ɯei"dZddlZddlZddlmZddlmZmZmZmZm Z m Z m Z ddl Z ddlmcmcmcmcmZddlmZddlmZmZmZddlmZddlmZdd lmZdd l m!Z!dd l"m#Z#m$Z$m%Z%m&Z&m'Z'm(Z(m)Z)m*Z*m+Z+m,Z,m-Z-m.Z.m/Z/dd l0m1Z1m2Z2m3Z3dd l4m5Z5m6Z6ejndkrddlm8Z8nddl9m8Z8GddZ:GddZ;y)zStored procedures in Snowpark. Refer to :class:`~snowflake.snowpark.stored_procedure.StoredProcedure` for details and sample code.N) ModuleType)AnyCallableDictListOptionalTupleUnion)ProgrammingError) build_sprocbuild_sproc_applywith_src_position)SnowparkClientExceptionMessages)"open_telemetry_udf_context_manager)TelemetryField)convert_sp_to_sf_type) UDFColumnRegistrationType&add_snowpark_package_to_sproc_packagescheck_execute_as_argcheck_python_runtime_versioncheck_register_args%cleanup_failed_permanent_registrationcreate_python_udf_or_sp generate_anonymous_python_sp_sqlgenerate_call_python_sp_sqlprocess_file_pathprocess_registration_inputsresolve_imports_and_packages)TempObjectTypecheck_imports_type publicapi)DataType StructType) )Iterablec4eZdZdZ ddededeededejdd e ed e ee ee fd e e jd e ed e e j ddfdZ ddeede dfdZeddddddede dde eeefdededef dZy)StoredProcedurea% Encapsulates a user defined lambda or function that is returned by :func:`~snowflake.snowpark.functions.sproc`, :meth:`StoredProcedureRegistration.register` or :meth:`StoredProcedureRegistration.register_from_file`. The constructor of this class is not supposed to be called directly. Call an instance of :class:`StoredProcedure` to invoke a stored procedure. The input should be Python literal values. See Also: - :class:`StoredProcedureRegistration` - :func:`~snowflake.snowpark.functions.sproc` Nfunc return_type input_typesname execute_ascallerownerzrestricted calleranonymous_sp_sqlpackages_ast_ast_id _ast_stmtreturnc ||_||_||_||_||_||_t |t|_||_ ||_ | |_ | |_ yN) r*r- _return_type _input_types _execute_as_anonymous_sp_sql isinstancer$_is_return_table _packagesr4r5r6) selfr*r+r,r-r.r2r3r4r5r6s e/var/www/html/glpi_dashboard/venv/lib/python3.12/site-packages/snowflake/snowpark/stored_procedure.py__init__zStoredProcedure.__init__Hs_#  ''%!1 *; C!  "argssession"snowflake.snowpark.session.Sessionc|rIt|dtjjjr|r t d|d}|dd}n,|xs(tjjj }t|jt|kr-t dt|jdt|||fS)NrzTwo sessions specified in arguments. Session should either be the first argument or as a named argument at the end, but not bothzJIncorrect number of arguments passed to the stored procedure. Expected: <=z , Found: ) r> snowflakesnowparkrFSession ValueError_get_active_sessionlenr;)rArErFs rB_validate_callzStoredProcedure._validate_callgs JtAw (:(:(B(B(J(JK @1gG8DQ!3!3!;!;!O!O!QG t  !CI -"4#4#456iD {L  W}rDT)rFstatement_paramsblock _emit_astrQrRrSc,|j||\}}|jjjdtj j d}|r^|jR|jJd|jJdtj}t||j|g||jrUt||jg|}|j|}|j||j |||j"} |s9| S|j$|jg||j ||j |d} |j r || _| S|j"%|j&j)|j"| S)NzStoredProcedure.__call__zENeed to ensure _emit_ast is True when registering a stored procedure.z-Need to assign an ID to the stored procedure.)queryis_return_tablerRrQast_stmt)rVrQrSrR)rP_conn_telemetry_clientsend_function_usage_telemetryrFUNC_CAT_USAGEvaluer4r5protoExprr r=rr-_execute_sproc_internalr?r6_call _ast_batcheval) rArFrQrRrSrE sproc_exprcall_sqlrUress rB__call__zStoredProcedure.__call__s++D': g ''EE &(E(E(K(K  . % WV W% ( ?> ?(J j$,,8H P4 P  ! !27DIIMMH--.xj9E11 $ 5 5!1 2C '-- !% 5 5!1// C  "CH  ^^ '    # #DNN 3 rD)r1NNNNNr9)__name__ __module__ __qualname____doc__rr#rstrtypingLiteralrr rr]r)intBindrCrrPr"rboolrfrDrBr)r)9se (NU*.;?04!%*.###(^ #  # NN#IJ ##3-#4c:o 678#u,,-###EJJ'# #DCG3i>?0CG59 99>?9#4S>2 9  9  9 99rDr)c9~eZdZdZdedddfdZdeddfd Ze d2dd d d de e e e e ffdee deee dee e ee fdedee deee e e e e ffdeee e efdedededej(ddedeee deee e fdee ded ee d!eee e fd"eee e fd#ed$edef.d%Ze d2dd d d d&d'e d(e dee deee dee e ee fdedee deee e e e e ffdeee e efdedededej(ddedeee deee e fdee ded ee d!eee e fd"eee e fd#ed)ed$edef2d*Zd dd d d d ddd ddd ddd d+de e e e e ffde dee d,e dee deee e e e e ffdeee e efdedededed#ed"eee e fdej(dd-ed.e d)ededeee deee e fd/edee d0eee efded ee d!eee e fd$edef8d1Zy)3StoredProcedureRegistrationak; Provides methods to register lambdas and functions as stored procedures in the Snowflake database. For more information about Snowflake Python stored procedures, see `Python stored procedures `__. :attr:`session.sproc ` returns an object of this class. You can use this object to register stored procedures that you plan to use in the current session or permanently. The methods that register a stored procedure return a :class:`StoredProcedure` object. Note that the first parameter of your function should be a snowpark Session. Also, you need to add `snowflake-snowpark-python` package (version >= 0.4.0) to your session before trying to create a stored procedure. There are two ways to register a stored procedure with Snowpark: - Use :func:`~snowflake.snowpark.functions.sproc` or :meth:`register`. By pointing to a `runtime Python function`, Snowpark uses `cloudpickle `_ to serialize this function to bytecode, and deserialize the bytecode to a Python function on the Snowflake server during stored procedure creation. During the serialization, the global variables used in the Python function will be serialized into the bytecode, but only the name of the module object or any objects from a module that are used in the Python function will be serialized. If the size of the serialized bytecode is over 8K bytes, it will be uploaded to a stage location as a Python file. If it's under 8K, it will be added to the `Stored Procedure in-line code `__. During the deserialization, Python will look up the corresponding modules and objects by names. Details could be found in :class:`snowflake.snowpark.udf.UDFRegistration`. - Use :meth:`register_from_file`. By pointing to a `Python file` or a `zip file containing Python source code` and the target function name, Snowpark uploads this file to a stage (which can also be customized), and load the corresponding function from this file to the Python runtime on the Snowflake server during stored procedure creation. Then this function will be invoked when calling this stored procedure. This approach can address the deficiency of the previous approach that uses cloudpickle, because the source code in this file other than the target function will be loaded during stored procedure creation. Therefore, this approach is useful and efficient when all your Python code is already in source files. Snowflake supports the following data types for the parameters for a stored procedure: ============================================= ================================================ ========= Python Type Snowpark Type SQL Type ============================================= ================================================ ========= ``int`` :class:`~snowflake.snowpark.types.LongType` NUMBER ``decimal.Decimal`` :class:`~snowflake.snowpark.types.DecimalType` NUMBER ``float`` :class:`~snowflake.snowpark.types.FloatType` FLOAT ``str`` :class:`~snowflake.snowpark.types.StringType` STRING ``bool`` :class:`~snowflake.snowpark.types.BooleanType` BOOL ``datetime.time`` :class:`~snowflake.snowpark.types.TimeType` TIME ``datetime.date`` :class:`~snowflake.snowpark.types.DateType` DATE ``datetime.datetime`` :class:`~snowflake.snowpark.types.TimestampType` TIMESTAMP ``bytes`` or ``bytearray`` :class:`~snowflake.snowpark.types.BinaryType` BINARY ``list`` :class:`~snowflake.snowpark.types.ArrayType` ARRAY ``dict`` :class:`~snowflake.snowpark.types.MapType` OBJECT Dynamically mapped to the native Python type :class:`~snowflake.snowpark.types.VariantType` VARIANT ``dict`` :class:`~snowflake.snowpark.types.GeographyType` GEOGRAPHY ============================================= ================================================ ========= Note: 1. Data with the VARIANT SQL type will be converted to a Python type dynamically inside a stored procedure. The following SQL types are converted to :class:`str` in stored procedures rather than native Python types: TIME, DATE, TIMESTAMP and BINARY. 2. Data returned as :class:`~snowflake.snowpark.types.ArrayType` (``list``), :class:`~snowflake.snowpark.types.MapType` (``dict``) or :class:`~snowflake.snowpark.types.VariantType` (:attr:`~snowflake.snowpark.types.Variant`) by a stored procedure will be represented as a json string. You can call ``eval()`` or ``json.loads()`` to convert the result to a native Python object. Data returned as :class:`~snowflake.snowpark.types.GeographyType` (:attr:`~snowflake.snowpark.types.Geography`) by a stored procedure will be represented as a `GeoJSON `_ string. 3. Currently calling stored procedure that requires VARIANT and GEOGRAPHY input types is not supported in snowpark API. 4. Dataframe returned from :meth:`~snowflake.snowpark.Session.call` does not support stacking dataframe operations when sql simplifier is disabled, and output columns in return type for the table stored procedure are not defined. Example 1 Use stored procedure to copy data from one table to another:: >>> import snowflake.snowpark >>> from snowflake.snowpark.functions import sproc >>> >>> session.add_packages('snowflake-snowpark-python') >>> >>> def my_copy(session: snowflake.snowpark.Session, from_table: str, to_table: str, count: int) -> str: ... session.table(from_table).limit(count).write.save_as_table(to_table) ... return "SUCCESS" >>> >>> my_copy_sp = session.sproc.register(my_copy, name="my_copy_sp", replace=True) >>> _ = session.sql("create or replace temp table test_from(test_str varchar) as select randstr(20, random()) from table(generator(rowCount => 100))").collect() >>> >>> # call using sql >>> _ = session.sql("drop table if exists test_to").collect() >>> session.sql("call my_copy_sp('test_from', 'test_to', 10)").collect() [Row(MY_COPY_SP='SUCCESS')] >>> session.table("test_to").count() 10 >>> # call using session.call API >>> _ = session.sql("drop table if exists test_to").collect() >>> session.call("my_copy_sp", "test_from", "test_to", 10) 'SUCCESS' >>> session.table("test_to").count() 10 Example 2 Create a temporary stored procedure from a lambda and call it:: >>> from snowflake.snowpark.functions import sproc >>> from snowflake.snowpark.types import IntegerType >>> >>> session.add_packages('snowflake-snowpark-python') >>> add_one_sp = sproc( ... lambda session_, x: session_.sql(f"select {x} + 1").collect()[0][0], ... return_type=IntegerType(), ... input_types=[IntegerType()] ... ) >>> add_one_sp(1) 2 Example 3 Create a stored procedure with type hints and ``@sproc`` decorator and call it:: >>> import snowflake.snowpark >>> from snowflake.snowpark.functions import sproc >>> >>> session.add_packages('snowflake-snowpark-python') >>> @sproc ... def add_sp(session_: snowflake.snowpark.Session, x: int, y: int) -> int: ... return session_.sql(f"select {x} + {y}").collect()[0][0] >>> add_sp(1, 2) 3 Example 4 Create a permanent stored procedure with a name and call it in SQL:: >>> from snowflake.snowpark.types import IntegerType >>> >>> session.add_packages('snowflake-snowpark-python') >>> _ = session.sql("create or replace temp stage mystage").collect() >>> _ = session.sproc.register( ... lambda session_, x, y: session_.sql(f"SELECT {x} * {y}").collect()[0][0], ... return_type=IntegerType(), ... input_types=[IntegerType(), IntegerType()], ... is_permanent=True, ... name="mul_sp", ... replace=True, ... stage_location="@mystage", ... ) >>> session.sql("call mul_sp(5, 6)").collect() [Row(MUL_SP=30)] >>> # skip stored proc creation if it already exists >>> _ = session.sproc.register( ... lambda session_, x, y: session_.sql(f"SELECT {x} * {y} + 1").collect()[0][0], ... return_type=IntegerType(), ... input_types=[IntegerType(), IntegerType()], ... is_permanent=True, ... name="mul_sp", ... if_not_exists=True, ... stage_location="@mystage", ... ) >>> session.sql("call mul_sp(5, 6)").collect() [Row(MUL_SP=30)] >>> # overwrite stored procedure >>> _ = session.sproc.register( ... lambda session_, x, y: session_.sql(f"SELECT {x} * {y} + 1").collect()[0][0], ... return_type=IntegerType(), ... input_types=[IntegerType(), IntegerType()], ... is_permanent=True, ... name="mul_sp", ... replace=True, ... stage_location="@mystage", ... ) >>> session.sql("call mul_sp(5, 6)").collect() [Row(MUL_SP=31)] Example 5 Create a stored procedure with stored-procedure-level imports and call it:: >>> import snowflake.snowpark >>> from resources.test_sp_dir.test_sp_file import mod5 >>> from snowflake.snowpark.functions import sproc >>> >>> session.add_packages('snowflake-snowpark-python') >>> @sproc(imports=[("tests/resources/test_sp_dir/test_sp_file.py", "resources.test_sp_dir.test_sp_file")]) ... def mod5_and_plus1_sp(session_: snowflake.snowpark.Session, x: int) -> int: ... return mod5(session_, x) + 1 >>> mod5_and_plus1_sp(2) 3 Example 6 Create a stored procedure with stored-procedure-level packages and call it:: >>> import snowflake.snowpark >>> from snowflake.snowpark.functions import sproc >>> import numpy as np >>> import math >>> >>> @sproc(packages=["snowflake-snowpark-python", "numpy"]) ... def sin_sp(_: snowflake.snowpark.Session, x: float) -> float: ... return np.sin(x) >>> sin_sp(0.5 * math.pi) 1.0 Example 7 Creating a stored procedure from a local Python file:: >>> session.add_packages('snowflake-snowpark-python') >>> # mod5() in that file has type hints >>> mod5_sp = session.sproc.register_from_file( ... file_path="tests/resources/test_sp_dir/test_sp_file.py", ... func_name="mod5", ... ) >>> mod5_sp(2) 2 Example 8 Creating a stored procedure from a Python file on an internal stage:: >>> from snowflake.snowpark.types import IntegerType >>> >>> session.add_packages('snowflake-snowpark-python') >>> _ = session.sql("create or replace temp stage mystage").collect() >>> _ = session.file.put("tests/resources/test_sp_dir/test_sp_file.py", "@mystage", auto_compress=False) >>> mod5_sp = session.sproc.register_from_file( ... file_path="@mystage/test_sp_file.py", ... func_name="mod5", ... return_type=IntegerType(), ... input_types=[IntegerType()], ... ) >>> mod5_sp(2) 2 Example 9 Creating a table stored procedure with return type while defining return columns and datatypes:: >>> from snowflake.snowpark.types import IntegerType, StructField, StructType >>> @sproc(return_type=StructType([StructField("A", IntegerType()), StructField("B", IntegerType())]), input_types=[IntegerType(), IntegerType()]) ... def select_sp(session_, x, y): ... return session_.sql(f"SELECT {x} as A, {y} as B") ... >>> select_sp(1, 2).show() ------------- |"A" |"B" | ------------- |1 |2 | ------------- Example 10 Creating a table stored procedure with return type with free return columns:: >>> from snowflake.snowpark.types import IntegerType, StructType >>> @sproc(return_type=StructType(), input_types=[IntegerType(), IntegerType()]) ... def select_sp(session_, x, y): ... return session_.sql(f"SELECT {x} as A, {y} as B") ... >>> select_sp(1, 2).show() ------------- |"A" |"B" | ------------- |1 |2 | ------------- Example 11 Creating a table stored procedure using implicit type hints:: >>> from snowflake.snowpark.dataframe import DataFrame >>> @sproc ... def select_sp(session_: snowflake.snowpark.Session, x: int, y: int) -> DataFrame: ... return session_.sql(f"SELECT {x} as A, {y} as B") ... >>> select_sp(1, 2).show() ------------- |"A" |"B" | ------------- |1 |2 | ------------- See Also: - :class:`snowflake.snowpark.udf.UDFRegistration` - :func:`~snowflake.snowpark.functions.sproc` - :meth:`register` - :meth:`register_from_file` - :meth:`~snowflake.snowpark.Session.add_import` - :meth:`~snowflake.snowpark.Session.add_packages` rFrGr7Nc||_yr9)_session)rArFs rBrCz$StoredProcedureRegistration.__init__s  rD sproc_objz&snowflake.snowpark.dataframe.DataFramec|jDcgc] }t|}}|jjd|jddj |dScc}w)a Returns a :class:`~snowflake.snowpark.DataFrame` that describes the properties of a stored procedure. Args: sproc_obj: A :class:`StoredProcedure` returned by :func:`~snowflake.snowpark.functions.sproc` or :meth:`register`. zdescribe procedure (,))r;rrusqlr-join)rArvt func_argss rBdescribez$StoredProcedureRegistration.describesb8A7M7MN!*1-N N}}  !)..!1388I3F2Gq I  OsAFr1T)rQsource_code_displayrSr*r+r,r- is_permanentstage_locationimportsr3replace if_not_existsparallelr.r/strictexternal_access_integrationssecretscomment copy_grantsartifact_repositoryresource_constraintrQrrSc t|j||5t|s(|jdt dt |t | ttj|||| |j|||||||| | | | f |||||| d||jdd||jdd|jd d|||d |cdddS#1swYyxYw) a Registers a Python function as a Snowflake Python stored procedure and returns the stored procedure. The usage, input arguments, and return value of this method are the same as they are for :func:`~snowflake.snowpark.functions.sproc`, but :meth:`register` cannot be used as a decorator. See examples in :class:`~snowflake.snowpark.stored_procedure.StoredProcedureRegistration`. Args: func: A Python function used for creating the stored procedure. Note that the first parameter of your function should be a snowpark Session. return_type: A :class:`~snowflake.snowpark.types.DataType` representing the return data type of the stored procedure. Optional if type hints are provided. input_types: A list of :class:`~snowflake.snowpark.types.DataType` representing the input data types of the stored procedure. Optional if type hints are provided. 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 stored procedure in Snowflake, which allows you to call this stored procedure in a SQL command or via :meth:`~snowflake.snowpark.Session.call`. If it is not provided, a name will be automatically generated for the stored procedure. A name must be specified when ``is_permanent`` is ``True``. is_permanent: Whether to create a permanent stored procedure. 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 stored procedure 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 stored procedure. You can use a string to represent a file path (similar to the ``path`` argument in :meth:`~snowflake.snowpark.Session.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 :meth:`~snowflake.snowpark.Session.add_import`). These stored procedure-level imports will override the session-level imports added by :meth:`~snowflake.snowpark.Session.add_import`. packages: A list of packages that only apply to this stored procedure. These stored procedure-level packages will override the session-level packages added by :meth:`~snowflake.snowpark.Session.add_packages` and :meth:`~snowflake.snowpark.Session.add_requirements`. replace: Whether to replace a stored procedure that already was registered. The default is ``False``. If it is ``False``, attempting to register a stored procedure with a name that already exists results in a ``SnowparkSQLException`` exception being thrown. If it is ``True``, an existing stored procedure with the same name is overwritten. if_not_exists: Whether to skip creation of a stored procedure the same procedure is already registered. 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 stored procedure is already registered, the registration is skipped. parallel: The number of threads to use for uploading stored procedure 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 stored procedure files. execute_as: What permissions should the procedure have while executing. This supports caller, or owner for now. strict: Whether the created stored procedure is strict. A strict stored procedure will not invoke the stored procedure if any input is null. Instead, a null value will always be returned. Note that the stored procedure might still return null for non-null inputs. statement_params: Dictionary of statement level parameters to be set while executing this action. source_code_display: Display the source code of the stored procedure `func` as comments in the generated script. The source code is dynamically generated therefore it may not be identical to how the `func` is originally defined. The default is ``True``. If it is ``False``, source code will not be generated or displayed. 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. comment: Adds a comment for the created object. See `COMMENT `_ copy_grants: Specifies to retain the access privileges from the original function when a new function is created using CREATE OR REPLACE PROCEDURE. artifact_repository: The name of an artifact_repository that packages are found in. If unspecified, packages are pulled from Anaconda. resource_constraint: A dictionary containing a resource properties of a warehouse and then constraints needed to run this function. Eg ``{"architecture": "x86"}`` requires an x86 warehouse be used for execution. See Also: - :func:`~snowflake.snowpark.functions.sproc` - :meth:`register_from_file` )r*r-_registered_object_nameNzHInvalid function: not a function or callable (__call__ is not defined): z$StoredProcedureRegistration.register anonymousFforce_inline_codenative_app_params)rrrrrQr.api_call_sourcerrrrrrrrS) rregistercallableget TypeErrortyperrr PROCEDURE_do_register_sppop)rAr*r+r,r-rrrr3rrrr.rrrrrrrrQrrSkwargss rBrz$StoredProcedureRegistration.registers\0 Dt T, D>fjj1J&K&S226t*? ! , (($ nh  (4''.J'!1% F$7 **[%8)#)**-@%"H"(**-@$"G$7$7#;<=, , , s B8CC$)rQrskip_upload_on_content_matchrS file_path func_namerc t|j|||5t|}ttj |||| t | |j||f|||||| | | | |f |||||| d||||||d |cdddS#1swYyxYw)a Registers a Python function as a Snowflake Python stored procedure from a Python or zip file, and returns the stored procedure. Apart from ``file_path`` and ``func_name``, the input arguments of this method are the same as :meth:`register`. See examples in :class:`~snowflake.snowpark.stored_procedure.StoredProcedureRegistration`. Args: file_path: The path of a local file or a remote file in the stage. See more details on ``path`` argument of :meth:`session.add_import() `. Note that unlike ``path`` argument of :meth:`session.add_import() `, here the file can only be a Python file or a compressed file (e.g., .zip file) containing Python modules. func_name: The Python function name in the file that will be created as a stored procedure. return_type: A :class:`~snowflake.snowpark.types.DataType` representing the return data type of the stored procedure. Optional if type hints are provided. input_types: A list of :class:`~snowflake.snowpark.types.DataType` representing the input data types of the stored procedure. Optional if type hints are provided. 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 stored procedure in Snowflake, which allows you to call this stored procedure in a SQL command or via :meth:`~snowflake.snowpark.Session.call`. If it is not provided, a name will be automatically generated for the stored procedure. A name must be specified when ``is_permanent`` is ``True``. is_permanent: Whether to create a permanent stored procedure. 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 stored procedure 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 stored procedure. You can use a string to represent a file path (similar to the ``path`` argument in :meth:`~snowflake.snowpark.Session.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 :meth:`~snowflake.snowpark.Session.add_import`). These stored procedure-level imports will override the session-level imports added by :meth:`~snowflake.snowpark.Session.add_import`. packages: A list of packages that only apply to this stored procedure. These stored procedure-level packages will override the session-level packages added by :meth:`~snowflake.snowpark.Session.add_packages` and :meth:`~snowflake.snowpark.Session.add_requirements`. replace: Whether to replace a stored procedure that already was registered. The default is ``False``. If it is ``False``, attempting to register a stored procedure with a name that already exists results in a ``SnowparkSQLException`` exception being thrown. If it is ``True``, an existing stored procedure with the same name is overwritten. if_not_exists: Whether to skip creation of a stored procedure the same procedure is already registered. 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 stored procedure is already registered, the registration is skipped. parallel: The number of threads to use for uploading stored procedure 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 stored procedure files. execute_as: What permissions should the procedure have while executing. This supports ``caller``, ``owner`` or ``restricted caller`` for now. strict: Whether the created stored procedure is strict. A strict stored procedure will not invoke the stored procedure if any input is null. Instead, a null value will always be returned. Note that the stored procedure might still return null for non-null inputs. statement_params: Dictionary of statement level parameters to be set while executing this action. source_code_display: Display the source code of the stored procedure `func` as comments in the generated script. The source code is dynamically generated therefore it may not be identical to how the `func` is originally defined. The default is ``True``. If it is ``False``, source code will not be generated or displayed. skip_upload_on_content_match: When set to ``True`` and a version of source file already exists on stage, the given source file will be uploaded to stage only if the contents of the current file differ from the remote file on stage. Defaults to ``False``. 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. comment: Adds a comment for the created object. See `COMMENT `_ copy_grants: Specifies to retain the access privileges from the original function when a new function is created using CREATE OR REPLACE PROCEDURE. artifact_repository: The name of an artifact_repository that packages are found in. If unspecified, packages are pulled from Anaconda. resource_constraint: A dictionary containing a resource properties of a warehouse and then constraints needed to run this function. Eg ``{"architecture": "x86"}`` requires an x86 warehouse be used for execution. Note:: The type hints can still be extracted from the source Python file if they are provided, but currently are not working for a zip file. Therefore, you have to provide ``return_type`` and ``input_types`` when ``path`` points to a zip file. See Also: - :func:`~snowflake.snowpark.functions.sproc` - :meth:`register` )rrr-z.StoredProcedureRegistration.register_from_file) rrrrrQr.rrrrrrrSN)rregister_from_filerrr rrr)rArrr+r,r-rrrr3rrrr.rrrrrrrrQrrrSrs rBrz.StoredProcedureRegistration.register_from_files~0  # #yITX $ *)4I (($ nh  ! ,(4''I&.J'!1% P$7-I)$7$7#123$ $ $ s A BB )rrQr.rrrrrrrrrrrrSsp_namerrrrc  bd\}}}|jdi|rP|jjj}t |j j |}|j}t||||d||||| St|dt|jtj|||||\} }!}"}}}#|r|jjj}t |j j |}|j}t||fid|d|d|d|d |d |d |d | d | d| d|d| d| d|d|d|d|d|jd| ||!r tddgtt!|D$cgc] }$d|$dz c}$z}%t#||%ddD&'cgc]\}&}'t%|&|'}(}&}'t'|j|}t)|jtj||%| |||| | | |||||jdd\})}*}+},}-}.d}/|j|jj*}/|.s t-|/d}0|rt/|||(|)| |+|,||*| |/|||}0nd}1 t1d3id|jd|d|d |(d!|#d"|)d#tjd$| d%|+d&|,d'|d(t2j4d|d |d | d)|*d|d*|d| d|d|d| d|d+|d,|d-|/d.|d/| |1rtE|j|-| t|||| ||0||||2 }5|5Scc}$wcc}'}&w#t6$rE}2d0}1t9j:d1}3t=j>|2}4|4jA|3dd}2~2wtB$rd0}1wxYw#|1rtE|j|-|wwxYw)4N)NNNr)r.r3r4r5r6zstored-proc-levelr+r,r-rrr3rrrrQr.rrrrrrrFz(pandas stored procedure is not supportedargrI)rFr3 _suppress_local_package_warningsF)rQrrrrrr)r*r+ input_argshandler object_name all_imports all_packages raw_importsinline_python_coderruntime_versionrrrr*ropt_arg_defaultsr object_typerrrrregistration_typerrrrrrrT)r.r2r3r4r5r6rq)#rrurabindrexprstored_procedureuidr)r!rr rr rrangerOziprrr!_runtime_version_from_requirementrrrrSPROCr sysexc_infor$SQL_EXCEPTION_FROM_PROGRAMMING_ERRORwith_traceback BaseExceptionr)6rAr*r+r,rrrr3rrrrrrQr.rrrrrrrrrrrrrSrstmtastast_id sproc_name is_pandas_udfis_dataframe_inputri arg_namesdtarg_namerrcoderrupload_file_stage_location%custom_python_runtime_version_allowed runtime_version_from_requirementr2raisedpetbnesprocs6 rBrz+StoredProcedureRegistration._do_register_sp4sU@-c6 ::/ 0 <}}//446' (B(BDI"01%!   7$78 ( MM  $ $             ==++002D#DII$>$>ECXXF  ( (      .    "   , " "2 &  %8  .J! " # $ % &*' ( ) *)3- 2 FG GKc+>N8O"P1S1;"PP 8;KSTSU8W (4HIb( #  :MM  ) MM  $ $       - 3)E%/ 3-3ZZ2E.!      & 1.,0( == $ ?? -5 ()I J ?'%&')##' @-I"3  "F1 ' MM!, *  &6  $ !/ 8 8!+!,".!('7&<&<".$#0 (,!" *#$%4%&"'(2N)*$+,&6-.$/0'812!,34%E56)<78)<9Z9 'A>    !-   K#Q N$ 6\\^A&4YY''+5   9 'A>s2L+*L06BL66 N?AM??NNN.)NNNFNNNFFr1FNNNFNN)rgrhrirjrrCr)rr"r rr rkr#rr'rprrnrlrmrrrrrrqrDrBrsrssDbH  )M N SW   (  1  +/0448"(,?C;?#MT<@,0!%!-18<)Y,6:$(1YHeCHo-.Yh'Yd8n- Y uS(3-/01 Y  Y! Y$uS%S/%9:;<Y4c:o 678YYYYNN#IJYY'/tCy&9Y $sCx.)!Y"##Y$%Y&&c]'Y(&d38n5)Y,#4S>2-Y."/Y01Y4 5YYv +/0448"(,?C;?#MT<@,0!%!-18<+b.6:$(-25bbbh' b d8n- b uS(3-/01 bb! b$uS%S/%9:;<b4c:o 678bbbbNN#IJbb '/tCy&9!b"$sCx.)#b$#%b&'b(&c])b*&d38n5+b.#4S>2/b0"1b2'+3b45b8 9bbd%*59MT-2"<@,0"'!%6:!-18<;mHeCHo-.mm(^ m  m ! m$uS%S/%9:;<m4c:o 678mmmmm"m#4S>2m NN#IJ!m"#m$%m&'+'m()m*'/tCy&9+m,$sCx.)-m. /m0#1m2$DcN33m45m6&c]7m8&d38n59m:;m> ?mrDrs)rs I DDDDDD0 WBI : v(@@Fe e rD