Ray (contrib)

⚠️ Contrib Plugin: RaySource is a contributed plugin shipped alongside the Ray offline store. It may not be as stable or fully supported as core data sources.

RaySource is a pure-metadata descriptor that tells Feast how to load a Ray Dataset from any source that Ray Data supports natively — Parquet, CSV, JSON, HuggingFace Datasets, MongoDB, binary files, images, TFRecords, and more.

It is the recommended data source when using the Ray offline store and replaces the need for FileSource for all non-Parquet and non-file-based data.

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When to use RaySource vs FileSource

Scenario	Recommended source
Parquet files on disk / S3 / GCS (existing setup)	FileSource (backward compatible)
Parquet via Ray reader (pipelines, remote auth)	RaySource(reader_type="parquet")
CSV, JSON, text, images via Ray	RaySource
HuggingFace datasets library	RaySource(reader_type="huggingface")
MongoDB, SQL, TFRecords, WebDataset	RaySource

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Installation

RaySource is bundled with the Ray offline store contrib package:

pip install 'feast[ray]'

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Supported reader_type values

reader_type	Underlying Ray API	Notes
parquet	ray.data.read_parquet	S3, GCS, HDFS, local
csv	ray.data.read_csv
json	ray.data.read_json
text	ray.data.read_text
images	ray.data.read_images
binary_files	ray.data.read_binary_files
tfrecords	ray.data.read_tfrecords
webdataset	ray.data.read_webdataset
huggingface	ray.data.from_huggingface	Wraps datasets.load_dataset
mongo	ray.data.read_mongo
sql	ray.data.read_sql	Pass connection_url in reader_options

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Configuration

Parameters

Parameter	Type	Required	Description
name	str	Yes	Unique name for this data source
reader_type	str	Yes	One of the supported reader types above
path	str	No	File or directory path (required for file-based readers)
reader_options	dict	No	Extra keyword arguments forwarded to the Ray reader
timestamp_field	str	No	Column containing event timestamps
created_timestamp_column	str	No	Column containing row creation timestamps
tags	dict	No	Arbitrary key-value metadata
description	str	No	Human-readable description
owner	str	No	Owning team or contact

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Usage examples

Parquet on S3

from feast.infra.offline_stores.contrib.ray_offline_store.ray_source import RaySource

driver_stats = RaySource(
    name="driver_stats_parquet",
    reader_type="parquet",
    path="s3://my-bucket/driver_stats/",
    timestamp_field="event_timestamp",
)

CSV

sensor_readings = RaySource(
    name="sensor_readings_csv",
    reader_type="csv",
    path="/data/sensors/",
    timestamp_field="ts",
)

HuggingFace dataset

Load a dataset from the HuggingFace Hub directly into Feast.

from feast.infra.offline_stores.contrib.ray_offline_store.ray_source import RaySource

cheque_images = RaySource(
    name="cheque_images_hf",
    reader_type="huggingface",
    reader_options={
        "dataset_name": "cheques_sample_data",
        "split": "train",
    },
    timestamp_field="event_timestamp",
)

MongoDB

transaction_log = RaySource(
    name="transactions_mongo",
    reader_type="mongo",
    reader_options={
        "uri": "mongodb://localhost:27017",
        "database": "featuredb",
        "collection": "transactions",
    },
    timestamp_field="created_at",
)

SQL (via connection URL)

user_features = RaySource(
    name="user_features_sql",
    reader_type="sql",
    reader_options={
        "connection_url": "postgresql+psycopg2://user:password@host:5432/db",  # pragma: allowlist secret
        "query": "SELECT * FROM user_features",
    },
    timestamp_field="event_timestamp",
)

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Using RaySource in a BatchFeatureView

from datetime import timedelta
from feast import BatchFeatureView, Entity, Field
from feast.types import Float32, Int64, String
from feast.infra.offline_stores.contrib.ray_offline_store.ray_source import RaySource

cheque = Entity(name="cheque_id", description="Unique cheque identifier")

cheque_source = RaySource(
    name="cheque_images_hf",
    reader_type="huggingface",
    reader_options={
        "dataset_name": "cheques_sample_data",
        "split": "train",
    },
    timestamp_field="event_timestamp",
)

cheque_ocr_fv = BatchFeatureView(
    name="cheque_ocr_features",
    entities=[cheque],
    ttl=timedelta(days=365),
    schema=[
        Field(name="cheque_id", dtype=Int64),
        Field(name="payee_name", dtype=String),
        Field(name="amount", dtype=String),
        Field(name="bank_name", dtype=String),
        Field(name="raw_text", dtype=String),
    ],
    source=cheque_source,
)

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Retrieving data as a Ray Dataset

Once the feature view is materialised you can retrieve the offline features directly as a Ray Dataset using the first-class to_ray_dataset() method:

from feast import FeatureStore

store = FeatureStore(".")

# Chain directly on the retrieval job — to_ray_dataset() is a first-class
# method on every RetrievalJobs.
ds = store.get_historical_features(
    features=["cheque_ocr_features:payee_name", "cheque_ocr_features:amount"],
    entity_df=entity_df,
).to_ray_dataset()

# Use the dataset downstream in Ray or ML pipelines
ds.show(3)

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Proto serialisation

RaySource is fully serialisable to Feast's protobuf registry format. The reader_type, path, and reader_options dict are all persisted and can be round-tripped via to_proto() / from_proto().

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Limitations

• The Ray offline store (and therefore RaySource) requires feast[ray].

• reader_type="sql" requires a serialisable connection_url; raw sqlalchemy.engine.Engine objects cannot be pickled across Ray workers.

• Streaming sources (Kafka, Kinesis) are not supported via RaySource; use the dedicated Kafka or Kinesis data sources.

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Related pages

• Ray Offline Store

• Ray Compute Engine

• Feature Retrieval