feast/sdk/python/feast/type_map.py at master · feast-dev/feast · GitHub

History

2430 lines (2156 loc) · 87.3 KB

Raw

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

# 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").astype(int)

logger = logging.getLogger(__name__)

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

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

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

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

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

"""

if tzinfo is None:

return ""

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

if key:

return key

name = str(tzinfo)

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

return name

def _zone_from_name(zone: str):

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

if not zone:

return timezone.utc

try:

from zoneinfo import ZoneInfo

return ZoneInfo(zone)

except Exception:

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

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

# original offset is preserved on round trips rather than silently

# shifting the wall-clock time to UTC.

offset = _fixed_offset_from_name(zone)

if offset is not None:

return offset

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

return timezone.utc

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

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

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

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

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

"""

text = zone.strip()

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

return timezone.utc

match = re.fullmatch(

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

text,

)

if not match:

return None

sign, hours, minutes = match.groups()

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

if sign == "-":

delta = -delta

return timezone(delta)

def feast_value_type_to_python_type(

field_value_proto: ProtoValue,

feature_type: Optional[ValueType] = None,

) -> Any:

"""

Converts field value Proto to Dict and returns each field's Feast Value Type value

in their respective Python value.

Args:

field_value_proto: Field value Proto

Returns:

Python native type representation/version of the given field_value_proto

"""

val_attr = field_value_proto.WhichOneof("val")

if val_attr is None:

return None

val = getattr(field_value_proto, val_attr)

# Handle JSON types — stored as strings but returned as parsed Python objects

if val_attr == "json_val":

try:

return json.loads(val)

except (json.JSONDecodeError, TypeError):

return val

elif val_attr == "json_list_val":

result = []

for v in val.val:

if isinstance(v, str):

try:

result.append(json.loads(v))

except (json.JSONDecodeError, TypeError):

result.append(v)

else:

result.append(v)

return result

# Handle nested collection types (list_val, set_val)

if val_attr in ("list_val", "set_val"):

return _handle_nested_collection_value(val)

# Handle Struct types — stored using Map proto, returned as dicts

if val_attr == "struct_val":

return _handle_map_value(val)

elif val_attr == "struct_list_val":

return _handle_map_list_value(val)

# Handle Map and MapList types FIRST (before generic list processing)

if val_attr == "map_val":

return _handle_map_value(val)

elif val_attr == "map_list_val":

return _handle_map_list_value(val)

elif val_attr == "scalar_map_val":

return _handle_scalar_map_value(val)

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

if val_attr == "zoned_timestamp_val":

if val.unix_timestamp == NULL_TIMESTAMP_INT_VALUE:

return None

tz = _zone_from_name(val.zone)

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

# If it's a _LIST or _SET type extract the values.

if hasattr(val, "val"):

val = list(val.val)

# Convert UNIX_TIMESTAMP values to `datetime`

if val_attr == "unix_timestamp_list_val":

val = [

(

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

if v != NULL_TIMESTAMP_INT_VALUE

else None

)

for v in val

]

elif val_attr == "unix_timestamp_set_val":

val = set(

[

(

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

if v != NULL_TIMESTAMP_INT_VALUE

else None

)

for v in val

]

)

elif val_attr == "unix_timestamp_val":

val = (

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

if val != NULL_TIMESTAMP_INT_VALUE

else None

)

# Convert _SET types to Python sets

elif val_attr.endswith("_set_val") and val_attr != "unix_timestamp_set_val":

val = set(val)

# Convert UUID values to uuid.UUID objects

if val_attr in ("uuid_val", "time_uuid_val"):

return uuid_module.UUID(val) if isinstance(val, str) else val

if val_attr in ("uuid_list_val", "time_uuid_list_val"):

return [uuid_module.UUID(v) if isinstance(v, str) else v for v in val]

if val_attr in ("uuid_set_val", "time_uuid_set_val"):

return {uuid_module.UUID(v) if isinstance(v, str) else v for v in val}

# Convert DECIMAL values to decimal.Decimal objects

if val_attr == "decimal_val":

return decimal.Decimal(val) if isinstance(val, str) else val

if val_attr == "decimal_list_val":

return [decimal.Decimal(v) if isinstance(v, str) else v for v in val]

if val_attr == "decimal_set_val":

return {decimal.Decimal(v) if isinstance(v, str) else v for v in val}

# Backward compatibility: handle UUIDs stored as string_val/string_list_val with feature_type hint

if feature_type in (ValueType.UUID, ValueType.TIME_UUID) and isinstance(val, str):

return uuid_module.UUID(val)

if feature_type in (ValueType.UUID_LIST, ValueType.TIME_UUID_LIST) and isinstance(

val, list

return [uuid_module.UUID(v) if isinstance(v, str) else v for v in val]

if feature_type in (ValueType.UUID_SET, ValueType.TIME_UUID_SET) and isinstance(

val, set

return {uuid_module.UUID(v) if isinstance(v, str) else v for v in val}

return val

def _handle_map_value(map_message) -> Dict[str, Any]:

"""Handle Map proto message containing map<string, Value> val."""

result = {}

for key, value in map_message.val.items():

# Recursively handle the Value message

result[key] = feast_value_type_to_python_type(value)

return result

def _handle_map_list_value(map_list_message) -> List[Dict[str, Any]]:

"""Handle MapList proto message containing repeated Map val."""

result = []

for map_item in map_list_message.val:

# Handle each Map in the list

processed_map = _handle_map_value(map_item)

result.append(processed_map)

return result

def _handle_nested_collection_value(repeated_value) -> List[Any]:

"""Handle nested collection proto (RepeatedValue containing Values).

Each inner Value is itself a list/set proto. We recursively convert

each inner Value to a Python list/set via feast_value_type_to_python_type.

"""

result = []

for inner_value in repeated_value.val:

result.append(feast_value_type_to_python_type(inner_value))

return result

def _map_key_to_python_value(map_key: MapKey) -> Any:

"""Convert a MapKey proto to its Python equivalent."""

key_attr = map_key.WhichOneof("key")

if key_attr is None:

return None

val = getattr(map_key, key_attr)

if key_attr in ("int32_key", "int64_key"):

return int(val)

if key_attr in ("float_key", "double_key"):

return float(val)

if key_attr == "bool_key":

return bool(val)

if key_attr == "unix_timestamp_key":

return (

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

if val != NULL_TIMESTAMP_INT_VALUE

else None

)

if key_attr == "bytes_key":

return bytes(val)

if key_attr in ("uuid_key", "time_uuid_key"):

return uuid_module.UUID(val)

if key_attr == "decimal_key":

return decimal.Decimal(val)

return val

def _handle_scalar_map_value(value_map_message: ScalarMap) -> Dict[Any, Any]:

"""Handle ScalarMap proto message (repeated ScalarMapEntry) → Python dict."""

result: Dict[Any, Any] = {}

for entry in value_map_message.val:

key = _map_key_to_python_value(entry.key)

value = feast_value_type_to_python_type(entry.value)

result[key] = value

return result

def feast_value_type_to_pandas_type(value_type: ValueType) -> Any:

value_type_to_pandas_type: Dict[ValueType, str] = {

ValueType.FLOAT: "float",

ValueType.INT32: "int",

ValueType.INT64: "int",

ValueType.STRING: "str",

ValueType.DOUBLE: "float",

ValueType.BYTES: "bytes",

ValueType.BOOL: "bool",

ValueType.UNIX_TIMESTAMP: "datetime64[ns]",

ValueType.UUID: "str",

ValueType.TIME_UUID: "str",

ValueType.DECIMAL: "object",

}

if (

value_type.name in ("MAP", "JSON", "STRUCT", "VALUE_LIST", "VALUE_SET")

or value_type.name.endswith("_LIST")

or value_type.name.endswith("_SET")

return "object"

if value_type in value_type_to_pandas_type:

return value_type_to_pandas_type[value_type]

raise TypeError(

f"Casting to pandas type for type {value_type} failed. "

f"Type {value_type} not found"

)

def python_type_to_feast_value_type(

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:

value: Value that will be inspected

recurse: Whether to recursively look for nested types in arrays

Returns:

Feast Value Type

"""

type_name = (type_name or type(value).__name__).lower()

type_map = {

"int": ValueType.INT64,

"str": ValueType.STRING,

"string": ValueType.STRING, # pandas.StringDtype

"float": ValueType.DOUBLE,

"bytes": ValueType.BYTES,

"float64": ValueType.DOUBLE,

"float32": ValueType.FLOAT,

"int64": ValueType.INT64,

"uint64": ValueType.INT64,

"int32": ValueType.INT32,

"uint32": ValueType.INT32,

"int16": ValueType.INT32,

"uint16": ValueType.INT32,

"uint8": ValueType.INT32,

"int8": ValueType.INT32,

"bool_": ValueType.BOOL, # np.bool_

"bool": ValueType.BOOL,

"boolean": ValueType.BOOL,

"timedelta": ValueType.UNIX_TIMESTAMP,

"timestamp": ValueType.UNIX_TIMESTAMP,

"datetime": ValueType.UNIX_TIMESTAMP,

"datetime64[ns]": ValueType.UNIX_TIMESTAMP,

"datetime64[ns, tz]": ValueType.UNIX_TIMESTAMP, # special dtype of pandas

"datetime64[ns, utc]": ValueType.UNIX_TIMESTAMP,

"date": ValueType.UNIX_TIMESTAMP,

"category": ValueType.STRING,

"uuid": ValueType.UUID,

"decimal": ValueType.DECIMAL,

}

if type_name in type_map:

return type_map[type_name]

# Handle pandas "object" dtype by inspecting the actual value

if type_name == "object" and value is not None:

# Check the actual type of the value

actual_type = type(value).__name__.lower()

if actual_type == "str":

return ValueType.STRING

# Check if it's a dictionary (could be a Map)

elif actual_type == "dict":

return ValueType.MAP

# If it's a different type wrapped in object, try to infer from the value

elif actual_type in type_map:

return type_map[actual_type]

if isinstance(value, np.ndarray) and str(value.dtype) in type_map:

item_type = type_map[str(value.dtype)]

return ValueType[item_type.name + "_LIST"]

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

# Check if it's a list of maps

if value and isinstance(value[0], dict):

return ValueType.MAP_LIST

# if the value's type is "ndarray" and we couldn't infer from "value.dtype"

# this is most probably array of "object",

# so we need to iterate over objects and try to infer type of each item

if not recurse:

raise ValueError(

f"Value type for field {name} is {type(value)} but "

f"recursion is not allowed. Nested collection types cannot be "

f"inferred automatically; use an explicit Field dtype instead "

f"(e.g., dtype=Array(Array(Int32)))."

)

# This is the final type which we infer from the list

common_item_value_type = None

for item in value:

if isinstance(item, ProtoValue):

current_item_value_type: ValueType = _proto_value_to_value_type(item)

else:

# Get the type from the current item, only one level deep

current_item_value_type = python_type_to_feast_value_type(

name=name, value=item, recurse=False

)

# Validate whether the type stays consistent

if (

common_item_value_type

and not common_item_value_type == current_item_value_type

raise ValueError(

f"List value type for field {name} is inconsistent. "

f"{common_item_value_type} different from "

f"{current_item_value_type}."

)

common_item_value_type = current_item_value_type

if common_item_value_type is None:

return ValueType.UNKNOWN

return ValueType[common_item_value_type.name + "_LIST"]

# Check if it's a set (Set type)

if isinstance(value, set):

if not recurse:

raise ValueError(

f"Value type for field {name} is {type(value)} but "

f"recursion is not allowed. Set types can only be one level "

f"deep."

)

# Infer the type from set elements

common_set_item_type = None

for item in value:

if isinstance(item, ProtoValue):

current_set_item_type: ValueType = _proto_value_to_value_type(item)

else:

# Get the type from the current item, only one level deep

current_set_item_type = python_type_to_feast_value_type(

name=name, value=item, recurse=False

)

# Validate whether the type stays consistent

if (

common_set_item_type

and not common_set_item_type == current_set_item_type

raise ValueError(

f"Set value type for field {name} is inconsistent. "

f"{common_set_item_type} different from "

f"{current_set_item_type}."

)

common_set_item_type = current_set_item_type

if common_set_item_type is None:

return ValueType.UNKNOWN

return ValueType[common_set_item_type.name + "_SET"]

# Check if it's a dictionary (Map type)

if isinstance(value, dict):

# Non-string keys require ScalarMap; string keys (or empty dict) use Map

if value and not isinstance(next(iter(value)), str):

return ValueType.SCALAR_MAP

return ValueType.MAP

raise ValueError(

f"Value with native type {type_name} cannot be converted into Feast value type"

)

def python_values_to_feast_value_type(

) -> 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,

}

return type_map.get(type_str, ValueType.STRING)

def _type_err(item, dtype):

raise TypeError(f'Value "{item}" is of type {type(item)} not of type {dtype}')

PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE: Dict[

ValueType, Tuple[ListType, str, List[Type]]

] = {

ValueType.FLOAT_LIST: (

FloatList,

"float_list_val",

[np.float32, np.float64, float],

ValueType.DOUBLE_LIST: (

DoubleList,

"double_list_val",

[np.float64, np.float32, float],

ValueType.INT32_LIST: (Int32List, "int32_list_val", [np.int64, np.int32, int]),

ValueType.INT64_LIST: (Int64List, "int64_list_val", [np.int64, np.int32, int]),

ValueType.UNIX_TIMESTAMP_LIST: (

Int64List,

"int64_list_val",

[np.datetime64, np.int64, np.int32, int, datetime, Timestamp],

ValueType.STRING_LIST: (StringList, "string_list_val", [np.str_, str]),

ValueType.BOOL_LIST: (BoolList, "bool_list_val", [np.bool_, bool]),

ValueType.BYTES_LIST: (BytesList, "bytes_list_val", [np.bytes_, bytes]),

ValueType.UUID_LIST: (

StringList,

"uuid_list_val",

[np.str_, str, uuid_module.UUID],

ValueType.TIME_UUID_LIST: (

StringList,

"time_uuid_list_val",

[np.str_, str, uuid_module.UUID],

ValueType.DECIMAL_LIST: (

StringList,

"decimal_list_val",

[np.str_, str, decimal.Decimal],

}

PYTHON_SET_VALUE_TYPE_TO_PROTO_VALUE: Dict[

ValueType, Tuple[SetType, str, List[Type]]

] = {

ValueType.FLOAT_SET: (

FloatSet,

"float_set_val",

[np.float32, np.float64, float],

ValueType.DOUBLE_SET: (

DoubleSet,

"double_set_val",

[np.float64, np.float32, float],

ValueType.INT32_SET: (Int32Set, "int32_set_val", [np.int64, np.int32, int]),

ValueType.INT64_SET: (Int64Set, "int64_set_val", [np.int64, np.int32, int]),

ValueType.UNIX_TIMESTAMP_SET: (

Int64Set,

"unix_timestamp_set_val",

[np.datetime64, np.int64, np.int32, int, datetime, Timestamp],

ValueType.STRING_SET: (StringSet, "string_set_val", [np.str_, str]),

ValueType.BOOL_SET: (BoolSet, "bool_set_val", [np.bool_, bool]),

ValueType.BYTES_SET: (BytesSet, "bytes_set_val", [np.bytes_, bytes]),

ValueType.UUID_SET: (StringSet, "uuid_set_val", [np.str_, str, uuid_module.UUID]),

ValueType.TIME_UUID_SET: (

StringSet,

"time_uuid_set_val",

[np.str_, str, uuid_module.UUID],

ValueType.DECIMAL_SET: (

StringSet,

"decimal_set_val",

[np.str_, str, decimal.Decimal],

}

PYTHON_SCALAR_VALUE_TYPE_TO_PROTO_VALUE: Dict[

ValueType, Tuple[str, Any, Optional[Set[Type]]]

] = {

ValueType.INT32: ("int32_val", lambda x: int(x), None),

ValueType.INT64: (

"int64_val",

lambda x: (

int(x.timestamp())

if isinstance(x, pd._libs.tslibs.timestamps.Timestamp)

else int(x)

None,

ValueType.FLOAT: ("float_val", lambda x: float(x), None),

ValueType.DOUBLE: (

"double_val",

lambda x: x,

{float, np.float64, int, np.int_, decimal.Decimal},

ValueType.STRING: ("string_val", lambda x: str(x), None),

ValueType.BYTES: ("bytes_val", lambda x: x, {bytes}),

ValueType.IMAGE_BYTES: ("bytes_val", lambda x: x, {bytes}),

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

ValueType.UUID: ("uuid_val", lambda x: str(x), {str, uuid_module.UUID}),

ValueType.TIME_UUID: ("time_uuid_val", lambda x: str(x), {str, uuid_module.UUID}),

ValueType.DECIMAL: ("decimal_val", lambda x: str(x), {decimal.Decimal, str}),

}

def _python_datetime_to_int_timestamp(

values: Sequence[Any],

) -> Sequence[Union[int, np.int_]]:

# Fast path for Numpy array.

if isinstance(values, np.ndarray) and isinstance(values.dtype, np.datetime64):

if values.ndim != 1:

raise ValueError("Only 1 dimensional arrays are supported.")

return cast(Sequence[np.int_], values.astype("datetime64[s]").astype(np.int_))

int_timestamps = []

for value in values:

if isinstance(value, datetime):

int_timestamps.append(int(value.timestamp()))

elif isinstance(value, Timestamp):

int_timestamps.append(int(value.ToSeconds()))

elif isinstance(value, np.datetime64):

int_timestamps.append(value.astype("datetime64[s]").astype(np.int_)) # type: ignore[attr-defined]

elif isinstance(value, type(np.nan)):

int_timestamps.append(NULL_TIMESTAMP_INT_VALUE)

else:

int_timestamps.append(int(value))

return int_timestamps

def _convert_timestamp_collection_to_proto(

values: List[Any],

proto_field: str,

proto_type: type,

) -> List[ProtoValue]:

"""Convert timestamp collection values (list or set) to proto.

Args:

values: List of timestamp collections to convert.

proto_field: The proto field name (e.g., 'unix_timestamp_list_val').

proto_type: The proto type class (e.g., Int64List).

Returns:

List of ProtoValue with converted timestamps.

"""

result = []

for value in values:

if value is not None:

result.append(

ProtoValue(

**{

proto_field: proto_type(

val=_python_datetime_to_int_timestamp(value)

)

} # type: ignore

)

else:

result.append(ProtoValue())

return result

def _convert_bool_collection_to_proto(

values: List[Any],

proto_field: str,

proto_type: type,

) -> List[ProtoValue]:

"""Convert boolean collection values (list or set) to proto.

ProtoValue does not support direct conversion of np.bool_, so we need to

explicitly convert each element to Python bool.

Args:

values: List of boolean collections to convert.

proto_field: The proto field name (e.g., 'bool_list_val').

proto_type: The proto type class (e.g., BoolList).

Returns:

List of ProtoValue with converted booleans.

"""

result = []

for value in values:

if value is not None:

result.append(

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

)

else:

result.append(ProtoValue())

return result

def _validate_collection_item_types(

sample: Any,

valid_types: List[Type],

feast_value_type: ValueType,

) -> None:

"""Validate that collection items match expected types.

Args:

sample: A sample collection value to check.

valid_types: List of valid Python types for items.

feast_value_type: The Feast value type for error messages.

Raises:

TypeError: If any item in sample is not a valid type.

"""

if sample is None:

return

if all(type(item) in valid_types for item in sample if item is not None):

return

# to_numpy() upcasts INT32/INT64 with NULL to Float64 automatically

int_collection_types = [

ValueType.INT32_LIST,

ValueType.INT64_LIST,

ValueType.INT32_SET,

ValueType.INT64_SET,

]

for item in sample:

if item is None:

continue # None elements in STRING_LIST are replaced with ""; for other types they are dropped

if type(item) not in valid_types:

if feast_value_type in int_collection_types:

# Check if the float values are due to NULL upcast

if not any(np.isnan(i) for i in sample if isinstance(i, float)):

logger.error(

f"{feast_value_type.name} has NULL values. to_numpy() upcasts to Float64 automatically."

)

raise _type_err(item, valid_types[0])

def _python_set_to_proto_values(

feast_value_type: ValueType, values: List[Any]

) -> List[ProtoValue]:

"""

Converts Python set values to Feast Proto Values.

Args:

feast_value_type: The target set value type

values: List of set values that will be converted

Returns:

List of Feast Value Proto

"""

# Feature can be set but None is still valid

if feast_value_type not in PYTHON_SET_VALUE_TYPE_TO_PROTO_VALUE:

return []

set_proto_type, set_field_name, set_valid_types = (

PYTHON_SET_VALUE_TYPE_TO_PROTO_VALUE[feast_value_type]

)

# Convert set to list for proto (proto doesn't have native set type)

def convert_set_to_list(value: Any) -> Any:

if value is None:

return None

if isinstance(value, set):

return list(value)

if isinstance(value, (list, tuple, np.ndarray)):

return list(set(value))

return value

converted_values = [convert_set_to_list(v) for v in values]

sample = next(filter(_non_empty_value, converted_values), None)

# Bytes to array type conversion

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

if feast_value_type == ValueType.BYTES_SET:

raise _type_err(sample, ValueType.BYTES_SET)

json_sample = json.loads(sample)

if isinstance(json_sample, list):

json_values = [

json.loads(value) if value is not None else None

for value in converted_values

]

if feast_value_type == ValueType.BOOL_SET:

json_values = [

[bool(item) for item in list_item]

if list_item is not None

else None

for list_item in json_values

]

return [

ProtoValue(**{set_field_name: set_proto_type(val=v)}) # type: ignore[arg-type]

if v is not None

else ProtoValue()

for v in json_values

]

raise _type_err(sample, set_valid_types[0])

# Validate item types using shared helper

_validate_collection_item_types(sample, set_valid_types, feast_value_type)

# Handle special types using shared helpers

if feast_value_type == ValueType.UNIX_TIMESTAMP_SET:

return _convert_timestamp_collection_to_proto(

converted_values, "unix_timestamp_set_val", Int64Set

)

if feast_value_type == ValueType.BOOL_SET:

return _convert_bool_collection_to_proto(

converted_values, set_field_name, set_proto_type

)

if feast_value_type in (ValueType.UUID_SET, ValueType.TIME_UUID_SET):

# uuid.UUID objects must be converted to str for StringSet proto.

return [

(

ProtoValue(

**{set_field_name: set_proto_type(val=[str(e) for e in value])} # type: ignore[arg-type, misc]

)

if value is not None

else ProtoValue()

)

for value in converted_values

]

if feast_value_type == ValueType.DECIMAL_SET:

# decimal.Decimal objects must be converted to str for StringSet proto.

return [

(

ProtoValue(

**{set_field_name: set_proto_type(val=[str(e) for e in value])} # type: ignore[arg-type, misc]

)

if value is not None

else ProtoValue()

)

for value in converted_values

]

# Generic set conversion

return [

ProtoValue(**{set_field_name: set_proto_type(val=value)}) # type: ignore[arg-type]

if value is not None

else ProtoValue()

for value in converted_values

]

# Per-type default values substituted for None elements inside list columns.

# Protobuf repeated fields do not accept None, so we replace with a

# type-appropriate zero/empty value.

_LIST_NONE_DEFAULTS: Dict[ValueType, Any] = {

ValueType.STRING_LIST: "",

ValueType.BYTES_LIST: b"",

ValueType.INT32_LIST: 0,

ValueType.INT64_LIST: 0,

ValueType.FLOAT_LIST: 0.0,

ValueType.DOUBLE_LIST: 0.0,

ValueType.BOOL_LIST: False,

ValueType.UNIX_TIMESTAMP_LIST: NULL_TIMESTAMP_INT_VALUE,

ValueType.UUID_LIST: "",

ValueType.TIME_UUID_LIST: "",

ValueType.DECIMAL_LIST: "",

}

def _sanitize_list_value(value: Any, feast_value_type: ValueType) -> Any:

"""Convert ndarray to list and replace None elements with a type-appropriate default.

Arrow/Athena may deserialize array columns as numpy.ndarray with object dtype

instead of plain Python lists. Protobuf repeated fields do not accept ndarrays

or None elements, so we normalise here before building proto messages.

"""

if isinstance(value, np.ndarray):

value = value.tolist()

none_default = _LIST_NONE_DEFAULTS.get(feast_value_type)

if none_default is not None and isinstance(value, list):

value = [none_default if v is None else v for v in value]

return value

def _convert_list_values_to_proto(

feast_value_type: ValueType,

values: List[Any],

sample: Any,

) -> List[ProtoValue]:

"""Convert list-type values to proto.

Args:

feast_value_type: The target list value type.

values: List of list values to convert.

sample: First non-empty value for type checking.

Returns:

List of ProtoValue.

"""

if feast_value_type not in PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE:

raise Exception(f"Unsupported list type: {feast_value_type}")

proto_type, field_name, valid_types = PYTHON_LIST_VALUE_TYPE_TO_PROTO_VALUE[

feast_value_type

]

values = [

_sanitize_list_value(v, feast_value_type) if v is not None else v

View remainder of file in raw view

feast/sdk/python/feast/type_map.py at master · feast-dev/feast · GitHub

Uh oh!

Expand file tree

Breadcrumbs

type_map.py

Latest commit

History

Breadcrumbs

type_map.py

File metadata and controls