snowflake.snowpark.udtf.UDTFRegistration.register

UDTFRegistration.register(handler: Type, output_schema: Union[StructType, Iterable[str], PandasDataFrameType], input_types: Optional[List[DataType]] = None, input_names: Optional[List[str]] = None, name: Optional[Union[str, Iterable[str]]] = None, is_permanent: bool = False, stage_location: Optional[str] = None, imports: Optional[List[Union[str, Tuple[str, str]]]] = None, packages: Optional[List[Union[str, module]]] = None, replace: bool = False, if_not_exists: bool = False, parallel: int = 4, strict: bool = False, secure: bool = False, external_access_integrations: Optional[List[str]] = None, secrets: Optional[Dict[str, str]] = None, immutable: bool = False, max_batch_size: Optional[int] = None, comment: Optional[str] = None, *, statement_params: Optional[Dict[str, str]] = None, **kwargs) UserDefinedTableFunction[source]

Registers a Python class as a Snowflake Python UDTF and returns the UDTF. The usage, input arguments, and return value of this method are the same as they are for udtf(), but register() cannot be used as a decorator. See examples in UDTFRegistration.

Parameters:
  • handler – A Python class used for creating the UDTF.

  • output_schema – A list of column names, or a StructType instance that represents the table function’s columns, or a PandasDataFrameType instance for vectorized UDTF. If a list of column names is provided, the process method of the handler class must have a return type hint to indicate the output schema data types.

  • input_types – A list of DataType representing the input data types of the UDTF. Optional if type hints are provided.

  • input_names – A list of str representing the input column names of the UDTF, this only applies to vectorized UDTF and is essentially a noop for regular UDTFs. If unspecified, default column names will be ARG1, ARG2, etc.

  • name – A string or list of strings that specify the name or fully-qualified object identifier (database name, schema name, and function name) for the UDTF in Snowflake. If it is not provided, a name will be automatically generated for the UDTF. A name must be specified when is_permanent is True.

  • is_permanent – Whether to create a permanent UDTF. The default is False. If it is True, a valid stage_location must be provided.

  • stage_location – The stage location where the Python file for the UDTF and its dependencies should be uploaded. The stage location must be specified when is_permanent is True, and it will be ignored when is_permanent is False. It can be any stage other than temporary stages and external stages.

  • imports – A list of imports that only apply to this UDTF. You can use a string to represent a file path (similar to the path argument in add_import()) in this list, or a tuple of two strings to represent a file path and an import path (similar to the import_path argument in add_import()). These UDTF-level imports will override the session-level imports added by add_import().

  • packages – A list of packages that only apply to this UDTF. These UDTF-level packages will override the session-level packages added by add_packages() and add_requirements(). To use Python packages that are not available in Snowflake, refer to custom_package_usage_config().

  • replace – Whether to replace a UDTF that already was registered. The default is False. If it is False, attempting to register a UDTF with a name that already exists results in a SnowparkSQLException exception being thrown. If it is True, an existing UDTF with the same name is overwritten.

  • if_not_exists – Whether to skip creation of a UDTF when one with the same signature already exists. The default is False. if_not_exists and replace are mutually exclusive and a ValueError is raised when both are set. If it is True and a UDTF with the same signature exists, the UDTF creation is skipped.

  • session – Use this session to register the UDTF. If it’s not specified, the session that you created before calling this function will be used. You need to specify this parameter if you have created multiple sessions before calling this method.

  • parallel – The number of threads to use for uploading UDTF files with the PUT command. The default value is 4 and supported values are from 1 to 99. Increasing the number of threads can improve performance when uploading large UDTF files.

  • strict – Whether the created UDTF is strict. A strict UDTF will not invoke the UDTF if any input is null. Instead, a null value will always be returned for that row. Note that the UDTF might still return null for non-null inputs.

  • secure – Whether the created UDTF is secure. For more information about secure functions, see Secure UDFs.

  • statement_params – Dictionary of statement level parameters to be set while executing this action.

  • external_access_integrations – The names of one or more external access integrations. Each integration you specify allows access to the external network locations and secrets the integration specifies.

  • secrets – The key-value pairs of string types of secrets used to authenticate the external network location. The secrets can be accessed from handler code. The secrets specified as values must also be specified in the external access integration and the keys are strings used to retrieve the secrets using secret API.

  • immutable – Whether the UDTF result is deterministic or not for the same input.

  • max_batch_size – The maximum number of rows per input pandas DataFrame or pandas Series inside a vectorized UDTF. Because a vectorized UDTF will be executed within a time limit, which is 60 seconds, this optional argument can be used to reduce the running time of every batch by setting a smaller batch size. Note that setting a larger value does not guarantee that Snowflake will encode batches with the specified number of rows. It will be ignored when registering a non-vectorized UDTF.

  • comment – Adds a comment for the created object object. See COMMENT