • values (dict[str, Any]) – The values of the schema.

  • info (ValidationInfo) – Validation context used to determine if ID generation is enabled.

Returns:

The values of the schema with generated IDs.

Return type:

dict [ str, Any ]

get_table_by_column(column)

Find the table that contains a column.

Parameters:

column (Column) – The column to find.

Returns:

The table that contains the column.

Return type:

Table

Raises:

ValueError – If the column is not found in any table.

model_post_init(ctx)

Post-initialization hook for the model.

Parameters:

ctx (Any) – The context object which was passed to the model.

Return type:

None

Notes

This method is called automatically by Pydantic after the model is initialized. It is used to create the ID map for the schema.

The ctx argument has the type Any because this is the function signature in Pydantic itself.