feast/sdk/python/feast/data_source.py at v0.62.0 · feast-dev/feast · GitHub

History

951 lines (810 loc) · 34.8 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

# 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 enum

import warnings

from abc import ABC, abstractmethod

from datetime import datetime, timedelta, timezone

from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple

from google.protobuf.duration_pb2 import Duration

from google.protobuf.json_format import MessageToJson

from typeguard import typechecked

from feast import type_map

from feast.data_format import StreamFormat

from feast.field import Field

from feast.protos.feast.core.DataSource_pb2 import DataSource as DataSourceProto

from feast.repo_config import RepoConfig, get_data_source_class_from_type

from feast.types import from_value_type

from feast.utils import _utc_now

from feast.value_type import ValueType

class KafkaOptions:

"""

DataSource Kafka options used to source features from Kafka messages

"""

def __init__(

self,

kafka_bootstrap_servers: str,

message_format: StreamFormat,

topic: str,

watermark_delay_threshold: Optional[timedelta] = None,

self.kafka_bootstrap_servers = kafka_bootstrap_servers

self.message_format = message_format

self.topic = topic

self.watermark_delay_threshold = watermark_delay_threshold or None

@classmethod

def from_proto(cls, kafka_options_proto: DataSourceProto.KafkaOptions):

"""

Creates a KafkaOptions from a protobuf representation of a kafka option

Args:

kafka_options_proto: A protobuf representation of a DataSource

Returns:

Returns a KafkaOptions object based on the kafka_options protobuf

"""

watermark_delay_threshold = None

if kafka_options_proto.HasField("watermark_delay_threshold"):

watermark_delay_threshold = (

timedelta(days=0)

if kafka_options_proto.watermark_delay_threshold.ToNanoseconds() == 0

else kafka_options_proto.watermark_delay_threshold.ToTimedelta()

)

kafka_options = cls(

kafka_bootstrap_servers=kafka_options_proto.kafka_bootstrap_servers,

message_format=StreamFormat.from_proto(kafka_options_proto.message_format),

topic=kafka_options_proto.topic,

watermark_delay_threshold=watermark_delay_threshold,

)

return kafka_options

def to_proto(self) -> DataSourceProto.KafkaOptions:

"""

Converts an KafkaOptionsProto object to its protobuf representation.

Returns:

KafkaOptionsProto protobuf

"""

watermark_delay_threshold = None

if self.watermark_delay_threshold is not None:

watermark_delay_threshold = Duration()

watermark_delay_threshold.FromTimedelta(self.watermark_delay_threshold)

kafka_options_proto = DataSourceProto.KafkaOptions(

kafka_bootstrap_servers=self.kafka_bootstrap_servers,

message_format=self.message_format.to_proto(),

topic=self.topic,

watermark_delay_threshold=watermark_delay_threshold,

)

return kafka_options_proto

class KinesisOptions:

"""

DataSource Kinesis options used to source features from Kinesis records

"""

def __init__(

self,

record_format: StreamFormat,

region: str,

stream_name: str,

self.record_format = record_format

self.region = region

self.stream_name = stream_name

@classmethod

def from_proto(cls, kinesis_options_proto: DataSourceProto.KinesisOptions):

"""

Creates a KinesisOptions from a protobuf representation of a kinesis option

Args:

kinesis_options_proto: A protobuf representation of a DataSource

Returns:

Returns a KinesisOptions object based on the kinesis_options protobuf

"""

kinesis_options = cls(

record_format=StreamFormat.from_proto(kinesis_options_proto.record_format),

region=kinesis_options_proto.region,

stream_name=kinesis_options_proto.stream_name,

)

return kinesis_options

def to_proto(self) -> DataSourceProto.KinesisOptions:

"""

Converts an KinesisOptionsProto object to its protobuf representation.

Returns:

KinesisOptionsProto protobuf

"""

kinesis_options_proto = DataSourceProto.KinesisOptions(

record_format=self.record_format.to_proto(),

region=self.region,

stream_name=self.stream_name,

)

return kinesis_options_proto

_DATA_SOURCE_OPTIONS = {

DataSourceProto.SourceType.BATCH_FILE: "feast.infra.offline_stores.file_source.FileSource",

DataSourceProto.SourceType.BATCH_BIGQUERY: "feast.infra.offline_stores.bigquery_source.BigQuerySource",

DataSourceProto.SourceType.BATCH_REDSHIFT: "feast.infra.offline_stores.redshift_source.RedshiftSource",

DataSourceProto.SourceType.BATCH_SNOWFLAKE: "feast.infra.offline_stores.snowflake_source.SnowflakeSource",

DataSourceProto.SourceType.BATCH_TRINO: "feast.infra.offline_stores.contrib.trino_offline_store.trino_source.TrinoSource",

DataSourceProto.SourceType.BATCH_SPARK: "feast.infra.offline_stores.contrib.spark_offline_store.spark_source.SparkSource",

DataSourceProto.SourceType.BATCH_ATHENA: "feast.infra.offline_stores.contrib.athena_offline_store.athena_source.AthenaSource",

DataSourceProto.SourceType.STREAM_KAFKA: "feast.data_source.KafkaSource",

DataSourceProto.SourceType.STREAM_KINESIS: "feast.data_source.KinesisSource",

DataSourceProto.SourceType.REQUEST_SOURCE: "feast.data_source.RequestSource",

DataSourceProto.SourceType.PUSH_SOURCE: "feast.data_source.PushSource",

}

_DATA_SOURCE_FOR_OFFLINE_STORE = {

DataSourceProto.SourceType.BATCH_FILE: "feast.infra.offline_stores.dask.DaskOfflineStore",

DataSourceProto.SourceType.BATCH_BIGQUERY: "feast.infra.offline_stores.bigquery.BigQueryOfflineStore",

DataSourceProto.SourceType.BATCH_REDSHIFT: "feast.infra.offline_stores.redshift.RedshiftOfflineStore",

DataSourceProto.SourceType.BATCH_SNOWFLAKE: "feast.infra.offline_stores.snowflake.SnowflakeOfflineStore",

DataSourceProto.SourceType.BATCH_TRINO: "feast.infra.offline_stores.contrib.trino_offline_store.trino.TrinoOfflineStore",

DataSourceProto.SourceType.BATCH_SPARK: "feast.infra.offline_stores.contrib.spark_offline_store.spark.SparkOfflineStore",

DataSourceProto.SourceType.BATCH_ATHENA: "feast.infra.offline_stores.contrib.athena_offline_store.athena.AthenaOfflineStore",

}

@typechecked

class DataSource(ABC):

"""

DataSource that can be used to source features.

Args:

timestamp_field (optional): Event timestamp field used for point-in-time joins of

feature values.

created_timestamp_column (optional): Timestamp column indicating when the row

was created, used for deduplicating rows.

field_mapping (optional): A dictionary mapping of column names in this data

source to feature names in a feature table or view. Only used for feature

columns and timestamp columns, not entity columns.

description (optional) A human-readable description.

tags (optional): A dictionary of key-value pairs to store arbitrary metadata.

owner (optional): The owner of the data source, typically the email of the primary

maintainer.

date_partition_column (optional): Timestamp column used for partitioning. Not supported by all offline stores.

created_timestamp: The time when the data source was created.

last_updated_timestamp: The time when the data source was last updated.

"""

timestamp_field: str

created_timestamp_column: str

field_mapping: Dict[str, str]

description: str

tags: Dict[str, str]

owner: str

date_partition_column: str

created_timestamp: Optional[datetime]

last_updated_timestamp: Optional[datetime]

def __init__(

self,

timestamp_field: Optional[str] = None,

created_timestamp_column: Optional[str] = None,

field_mapping: Optional[Dict[str, str]] = None,

description: Optional[str] = "",

tags: Optional[Dict[str, str]] = None,

owner: Optional[str] = "",

date_partition_column: Optional[str] = None,

"""

Creates a DataSource object.

Args:

timestamp_field (optional): Event timestamp field used for point-in-time joins of

feature values.

created_timestamp_column (optional): Timestamp column indicating when the row

was created, used for deduplicating rows.

field_mapping (optional): A dictionary mapping of column names in this data

source to feature names in a feature table or view. Only used for feature

columns, not entity or timestamp columns.

description (optional): A human-readable description.

tags (optional): A dictionary of key-value pairs to store arbitrary metadata.

owner (optional): The owner of the data source, typically the email of the primary

maintainer.

date_partition_column (optional): Timestamp column used for partitioning. Not supported by all stores

"""

self.name = name

self.timestamp_field = timestamp_field or ""

self.created_timestamp_column = (

created_timestamp_column if created_timestamp_column else ""

)

self.field_mapping = field_mapping if field_mapping else {}

if (

self.timestamp_field

and self.timestamp_field == self.created_timestamp_column

raise ValueError(

"Please do not use the same column for 'timestamp_field' and 'created_timestamp_column'."

)

self.description = description or ""

self.tags = tags or {}

self.owner = owner or ""

self.date_partition_column = (

date_partition_column if date_partition_column else ""

)

now = _utc_now()

self.created_timestamp = now

self.last_updated_timestamp = now

def __hash__(self):

return hash((self.name, self.timestamp_field))

def __str__(self):

return str(MessageToJson(self.to_proto()))

def __eq__(self, other):

if other is None:

return False

if not isinstance(other, DataSource):

raise TypeError("Comparisons should only involve DataSource class objects.")

if (

self.name != other.name

or self.timestamp_field != other.timestamp_field

or self.created_timestamp_column != other.created_timestamp_column

or self.field_mapping != other.field_mapping

or self.date_partition_column != other.date_partition_column

or self.description != other.description

or self.tags != other.tags

or self.owner != other.owner

return False

return True

@staticmethod

@abstractmethod

def from_proto(data_source: DataSourceProto) -> Any:

"""

Converts data source config in protobuf spec to a DataSource class object.

Args:

data_source: A protobuf representation of a DataSource.

Returns:

A DataSource class object.

Raises:

ValueError: The type of DataSource could not be identified.

"""

data_source_type = data_source.type

if not data_source_type or (

data_source_type

not in list(_DATA_SOURCE_OPTIONS.keys())

+ [DataSourceProto.SourceType.CUSTOM_SOURCE]

raise ValueError("Could not identify the source type being added.")

if data_source_type == DataSourceProto.SourceType.CUSTOM_SOURCE:

cls = get_data_source_class_from_type(data_source.data_source_class_type)

data_source_instance = cls.from_proto(data_source)

else:

cls = get_data_source_class_from_type(

_DATA_SOURCE_OPTIONS[data_source_type]

)

data_source_instance = cls.from_proto(data_source)

data_source_instance._extract_timestamps_from_proto(data_source)

return data_source_instance

def to_proto(self) -> DataSourceProto:

"""

Converts a DataSourceProto object to its protobuf representation.

"""

proto = self._to_proto_impl()

self._set_timestamps_in_proto(proto)

return proto

@abstractmethod

def _to_proto_impl(self) -> DataSourceProto:

"""

Subclass implementation of protobuf conversion.

This should be implemented by each DataSource subclass.

"""

raise NotImplementedError

def validate(self, config: RepoConfig):

"""

Validates the underlying data source.

Args:

config: Configuration object used to configure a feature store.

"""

raise NotImplementedError

@staticmethod

@abstractmethod

def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:

"""

Returns the callable method that returns Feast type given the raw column type.

"""

raise NotImplementedError

def get_table_column_names_and_types(

self, config: RepoConfig

) -> Iterable[Tuple[str, str]]:

"""

Returns the list of column names and raw column types.

Args:

config: Configuration object used to configure a feature store.

"""

raise NotImplementedError

def get_table_query_string(self) -> str:

"""

Returns a string that can directly be used to reference this table in SQL.

"""

raise NotImplementedError

def _extract_timestamps_from_proto(self, data_source_proto: DataSourceProto):

"""

Internal method to extract created_timestamp and last_updated_timestamp from protobuf.

Called automatically by the base from_proto method.

"""

if data_source_proto.HasField("meta"):

if data_source_proto.meta.HasField("created_timestamp"):

self.created_timestamp = (

data_source_proto.meta.created_timestamp.ToDatetime().replace(

tzinfo=timezone.utc

)

if data_source_proto.meta.HasField("last_updated_timestamp"):

self.last_updated_timestamp = (

data_source_proto.meta.last_updated_timestamp.ToDatetime().replace(

tzinfo=timezone.utc

)

def _set_timestamps_in_proto(self, data_source_proto: DataSourceProto):

"""

Internal method to set created_timestamp and last_updated_timestamp in protobuf.

Called automatically by the base to_proto method.

"""

if not data_source_proto.HasField("meta"):

data_source_proto.meta.CopyFrom(DataSourceProto.SourceMeta())

if self.created_timestamp:

data_source_proto.meta.created_timestamp.FromDatetime(

self.created_timestamp

)

if self.last_updated_timestamp:

data_source_proto.meta.last_updated_timestamp.FromDatetime(

self.last_updated_timestamp

)

@abstractmethod

def source_type(self) -> DataSourceProto.SourceType.ValueType: ...

@typechecked

class KafkaSource(DataSource):

"""A KafkaSource allow users to register Kafka streams as data sources."""

def __init__(

self,

timestamp_field: str,

message_format: StreamFormat,

bootstrap_servers: Optional[str] = None,

kafka_bootstrap_servers: Optional[str] = None,

topic: Optional[str] = None,

created_timestamp_column: Optional[str] = "",

field_mapping: Optional[Dict[str, str]] = None,

description: Optional[str] = "",

tags: Optional[Dict[str, str]] = None,

owner: Optional[str] = "",

batch_source: Optional[DataSource] = None,

watermark_delay_threshold: Optional[timedelta] = None,

"""

Creates a KafkaSource object.

Args:

timestamp_field: Event timestamp field used for point-in-time joins of feature values.

message_format: StreamFormat of serialized messages.

bootstrap_servers: (Deprecated) The servers of the kafka broker in the form "localhost:9092".

kafka_bootstrap_servers (optional): The servers of the kafka broker in the form "localhost:9092".

topic (optional): The name of the topic to read from in the kafka source.

created_timestamp_column (optional): Timestamp column indicating when the row

was created, used for deduplicating rows.

field_mapping (optional): A dictionary mapping of column names in this data

source to feature names in a feature table or view. Only used for feature

columns, not entity or timestamp columns.

description (optional): A human-readable description.

tags (optional): A dictionary of key-value pairs to store arbitrary metadata.

owner (optional): The owner of the data source, typically the email of the primary

maintainer.

batch_source (optional): The datasource that acts as a batch source.

watermark_delay_threshold (optional): The watermark delay threshold for stream data.

Specifically how late stream data can arrive without being discarded.

"""

if bootstrap_servers:

warnings.warn(

(

"The 'bootstrap_servers' parameter has been deprecated in favor of 'kafka_bootstrap_servers'. "

"Feast 0.25 and onwards will not support the 'bootstrap_servers' parameter."

DeprecationWarning,

)

super().__init__(

name=name,

timestamp_field=timestamp_field,

created_timestamp_column=created_timestamp_column,

field_mapping=field_mapping,

description=description,

tags=tags,

owner=owner,

)

self.batch_source = batch_source

kafka_bootstrap_servers = kafka_bootstrap_servers or bootstrap_servers or ""

topic = topic or ""

self.kafka_options = KafkaOptions(

kafka_bootstrap_servers=kafka_bootstrap_servers,

message_format=message_format,

topic=topic,

watermark_delay_threshold=watermark_delay_threshold,

)

def __eq__(self, other):

if not isinstance(other, KafkaSource):

raise TypeError(

"Comparisons should only involve KafkaSource class objects."

)

if not super().__eq__(other):

return False

if (

self.kafka_options.kafka_bootstrap_servers

!= other.kafka_options.kafka_bootstrap_servers

or self.kafka_options.message_format != other.kafka_options.message_format

or self.kafka_options.topic != other.kafka_options.topic

or self.kafka_options.watermark_delay_threshold

!= other.kafka_options.watermark_delay_threshold

return False

return True

def __hash__(self):

return super().__hash__()

@staticmethod

def from_proto(data_source: DataSourceProto):

watermark_delay_threshold = None

if data_source.kafka_options.watermark_delay_threshold:

watermark_delay_threshold = (

timedelta(days=0)

if data_source.kafka_options.watermark_delay_threshold.ToNanoseconds()

== 0

else data_source.kafka_options.watermark_delay_threshold.ToTimedelta()

)

return KafkaSource(

name=data_source.name,

field_mapping=dict(data_source.field_mapping),

kafka_bootstrap_servers=data_source.kafka_options.kafka_bootstrap_servers,

message_format=StreamFormat.from_proto(

data_source.kafka_options.message_format

watermark_delay_threshold=watermark_delay_threshold,

topic=data_source.kafka_options.topic,

created_timestamp_column=data_source.created_timestamp_column,

timestamp_field=data_source.timestamp_field,

description=data_source.description,

tags=dict(data_source.tags),

owner=data_source.owner,

batch_source=(

DataSource.from_proto(data_source.batch_source)

if data_source.batch_source

else None

)

def _to_proto_impl(self) -> DataSourceProto:

data_source_proto = DataSourceProto(

name=self.name,

type=DataSourceProto.STREAM_KAFKA,

field_mapping=self.field_mapping,

kafka_options=self.kafka_options.to_proto(),

description=self.description,

tags=self.tags,

owner=self.owner,

)

data_source_proto.timestamp_field = self.timestamp_field

data_source_proto.created_timestamp_column = self.created_timestamp_column

if self.batch_source:

data_source_proto.batch_source.MergeFrom(self.batch_source.to_proto())

return data_source_proto

def validate(self, config: RepoConfig):

raise NotImplementedError

def get_table_column_names_and_types(

self, config: RepoConfig

) -> Iterable[Tuple[str, str]]:

raise NotImplementedError

@staticmethod

def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:

return type_map.redshift_to_feast_value_type

def get_table_query_string(self) -> str:

raise NotImplementedError

def source_type(self) -> DataSourceProto.SourceType.ValueType:

return DataSourceProto.STREAM_KAFKA

@typechecked

class RequestSource(DataSource):

"""

RequestSource that can be used to provide input features for on demand transforms

Attributes:

schema: Schema mapping from the input feature name to a ValueType

description: A human-readable description.

tags: A dictionary of key-value pairs to store arbitrary metadata.

owner: The owner of the request data source, typically the email of the primary

maintainer.

"""

schema: List[Field]

description: str

tags: Dict[str, str]

owner: str

def __init__(

self,

schema: List[Field],

timestamp_field: Optional[str] = None,

description: Optional[str] = "",

tags: Optional[Dict[str, str]] = None,

owner: Optional[str] = "",

"""Creates a RequestSource object."""

super().__init__(

name=name,

timestamp_field=timestamp_field,

description=description,

tags=tags,

owner=owner,

)

self.schema = schema

def validate(self, config: RepoConfig):

raise NotImplementedError

def get_table_column_names_and_types(

self, config: RepoConfig

) -> Iterable[Tuple[str, str]]:

raise NotImplementedError

def __eq__(self, other):

if not isinstance(other, RequestSource):

raise TypeError(

"Comparisons should only involve RequestSource class objects."

)

if not super().__eq__(other):

return False

if isinstance(self.schema, List) and isinstance(other.schema, List):

for field1, field2 in zip(self.schema, other.schema):

if field1 != field2:

return False

return True

else:

return False

def __hash__(self):

return super().__hash__()

@staticmethod

def from_proto(data_source: DataSourceProto):

schema_pb = data_source.request_data_options.schema

list_schema = []

for field_proto in schema_pb:

list_schema.append(Field.from_proto(field_proto))

return RequestSource(

name=data_source.name,

schema=list_schema,

timestamp_field=data_source.timestamp_field,

description=data_source.description,

tags=dict(data_source.tags),

owner=data_source.owner,

)

def _to_proto_impl(self) -> DataSourceProto:

schema_pb = []

if isinstance(self.schema, Dict):

for key, value in self.schema.items():

schema_pb.append(

Field(name=key, dtype=from_value_type(value.value)).to_proto()

)

else:

for field in self.schema:

schema_pb.append(field.to_proto())

data_source_proto = DataSourceProto(

name=self.name,

type=DataSourceProto.REQUEST_SOURCE,

description=self.description,

tags=self.tags,

owner=self.owner,

)

data_source_proto.timestamp_field = self.timestamp_field

data_source_proto.request_data_options.schema.extend(schema_pb)

return data_source_proto

def get_table_query_string(self) -> str:

raise NotImplementedError

@staticmethod

def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:

raise NotImplementedError

def source_type(self) -> DataSourceProto.SourceType.ValueType:

return DataSourceProto.REQUEST_SOURCE

@typechecked

class KinesisSource(DataSource):

"""A KinesisSource allows users to register Kinesis streams as data sources."""

def validate(self, config: RepoConfig):

raise NotImplementedError

def get_table_column_names_and_types(

self, config: RepoConfig

) -> Iterable[Tuple[str, str]]:

raise NotImplementedError

@staticmethod

def from_proto(data_source: DataSourceProto):

return KinesisSource(

name=data_source.name,

timestamp_field=data_source.timestamp_field,

field_mapping=dict(data_source.field_mapping),

record_format=StreamFormat.from_proto(

data_source.kinesis_options.record_format

region=data_source.kinesis_options.region,

stream_name=data_source.kinesis_options.stream_name,

created_timestamp_column=data_source.created_timestamp_column,

description=data_source.description,

tags=dict(data_source.tags),

owner=data_source.owner,

batch_source=(

DataSource.from_proto(data_source.batch_source)

if data_source.batch_source

else None

)

@staticmethod

def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:

raise NotImplementedError

def get_table_query_string(self) -> str:

raise NotImplementedError

def __init__(

self,

record_format: StreamFormat,

region: str,

stream_name: str,

timestamp_field: Optional[str] = "",

created_timestamp_column: Optional[str] = "",

field_mapping: Optional[Dict[str, str]] = None,

description: Optional[str] = "",

tags: Optional[Dict[str, str]] = None,

owner: Optional[str] = "",

batch_source: Optional[DataSource] = None,

"""

Args:

record_format: The record format of the Kinesis stream.

region: The AWS region of the Kinesis stream.

stream_name: The name of the Kinesis stream.

timestamp_field: Event timestamp field used for point-in-time joins of

feature values.

created_timestamp_column: Timestamp column indicating when the row

was created, used for deduplicating rows.

field_mapping: A dictionary mapping of column names in this data

source to feature names in a feature table or view. Only used for feature

columns, not entity or timestamp columns.

description: A human-readable description.

tags: A dictionary of key-value pairs to store arbitrary metadata.

owner: The owner of the Kinesis source, typically the email of the primary

maintainer.

batch_source: A DataSource backing the Kinesis stream (used for retrieving historical features).

"""

if record_format is None:

raise ValueError("Record format must be specified for kinesis source")

super().__init__(

name=name,

timestamp_field=timestamp_field,

created_timestamp_column=created_timestamp_column,

field_mapping=field_mapping,

description=description,

tags=tags,

owner=owner,

)

self.batch_source = batch_source

self.kinesis_options = KinesisOptions(

record_format=record_format, region=region, stream_name=stream_name

)

def __eq__(self, other):

if not isinstance(other, KinesisSource):

raise TypeError(

"Comparisons should only involve KinesisSource class objects."

)

if not super().__eq__(other):

return False

if (

self.kinesis_options.record_format != other.kinesis_options.record_format

or self.kinesis_options.region != other.kinesis_options.region

or self.kinesis_options.stream_name != other.kinesis_options.stream_name

return False

return True

def __hash__(self):

return super().__hash__()

def _to_proto_impl(self) -> DataSourceProto:

data_source_proto = DataSourceProto(

name=self.name,

type=DataSourceProto.STREAM_KINESIS,

field_mapping=self.field_mapping,

kinesis_options=self.kinesis_options.to_proto(),

description=self.description,

tags=self.tags,

owner=self.owner,

)

data_source_proto.timestamp_field = self.timestamp_field

data_source_proto.created_timestamp_column = self.created_timestamp_column

if self.batch_source:

data_source_proto.batch_source.MergeFrom(self.batch_source.to_proto())

return data_source_proto

def source_type(self) -> DataSourceProto.SourceType.ValueType:

return DataSourceProto.STREAM_KINESIS

class PushMode(enum.Enum):

ONLINE = 1

OFFLINE = 2

ONLINE_AND_OFFLINE = 3

@typechecked

class PushSource(DataSource):

"""

A source that can be used to ingest features on request

"""

# TODO(adchia): consider adding schema here in case where Feast manages pushing events to the offline store

# TODO(adchia): consider a "mode" to support pushing raw vs transformed events

batch_source: Optional[DataSource] = None

def __init__(

self,

batch_source: Optional[DataSource] = None,

description: Optional[str] = "",

tags: Optional[Dict[str, str]] = None,

owner: Optional[str] = "",

"""

Creates a PushSource object.

Args:

batch_source: The batch source that backs this push source. It's used when materializing from the offline

store to the online store, and when retrieving historical features.

description (optional): A human-readable description.

tags (optional): A dictionary of key-value pairs to store arbitrary metadata.

owner (optional): The owner of the data source, typically the email of the primary

maintainer.

"""

super().__init__(name=name, description=description, tags=tags, owner=owner)

self.batch_source = batch_source

def __eq__(self, other):

if not isinstance(other, PushSource):

return False

if not super().__eq__(other):

return False

if self.batch_source != other.batch_source:

return False

return True

def __hash__(self):

return super().__hash__()

def validate(self, config: RepoConfig):

raise NotImplementedError

def get_table_column_names_and_types(

self, config: RepoConfig

) -> Iterable[Tuple[str, str]]:

raise NotImplementedError

@staticmethod

def from_proto(data_source: DataSourceProto):

batch_source = (

DataSource.from_proto(data_source.batch_source)

if data_source.HasField("batch_source")

else None

)

return PushSource(

name=data_source.name,

batch_source=batch_source,

description=data_source.description,

tags=dict(data_source.tags),

owner=data_source.owner,

)

def _to_proto_impl(self) -> DataSourceProto:

data_source_proto = DataSourceProto(

name=self.name,

type=DataSourceProto.PUSH_SOURCE,

description=self.description,

tags=self.tags,

owner=self.owner,

)

# Only set timestamp fields if we have a batch source and this PushSource doesn't have its own fields

if self.batch_source and not (

self.timestamp_field or self.created_timestamp_column or self.field_mapping

data_source_proto.timestamp_field = self.batch_source.timestamp_field

data_source_proto.created_timestamp_column = (

self.batch_source.created_timestamp_column

)

data_source_proto.field_mapping.update(self.batch_source.field_mapping)

data_source_proto.date_partition_column = (

self.batch_source.date_partition_column

)

if self.batch_source:

data_source_proto.batch_source.MergeFrom(self.batch_source.to_proto())

return data_source_proto

def get_table_query_string(self) -> str:

raise NotImplementedError

@staticmethod

def source_datatype_to_feast_value_type() -> Callable[[str], ValueType]:

raise NotImplementedError

def source_type(self) -> DataSourceProto.SourceType.ValueType:

return DataSourceProto.PUSH_SOURCE

feast/sdk/python/feast/data_source.py at v0.62.0 · feast-dev/feast · GitHub

Uh oh!

Expand file tree

Breadcrumbs

data_source.py

Latest commit

History

Breadcrumbs

data_source.py

File metadata and controls