# 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

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,

BytesList,

DoubleList,

FloatList,

Int32List,

Int64List,

StringList,

)

from feast.protos.feast.types.Value_pb2 import Value as ProtoValue

from feast.value_type import ListType, 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) -> 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)

# If it's a _LIST type extract the list.

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_val":

val = (

datetime.fromtimestamp(val, tz=timezone.utc)

if val != NULL_TIMESTAMP_INT_VALUE

else None

)

return val

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]",

}

if value_type.name.endswith("_LIST"):

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,

}

if type_name in type_map:

return type_map[type_name]

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)):

# 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. Array types can only be one level "

f"deep."

)

# 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"]

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,

"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,

}

return type_map[type_str]

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]),

}

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.BOOL: ("bool_val", lambda x: x, {bool, np.bool_, int, np.int_}),

}

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 _python_value_to_proto_value(

feast_value_type: ValueType, values: List[Any]

) -> List[ProtoValue]:

"""

Converts a Python (native, pandas) value to a Feast Proto Value based

on a provided value type

Args:

feast_value_type: The target value type

values: List of Values that will be converted

Returns:

List of Feast Value Proto

"""

# ToDo: make a better sample for type checks (more than one element)

sample = next(filter(_non_empty_value, values), None) # first not empty value

# Detect list type and handle separately

if "list" in feast_value_type.name.lower():

# Feature can be list but None is still valid

if feast_value_type in PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE:

proto_type, field_name, valid_types = PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE[

feast_value_type

]

# Bytes to array type conversion

if isinstance(sample, (bytes, bytearray)):

# Bytes of an array containing elements of bytes not supported

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

for v in json_values

]

raise _type_err(sample, valid_types[0])

if sample is not None and not all(

type(item) in valid_types for item in sample

):

# to_numpy() in utils._convert_arrow_to_proto() upcasts values of type Array of INT32 or INT64 with NULL values to Float64 automatically.

for item in sample:

if type(item) not in valid_types:

if feast_value_type in [

ValueType.INT32_LIST,

ValueType.INT64_LIST,

]:

if not any(np.isnan(item) for item in sample):

logger.error(

"Array of Int32 or Int64 type has NULL values. to_numpy() upcasts to Float64 automatically."

)

raise _type_err(item, valid_types[0])

if feast_value_type == ValueType.UNIX_TIMESTAMP_LIST:

return [

(

# ProtoValue does actually accept `np.int_` but the typing complains.

ProtoValue(

unix_timestamp_list_val=Int64List(

val=_python_datetime_to_int_timestamp(value) # type: ignore

)

)

if value is not None

else ProtoValue()

)

for value in values

]

if feast_value_type == ValueType.BOOL_LIST:

# ProtoValue does not support conversion of np.bool_ so we need to convert it to support np.bool_.

return [

(

ProtoValue(

**{field_name: proto_type(val=[bool(e) for e in value])} # type: ignore

)

if value is not None

else ProtoValue()

)

for value in values

]

return [

(

ProtoValue(**{field_name: proto_type(val=value)}) # type: ignore

if value is not None

else ProtoValue()

)

for value in values

]

# Handle scalar types below

else:

if sample is None:

# all input values are None

return [ProtoValue()] * len(values)

if feast_value_type == ValueType.UNIX_TIMESTAMP:

int_timestamps = _python_datetime_to_int_timestamp(values)

# ProtoValue does actually accept `np.int_` but the typing complains.

return [ProtoValue(unix_timestamp_val=ts) for ts in int_timestamps] # type: ignore

(

field_name,

func,

valid_scalar_types,

) = PYTHON_SCALAR_VALUE_TYPE_TO_PROTO_VALUE[feast_value_type]

if valid_scalar_types:

if (sample == 0 or sample == 0.0) and feast_value_type != ValueType.BOOL:

# Numpy convert 0 to int. However, in the feature view definition, the type of column may be a float.

# So, if value is 0, type validation must pass if scalar_types are either int or float.

allowed_types = {np.int64, int, np.float64, float, decimal.Decimal}

assert type(sample) in allowed_types, (

f"Type `{type(sample)}` not in {allowed_types}"

)

else:

assert type(sample) in valid_scalar_types, (

f"Type `{type(sample)}` not in {valid_scalar_types}"

)

if feast_value_type == ValueType.BOOL:

# ProtoValue does not support conversion of np.bool_ so we need to convert it to support np.bool_.

return [

(

ProtoValue(

**{

field_name: func(

bool(value) if type(value) is np.bool_ else value # type: ignore

)

}

)

if not pd.isnull(value)

else ProtoValue()

)

for value in values

]

if feast_value_type in PYTHON_SCALAR_VALUE_TYPE_TO_PROTO_VALUE:

out = []

for value in values:

if isinstance(value, ProtoValue):

out.append(value)

elif not pd.isnull(value):

out.append(ProtoValue(**{field_name: func(value)}))

else:

out.append(ProtoValue())

return out

raise Exception(f"Unsupported data type: ${str(type(values[0]))}")

def python_values_to_proto_values(

values: List[Any], feature_type: ValueType = ValueType.UNKNOWN

) -> List[ProtoValue]:

value_type = feature_type

sample = next(filter(_non_empty_value, values), None) # first not empty value

if sample is not None and feature_type == ValueType.UNKNOWN:

if isinstance(sample, (list, np.ndarray)):

value_type = (

feature_type

if len(sample) == 0

else python_type_to_feast_value_type("", sample)

)

else:

value_type = python_type_to_feast_value_type("", sample)

if value_type == ValueType.UNKNOWN:

raise TypeError("Couldn't infer value type from empty value")

proto_values = _python_value_to_proto_value(value_type, values)

if len(proto_values) != len(values):

raise ValueError(

f"Number of proto values {len(proto_values)} does not match number of values {len(values)}"

)

return proto_values

PROTO_VALUE_TO_VALUE_TYPE_MAP: Dict[str, ValueType] = {

"int32_val": ValueType.INT32,

"int64_val": ValueType.INT64,

"double_val": ValueType.DOUBLE,

"float_val": ValueType.FLOAT,

"string_val": ValueType.STRING,

"bytes_val": ValueType.BYTES,

"bool_val": ValueType.BOOL,

"int32_list_val": ValueType.INT32_LIST,

"int64_list_val": ValueType.INT64_LIST,

"double_list_val": ValueType.DOUBLE_LIST,

"float_list_val": ValueType.FLOAT_LIST,

"string_list_val": ValueType.STRING_LIST,

"bytes_list_val": ValueType.BYTES_LIST,

"bool_list_val": ValueType.BOOL_LIST,

}

VALUE_TYPE_TO_PROTO_VALUE_MAP: Dict[ValueType, str] = {

v: k for k, v in PROTO_VALUE_TO_VALUE_TYPE_MAP.items()

}

def _proto_value_to_value_type(proto_value: ProtoValue) -> ValueType:

"""

Returns Feast ValueType given Feast ValueType string.

Args:

proto_str: str

Returns:

A variant of ValueType.

"""

proto_str = proto_value.WhichOneof("val")

if proto_str is None:

return ValueType.UNKNOWN

return PROTO_VALUE_TO_VALUE_TYPE_MAP[proto_str]

def pa_to_feast_value_type(pa_type_as_str: str) -> ValueType:

is_list = False

if pa_type_as_str.startswith("list<item: "):

is_list = True

pa_type_as_str = pa_type_as_str.replace("list<item: ", "").replace(">", "")

if pa_type_as_str.startswith("timestamp"):

value_type = ValueType.UNIX_TIMESTAMP

else:

type_map = {

"int32": ValueType.INT32,

"int64": ValueType.INT64,

"double": ValueType.DOUBLE,

"float": ValueType.FLOAT,

"string": ValueType.STRING,

"binary": ValueType.BYTES,

"bool": ValueType.BOOL,

"null": ValueType.NULL,

"list<element: double>": ValueType.DOUBLE_LIST,

"list<element: int64>": ValueType.INT64_LIST,

"list<element: int32>": ValueType.INT32_LIST,

"list<element: str>": ValueType.STRING_LIST,

"list<element: bool>": ValueType.BOOL_LIST,

"list<element: bytes>": ValueType.BYTES_LIST,

"list<element: float>": ValueType.FLOAT_LIST,

}

value_type = type_map[pa_type_as_str]

if is_list:

value_type = ValueType[value_type.name + "_LIST"]

return value_type

def bq_to_feast_value_type(bq_type_as_str: str) -> ValueType:

is_list = False

if bq_type_as_str.startswith("ARRAY<"):

is_list = True

bq_type_as_str = bq_type_as_str[6:-1]

type_map: Dict[str, ValueType] = {

"DATETIME": ValueType.UNIX_TIMESTAMP,

"TIMESTAMP": ValueType.UNIX_TIMESTAMP,

"INTEGER": ValueType.INT64,

"NUMERIC": ValueType.INT64,

"INT64": ValueType.INT64,

"STRING": ValueType.STRING,

"FLOAT": ValueType.DOUBLE,

"FLOAT64": ValueType.DOUBLE,

"BYTES": ValueType.BYTES,

"BOOL": ValueType.BOOL,

"BOOLEAN": ValueType.BOOL, # legacy sql data type

"NULL": ValueType.NULL,

}

value_type = type_map.get(bq_type_as_str, ValueType.STRING)

if is_list:

value_type = ValueType[value_type.name + "_LIST"]

return value_type

def mssql_to_feast_value_type(mssql_type_as_str: str) -> ValueType:

type_map = {

"bigint": ValueType.FLOAT,

"binary": ValueType.BYTES,

"bit": ValueType.BOOL,

"char": ValueType.STRING,

"date": ValueType.UNIX_TIMESTAMP,

"datetime": ValueType.UNIX_TIMESTAMP,

"datetimeoffset": ValueType.UNIX_TIMESTAMP,

"float": ValueType.FLOAT,

"int": ValueType.INT32,

"nchar": ValueType.STRING,

"nvarchar": ValueType.STRING,

"nvarchar(max)": ValueType.STRING,

"real": ValueType.FLOAT,

"smallint": ValueType.INT32,

"tinyint": ValueType.INT32,

"varbinary": ValueType.BYTES,

"varchar": ValueType.STRING,

"None": ValueType.NULL,

# skip date, geometry, hllsketch, time, timetz

}

if mssql_type_as_str.lower() not in type_map:

raise ValueError(f"Mssql type not supported by feast {mssql_type_as_str}")

return type_map[mssql_type_as_str.lower()]

def pa_to_mssql_type(pa_type: "pyarrow.DataType") -> str:

# PyArrow types: https://arrow.apache.org/docs/python/api/datatypes.html

# MS Sql types: https://docs.microsoft.com/en-us/sql/t-sql/data-types/data-types-transact-sql?view=sql-server-ver16

pa_type_as_str = str(pa_type).lower()

if pa_type_as_str.startswith("timestamp"):

if "tz=" in pa_type_as_str:

return "datetime2"

else:

return "datetime"

if pa_type_as_str.startswith("date"):

return "date"

if pa_type_as_str.startswith("decimal"):

return pa_type_as_str

# We have to take into account how arrow types map to parquet types as well.

# For example, null type maps to int32 in parquet, so we have to use int4 in Redshift.

# Other mappings have also been adjusted accordingly.

type_map = {

"null": "None",

"bool": "bit",

"int8": "tinyint",

"int16": "smallint",

"int32": "int",

"int64": "bigint",

"uint8": "tinyint",

"uint16": "smallint",

"uint32": "int",

"uint64": "bigint",

"float": "float",

"double": "real",

"binary": "binary",

"string": "varchar",

}

if pa_type_as_str.lower() not in type_map:

raise ValueError(f"MS SQL Server type not supported by feast {pa_type_as_str}")

return type_map[pa_type_as_str]

def redshift_to_feast_value_type(redshift_type_as_str: str) -> ValueType:

# Type names from https://docs.aws.amazon.com/redshift/latest/dg/c_Supported_data_types.html

type_map = {

"int2": ValueType.INT32,

"int4": ValueType.INT32,

"int8": ValueType.INT64,

"numeric": ValueType.DOUBLE,

"float4": ValueType.FLOAT,

"float8": ValueType.DOUBLE,

"bool": ValueType.BOOL,

"character": ValueType.STRING,

"varchar": ValueType.STRING,

"timestamp": ValueType.UNIX_TIMESTAMP,

"timestamptz": ValueType.UNIX_TIMESTAMP,

"super": ValueType.BYTES,

# skip date, geometry, hllsketch, time, timetz

}

return type_map[redshift_type_as_str.lower()]

def snowflake_type_to_feast_value_type(snowflake_type: str) -> ValueType:

type_map = {

"BINARY": ValueType.BYTES,

"VARCHAR": ValueType.STRING,

"NUMBER32": ValueType.INT32,

"NUMBER64": ValueType.INT64,

"NUMBERwSCALE": ValueType.DOUBLE,

"DOUBLE": ValueType.DOUBLE,

"BOOLEAN": ValueType.BOOL,

"DATE": ValueType.UNIX_TIMESTAMP,

"TIMESTAMP": ValueType.UNIX_TIMESTAMP,

"TIMESTAMP_TZ": ValueType.UNIX_TIMESTAMP,

"TIMESTAMP_LTZ": ValueType.UNIX_TIMESTAMP,

"TIMESTAMP_NTZ": ValueType.UNIX_TIMESTAMP,

}

return type_map[snowflake_type]

def _convert_value_name_to_snowflake_udf(value_name: str, project_name: str) -> str:

name_map = {

"BYTES": f"feast_{project_name}_snowflake_binary_to_bytes_proto",

"STRING": f"feast_{project_name}_snowflake_varchar_to_string_proto",

"INT32": f"feast_{project_name}_snowflake_number_to_int32_proto",

"INT64": f"feast_{project_name}_snowflake_number_to_int64_proto",

"DOUBLE": f"feast_{project_name}_snowflake_float_to_double_proto",

"FLOAT": f"feast_{project_name}_snowflake_float_to_double_proto",

"BOOL": f"feast_{project_name}_snowflake_boolean_to_bool_proto",

"UNIX_TIMESTAMP": f"feast_{project_name}_snowflake_timestamp_to_unix_timestamp_proto",

"BYTES_LIST": f"feast_{project_name}_snowflake_array_bytes_to_list_bytes_proto",

"STRING_LIST": f"feast_{project_name}_snowflake_array_varchar_to_list_string_proto",

"INT32_LIST": f"feast_{project_name}_snowflake_array_number_to_list_int32_proto",

"INT64_LIST": f"feast_{project_name}_snowflake_array_number_to_list_int64_proto",

"DOUBLE_LIST": f"feast_{project_name}_snowflake_array_float_to_list_double_proto",

"FLOAT_LIST": f"feast_{project_name}_snowflake_array_float_to_list_double_proto",

"BOOL_LIST": f"feast_{project_name}_snowflake_array_boolean_to_list_bool_proto",

"UNIX_TIMESTAMP_LIST": f"feast_{project_name}_snowflake_array_timestamp_to_list_unix_timestamp_proto",

}

return name_map[value_name].upper()

def pa_to_redshift_value_type(pa_type: "pyarrow.DataType") -> str:

# PyArrow types: https://arrow.apache.org/docs/python/api/datatypes.html

# Redshift type: https://docs.aws.amazon.com/redshift/latest/dg/c_Supported_data_types.html

pa_type_as_str = str(pa_type).lower()

if pa_type_as_str.startswith("timestamp"):

if "tz=" in pa_type_as_str:

return "timestamptz"

else:

return "timestamp"

if pa_type_as_str.startswith("date"):

return "date"

if pa_type_as_str.startswith("decimal"):

# PyArrow decimal types (e.g. "decimal(38,37)") luckily directly map to the Redshift type.

return pa_type_as_str

if pa_type_as_str.startswith("list"):

return "super"

# We have to take into account how arrow types map to parquet types as well.

# For example, null type maps to int32 in parquet, so we have to use int4 in Redshift.

# Other mappings have also been adjusted accordingly.

type_map = {

"null": "int4",

"bool": "bool",

"int8": "int4",

"int16": "int4",

"int32": "int4",

"int64": "int8",

"uint8": "int4",

"uint16": "int4",

"uint32": "int8",

"uint64": "int8",

"float": "float4",

"double": "float8",

"binary": "varchar",

"string": "varchar",

}

return type_map[pa_type_as_str]

def _non_empty_value(value: Any) -> bool:

"""

Check that there's enough data we can use for type inference.

If primitive type - just checking that it's not None

If iterable - checking that there's some elements (len > 0)

String is special case: "" - empty string is considered non empty

"""

return value is not None and (

not isinstance(value, Sized) or len(value) > 0 or isinstance(value, str)

)

def spark_to_feast_value_type(spark_type_as_str: str) -> ValueType:

# TODO not all spark types are convertible

# Current non-convertible types: interval, map, struct, structfield, binary

type_map: Dict[str, ValueType] = {

"null": ValueType.UNKNOWN,

"byte": ValueType.BYTES,

"string": ValueType.STRING,

"int": ValueType.INT32,

"short": ValueType.INT32,

"bigint": ValueType.INT64,

"long": ValueType.INT64,

"double": ValueType.DOUBLE,

"decimal": ValueType.DOUBLE,

"float": ValueType.FLOAT,

"boolean": ValueType.BOOL,

"timestamp": ValueType.UNIX_TIMESTAMP,

"date": ValueType.UNIX_TIMESTAMP,

"array<byte>": ValueType.BYTES_LIST,

"array<string>": ValueType.STRING_LIST,

"array<int>": ValueType.INT32_LIST,

"array<bigint>": ValueType.INT64_LIST,

"array<double>": ValueType.DOUBLE_LIST,

"array<decimal>": ValueType.DOUBLE_LIST,

"array<float>": ValueType.FLOAT_LIST,

"array<boolean>": ValueType.BOOL_LIST,

"array<timestamp>": ValueType.UNIX_TIMESTAMP_LIST,

"array<date>": ValueType.UNIX_TIMESTAMP_LIST,

}

if spark_type_as_str.startswith("decimal"):

spark_type_as_str = "decimal"

if spark_type_as_str.startswith("array<decimal"):

spark_type_as_str = "array<decimal>"

# TODO: Find better way of doing this.

if not isinstance(spark_type_as_str, str) or spark_type_as_str not in type_map:

return ValueType.NULL

return type_map[spark_type_as_str.lower()]

def spark_schema_to_np_dtypes(dtypes: List[Tuple[str, str]]) -> Iterator[np.dtype]:

# TODO recheck all typing (also tz for timestamp)

# https://spark.apache.org/docs/latest/api/python/user_guide/arrow_pandas.html#timestamp-with-time-zone-semantics

type_map = defaultdict(

lambda: np.dtype("O"),

{

"boolean": np.dtype("bool"),

"double": np.dtype("float64"),

"float": np.dtype("float64"),

"int": np.dtype("int64"),

"bigint": np.dtype("int64"),

"smallint": np.dtype("int64"),

"timestamp": np.dtype("datetime64[ns]"),

},

)

return (type_map[t] for _, t in dtypes)

def arrow_to_pg_type(t_str: str) -> str:

try:

if t_str.startswith("timestamp") or t_str.startswith("datetime"):

return "timestamptz" if "tz=" in t_str else "timestamp"

return {

"null": "null",

"bool": "boolean",

"int8": "tinyint",

"int16": "smallint",

"int32": "int",

"int64": "bigint",

"list<item: int32>": "int[]",

"list<item: int64>": "bigint[]",

"list<item: bool>": "boolean[]",

"list<item: double>": "double precision[]",

"list<item: timestamp[us]>": "timestamp[]",

"uint8": "smallint",

"uint16": "int",

"uint32": "bigint",

"uint64": "bigint",

"float": "float",

"double": "double precision",

"binary": "binary",

"string": "text",

}[t_str]

except KeyError:

raise ValueError(f"Unsupported type: {t_str}")

def pg_type_to_feast_value_type(type_str: str) -> ValueType:

type_map: Dict[str, ValueType] = {

"boolean": ValueType.BOOL,

"bytea": ValueType.BYTES,

"char": ValueType.STRING,

"bigint": ValueType.INT64,

"smallint": ValueType.INT32,

"integer": ValueType.INT32,

"real": ValueType.DOUBLE,

"double precision": ValueType.DOUBLE,

"boolean[]": ValueType.BOOL_LIST,

"bytea[]": ValueType.BYTES_LIST,

"char[]": ValueType.STRING_LIST,

"smallint[]": ValueType.INT32_LIST,

"integer[]": ValueType.INT32_LIST,

"text": ValueType.STRING,

"text[]": ValueType.STRING_LIST,

"character[]": ValueType.STRING_LIST,

"bigint[]": ValueType.INT64_LIST,

"real[]": ValueType.DOUBLE_LIST,

"double precision[]": ValueType.DOUBLE_LIST,

"character": ValueType.STRING,

"character varying": ValueType.STRING,

"date": ValueType.UNIX_TIMESTAMP,

"time without time zone": ValueType.UNIX_TIMESTAMP,

"timestamp without time zone": ValueType.UNIX_TIMESTAMP,

"timestamp without time zone[]": ValueType.UNIX_TIMESTAMP_LIST,

"date[]": ValueType.UNIX_TIMESTAMP_LIST,

"time without time zone[]": ValueType.UNIX_TIMESTAMP_LIST,

"timestamp with time zone": ValueType.UNIX_TIMESTAMP,

"timestamp with time zone[]": ValueType.UNIX_TIMESTAMP_LIST,

"numeric[]": ValueType.DOUBLE_LIST,

"numeric": ValueType.DOUBLE,

"uuid": ValueType.STRING,

"uuid[]": ValueType.STRING_LIST,

}

value = (

type_map[type_str.lower()]

if type_str.lower() in type_map

else ValueType.UNKNOWN

)

if value == ValueType.UNKNOWN:

print("unknown type:", type_str)

return value

def feast_value_type_to_pa(

feast_type: ValueType, timestamp_unit: str = "us"

) -> "pyarrow.DataType":

import pyarrow

type_map = {

ValueType.INT32: pyarrow.int32(),

ValueType.INT64: pyarrow.int64(),

ValueType.DOUBLE: pyarrow.float64(),

ValueType.FLOAT: pyarrow.float32(),

ValueType.STRING: pyarrow.string(),

ValueType.BYTES: pyarrow.binary(),

ValueType.BOOL: pyarrow.bool_(),

ValueType.UNIX_TIMESTAMP: pyarrow.timestamp(timestamp_unit),

ValueType.INT32_LIST: pyarrow.list_(pyarrow.int32()),

ValueType.INT64_LIST: pyarrow.list_(pyarrow.int64()),

ValueType.DOUBLE_LIST: pyarrow.list_(pyarrow.float64()),

ValueType.FLOAT_LIST: pyarrow.list_(pyarrow.float32()),

ValueType.STRING_LIST: pyarrow.list_(pyarrow.string()),

ValueType.BYTES_LIST: pyarrow.list_(pyarrow.binary()),

ValueType.BOOL_LIST: pyarrow.list_(pyarrow.bool_()),

ValueType.UNIX_TIMESTAMP_LIST: pyarrow.list_(pyarrow.timestamp(timestamp_unit)),

ValueType.NULL: pyarrow.null(),

}

return type_map[feast_type]

def pg_type_code_to_pg_type(code: int) -> str:

"""Map the postgres type code a Feast type string

Rather than raise an exception on an unknown type, we return the

string representation of the type code. This way rather than raising

an exception on unknown types, Feast will just skip the problem columns.

Note that json and jsonb are not supported but this shows up in the

log as a warning. Since postgres allows custom types we return an unknown for those cases.

See: https://jdbc.postgresql.org/documentation/publicapi/index.html?constant-values.html

"""

PG_TYPE_MAP = {

16: "boolean",

17: "bytea",

20: "bigint",

21: "smallint",

23: "integer",

25: "text",

114: "json",

199: "json[]",

700: "real",

701: "double precision",

1000: "boolean[]",