Feast Spark

Contains

• Spark ingestion jobs for Feast versions 0.9 and below
• Feast Job Service
• Feast Python SDK Spark extensions

Usage:

import feast_spark
import feast

client = feast.Client()

client.set_project("project1")
entity = feast.Entity(
    name="driver_car_id",
    description="Car driver id",
    value_type=ValueType.STRING,
    labels={"team": "matchmaking"},
)

# Create Feature Tables using Feast SDK
batch_source = feast.FileSource(
    file_format=ParquetFormat(),
    file_url="file://feast/*",
    event_timestamp_column="ts_col",
    created_timestamp_column="timestamp",
    date_partition_column="date_partition_col",
)

stream_source = feast.KafkaSource(
    bootstrap_servers="localhost:9094",
    message_format=ProtoFormat("class.path"),
    topic="test_topic",
    event_timestamp_column="ts_col",
)

ft = feast.FeatureTable(
    name="my-feature-table-1",
    features=[
        Feature(name="fs1-my-feature-1", dtype=ValueType.INT64),
        Feature(name="fs1-my-feature-2", dtype=ValueType.STRING),
        Feature(name="fs1-my-feature-3", dtype=ValueType.STRING_LIST),
        Feature(name="fs1-my-feature-4", dtype=ValueType.BYTES_LIST),
    ],
    entities=["fs1-my-entity-1"],
    labels={"team": "matchmaking"},
    batch_source=batch_source,
    stream_source=stream_source,
)

# Register objects in Feast
client.apply(entity, ft)

# Start spark streaming ingestion job that reads from kafka and writes to the online store
feast_spark.Client(client).start_stream_to_online_ingestion(ft)