SNOW-1183322: [Local Testing] Add support for registering sprocs

CHANGELOG.md

@@ -4,6 +4,7 @@
 
 ### New Features
 
+- Added support for registering udfs and stored procedure to local testing.
 - Added support for the following local testing APIs:
   - snowflake.snowpark.Session:
     - file.put
@@ -25,9 +26,10 @@
     - current_database
     - current_session
     - date_trunc
-    - udf
     - object_construct
     - object_construct_keep_null
+    - pow
+    - sqrt
 - Added the function `DataFrame.write.csv` to unload data from a ``DataFrame`` into one or more CSV files in a stage.
 - Added distributed tracing using open telemetry apis for action functions in `DataFrame` and `DataFrameWriter`:
   - snowflake.snowpark.DataFrame:

src/snowflake/snowpark/mock/_functions.py

@@ -273,6 +273,20 @@ def mock_listagg(column: ColumnEmulator, delimiter: str, is_distinct: bool):
     )
 
 
+@patch("sqrt")
+def mock_sqrt(column: ColumnEmulator):
+    result = column.apply(math.sqrt)
+    result.sf_type = ColumnType(FloatType(), column.sf_type.nullable)
+    return result
+
+
+@patch("pow")
+def mock_pow(left: ColumnEmulator, right: ColumnEmulator):
+    result = left.combine(right, lambda l, r: l**r)
+    result.sf_type = ColumnType(FloatType(), left.sf_type.nullable)
+    return result
+
+
 @patch("to_date")
 def mock_to_date(
     column: ColumnEmulator,

src/snowflake/snowpark/mock/_stored_procedure.py

@@ -0,0 +1,238 @@
+#
+# Copyright (c) 2012-2024 Snowflake Computing Inc. All rights reserved.
+#
+
+import json
+import sys
+import typing
+from types import ModuleType
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+import snowflake.snowpark
+from snowflake.snowpark._internal.udf_utils import (
+    check_python_runtime_version,
+    process_registration_inputs,
+)
+from snowflake.snowpark._internal.utils import TempObjectType
+from snowflake.snowpark.column import Column
+from snowflake.snowpark.dataframe import DataFrame
+from snowflake.snowpark.exceptions import SnowparkSQLException
+from snowflake.snowpark.mock import CUSTOM_JSON_ENCODER
+from snowflake.snowpark.mock._plan import calculate_expression
+from snowflake.snowpark.mock._snowflake_data_type import ColumnEmulator
+from snowflake.snowpark.stored_procedure import (
+    StoredProcedure,
+    StoredProcedureRegistration,
+)
+from snowflake.snowpark.types import (
+    ArrayType,
+    DataType,
+    MapType,
+    StructType,
+    _FractionalType,
+    _IntegralType,
+)
+
+from ._telemetry import LocalTestOOBTelemetryService
+
+if sys.version_info <= (3, 9):
+    from typing import Iterable
+else:
+    from collections.abc import Iterable
+
+
+def sproc_types_are_compatible(x, y):
+    if (
+        isinstance(x, type(y))
+        or isinstance(x, _IntegralType)
+        and isinstance(y, _IntegralType)
+        or isinstance(x, _FractionalType)
+        and isinstance(y, _FractionalType)
+    ):
+        return True
+    return False
+
+
+class MockStoredProcedure(StoredProcedure):
+    def __call__(
+        self,
+        *args: Any,
+        session: Optional["snowflake.snowpark.session.Session"] = None,
+        statement_params: Optional[Dict[str, str]] = None,
+    ) -> Any:
+        args, session = self._validate_call(args, session)
+
+        # Unpack columns if passed
+        parsed_args = []
+        for arg, expected_type in zip(args, self._input_types):
+            if isinstance(arg, Column):
+                expr = arg._expression
+
+                # If expression does not define its datatype we cannot verify it's compatibale.
+                # This is potentially unsafe.
+                if expr.datatype and not sproc_types_are_compatible(
+                    expr.datatype, expected_type
+                ):
+                    raise ValueError(
+                        f"Unexpected type {expr.datatype} for sproc argument of type {expected_type}"
+                    )
+
+                # Expression may be a nested expression. Expression should not need any input data
+                # and should only return one value so that it can be passed as a literal value.
+                # We pass in a single None value so that the expression evaluator has some data to
+                # pass to the expressions.
+                resolved_expr = calculate_expression(
+                    expr,
+                    ColumnEmulator(data=[None]),
+                    session._analyzer,
+                    {},
+                )
+
+                # If the length of the resolved expression is not a single value we cannot pass it as a literal.
+                if len(resolved_expr) != 1:
+                    raise ValueError(
+                        "[Local Testing] Unexpected argument type {expr.__class__.__name__} for call to sproc"
+                    )
+                parsed_args.append(resolved_expr[0])
+            else:
+                parsed_args.append(arg)
+
+        result = self.func(session, *parsed_args)
+
+        # Semi-structured types are serialized in json
+        if isinstance(
+            self._return_type,
+            (
+                ArrayType,
+                MapType,
+                StructType,
+            ),
+        ) and not isinstance(result, DataFrame):
+            result = json.dumps(result, indent=2, cls=CUSTOM_JSON_ENCODER)
+
+        return result
+
+
+class MockStoredProcedureRegistration(StoredProcedureRegistration):
+    def __init__(self, *args, **kwargs) -> None:
+        super().__init__(*args, **kwargs)
+        self._registry: Dict[str, Callable] = dict()
+
+    def register_from_file(
+        self,
+        file_path: str,
+        func_name: str,
+        return_type: Optional[DataType] = None,
+        input_types: Optional[List[DataType]] = 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, ModuleType]]] = None,
+        replace: bool = False,
+        if_not_exists: bool = False,
+        parallel: int = 4,
+        execute_as: typing.Literal["caller", "owner"] = "owner",
+        strict: bool = False,
+        external_access_integrations: Optional[List[str]] = None,
+        secrets: Optional[Dict[str, str]] = None,
+        *,
+        statement_params: Optional[Dict[str, str]] = None,
+        source_code_display: bool = True,
+        skip_upload_on_content_match: bool = False,
+    ) -> StoredProcedure:
+        LocalTestOOBTelemetryService.get_instance().log_not_supported_error(
+            external_feature_name="register sproc from file",
+            internal_feature_name="MockStoredProcedureRegistration.register_from_file",
+            parameters_info={},
+            raise_error=NotImplementedError,
+        )
+
+    def _do_register_sp(
+        self,
+        func: Union[Callable, Tuple[str, str]],
+        return_type: DataType,
+        input_types: List[DataType],
+        sp_name: str,
+        stage_location: Optional[str],
+        imports: Optional[List[Union[str, Tuple[str, str]]]],
+        packages: Optional[List[Union[str, ModuleType]]],
+        replace: bool,
+        if_not_exists: bool,
+        parallel: int,
+        strict: bool,
+        *,
+        source_code_display: bool = False,
+        statement_params: Optional[Dict[str, str]] = None,
+        execute_as: typing.Literal["caller", "owner"] = "owner",
+        anonymous: bool = False,
+        api_call_source: str,
+        skip_upload_on_content_match: bool = False,
+        is_permanent: bool = False,
+        external_access_integrations: Optional[List[str]] = None,
+        secrets: Optional[Dict[str, str]] = None,
+        force_inline_code: bool = False,
+    ) -> StoredProcedure:
+        (
+            udf_name,
+            is_pandas_udf,
+            is_dataframe_input,
+            return_type,
+            input_types,
+        ) = process_registration_inputs(
+            self._session,
+            TempObjectType.PROCEDURE,
+            func,
+            return_type,
+            input_types,
+            sp_name,
+            anonymous,
+        )
+
+        if is_pandas_udf:
+            raise TypeError("pandas stored procedure is not supported")
+
+        if packages or imports:
+            LocalTestOOBTelemetryService.get_instance().log_not_supported_error(
+                external_feature_name="uploading imports and packages for sprocs",
+                internal_feature_name="MockStoredProcedureRegistration._do_register_sp",
+                parameters_info={},
+                raise_error=NotImplementedError,
+            )
+
+        check_python_runtime_version(self._session._runtime_version_from_requirement)
+
+        if udf_name in self._registry and not replace:
+            raise SnowparkSQLException(
+                f"002002 (42710): SQL compilation error: \nObject '{udf_name}' already exists.",
+                error_code="1304",
+            )
+
+        sproc = MockStoredProcedure(
+            func,
+            return_type,
+            input_types,
+            udf_name,
+            execute_as=execute_as,
+        )
+
+        self._registry[udf_name] = sproc
+
+        return sproc
+
+    def call(
+        self,
+        sproc_name: str,
+        *args: Any,
+        session: Optional["snowflake.snowpark.session.Session"] = None,
+        statement_params: Optional[Dict[str, str]] = None,
+    ):
+
+        if sproc_name not in self._registry:
+            raise SnowparkSQLException(
+                f"[Local Testing] sproc {sproc_name} does not exist."
+            )
+
+        return self._registry[sproc_name](
+            *args, session=session, statement_params=statement_params
+        )

src/snowflake/snowpark/session.py

@@ -136,6 +136,7 @@
     _extract_schema_and_data_from_pandas_df,
 )
 from snowflake.snowpark.mock._plan_builder import MockSnowflakePlanBuilder
+from snowflake.snowpark.mock._stored_procedure import MockStoredProcedureRegistration
 from snowflake.snowpark.mock._udf import MockUDFRegistration
 from snowflake.snowpark.query_history import QueryHistory
 from snowflake.snowpark.row import Row
@@ -441,12 +442,14 @@ def __init__(
 
         if isinstance(conn, MockServerConnection):
             self._udf_registration = MockUDFRegistration(self)
+            self._sp_registration = MockStoredProcedureRegistration(self)
         else:
             self._udf_registration = UDFRegistration(self)
+            self._sp_registration = StoredProcedureRegistration(self)
 
         self._udtf_registration = UDTFRegistration(self)
         self._udaf_registration = UDAFRegistration(self)
-        self._sp_registration = StoredProcedureRegistration(self)
+
         self._plan_builder = (
             SnowflakePlanBuilder(self)
             if isinstance(self._conn, ServerConnection)
@@ -2805,11 +2808,6 @@ def sproc(self) -> StoredProcedureRegistration:
         Returns a :class:`stored_procedure.StoredProcedureRegistration` object that you can use to register stored procedures.
         See details of how to use this object in :class:`stored_procedure.StoredProcedureRegistration`.
         """
-        if isinstance(self, MockServerConnection):
-            self._conn.log_not_supported_error(
-                external_feature_name="Session.sproc",
-                raise_error=NotImplementedError,
-            )
         return self._sp_registration
 
     def _infer_is_return_table(
@@ -2917,6 +2915,11 @@ def _call(
             is_return_table: When set to a non-null value, it signifies whether the return type of sproc_name
                 is a table return type. This skips infer check and returns a dataframe with appropriate sql call.
         """
+        if isinstance(self._sp_registration, MockStoredProcedureRegistration):
+            return self._sp_registration.call(
+                sproc_name, *args, session=self, statement_params=statement_params
+            )
+
         validate_object_name(sproc_name)
         query = generate_call_python_sp_sql(self, sproc_name, *args)
 

src/snowflake/snowpark/stored_procedure.py

@@ -76,12 +76,11 @@ def __init__(
         self._anonymous_sp_sql = anonymous_sp_sql
         self._is_return_table = isinstance(return_type, StructType)
 
-    def __call__(
+    def _validate_call(
         self,
-        *args: Any,
+        args: List[Any],
         session: Optional["snowflake.snowpark.session.Session"] = None,
-        statement_params: Optional[Dict[str, str]] = None,
-    ) -> Any:
+    ):
         if args and isinstance(args[0], snowflake.snowpark.session.Session):
             if session:
                 raise ValueError(
@@ -98,6 +97,16 @@ def __call__(
                 f"Incorrect number of arguments passed to the stored procedure. Expected: {len(self._input_types)}, Found: {len(args)}"
             )
 
+        return args, session
+
+    def __call__(
+        self,
+        *args: Any,
+        session: Optional["snowflake.snowpark.session.Session"] = None,
+        statement_params: Optional[Dict[str, str]] = None,
+    ) -> Any:
+        args, session = self._validate_call(args, session)
+
         session._conn._telemetry_client.send_function_usage_telemetry(
             "StoredProcedure.__call__", TelemetryField.FUNC_CAT_USAGE.value
         )

tests/integ/scala/test_function_suite.py

@@ -482,6 +482,7 @@ def test_random(session):
     df.select(random()).collect()
 
 
+@pytest.mark.localtest
 def test_sqrt(session):
     Utils.check_answer(
         TestData.test_data1(session).select(sqrt(col("NUM"))),
@@ -552,6 +553,7 @@ def test_log(session):
     )
 
 
+@pytest.mark.localtest
 def test_pow(session):
     Utils.check_answer(
         TestData.double2(session).select(pow(col("A"), col("B"))),

tests/integ/test_function.py

@@ -410,7 +410,7 @@ def test_strtok_to_array(session):
     assert res[0] == "a" and res[1] == "b" and res[2] == "c"
 
 
-@pytest.mark.local
+@pytest.mark.localtest
 @pytest.mark.parametrize("use_col", [True, False])
 @pytest.mark.parametrize(
     "values,expected",
@@ -431,7 +431,7 @@ def test_greatest(session, use_col, values, expected):
     assert res[0][0] == expected
 
 
-@pytest.mark.local
+@pytest.mark.localtest
 @pytest.mark.parametrize("use_col", [True, False])
 @pytest.mark.parametrize(
     "values,expected",