def to_proto(self) -> DataSourceProto:
data_source_proto = DataSourceProto(
type=DataSourceProto.STREAM_KINESIS,
field_mapping=self.field_mapping,
kinesis_options=self.kinesis_options.to_proto(),
)
data_source_proto.timestamp_column = self.timestamp_column
data_source_proto.date_partition_column = self.date_partition_column
return data_source_proto
def to_proto(self) -> DataSourceProto:
data_source_proto = DataSourceProto(
type=DataSourceProto.BATCH_BIGQUERY,
field_mapping=self.field_mapping,
bigquery_options=self.bigquery_options.to_proto(),
)
data_source_proto.timestamp_column = self.timestamp_column
data_source_proto.date_partition_column = self.date_partition_column
return data_source_proto
def to_proto(self) -> DataSourceProto:
data_source_proto = DataSourceProto(
type=DataSourceProto.STREAM_KAFKA,
field_mapping=self.field_mapping,
kafka_options=self.kafka_options.to_proto(),
)
data_source_proto.event_timestamp_column = self.event_timestamp_column
data_source_proto.created_timestamp_column = self.created_timestamp_column
data_source_proto.date_partition_column = self.date_partition_column
return data_source_proto
def to_proto(self) -> DataSourceProto:
data_source_proto = DataSourceProto(
type=DataSourceProto.BATCH_FILE,
field_mapping=self.field_mapping,
file_options=self.file_options.to_proto(),
)
data_source_proto.event_timestamp_column = self.event_timestamp_column
data_source_proto.created_timestamp_column = self.created_timestamp_column
data_source_proto.date_partition_column = self.date_partition_column
return data_source_proto
def _ingest_test_getfeaturetable_mocked_resp(file_url: str,
date_partition_col: str = None):
return GetFeatureTableResponse(table=FeatureTableProto(
spec=FeatureTableSpecProto(
name="ingest_featuretable",
max_age=Duration(seconds=3600),
features=[
FeatureSpecProto(
name="dev_feature_float",
value_type=ValueProto.ValueType.FLOAT,
),
FeatureSpecProto(
name="dev_feature_string",
value_type=ValueProto.ValueType.STRING,
),
],
entities=["dev_entity"],
batch_source=DataSourceProto(
file_options=DataSourceProto.FileOptions(
file_format=ParquetFormat().to_proto(), file_url=file_url),
event_timestamp_column="datetime",
created_timestamp_column="timestamp",
date_partition_column=date_partition_col
if date_partition_col is not None else None,
),
),
meta=FeatureTableMetaProto(),
))
def to_proto(self) -> DataSourceProto.BigQueryOptions:
"""
Converts an BigQueryOptionsProto object to its protobuf representation.
Returns:
BigQueryOptionsProto protobuf
"""
bigquery_options_proto = DataSourceProto.BigQueryOptions(
table_ref=self.table_ref, )
return bigquery_options_proto
def to_proto(self) -> DataSourceProto.FileOptions:
"""
Converts an FileOptionsProto object to its protobuf representation.
Returns:
FileOptionsProto protobuf
"""
file_options_proto = DataSourceProto.FileOptions(
file_format=self.file_format, file_url=self.file_url,
)
return file_options_proto
def to_proto(self) -> DataSourceProto.KinesisOptions:
"""
Converts an KinesisOptionsProto object to its protobuf representation.
Returns:
KinesisOptionsProto protobuf
"""
kinesis_options_proto = DataSourceProto.KinesisOptions(
class_path=self.class_path,
region=self.region,
stream_name=self.stream_name,
)
return kinesis_options_proto
def to_proto(self) -> DataSourceProto.KafkaOptions:
"""
Converts an KafkaOptionsProto object to its protobuf representation.
Returns:
KafkaOptionsProto protobuf
"""
kafka_options_proto = DataSourceProto.KafkaOptions(
bootstrap_servers=self.bootstrap_servers,
class_path=self.class_path,
topic=self.topic,
)
return kafka_options_proto
