Copilot commented on code in PR #10877:
URL: https://github.com/apache/gravitino/pull/10877#discussion_r3167149465
##########
clients/client-python/gravitino/api/rel/types/json_serdes/base.py:
##########
@@ -25,13 +25,21 @@
from gravitino.api.rel.expressions.sorts.sort_order import SortOrder
from gravitino.api.rel.indexes.index import Index
from gravitino.api.rel.types.types import Type
+from gravitino.api.stats.statistic_value import StatisticValue
from gravitino.dto.rel.partitioning.partitioning import Partitioning
from gravitino.dto.rel.partitions.partition_dto import PartitionDTO
_GravitinoTypeT = TypeVar(
"_GravitinoTypeT",
bound=Union[
- Expression, Type, Partitioning, PartitionDTO, Distribution, Index,
SortOrder
+ Expression,
+ Type,
+ Partitioning,
+ PartitionDTO,
+ Distribution,
+ Index,
+ SortOrder,
+ StatisticValue,
],
Review Comment:
Adding `StatisticValue` to the `JsonSerializable` type bound introduces an
unnecessary dependency from `api/rel/types/json_serdes` into `api/stats` (and
forces importing stats whenever `JsonSerializable` is imported). Since
`JsonSerializable` is a generic serializer interface, consider removing the
restrictive `bound=Union[...]` (or loosening it to a generic unconstrained
`TypeVar`) so new serdes types don’t require editing this base module and to
keep package layering clean.
##########
clients/client-python/tests/unittests/api/stats/test_statistic_values.py:
##########
@@ -0,0 +1,204 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+import random
+import unittest
+
+from gravitino.api.rel.types.types import Types
+from gravitino.api.stats.statistic_value import StatisticValue
+from gravitino.api.stats.statistic_values import StatisticValues
+from gravitino.exceptions.base import IllegalArgumentException
+
+
+class TestStatisticValues(unittest.TestCase):
+ @classmethod
+ def setUpClass(cls) -> None:
+ super().setUpClass()
+ cls._rand_int = random.randint(0, 500)
+ cls._rand_int_another = random.randint(cls._rand_int + 1, 1000)
+ cls._rand_float = random.uniform(0, 500)
+ cls._rand_float_another = random.uniform(cls._rand_float + 1, 1000)
+ cls._rand_str = f"str-{cls._rand_int}"
+ cls._rand_str_another = f"str-{cls._rand_int_another}"
+
+ def test_long_value(self):
+ value = StatisticValues.LongValue(self._rand_int)
+ twin_value = StatisticValues.long_value(self._rand_int)
+ another_value = StatisticValues.LongValue(self._rand_int_another)
+
+ self.assertEqual(value.value(), self._rand_int)
+ self.assertEqual(value.data_type().name(), Types.LongType.get().name())
+ self.assertEqual(hash(value), hash(self._rand_int))
+ self.assertEqual(value, twin_value)
+ self.assertNotEqual(value, another_value)
+ self.assertNotEqual(value,
StatisticValues.DoubleValue(self._rand_float))
+
+ def test_double_value(self):
+ value = StatisticValues.DoubleValue(float(self._rand_float))
+ twin_value = StatisticValues.double_value(float(self._rand_float))
+ another_value =
StatisticValues.DoubleValue(float(self._rand_float_another))
+
+ self.assertEqual(value.value(), float(self._rand_float))
+ self.assertEqual(value.data_type().name(),
Types.DoubleType.get().name())
+ self.assertEqual(hash(value), hash(float(self._rand_float)))
+ self.assertEqual(value, twin_value)
+ self.assertNotEqual(value, another_value)
+ self.assertNotEqual(value, StatisticValues.LongValue(self._rand_int))
+
+ def test_string_value(self):
+ value = StatisticValues.StringValue(self._rand_str)
+ twin_value = StatisticValues.string_value(self._rand_str)
+ another_value = StatisticValues.StringValue(self._rand_str_another)
+
+ self.assertEqual(value.value(), self._rand_str)
+ self.assertEqual(value.data_type().name(),
Types.StringType.get().name())
+ self.assertEqual(hash(value), hash(self._rand_str))
+ self.assertEqual(value, twin_value)
+ self.assertNotEqual(value, another_value)
+ self.assertNotEqual(value, StatisticValues.LongValue(self._rand_int))
+
+ def test_list_value(self):
+ value_list: list[StatisticValue[int]] = [
+ StatisticValues.LongValue(random.randint(0, 100)) for i in
range(10)
+ ]
+ another_value_list: list[StatisticValue[int]] = [
+ StatisticValues.LongValue(random.randint(0, 100)) for i in
range(10)
+ ]
+ value = StatisticValues.ListValue(value_list)
+ twin_value: StatisticValues.ListValue[int] =
StatisticValues.list_value(
+ value_list
+ )
+ another_value = StatisticValues.ListValue(another_value_list)
+
+ self.assertEqual(value.value(), value_list)
+ self.assertEqual(
+ value.data_type().name(),
+ Types.ListType.nullable(Types.LongType.get()).name(),
+ )
+ self.assertEqual(hash(value), hash(tuple(v.value() for v in
value_list)))
+ self.assertEqual(value, twin_value)
+ self.assertNotEqual(value, another_value)
+ self.assertNotEqual(value, StatisticValues.LongValue(self._rand_int))
+
+ def test_object_value(self):
+ value_dict: dict[str, StatisticValue[int]] = {
+ f"key_{i}": StatisticValues.LongValue(random.randint(0, 100))
+ for i in range(10)
+ }
+ another_value_dict: dict[str, StatisticValue[int]] = {
+ f"key_{i}": StatisticValues.LongValue(random.randint(0, 100))
+ for i in range(10)
+ }
+ value = StatisticValues.ObjectValue(value_dict)
+ twin_value: StatisticValues.ObjectValue[int] =
StatisticValues.object_value(
+ value_dict
+ )
+ another_value = StatisticValues.ObjectValue(another_value_dict)
+
+ expected_data_type = Types.StructType.of(
+ *[
+ Types.StructType.Field.nullable_field(key,
statistic_value.data_type())
+ for key, statistic_value in value_dict.items()
+ ]
+ )
+ self.assertEqual(value.value(), value_dict)
+ self.assertEqual(
+ value.data_type().simple_string(),
expected_data_type.simple_string()
+ )
+ self.assertEqual(
+ hash(value), hash(tuple(v.value() for v in value_dict.values()))
+ )
Review Comment:
`ObjectValue.__hash__` is computed via `StatisticValues._make_hash`, which
includes keys (sorted) and hashes of nested values. This test’s expected hash
(`hash(tuple(v.value() for v in value_dict.values()))`) ignores keys and
insertion-order differences, so it won’t match the actual implementation.
Update the assertion to either compare `hash(value)` with `hash(twin_value)`
only, or compute the expected hash using the same key-inclusive,
order-independent representation.
```suggestion
self.assertEqual(hash(value), hash(twin_value))
```
##########
clients/client-python/gravitino/api/stats/statistic_values.py:
##########
@@ -0,0 +1,265 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+from typing import Any, TypeVar
+
+from gravitino.api.rel.types.type import Type
+from gravitino.api.rel.types.types import Types
+from gravitino.api.stats.statistic_value import StatisticValue
+from gravitino.utils.precondition import Precondition
+
+T = TypeVar("T")
+
+
+class StatisticValues:
+ """A class representing a collection of statistic values."""
+
+ @staticmethod
+ def boolean_value(value: bool) -> "BooleanValue":
+ """Creates a statistic value that holds a boolean value.
+
+ Args:
+ value: the boolean value to be held by this statistic value
+
+ Returns:
+ A BooleanValue instance containing the provided boolean value
+ """
+ return StatisticValues.BooleanValue(value)
+
+ @staticmethod
+ def long_value(value: int) -> "LongValue":
+ """Creates a statistic value that holds a long value.
+
+ Args:
+ value: the long value to be held by this statistic value
+
+ Returns:
+ A LongValue instance containing the provided long value
+ """
+ return StatisticValues.LongValue(value)
+
+ @staticmethod
+ def double_value(value: float) -> "DoubleValue":
+ """Creates a statistic value that holds a double value.
+
+ Args:
+ value: the double value to be held by this statistic value
+
+ Returns:
+ A DoubleValue instance containing the provided double value
+ """
+ return StatisticValues.DoubleValue(value)
+
+ @staticmethod
+ def string_value(value: str) -> "StringValue":
+ """Creates a statistic value that holds a string value.
+
+ Args:
+ value: the string value to be held by this statistic value
+
+ Returns:
+ A StringValue instance containing the provided string value
+ """
+ return StatisticValues.StringValue(value)
+
+ @staticmethod
+ def list_value(value: list[StatisticValue[T]]) -> "ListValue[T]":
+ """Creates a statistic value that holds a list of other statistic
values.
+
+ Args:
+ value: the list of statistic values to be held by this statistic
value
+
+ Returns:
+ A ListValue instance containing the provided list of statistic
values
+ """
+ return StatisticValues.ListValue(value)
+
+ @staticmethod
+ def object_value(value: dict[str, StatisticValue[Any]]) ->
"ObjectValue[Any]":
+ """Creates a statistic value that holds a map of string keys to other
statistic values.
+
+ Args:
+ value: the map of string keys to statistic values to be held by
this statistic value
+
+ Returns:
+ An ObjectValue instance containing the provided map of statistic
values
+ """
+ return StatisticValues.ObjectValue(value)
+
+ @staticmethod
+ def _make_hash(value: StatisticValue[Any]) -> int:
+ """Recursively compute hash for any StatisticValue.
+
+ Args:
+ value: the StatisticValue to hash
+
+ Returns:
+ Hash code for the StatisticValue
+ """
+ match value:
+ case StatisticValues.ListValue():
+ return hash(tuple(StatisticValues._make_hash(v) for v in
value.value()))
+ case StatisticValues.ObjectValue():
+ return hash(
+ tuple(
+ sorted(
+ (k, StatisticValues._make_hash(v))
+ for k, v in value.value().items()
+ )
+ )
+ )
+ case _:
+ return hash(value.value())
+
+ class BooleanValue(StatisticValue[bool]):
+ """A statistic value that holds a Boolean value."""
+
+ def __init__(self, value: bool) -> None:
+ self._value = value
+
+ def value(self) -> bool:
+ return self._value
+
+ def data_type(self) -> Type:
+ return Types.BooleanType.get()
+
+ def __hash__(self) -> int:
+ return hash(self._value)
+
+ def __eq__(self, other) -> bool:
+ if not isinstance(other, StatisticValues.BooleanValue):
+ return False
+ return self._value == other._value
+
+ class LongValue(StatisticValue[int]):
+ """A statistic value that holds a Long value."""
+
+ def __init__(self, value: int) -> None:
+ self._value = value
+
+ def value(self) -> int:
+ return self._value
+
+ def data_type(self) -> Type:
+ return Types.LongType.get()
+
+ def __hash__(self) -> int:
+ return hash(self._value)
+
+ def __eq__(self, other) -> bool:
+ if not isinstance(other, StatisticValues.LongValue):
+ return False
+ return self._value == other._value
+
+ class DoubleValue(StatisticValue[float]):
+ """A statistic value that holds a Double value."""
+
+ def __init__(self, value: float) -> None:
+ self._value = value
+
+ def value(self) -> float:
+ return self._value
+
+ def data_type(self) -> Type:
+ return Types.DoubleType.get()
+
+ def __hash__(self) -> int:
+ return hash(self._value)
+
+ def __eq__(self, other) -> bool:
+ if not isinstance(other, StatisticValues.DoubleValue):
+ return False
+ return self._value == other._value
+
+ class StringValue(StatisticValue[str]):
+ """A statistic value that holds a String value."""
+
+ def __init__(self, value: str) -> None:
+ self._value = value
+
+ def value(self) -> str:
+ return self._value
+
+ def data_type(self) -> Type:
+ return Types.StringType.get()
+
+ def __hash__(self) -> int:
+ return hash(self._value)
+
+ def __eq__(self, other) -> bool:
+ if not isinstance(other, StatisticValues.StringValue):
+ return False
+ return self._value == other._value
+
+ class ListValue(StatisticValue[list[StatisticValue[T]]]):
+ """A statistic value that holds a List of other statistic values."""
+
+ def __init__(self, value_list: list[StatisticValue[T]]) -> None:
+ Precondition.check_argument(
+ value_list is not None and len(value_list) > 0,
+ "Values cannot be null or empty",
+ )
+ data_type = value_list[0].data_type()
+ Precondition.check_argument(
+ all(value.data_type() == data_type for value in value_list),
+ "All values in the list must have the same data type",
+ )
+ self._value_list = value_list
+
+ def value(self) -> list[StatisticValue[T]]:
+ return self._value_list
+
+ def data_type(self) -> Type:
+ return Types.ListType.nullable(self._value_list[0].data_type())
+
+ def __hash__(self) -> int:
+ return StatisticValues._make_hash(self)
+
+ def __eq__(self, other) -> bool:
+ if not isinstance(other, StatisticValues.ListValue):
+ return False
+ return self._value_list == other._value_list
+
+ class ObjectValue(StatisticValue[dict[str, StatisticValue[T]]]):
+ """A statistic value that holds a Map of String keys to other
statistic values."""
+
+ def __init__(self, value_map: dict[str, StatisticValue[T]]) -> None:
+ Precondition.check_argument(
+ value_map is not None and len(value_map) > 0,
+ "Values cannot be null or empty",
+ )
+ self._value_map = value_map
+
+ def value(self) -> dict[str, StatisticValue[T]]:
+ return self._value_map
+
+ def data_type(self) -> Type:
+ return Types.StructType.of(
+ *[
+ Types.StructType.Field.nullable_field(key,
value.data_type())
+ for key, value in self._value_map.items()
Review Comment:
`ObjectValue.data_type()` builds a `StructType` using
`self._value_map.items()` iteration order. Since `Types.StructType.__eq__` is
order-sensitive (`fields` list equality), two `ObjectValue`s with identical
key/value pairs but different insertion order will report different
`data_type()`, and `ListValue` type-homogeneity checks can fail for lists of
such objects. Consider constructing struct fields in a deterministic order
(e.g., sort by key) so the schema is stable and independent of dict insertion
order.
```suggestion
Types.StructType.Field.nullable_field(
key, self._value_map[key].data_type()
)
for key in sorted(self._value_map)
```
##########
clients/client-python/tests/unittests/api/stats/test_statistic_value_serdes.py:
##########
@@ -0,0 +1,173 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+
+import random
+import unittest
+
+from gravitino.api.stats.json_serdes.statistic_value_serdes import (
+ StatisticValueSerdes,
+)
+from gravitino.api.stats.statistic_values import StatisticValues
+
+
+class TestStatisticValueJsonSerdes(unittest.TestCase):
+ @classmethod
+ def setUpClass(cls) -> None:
+ super().setUpClass()
+ cls._rand_int = random.randint(0, 500)
+ cls._rand_float = random.uniform(0, 500)
+ cls._rand_str = f"str-{cls._rand_int}"
+
+ def test_deserialize_naive_types(self):
+ self.assertEqual(
+ StatisticValueSerdes.deserialize(self._rand_int),
+ StatisticValues.long_value(self._rand_int),
+ )
+ self.assertEqual(
+ StatisticValueSerdes.deserialize(self._rand_float),
+ StatisticValues.double_value(self._rand_float),
+ )
+ self.assertEqual(
+ StatisticValueSerdes.deserialize(self._rand_str),
+ StatisticValues.string_value(self._rand_str),
+ )
+ self.assertEqual(
+ StatisticValueSerdes.deserialize(True),
StatisticValues.boolean_value(True)
+ )
+
+ def test_deserialize_object_type(self):
+ data = {
+ "boolean": True,
+ "long": self._rand_int,
+ "double": self._rand_float,
+ "string": self._rand_str,
+ "list": [self._rand_int, self._rand_int + 1, self._rand_int + 2],
+ "struct": {
+ "nested_boolean": False,
+ "nested_string": "nested",
+ "nested_list": [self._rand_str, self._rand_str + "_another"],
+ },
+ }
+
+ deserialized_value = StatisticValueSerdes.deserialize(data)
+
+ expected_value = StatisticValues.object_value(
+ {
+ "boolean": StatisticValues.boolean_value(True),
+ "long": StatisticValues.long_value(self._rand_int),
+ "double": StatisticValues.double_value(self._rand_float),
+ "string": StatisticValues.string_value(self._rand_str),
+ "list": StatisticValues.list_value(
+ [
+ StatisticValues.long_value(self._rand_int),
+ StatisticValues.long_value(self._rand_int + 1),
+ StatisticValues.long_value(self._rand_int + 2),
+ ]
+ ),
+ "struct": StatisticValues.object_value(
+ {
+ "nested_boolean": StatisticValues.boolean_value(False),
+ "nested_string":
StatisticValues.string_value("nested"),
+ "nested_list": StatisticValues.list_value(
+ [
+ StatisticValues.string_value(self._rand_str),
+ StatisticValues.string_value(
+ self._rand_str + "_another"
+ ),
+ ]
+ ),
+ }
+ ),
+ }
+ )
+
+ self.assertEqual(deserialized_value, expected_value)
+
+ def test_deserialize_unsupported_type(self):
+ with self.assertRaises(ValueError):
+ StatisticValueSerdes.deserialize(None)
+
Review Comment:
`StatisticValueSerdes.deserialize(None)` triggers
`Precondition.check_argument(data is not None, ...)` which raises
`IllegalArgumentException`, not `ValueError`. This test will fail as written;
update it to expect `IllegalArgumentException` (and optionally assert on the
message).
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