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new 8ae9c512a55 [SPARK-43178][CONNECT][PYTHON] Migrate UDF errors into
PySpark error framework
8ae9c512a55 is described below
commit 8ae9c512a55df1651508dc0de468fd6826955344
Author: itholic <[email protected]>
AuthorDate: Mon Apr 24 18:17:52 2023 +0800
[SPARK-43178][CONNECT][PYTHON] Migrate UDF errors into PySpark error
framework
### What changes were proposed in this pull request?
This PR proposes to migrate UDF errors into PySpark error framework.
### Why are the changes needed?
To leverage the PySpark error framework so that we can provide more
actionable and consistent errors for users.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
The existing CI should pass.
Closes #40866 from itholic/udf_errors.
Authored-by: itholic <[email protected]>
Signed-off-by: Ruifeng Zheng <[email protected]>
---
python/pyspark/errors/error_classes.py | 15 ++++++
python/pyspark/sql/connect/udf.py | 33 +++++++-----
.../sql/tests/pandas/test_pandas_grouped_map.py | 13 +++--
python/pyspark/sql/tests/pandas/test_pandas_udf.py | 22 ++++++--
python/pyspark/sql/tests/test_udf.py | 11 +++-
python/pyspark/sql/udf.py | 62 +++++++++++++---------
6 files changed, 108 insertions(+), 48 deletions(-)
diff --git a/python/pyspark/errors/error_classes.py
b/python/pyspark/errors/error_classes.py
index 2b41f54def9..e3742441fe4 100644
--- a/python/pyspark/errors/error_classes.py
+++ b/python/pyspark/errors/error_classes.py
@@ -74,6 +74,11 @@ ERROR_CLASSES_JSON = """
"<arg_list> should not be set together."
]
},
+ "CANNOT_SPECIFY_RETURN_TYPE_FOR_UDF": {
+ "message": [
+ "returnType can not be specified when `<arg_name>` is a user-defined
function, but got <return_type>."
+ ]
+ },
"COLUMN_IN_LIST": {
"message": [
"`<func_name>` does not allow a Column in a list."
@@ -99,11 +104,21 @@ ERROR_CLASSES_JSON = """
"All items in `<arg_name>` should be in <allowed_types>, got
<item_type>."
]
},
+ "INVALID_RETURN_TYPE_FOR_PANDAS_UDF": {
+ "message": [
+ "Pandas UDF should return StructType for <eval_type>, got <return_type>."
+ ]
+ },
"INVALID_TIMEOUT_TIMESTAMP" : {
"message" : [
"Timeout timestamp (<timestamp>) cannot be earlier than the current
watermark (<watermark>)."
]
},
+ "INVALID_UDF_EVAL_TYPE" : {
+ "message" : [
+ "Eval type for UDF must be SQL_BATCHED_UDF, SQL_SCALAR_PANDAS_UDF,
SQL_SCALAR_PANDAS_ITER_UDF or SQL_GROUPED_AGG_PANDAS_UDF."
+ ]
+ },
"INVALID_WHEN_USAGE": {
"message": [
"when() can only be applied on a Column previously generated by when()
function, and cannot be applied once otherwise() is applied."
diff --git a/python/pyspark/sql/connect/udf.py
b/python/pyspark/sql/connect/udf.py
index aab7bb3c0d3..89d126e43d1 100644
--- a/python/pyspark/sql/connect/udf.py
+++ b/python/pyspark/sql/connect/udf.py
@@ -37,6 +37,7 @@ from pyspark.sql.connect.column import Column
from pyspark.sql.connect.types import UnparsedDataType
from pyspark.sql.types import ArrayType, DataType, MapType, StringType,
StructType
from pyspark.sql.udf import UDFRegistration as PySparkUDFRegistration
+from pyspark.errors import PySparkTypeError
if TYPE_CHECKING:
@@ -125,20 +126,24 @@ class UserDefinedFunction:
deterministic: bool = True,
):
if not callable(func):
- raise TypeError(
- "Invalid function: not a function or callable (__call__ is not
defined): "
- "{0}".format(type(func))
+ raise PySparkTypeError(
+ error_class="NOT_CALLABLE",
+ message_parameters={"arg_name": "func", "arg_type":
type(func).__name__},
)
if not isinstance(returnType, (DataType, str)):
- raise TypeError(
- "Invalid return type: returnType should be DataType or str "
- "but is {}".format(returnType)
+ raise PySparkTypeError(
+ error_class="NOT_DATATYPE_OR_STR",
+ message_parameters={
+ "arg_name": "returnType",
+ "arg_type": type(returnType).__name__,
+ },
)
if not isinstance(evalType, int):
- raise TypeError(
- "Invalid evaluation type: evalType should be an int but is
{}".format(evalType)
+ raise PySparkTypeError(
+ error_class="NOT_INT",
+ message_parameters={"arg_name": "evalType", "arg_type":
type(evalType).__name__},
)
self.func = func
@@ -241,9 +246,9 @@ class UDFRegistration:
# Python function.
if hasattr(f, "asNondeterministic"):
if returnType is not None:
- raise TypeError(
- "Invalid return type: data type can not be specified when
f is"
- "a user-defined function, but got %s." % returnType
+ raise PySparkTypeError(
+ error_class="CANNOT_SPECIFY_RETURN_TYPE_FOR_UDF",
+ message_parameters={"arg_name": "f", "return_type":
str(returnType)},
)
f = cast("UserDefinedFunctionLike", f)
if f.evalType not in [
@@ -252,9 +257,9 @@ class UDFRegistration:
PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
]:
- raise ValueError(
- "Invalid f: f must be SQL_BATCHED_UDF,
SQL_SCALAR_PANDAS_UDF, "
- "SQL_SCALAR_PANDAS_ITER_UDF or SQL_GROUPED_AGG_PANDAS_UDF."
+ raise PySparkTypeError(
+ error_class="INVALID_UDF_EVAL_TYPE",
+ message_parameters={},
)
return_udf = f
self.sparkSession._client.register_udf(
diff --git a/python/pyspark/sql/tests/pandas/test_pandas_grouped_map.py
b/python/pyspark/sql/tests/pandas/test_pandas_grouped_map.py
index 36bdae02944..f5051c412ff 100644
--- a/python/pyspark/sql/tests/pandas/test_pandas_grouped_map.py
+++ b/python/pyspark/sql/tests/pandas/test_pandas_grouped_map.py
@@ -51,7 +51,7 @@ from pyspark.sql.types import (
NullType,
TimestampType,
)
-from pyspark.errors import PythonException
+from pyspark.errors import PythonException, PySparkTypeError
from pyspark.testing.sqlutils import (
ReusedSQLTestCase,
have_pandas,
@@ -212,12 +212,15 @@ class GroupedApplyInPandasTestsMixin:
def test_register_grouped_map_udf(self):
foo_udf = pandas_udf(lambda x: x, "id long", PandasUDFType.GROUPED_MAP)
with QuietTest(self.sc):
- with self.assertRaisesRegex(
- ValueError,
-
"f.*SQL_BATCHED_UDF.*SQL_SCALAR_PANDAS_UDF.*SQL_GROUPED_AGG_PANDAS_UDF.*",
- ):
+ with self.assertRaises(PySparkTypeError) as pe:
self.spark.catalog.registerFunction("foo_udf", foo_udf)
+ self.check_error(
+ exception=pe.exception,
+ error_class="INVALID_UDF_EVAL_TYPE",
+ message_parameters={},
+ )
+
def test_decorator(self):
df = self.data
diff --git a/python/pyspark/sql/tests/pandas/test_pandas_udf.py
b/python/pyspark/sql/tests/pandas/test_pandas_udf.py
index 4e1eec38a0c..8278c03ea04 100644
--- a/python/pyspark/sql/tests/pandas/test_pandas_udf.py
+++ b/python/pyspark/sql/tests/pandas/test_pandas_udf.py
@@ -21,7 +21,7 @@ from typing import cast
from pyspark.sql.functions import udf, pandas_udf, PandasUDFType, assert_true,
lit
from pyspark.sql.types import DoubleType, StructType, StructField, LongType,
DayTimeIntervalType
-from pyspark.errors import ParseException, PythonException
+from pyspark.errors import ParseException, PythonException, PySparkTypeError
from pyspark.rdd import PythonEvalType
from pyspark.testing.sqlutils import (
ReusedSQLTestCase,
@@ -153,18 +153,34 @@ class PandasUDFTestsMixin:
def zero_with_type():
return 1
- with self.assertRaisesRegex(TypeError, "Invalid return type"):
+ with self.assertRaises(PySparkTypeError) as pe:
@pandas_udf(returnType=PandasUDFType.GROUPED_MAP)
def foo(df):
return df
- with self.assertRaisesRegex(TypeError, "Invalid return type"):
+ self.check_error(
+ exception=pe.exception,
+ error_class="NOT_DATATYPE_OR_STR",
+ message_parameters={"arg_name": "returnType", "arg_type":
"int"},
+ )
+
+ with self.assertRaises(PySparkTypeError) as pe:
@pandas_udf(returnType="double",
functionType=PandasUDFType.GROUPED_MAP)
def foo(df):
return df
+ self.check_error(
+ exception=pe.exception,
+ error_class="INVALID_RETURN_TYPE_FOR_PANDAS_UDF",
+ message_parameters={
+ "eval_type": "SQL_GROUPED_MAP_PANDAS_UDF "
+ "or SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE",
+ "return_type": "DoubleType()",
+ },
+ )
+
with self.assertRaisesRegex(ValueError, "Invalid function"):
@pandas_udf(returnType="k int, v double",
functionType=PandasUDFType.GROUPED_MAP)
diff --git a/python/pyspark/sql/tests/test_udf.py
b/python/pyspark/sql/tests/test_udf.py
index d8a464b006f..2e6a7813524 100644
--- a/python/pyspark/sql/tests/test_udf.py
+++ b/python/pyspark/sql/tests/test_udf.py
@@ -38,7 +38,7 @@ from pyspark.sql.types import (
TimestampNTZType,
DayTimeIntervalType,
)
-from pyspark.errors import AnalysisException
+from pyspark.errors import AnalysisException, PySparkTypeError
from pyspark.testing.sqlutils import ReusedSQLTestCase, test_compiled,
test_not_compiled_message
from pyspark.testing.utils import QuietTest
@@ -109,11 +109,18 @@ class BaseUDFTestsMixin(object):
self.check_udf_registration_return_type_not_none()
def check_udf_registration_return_type_not_none(self):
- with self.assertRaisesRegex(TypeError, "Invalid return type"):
+ # negative test for incorrect type
+ with self.assertRaises(PySparkTypeError) as pe:
self.spark.catalog.registerFunction(
"f", UserDefinedFunction(lambda x, y: len(x) + y,
StringType()), StringType()
)
+ self.check_error(
+ exception=pe.exception,
+ error_class="CANNOT_SPECIFY_RETURN_TYPE_FOR_UDF",
+ message_parameters={"arg_name": "f", "return_type":
"StringType()"},
+ )
+
def test_nondeterministic_udf(self):
# Test that nondeterministic UDFs are evaluated only once in chained
UDF evaluations
import random
diff --git a/python/pyspark/sql/udf.py b/python/pyspark/sql/udf.py
index c486d869cba..58be9ed17ff 100644
--- a/python/pyspark/sql/udf.py
+++ b/python/pyspark/sql/udf.py
@@ -43,6 +43,7 @@ from pyspark.sql.types import (
from pyspark.sql.utils import get_active_spark_context
from pyspark.sql.pandas.types import to_arrow_type
from pyspark.sql.pandas.utils import require_minimum_pandas_version,
require_minimum_pyarrow_version
+from pyspark.errors import PySparkTypeError
if TYPE_CHECKING:
from pyspark.sql._typing import DataTypeOrString, ColumnOrName,
UserDefinedFunctionLike
@@ -218,20 +219,24 @@ class UserDefinedFunction:
deterministic: bool = True,
):
if not callable(func):
- raise TypeError(
- "Invalid function: not a function or callable (__call__ is not
defined): "
- "{0}".format(type(func))
+ raise PySparkTypeError(
+ error_class="NOT_CALLABLE",
+ message_parameters={"arg_name": "func", "arg_type":
type(func).__name__},
)
if not isinstance(returnType, (DataType, str)):
- raise TypeError(
- "Invalid return type: returnType should be DataType or str "
- "but is {}".format(returnType)
+ raise PySparkTypeError(
+ error_class="NOT_DATATYPE_OR_STR",
+ message_parameters={
+ "arg_name": "returnType",
+ "arg_type": type(returnType).__name__,
+ },
)
if not isinstance(evalType, int):
- raise TypeError(
- "Invalid evaluation type: evalType should be an int but is
{}".format(evalType)
+ raise PySparkTypeError(
+ error_class="NOT_INT",
+ message_parameters={"arg_name": "evalType", "arg_type":
type(evalType).__name__},
)
self.func = func
@@ -280,10 +285,13 @@ class UserDefinedFunction:
% str(self._returnType_placeholder)
)
else:
- raise TypeError(
- "Invalid return type for grouped map Pandas "
- "UDFs or at groupby.applyInPandas(WithState): return type
must be a "
- "StructType."
+ raise PySparkTypeError(
+ error_class="INVALID_RETURN_TYPE_FOR_PANDAS_UDF",
+ message_parameters={
+ "eval_type": "SQL_GROUPED_MAP_PANDAS_UDF or "
+ "SQL_GROUPED_MAP_PANDAS_UDF_WITH_STATE",
+ "return_type": str(self._returnType_placeholder),
+ },
)
elif (
self.evalType == PythonEvalType.SQL_MAP_PANDAS_ITER_UDF
@@ -298,9 +306,12 @@ class UserDefinedFunction:
"%s is not supported" %
str(self._returnType_placeholder)
)
else:
- raise TypeError(
- "Invalid return type in mapInPandas/mapInArrow: "
- "return type must be a StructType."
+ raise PySparkTypeError(
+ error_class="INVALID_RETURN_TYPE_FOR_PANDAS_UDF",
+ message_parameters={
+ "eval_type": "SQL_MAP_PANDAS_ITER_UDF or
SQL_MAP_ARROW_ITER_UDF",
+ "return_type": str(self._returnType_placeholder),
+ },
)
elif self.evalType == PythonEvalType.SQL_COGROUPED_MAP_PANDAS_UDF:
if isinstance(self._returnType_placeholder, StructType):
@@ -312,9 +323,12 @@ class UserDefinedFunction:
"%s is not supported" %
str(self._returnType_placeholder)
)
else:
- raise TypeError(
- "Invalid return type in cogroup.applyInPandas: "
- "return type must be a StructType."
+ raise PySparkTypeError(
+ error_class="INVALID_RETURN_TYPE_FOR_PANDAS_UDF",
+ message_parameters={
+ "eval_type": "SQL_COGROUPED_MAP_PANDAS_UDF",
+ "return_type": str(self._returnType_placeholder),
+ },
)
elif self.evalType == PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF:
try:
@@ -591,9 +605,9 @@ class UDFRegistration:
# Python function.
if hasattr(f, "asNondeterministic"):
if returnType is not None:
- raise TypeError(
- "Invalid return type: data type can not be specified when
f is"
- "a user-defined function, but got %s." % returnType
+ raise PySparkTypeError(
+ error_class="CANNOT_SPECIFY_RETURN_TYPE_FOR_UDF",
+ message_parameters={"arg_name": "f", "return_type":
str(returnType)},
)
f = cast("UserDefinedFunctionLike", f)
if f.evalType not in [
@@ -602,9 +616,9 @@ class UDFRegistration:
PythonEvalType.SQL_SCALAR_PANDAS_ITER_UDF,
PythonEvalType.SQL_GROUPED_AGG_PANDAS_UDF,
]:
- raise ValueError(
- "Invalid f: f must be SQL_BATCHED_UDF,
SQL_SCALAR_PANDAS_UDF, "
- "SQL_SCALAR_PANDAS_ITER_UDF or SQL_GROUPED_AGG_PANDAS_UDF."
+ raise PySparkTypeError(
+ error_class="INVALID_UDF_EVAL_TYPE",
+ message_parameters={},
)
register_udf = _create_udf(
f.func,
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