# 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 re

import uuid as uuid_module

from collections import defaultdict

from datetime import datetime, timedelta, 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,

ZonedTimestamp,

)

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", "ns").astype(int)

logger = logging.getLogger(__name__)

def _zone_name(tzinfo: Optional[Any]) -> str:

"""Return a storable zone string for a datetime's tzinfo.

Prefers the IANA name (e.g. ``zoneinfo.ZoneInfo`` key) so DST is preserved;

falls back to a fixed-offset string (e.g. ``-07:00``). A naive datetime

(``tzinfo is None``) yields ``""``, which decodes back as UTC.

"""

if tzinfo is None:

return ""

key = getattr(tzinfo, "key", None) # zoneinfo.ZoneInfo

if key:

return key

name = str(tzinfo)

# zoneinfo prints as the key; pytz prints the name; offsets print as "UTC-07:00"

return name

def _zone_from_name(zone: str):

"""Resolve a stored zone string back to a tzinfo. Empty → UTC."""

if not zone:

return timezone.utc

try:

from zoneinfo import ZoneInfo

return ZoneInfo(zone)

except Exception:

# Not an IANA name. It may be a fixed-offset string produced by

# ``_zone_name`` (e.g. "UTC", "UTC-07:00", "+05:30"); parse it so the

# original offset is preserved on round trips rather than silently

# shifting the wall-clock time to UTC.

offset = _fixed_offset_from_name(zone)

if offset is not None:

return offset

logger.warning("Could not resolve zone %r; decoding as UTC", zone)

return timezone.utc

def _fixed_offset_from_name(zone: str) -> Optional[timezone]:

"""Parse a fixed-offset zone string into a ``timezone``.

Accepts the forms ``_zone_name`` emits for offset-only tzinfos: a bare

``UTC``/``GMT``, or an offset like ``UTC-07:00``, ``-07:00``, ``+05:30`` or

``+0530``. Returns ``None`` if the string is not a recognizable offset.

"""

text = zone.strip()

if text in ("UTC", "GMT"):

return timezone.utc

match = re.fullmatch(

r"(?:UTC|GMT)?([+-])(\d{2}):?(\d{2})",

text,

)

if not match:

return None

sign, hours, minutes = match.groups()

delta = timedelta(hours=int(hours), minutes=int(minutes))

if sign == "-":

delta = -delta

return timezone(delta)

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)

# Zoned timestamp: a (instant, zone) message → tz-aware datetime in its own zone.

if val_attr == "zoned_timestamp_val":

if val.unix_timestamp == NULL_TIMESTAMP_INT_VALUE:

return None

tz = _zone_from_name(val.zone)

return datetime.fromtimestamp(val.unix_timestamp, tz=tz)

# 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.INT64,

"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]

# datetimes in any other unit or timezone, e.g. "datetime64[us]" or

# "datetime64[ns, america/new_york]"

if type_name.startswith("datetime64"):

return ValueType.UNIX_TIMESTAMP

# 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,

"SCALAR_MAP": ValueType.SCALAR_MAP,

"ZONED_TIMESTAMP": ValueType.ZONED_TIMESTAMP,

"PDF_BYTES": ValueType.PDF_BYTES,

"IMAGE_BYTES": ValueType.IMAGE_BYTES,

}

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.PDF_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):

# A naive datetime is UTC, as everywhere else in Feast. Without this,

# datetime.timestamp() would read it in the machine's local timezone.

if value.utcoffset() is None:

value = value.replace(tzinfo=timezone.utc)

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.