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GeezerVison is showing github.com — feast/sdk/python/feast/type_map.py at master · luodinglin/feast Expand file tree/ Copy pathtype_map.pyMore file actions 1024 lines (899 loc) · 36.2 KB / Copy pathtype_map.pyFile metadata and controls1024 lines (899 loc) · 36.2 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 json 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) 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, "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, "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} " f"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_}), 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_value = json.loads(sample) if isinstance(json_value, list): if feast_value_type == ValueType.BOOL_LIST: json_value = [bool(item) for item in json_value] return [ProtoValue(**{field_name: proto_type(val=json_value)})] # type: ignore raise _type_err(sample, valid_types[0]) if sample is not None and not all( type(item) in valid_types for item in sample ): first_invalid = next( item for item in sample if type(item) not in valid_types ) raise _type_err(first_invalid, valid_types[0]) if feast_value_type == ValueType.UNIX_TIMESTAMP_LIST: int_timestamps_lists = ( _python_datetime_to_int_timestamp(value) for value in values ) return [ # ProtoValue does actually accept `np.int_` but the typing complains. ProtoValue(unix_timestamp_list_val=Int64List(val=ts)) # type: ignore for ts in int_timestamps_lists ] 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} 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") return _python_value_to_proto_value(value_type, values) 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") type_map = { "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, None: ValueType.NULL, } return 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, } 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, "float": ValueType.FLOAT, "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, "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, } 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[]", 1001: "bytea[]", 1005: "smallint[]", 1007: "integer[]", 1009: "text[]", 1014: "character[]", 1016: "bigint[]", 1021: "real[]", 1022: "double precision[]", 1042: "character", 1043: "character varying", 1082: "date", 1083: "time without time zone", 1114: "timestamp without time zone", 1115: "timestamp without time zone[]", 1182: "date[]", 1183: "time without time zone[]", 1184: "timestamp with time zone", 1185: "timestamp with time zone[]", 1231: "numeric[]", 1700: "numeric", 2950: "uuid", 2951: "uuid[]", 3802: "jsonb", 3807: "jsonb[]", } return PG_TYPE_MAP.get(code, "unknown") def pg_type_code_to_arrow(code: int) -> str: return feast_value_type_to_pa( pg_type_to_feast_value_type(pg_type_code_to_pg_type(code)) ) def athena_to_feast_value_type(athena_type_as_str: str) -> ValueType: # Type names from https://docs.aws.amazon.com/athena/latest/ug/data-types.html type_map = { "null": ValueType.UNKNOWN, "boolean": ValueType.BOOL, "tinyint": ValueType.INT32, "smallint": ValueType.INT32, "int": ValueType.INT32, "bigint": ValueType.INT64, "double": ValueType.DOUBLE, "float": ValueType.FLOAT, "binary": ValueType.BYTES, "char": ValueType.STRING, "varchar": ValueType.STRING, "string": ValueType.STRING, "timestamp": ValueType.UNIX_TIMESTAMP, # skip date,decimal,array,map,struct } return type_map[athena_type_as_str.lower()] def pa_to_athena_value_type(pa_type: "pyarrow.DataType") -> str: # PyArrow types: https://arrow.apache.org/docs/python/api/datatypes.html # Type names from https://docs.aws.amazon.com/athena/latest/ug/data-types.html pa_type_as_str = str(pa_type).lower() if pa_type_as_str.startswith("timestamp"): return "timestamp" if pa_type_as_str.startswith("date"): return "date" |
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