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type_map.py
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2288 lines (2030 loc) · 81.6 KB
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# Copyright 2019 The Feast Authors
#
# Licensed 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
#
# https://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 decimal
import json
import logging
import uuid as uuid_module
from collections import defaultdict
from datetime import datetime, timezone
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterator,
List,
Optional,
Sequence,
Set,
Sized,
Tuple,
Type,
Union,
cast,
)
import numpy as np
import pandas as pd
from google.protobuf.timestamp_pb2 import Timestamp
from feast.protos.feast.types.Value_pb2 import (
BoolList,
BoolSet,
BytesList,
BytesSet,
DoubleList,
DoubleSet,
FloatList,
FloatSet,
Int32List,
Int32Set,
Int64List,
Int64Set,
Map,
MapKey,
MapList,
RepeatedValue,
ScalarMap,
ScalarMapEntry,
StringList,
StringSet,
)
from feast.protos.feast.types.Value_pb2 import Value as ProtoValue
from feast.value_type import ListType, SetType, ValueType
if TYPE_CHECKING:
import pyarrow
# null timestamps get converted to -9223372036854775808
NULL_TIMESTAMP_INT_VALUE: int = np.datetime64("NaT").astype(int)
logger = logging.getLogger(__name__)
def feast_value_type_to_python_type(
field_value_proto: ProtoValue,
feature_type: Optional[ValueType] = None,
) -> Any:
"""
Converts field value Proto to Dict and returns each field's Feast Value Type value
in their respective Python value.
Args:
field_value_proto: Field value Proto
Returns:
Python native type representation/version of the given field_value_proto
"""
val_attr = field_value_proto.WhichOneof("val")
if val_attr is None:
return None
val = getattr(field_value_proto, val_attr)
# Handle JSON types — stored as strings but returned as parsed Python objects
if val_attr == "json_val":
try:
return json.loads(val)
except (json.JSONDecodeError, TypeError):
return val
elif val_attr == "json_list_val":
result = []
for v in val.val:
if isinstance(v, str):
try:
result.append(json.loads(v))
except (json.JSONDecodeError, TypeError):
result.append(v)
else:
result.append(v)
return result
# Handle nested collection types (list_val, set_val)
if val_attr in ("list_val", "set_val"):
return _handle_nested_collection_value(val)
# Handle Struct types — stored using Map proto, returned as dicts
if val_attr == "struct_val":
return _handle_map_value(val)
elif val_attr == "struct_list_val":
return _handle_map_list_value(val)
# Handle Map and MapList types FIRST (before generic list processing)
if val_attr == "map_val":
return _handle_map_value(val)
elif val_attr == "map_list_val":
return _handle_map_list_value(val)
elif val_attr == "scalar_map_val":
return _handle_scalar_map_value(val)
# If it's a _LIST or _SET type extract the values.
if hasattr(val, "val"):
val = list(val.val)
# Convert UNIX_TIMESTAMP values to `datetime`
if val_attr == "unix_timestamp_list_val":
val = [
(
datetime.fromtimestamp(v, tz=timezone.utc)
if v != NULL_TIMESTAMP_INT_VALUE
else None
)
for v in val
]
elif val_attr == "unix_timestamp_set_val":
val = set(
[
(
datetime.fromtimestamp(v, tz=timezone.utc)
if v != NULL_TIMESTAMP_INT_VALUE
else None
)
for v in val
]
)
elif val_attr == "unix_timestamp_val":
val = (
datetime.fromtimestamp(val, tz=timezone.utc)
if val != NULL_TIMESTAMP_INT_VALUE
else None
)
# Convert _SET types to Python sets
elif val_attr.endswith("_set_val") and val_attr != "unix_timestamp_set_val":
val = set(val)
# Convert UUID values to uuid.UUID objects
if val_attr in ("uuid_val", "time_uuid_val"):
return uuid_module.UUID(val) if isinstance(val, str) else val
if val_attr in ("uuid_list_val", "time_uuid_list_val"):
return [uuid_module.UUID(v) if isinstance(v, str) else v for v in val]
if val_attr in ("uuid_set_val", "time_uuid_set_val"):
return {uuid_module.UUID(v) if isinstance(v, str) else v for v in val}
# Convert DECIMAL values to decimal.Decimal objects
if val_attr == "decimal_val":
return decimal.Decimal(val) if isinstance(val, str) else val
if val_attr == "decimal_list_val":
return [decimal.Decimal(v) if isinstance(v, str) else v for v in val]
if val_attr == "decimal_set_val":
return {decimal.Decimal(v) if isinstance(v, str) else v for v in val}
# Backward compatibility: handle UUIDs stored as string_val/string_list_val with feature_type hint
if feature_type in (ValueType.UUID, ValueType.TIME_UUID) and isinstance(val, str):
return uuid_module.UUID(val)
if feature_type in (ValueType.UUID_LIST, ValueType.TIME_UUID_LIST) and isinstance(
val, list
):
return [uuid_module.UUID(v) if isinstance(v, str) else v for v in val]
if feature_type in (ValueType.UUID_SET, ValueType.TIME_UUID_SET) and isinstance(
val, set
):
return {uuid_module.UUID(v) if isinstance(v, str) else v for v in val}
return val
def _handle_map_value(map_message) -> Dict[str, Any]:
"""Handle Map proto message containing map<string, Value> val."""
result = {}
for key, value in map_message.val.items():
# Recursively handle the Value message
result[key] = feast_value_type_to_python_type(value)
return result
def _handle_map_list_value(map_list_message) -> List[Dict[str, Any]]:
"""Handle MapList proto message containing repeated Map val."""
result = []
for map_item in map_list_message.val:
# Handle each Map in the list
processed_map = _handle_map_value(map_item)
result.append(processed_map)
return result
def _handle_nested_collection_value(repeated_value) -> List[Any]:
"""Handle nested collection proto (RepeatedValue containing Values).
Each inner Value is itself a list/set proto. We recursively convert
each inner Value to a Python list/set via feast_value_type_to_python_type.
"""
result = []
for inner_value in repeated_value.val:
result.append(feast_value_type_to_python_type(inner_value))
return result
def _map_key_to_python_value(map_key: MapKey) -> Any:
"""Convert a MapKey proto to its Python equivalent."""
key_attr = map_key.WhichOneof("key")
if key_attr is None:
return None
val = getattr(map_key, key_attr)
if key_attr in ("int32_key", "int64_key"):
return int(val)
if key_attr in ("float_key", "double_key"):
return float(val)
if key_attr == "bool_key":
return bool(val)
if key_attr == "unix_timestamp_key":
return (
datetime.fromtimestamp(val, tz=timezone.utc)
if val != NULL_TIMESTAMP_INT_VALUE
else None
)
if key_attr == "bytes_key":
return bytes(val)
if key_attr in ("uuid_key", "time_uuid_key"):
return uuid_module.UUID(val)
if key_attr == "decimal_key":
return decimal.Decimal(val)
return val
def _handle_scalar_map_value(value_map_message: ScalarMap) -> Dict[Any, Any]:
"""Handle ScalarMap proto message (repeated ScalarMapEntry) → Python dict."""
result: Dict[Any, Any] = {}
for entry in value_map_message.val:
key = _map_key_to_python_value(entry.key)
value = feast_value_type_to_python_type(entry.value)
result[key] = value
return result
def feast_value_type_to_pandas_type(value_type: ValueType) -> Any:
value_type_to_pandas_type: Dict[ValueType, str] = {
ValueType.FLOAT: "float",
ValueType.INT32: "int",
ValueType.INT64: "int",
ValueType.STRING: "str",
ValueType.DOUBLE: "float",
ValueType.BYTES: "bytes",
ValueType.BOOL: "bool",
ValueType.UNIX_TIMESTAMP: "datetime64[ns]",
ValueType.UUID: "str",
ValueType.TIME_UUID: "str",
ValueType.DECIMAL: "object",
}
if (
value_type.name in ("MAP", "JSON", "STRUCT", "VALUE_LIST", "VALUE_SET")
or value_type.name.endswith("_LIST")
or value_type.name.endswith("_SET")
):
return "object"
if value_type in value_type_to_pandas_type:
return value_type_to_pandas_type[value_type]
raise TypeError(
f"Casting to pandas type for type {value_type} failed. "
f"Type {value_type} not found"
)
def python_type_to_feast_value_type(
name: str,
value: Optional[Any] = None,
recurse: bool = True,
type_name: Optional[str] = None,
) -> ValueType:
"""
Finds the equivalent Feast Value Type for a Python value. Both native
and Pandas types are supported. This function will recursively look
for nested types when arrays are detected. All types must be homogenous.
Args:
name: Name of the value or field
value: Value that will be inspected
recurse: Whether to recursively look for nested types in arrays
Returns:
Feast Value Type
"""
type_name = (type_name or type(value).__name__).lower()
type_map = {
"int": ValueType.INT64,
"str": ValueType.STRING,
"string": ValueType.STRING, # pandas.StringDtype
"float": ValueType.DOUBLE,
"bytes": ValueType.BYTES,
"float64": ValueType.DOUBLE,
"float32": ValueType.FLOAT,
"int64": ValueType.INT64,
"uint64": ValueType.INT64,
"int32": ValueType.INT32,
"uint32": ValueType.INT32,
"int16": ValueType.INT32,
"uint16": ValueType.INT32,
"uint8": ValueType.INT32,
"int8": ValueType.INT32,
"bool_": ValueType.BOOL, # np.bool_
"bool": ValueType.BOOL,
"boolean": ValueType.BOOL,
"timedelta": ValueType.UNIX_TIMESTAMP,
"timestamp": ValueType.UNIX_TIMESTAMP,
"datetime": ValueType.UNIX_TIMESTAMP,
"datetime64[ns]": ValueType.UNIX_TIMESTAMP,
"datetime64[ns, tz]": ValueType.UNIX_TIMESTAMP, # special dtype of pandas
"datetime64[ns, utc]": ValueType.UNIX_TIMESTAMP,
"date": ValueType.UNIX_TIMESTAMP,
"category": ValueType.STRING,
"uuid": ValueType.UUID,
"decimal": ValueType.DECIMAL,
}
if type_name in type_map:
return type_map[type_name]
# Handle pandas "object" dtype by inspecting the actual value
if type_name == "object" and value is not None:
# Check the actual type of the value
actual_type = type(value).__name__.lower()
if actual_type == "str":
return ValueType.STRING
# Check if it's a dictionary (could be a Map)
elif actual_type == "dict":
return ValueType.MAP
# If it's a different type wrapped in object, try to infer from the value
elif actual_type in type_map:
return type_map[actual_type]
if isinstance(value, np.ndarray) and str(value.dtype) in type_map:
item_type = type_map[str(value.dtype)]
return ValueType[item_type.name + "_LIST"]
if isinstance(value, (list, np.ndarray)):
# Check if it's a list of maps
if value and isinstance(value[0], dict):
return ValueType.MAP_LIST
# if the value's type is "ndarray" and we couldn't infer from "value.dtype"
# this is most probably array of "object",
# so we need to iterate over objects and try to infer type of each item
if not recurse:
raise ValueError(
f"Value type for field {name} is {type(value)} but "
f"recursion is not allowed. Nested collection types cannot be "
f"inferred automatically; use an explicit Field dtype instead "
f"(e.g., dtype=Array(Array(Int32)))."
)
# This is the final type which we infer from the list
common_item_value_type = None
for item in value:
if isinstance(item, ProtoValue):
current_item_value_type: ValueType = _proto_value_to_value_type(item)
else:
# Get the type from the current item, only one level deep
current_item_value_type = python_type_to_feast_value_type(
name=name, value=item, recurse=False
)
# Validate whether the type stays consistent
if (
common_item_value_type
and not common_item_value_type == current_item_value_type
):
raise ValueError(
f"List value type for field {name} is inconsistent. "
f"{common_item_value_type} different from "
f"{current_item_value_type}."
)
common_item_value_type = current_item_value_type
if common_item_value_type is None:
return ValueType.UNKNOWN
return ValueType[common_item_value_type.name + "_LIST"]
# Check if it's a set (Set type)
if isinstance(value, set):
if not recurse:
raise ValueError(
f"Value type for field {name} is {type(value)} but "
f"recursion is not allowed. Set types can only be one level "
f"deep."
)
# Infer the type from set elements
common_set_item_type = None
for item in value:
if isinstance(item, ProtoValue):
current_set_item_type: ValueType = _proto_value_to_value_type(item)
else:
# Get the type from the current item, only one level deep
current_set_item_type = python_type_to_feast_value_type(
name=name, value=item, recurse=False
)
# Validate whether the type stays consistent
if (
common_set_item_type
and not common_set_item_type == current_set_item_type
):
raise ValueError(
f"Set value type for field {name} is inconsistent. "
f"{common_set_item_type} different from "
f"{current_set_item_type}."
)
common_set_item_type = current_set_item_type
if common_set_item_type is None:
return ValueType.UNKNOWN
return ValueType[common_set_item_type.name + "_SET"]
# Check if it's a dictionary (Map type)
if isinstance(value, dict):
# Non-string keys require ScalarMap; string keys (or empty dict) use Map
if value and not isinstance(next(iter(value)), str):
return ValueType.SCALAR_MAP
return ValueType.MAP
raise ValueError(
f"Value with native type {type_name} cannot be converted into Feast value type"
)
def python_values_to_feast_value_type(
name: str, values: Any, recurse: bool = True
) -> ValueType:
inferred_dtype = ValueType.UNKNOWN
for row in values:
current_dtype = python_type_to_feast_value_type(
name, value=row, recurse=recurse
)
if inferred_dtype is ValueType.UNKNOWN:
inferred_dtype = current_dtype
else:
if current_dtype != inferred_dtype and current_dtype not in (
ValueType.UNKNOWN,
ValueType.NULL,
):
raise TypeError(
f"Input entity {name} has mixed types, {current_dtype} and {inferred_dtype}. That is not allowed. "
)
if inferred_dtype in (ValueType.UNKNOWN, ValueType.NULL):
raise ValueError(
f"field {name} cannot have all null values for type inference."
)
return inferred_dtype
def _convert_value_type_str_to_value_type(type_str: str) -> ValueType:
type_map = {
"UNKNOWN": ValueType.UNKNOWN,
"BYTES": ValueType.BYTES,
"STRING": ValueType.STRING,
"INT32": ValueType.INT32,
"INT64": ValueType.INT64,
"DOUBLE": ValueType.DOUBLE,
"FLOAT": ValueType.FLOAT,
"FLOAT32": ValueType.FLOAT,
"BOOL": ValueType.BOOL,
"NULL": ValueType.NULL,
"UNIX_TIMESTAMP": ValueType.UNIX_TIMESTAMP,
"BYTES_LIST": ValueType.BYTES_LIST,
"STRING_LIST": ValueType.STRING_LIST,
"INT32_LIST ": ValueType.INT32_LIST,
"INT64_LIST": ValueType.INT64_LIST,
"DOUBLE_LIST": ValueType.DOUBLE_LIST,
"FLOAT_LIST": ValueType.FLOAT_LIST,
"BOOL_LIST": ValueType.BOOL_LIST,
"UNIX_TIMESTAMP_LIST": ValueType.UNIX_TIMESTAMP_LIST,
"MAP": ValueType.MAP,
"MAP_LIST": ValueType.MAP_LIST,
"JSON": ValueType.JSON,
"JSON_LIST": ValueType.JSON_LIST,
"STRUCT": ValueType.STRUCT,
"STRUCT_LIST": ValueType.STRUCT_LIST,
"BYTES_SET": ValueType.BYTES_SET,
"STRING_SET": ValueType.STRING_SET,
"INT32_SET": ValueType.INT32_SET,
"INT64_SET": ValueType.INT64_SET,
"DOUBLE_SET": ValueType.DOUBLE_SET,
"FLOAT_SET": ValueType.FLOAT_SET,
"BOOL_SET": ValueType.BOOL_SET,
"UNIX_TIMESTAMP_SET": ValueType.UNIX_TIMESTAMP_SET,
"UUID": ValueType.UUID,
"TIME_UUID": ValueType.TIME_UUID,
"UUID_LIST": ValueType.UUID_LIST,
"TIME_UUID_LIST": ValueType.TIME_UUID_LIST,
"UUID_SET": ValueType.UUID_SET,
"TIME_UUID_SET": ValueType.TIME_UUID_SET,
"VALUE_LIST": ValueType.VALUE_LIST,
"VALUE_SET": ValueType.VALUE_SET,
"DECIMAL": ValueType.DECIMAL,
"DECIMAL_LIST": ValueType.DECIMAL_LIST,
"DECIMAL_SET": ValueType.DECIMAL_SET,
}
return type_map.get(type_str, ValueType.STRING)
def _type_err(item, dtype):
raise TypeError(f'Value "{item}" is of type {type(item)} not of type {dtype}')
PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE: Dict[
ValueType, Tuple[ListType, str, List[Type]]
] = {
ValueType.FLOAT_LIST: (
FloatList,
"float_list_val",
[np.float32, np.float64, float],
),
ValueType.DOUBLE_LIST: (
DoubleList,
"double_list_val",
[np.float64, np.float32, float],
),
ValueType.INT32_LIST: (Int32List, "int32_list_val", [np.int64, np.int32, int]),
ValueType.INT64_LIST: (Int64List, "int64_list_val", [np.int64, np.int32, int]),
ValueType.UNIX_TIMESTAMP_LIST: (
Int64List,
"int64_list_val",
[np.datetime64, np.int64, np.int32, int, datetime, Timestamp],
),
ValueType.STRING_LIST: (StringList, "string_list_val", [np.str_, str]),
ValueType.BOOL_LIST: (BoolList, "bool_list_val", [np.bool_, bool]),
ValueType.BYTES_LIST: (BytesList, "bytes_list_val", [np.bytes_, bytes]),
ValueType.UUID_LIST: (
StringList,
"uuid_list_val",
[np.str_, str, uuid_module.UUID],
),
ValueType.TIME_UUID_LIST: (
StringList,
"time_uuid_list_val",
[np.str_, str, uuid_module.UUID],
),
ValueType.DECIMAL_LIST: (
StringList,
"decimal_list_val",
[np.str_, str, decimal.Decimal],
),
}
PYTHON_SET_VALUE_TYPE_TO_PROTO_VALUE: Dict[
ValueType, Tuple[SetType, str, List[Type]]
] = {
ValueType.FLOAT_SET: (
FloatSet,
"float_set_val",
[np.float32, np.float64, float],
),
ValueType.DOUBLE_SET: (
DoubleSet,
"double_set_val",
[np.float64, np.float32, float],
),
ValueType.INT32_SET: (Int32Set, "int32_set_val", [np.int64, np.int32, int]),
ValueType.INT64_SET: (Int64Set, "int64_set_val", [np.int64, np.int32, int]),
ValueType.UNIX_TIMESTAMP_SET: (
Int64Set,
"unix_timestamp_set_val",
[np.datetime64, np.int64, np.int32, int, datetime, Timestamp],
),
ValueType.STRING_SET: (StringSet, "string_set_val", [np.str_, str]),
ValueType.BOOL_SET: (BoolSet, "bool_set_val", [np.bool_, bool]),
ValueType.BYTES_SET: (BytesSet, "bytes_set_val", [np.bytes_, bytes]),
ValueType.UUID_SET: (StringSet, "uuid_set_val", [np.str_, str, uuid_module.UUID]),
ValueType.TIME_UUID_SET: (
StringSet,
"time_uuid_set_val",
[np.str_, str, uuid_module.UUID],
),
ValueType.DECIMAL_SET: (
StringSet,
"decimal_set_val",
[np.str_, str, decimal.Decimal],
),
}
PYTHON_SCALAR_VALUE_TYPE_TO_PROTO_VALUE: Dict[
ValueType, Tuple[str, Any, Optional[Set[Type]]]
] = {
ValueType.INT32: ("int32_val", lambda x: int(x), None),
ValueType.INT64: (
"int64_val",
lambda x: (
int(x.timestamp())
if isinstance(x, pd._libs.tslibs.timestamps.Timestamp)
else int(x)
),
None,
),
ValueType.FLOAT: ("float_val", lambda x: float(x), None),
ValueType.DOUBLE: (
"double_val",
lambda x: x,
{float, np.float64, int, np.int_, decimal.Decimal},
),
ValueType.STRING: ("string_val", lambda x: str(x), None),
ValueType.BYTES: ("bytes_val", lambda x: x, {bytes}),
ValueType.IMAGE_BYTES: ("bytes_val", lambda x: x, {bytes}),
ValueType.BOOL: ("bool_val", lambda x: x, {bool, np.bool_, int, np.int_}),
ValueType.UUID: ("uuid_val", lambda x: str(x), {str, uuid_module.UUID}),
ValueType.TIME_UUID: ("time_uuid_val", lambda x: str(x), {str, uuid_module.UUID}),
ValueType.DECIMAL: ("decimal_val", lambda x: str(x), {decimal.Decimal, str}),
}
def _python_datetime_to_int_timestamp(
values: Sequence[Any],
) -> Sequence[Union[int, np.int_]]:
# Fast path for Numpy array.
if isinstance(values, np.ndarray) and isinstance(values.dtype, np.datetime64):
if values.ndim != 1:
raise ValueError("Only 1 dimensional arrays are supported.")
return cast(Sequence[np.int_], values.astype("datetime64[s]").astype(np.int_))
int_timestamps = []
for value in values:
if isinstance(value, datetime):
int_timestamps.append(int(value.timestamp()))
elif isinstance(value, Timestamp):
int_timestamps.append(int(value.ToSeconds()))
elif isinstance(value, np.datetime64):
int_timestamps.append(value.astype("datetime64[s]").astype(np.int_)) # type: ignore[attr-defined]
elif isinstance(value, type(np.nan)):
int_timestamps.append(NULL_TIMESTAMP_INT_VALUE)
else:
int_timestamps.append(int(value))
return int_timestamps
def _convert_timestamp_collection_to_proto(
values: List[Any],
proto_field: str,
proto_type: type,
) -> List[ProtoValue]:
"""Convert timestamp collection values (list or set) to proto.
Args:
values: List of timestamp collections to convert.
proto_field: The proto field name (e.g., 'unix_timestamp_list_val').
proto_type: The proto type class (e.g., Int64List).
Returns:
List of ProtoValue with converted timestamps.
"""
result = []
for value in values:
if value is not None:
result.append(
ProtoValue(
**{
proto_field: proto_type(
val=_python_datetime_to_int_timestamp(value)
)
} # type: ignore
)
)
else:
result.append(ProtoValue())
return result
def _convert_bool_collection_to_proto(
values: List[Any],
proto_field: str,
proto_type: type,
) -> List[ProtoValue]:
"""Convert boolean collection values (list or set) to proto.
ProtoValue does not support direct conversion of np.bool_, so we need to
explicitly convert each element to Python bool.
Args:
values: List of boolean collections to convert.
proto_field: The proto field name (e.g., 'bool_list_val').
proto_type: The proto type class (e.g., BoolList).
Returns:
List of ProtoValue with converted booleans.
"""
result = []
for value in values:
if value is not None:
result.append(
ProtoValue(**{proto_field: proto_type(val=[bool(e) for e in value])}) # type: ignore
)
else:
result.append(ProtoValue())
return result
def _validate_collection_item_types(
sample: Any,
valid_types: List[Type],
feast_value_type: ValueType,
) -> None:
"""Validate that collection items match expected types.
Args:
sample: A sample collection value to check.
valid_types: List of valid Python types for items.
feast_value_type: The Feast value type for error messages.
Raises:
TypeError: If any item in sample is not a valid type.
"""
if sample is None:
return
if all(type(item) in valid_types for item in sample if item is not None):
return
# to_numpy() upcasts INT32/INT64 with NULL to Float64 automatically
int_collection_types = [
ValueType.INT32_LIST,
ValueType.INT64_LIST,
ValueType.INT32_SET,
ValueType.INT64_SET,
]
for item in sample:
if item is None:
continue # None elements in STRING_LIST are replaced with ""; for other types they are dropped
if type(item) not in valid_types:
if feast_value_type in int_collection_types:
# Check if the float values are due to NULL upcast
if not any(np.isnan(i) for i in sample if isinstance(i, float)):
logger.error(
f"{feast_value_type.name} has NULL values. to_numpy() upcasts to Float64 automatically."
)
raise _type_err(item, valid_types[0])
def _python_set_to_proto_values(
feast_value_type: ValueType, values: List[Any]
) -> List[ProtoValue]:
"""
Converts Python set values to Feast Proto Values.
Args:
feast_value_type: The target set value type
values: List of set values that will be converted
Returns:
List of Feast Value Proto
"""
# Feature can be set but None is still valid
if feast_value_type not in PYTHON_SET_VALUE_TYPE_TO_PROTO_VALUE:
return []
set_proto_type, set_field_name, set_valid_types = (
PYTHON_SET_VALUE_TYPE_TO_PROTO_VALUE[feast_value_type]
)
# Convert set to list for proto (proto doesn't have native set type)
def convert_set_to_list(value: Any) -> Any:
if value is None:
return None
if isinstance(value, set):
return list(value)
if isinstance(value, (list, tuple, np.ndarray)):
return list(set(value))
return value
converted_values = [convert_set_to_list(v) for v in values]
sample = next(filter(_non_empty_value, converted_values), None)
# Bytes to array type conversion
if isinstance(sample, (bytes, bytearray)):
if feast_value_type == ValueType.BYTES_SET:
raise _type_err(sample, ValueType.BYTES_SET)
json_sample = json.loads(sample)
if isinstance(json_sample, list):
json_values = [
json.loads(value) if value is not None else None
for value in converted_values
]
if feast_value_type == ValueType.BOOL_SET:
json_values = [
[bool(item) for item in list_item]
if list_item is not None
else None
for list_item in json_values
]
return [
ProtoValue(**{set_field_name: set_proto_type(val=v)}) # type: ignore[arg-type]
if v is not None
else ProtoValue()
for v in json_values
]
raise _type_err(sample, set_valid_types[0])
# Validate item types using shared helper
_validate_collection_item_types(sample, set_valid_types, feast_value_type)
# Handle special types using shared helpers
if feast_value_type == ValueType.UNIX_TIMESTAMP_SET:
return _convert_timestamp_collection_to_proto(
converted_values, "unix_timestamp_set_val", Int64Set
)
if feast_value_type == ValueType.BOOL_SET:
return _convert_bool_collection_to_proto(
converted_values, set_field_name, set_proto_type
)
if feast_value_type in (ValueType.UUID_SET, ValueType.TIME_UUID_SET):
# uuid.UUID objects must be converted to str for StringSet proto.
return [
(
ProtoValue(
**{set_field_name: set_proto_type(val=[str(e) for e in value])} # type: ignore[arg-type, misc]
)
if value is not None
else ProtoValue()
)
for value in converted_values
]
if feast_value_type == ValueType.DECIMAL_SET:
# decimal.Decimal objects must be converted to str for StringSet proto.
return [
(
ProtoValue(
**{set_field_name: set_proto_type(val=[str(e) for e in value])} # type: ignore[arg-type, misc]
)
if value is not None
else ProtoValue()
)
for value in converted_values
]
# Generic set conversion
return [
ProtoValue(**{set_field_name: set_proto_type(val=value)}) # type: ignore[arg-type]
if value is not None
else ProtoValue()
for value in converted_values
]
# Per-type default values substituted for None elements inside list columns.
# Protobuf repeated fields do not accept None, so we replace with a
# type-appropriate zero/empty value.
_LIST_NONE_DEFAULTS: Dict[ValueType, Any] = {
ValueType.STRING_LIST: "",
ValueType.BYTES_LIST: b"",
ValueType.INT32_LIST: 0,
ValueType.INT64_LIST: 0,
ValueType.FLOAT_LIST: 0.0,
ValueType.DOUBLE_LIST: 0.0,
ValueType.BOOL_LIST: False,
ValueType.UNIX_TIMESTAMP_LIST: NULL_TIMESTAMP_INT_VALUE,
ValueType.UUID_LIST: "",
ValueType.TIME_UUID_LIST: "",
ValueType.DECIMAL_LIST: "",
}
def _sanitize_list_value(value: Any, feast_value_type: ValueType) -> Any:
"""Convert ndarray to list and replace None elements with a type-appropriate default.
Arrow/Athena may deserialize array columns as numpy.ndarray with object dtype
instead of plain Python lists. Protobuf repeated fields do not accept ndarrays
or None elements, so we normalise here before building proto messages.
"""
if isinstance(value, np.ndarray):
value = value.tolist()
none_default = _LIST_NONE_DEFAULTS.get(feast_value_type)
if none_default is not None and isinstance(value, list):
value = [none_default if v is None else v for v in value]
return value
def _convert_list_values_to_proto(
feast_value_type: ValueType,
values: List[Any],
sample: Any,
) -> List[ProtoValue]:
"""Convert list-type values to proto.
Args:
feast_value_type: The target list value type.
values: List of list values to convert.
sample: First non-empty value for type checking.
Returns:
List of ProtoValue.
"""
if feast_value_type not in PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE:
raise Exception(f"Unsupported list type: {feast_value_type}")
proto_type, field_name, valid_types = PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE[
feast_value_type
]
values = [
_sanitize_list_value(v, feast_value_type) if v is not None else v
for v in values
]
if sample is not None:
sample = _sanitize_list_value(sample, feast_value_type)
# Bytes to array type conversion
if isinstance(sample, (bytes, bytearray)):
if feast_value_type == ValueType.BYTES_LIST:
raise _type_err(sample, ValueType.BYTES_LIST)
json_sample = json.loads(sample)
if isinstance(json_sample, list):
json_values = [json.loads(value) for value in values]
if feast_value_type == ValueType.BOOL_LIST:
json_values = [
[bool(item) for item in list_item] for list_item in json_values
]
return [
ProtoValue(**{field_name: proto_type(val=v)}) # type: ignore[arg-type]
for v in json_values
]
raise _type_err(sample, valid_types[0])
# Validate item types using shared helper
_validate_collection_item_types(sample, valid_types, feast_value_type)
# Handle special types using shared helpers
if feast_value_type == ValueType.UNIX_TIMESTAMP_LIST:
return _convert_timestamp_collection_to_proto(
values, "unix_timestamp_list_val", Int64List
)
if feast_value_type == ValueType.BOOL_LIST:
return _convert_bool_collection_to_proto(values, field_name, proto_type)
if feast_value_type in (ValueType.UUID_LIST, ValueType.TIME_UUID_LIST):
# uuid.UUID objects must be converted to str for StringList proto.
return [
(
ProtoValue(
**{field_name: proto_type(val=[str(e) for e in value])} # type: ignore[arg-type, misc]
)
if value is not None
else ProtoValue()
)
for value in values
]
if feast_value_type == ValueType.DECIMAL_LIST:
# decimal.Decimal objects must be converted to str for StringList proto.
return [
(
ProtoValue(
**{field_name: proto_type(val=[str(e) for e in value])} # type: ignore[arg-type, misc]
)
if value is not None
else ProtoValue()
)
for value in values
]
# Generic list conversion
return [
ProtoValue(**{field_name: proto_type(val=value)}) # type: ignore[arg-type]
if value is not None
else ProtoValue()
for value in values
]
def _is_array_like(value: Any) -> bool:
"""Return True if *value* is array-like (numpy array or any sized,