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GeezerVison is showing github.com — feast/sdk/python/feast/type_map.py at master · feast-dev/feast Expand file tree/ Copy pathtype_map.pyMore file actions 2484 lines (2205 loc) · 89.6 KB / Copy pathtype_map.pyFile metadata and controls2484 lines (2205 loc) · 89.6 KB 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 # 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"): # None elements are stored as "" (see _LIST_NONE_DEFAULTS) return [ None if v == "" else 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 [ None if v == "" else 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]: |
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