def entity_create(filename, project):
"""
Create or update an entity
"""
entities = [Entity.from_dict(entity_dict) for entity_dict in yaml_loader(filename)]
feast_client = Client() # type: Client
feast_client.apply(entities, project)
def _mock_feature_table(self,
labels: dict = {},
add_stream_source: bool = False) -> None:
table_spec = {
"name": "driver_trips",
"entities": ["driver_id"],
"features": [{
"name": "trips_today",
"valueType": "INT32"
}],
"labels": labels,
"batchSource": {
"type": "BATCH_FILE",
"fileOptions": {
"fileFormat": {
"parquetFormat": {}
},
"fileUrl": "file:///some/location",
},
},
}
if add_stream_source:
avro_schema_json = json.dumps({
"type":
"record",
"name":
"DriverTrips",
"fields": [
{
"name": "driver_id",
"type": "long"
},
{
"name": "trips_today",
"type": "int"
},
{
"name": "datetime",
"type": {
"type": "long",
"logicalType": "timestamp-micros"
},
},
],
})
table_spec["streamSource"] = {
"type": "STREAM_KAFKA",
"eventTimestampColumn": "datetime",
"createdTimestampColumn": "datetime",
"kafkaOptions": {
"bootstrapServers": "broker1",
"topic": "driver_trips",
"messageFormat": {
"avroFormat": {
"schemaJson": avro_schema_json,
}
},
},
}
self.extractor._client.list_feature_tables.return_value = [
FeatureTable.from_dict({
"spec": table_spec,
"meta": {
"createdTimestamp": "2020-01-01T00:00:00Z"
},
})
]
self.extractor._client.get_entity.return_value = Entity.from_dict({
"spec": {
"name": "driver_id",
"valueType": "INT64",
"description": "Internal identifier of the driver",
}
})
