The Ray offline store provides:

- Ray-based data reading from file sources (Parquet, CSV, etc.)

- Support for both local and distributed Ray clusters

- Support for local, remote, and KubeRay (Kubernetes-managed) clusters

- Integration with various storage backends (local files, S3, GCS, HDFS)

- Efficient data filtering and column selection

- Timestamp-based data processing with timezone awareness

- Enterprise-ready KubeRay cluster support via CodeFlare SDK

## Functionality Matrix

## Configuration

The Ray offline store can be configured in your `feature_store.yaml` file. Below are two main configuration patterns:

The Ray offline store can be configured in your `feature_store.yaml` file. It supports **three execution modes**:

### Basic Ray Offline Store

1. **LOCAL**: Ray runs locally on the same machine (default)

2. **REMOTE**: Connects to a remote Ray cluster via `ray_address`

3. **KUBERAY**: Connects to Ray clusters on Kubernetes via CodeFlare SDK

### Execution Modes

#### Local Mode (Default)

For simple data I/O operations without distributed processing:

offline_store:

type: ray

storage_path: data/ray_storage # Optional: Path for storing datasets

ray_address: localhost:10001 # Optional: Ray cluster address

```

#### Remote Ray Cluster

Connect to an existing Ray cluster:

```yaml

offline_store:

type: ray

storage_path: s3://my-bucket/feast-data

ray_address: "ray://my-cluster.example.com:10001"

```

#### KubeRay Cluster (Kubernetes)

Connect to Ray clusters on Kubernetes using CodeFlare SDK:

```yaml

offline_store:

type: ray

storage_path: s3://my-bucket/feast-data

use_kuberay: true

kuberay_conf:

cluster_name: "feast-ray-cluster"

namespace: "feast-system"

auth_token: "${RAY_AUTH_TOKEN}"

auth_server: "https://api.openshift.com:6443"

skip_tls: false

enable_ray_logging: false

```

**Environment Variables** (alternative to config file):

```bash

export FEAST_RAY_USE_KUBERAY=true

export FEAST_RAY_CLUSTER_NAME=feast-ray-cluster

export FEAST_RAY_AUTH_TOKEN=your-token

export FEAST_RAY_AUTH_SERVER=https://api.openshift.com:6443

export FEAST_RAY_NAMESPACE=feast-system

```

### Ray Offline Store + Compute Engine

|--------|------|---------|-------------|

| `type` | string | Required | Must be `feast.offline_stores.contrib.ray_offline_store.ray.RayOfflineStore` or `ray` |

| `storage_path` | string | None | Path for storing temporary files and datasets |

| `ray_address` | string | None | Address of the Ray cluster (e.g., "localhost:10001") |

| `ray_address` | string | None | Ray cluster address (triggers REMOTE mode, e.g., "ray://host:10001") |

| `use_kuberay` | boolean | None | Enable KubeRay mode (overrides ray_address) |

| `kuberay_conf` | dict | None | **KubeRay configuration dict** with keys: `cluster_name` (required), `namespace` (default: "default"), `auth_token`, `auth_server`, `skip_tls` (default: false) |

| `enable_ray_logging` | boolean | false | Enable Ray progress bars and verbose logging |

| `ray_conf` | dict | None | Ray initialization parameters for resource management (e.g., memory, CPU limits) |

| `broadcast_join_threshold_mb` | int | 100 | Size threshold for broadcast joins (MB) |

| `enable_distributed_joins` | boolean | true | Enable distributed joins for large datasets |

| `max_parallelism_multiplier` | int | 2 | Parallelism as multiple of CPU cores |

| `target_partition_size_mb` | int | 64 | Target partition size (MB) |

| `window_size_for_joins` | string | "1H" | Time window for distributed joins |

#### Mode Detection Precedence

The Ray offline store automatically detects the execution mode using the following precedence:

1. **Environment Variables** (highest priority)

- `FEAST_RAY_USE_KUBERAY`, `FEAST_RAY_CLUSTER_NAME`, etc.

2. **Config `kuberay_conf`**

- If present → KubeRay mode

3. **Config `ray_address`**

- If present → Remote mode

4. **Default**

- Local mode (lowest priority)

#### Ray Compute Engine Options

### Using Ray Cluster

#### Standard Ray Cluster

To use Ray in cluster mode for distributed data access:

1. Start a Ray cluster:

ray start --address='head-node-ip:10001'

```

#### KubeRay Cluster (Kubernetes)

To use Feast with Ray clusters on Kubernetes via CodeFlare SDK:

**Prerequisites:**

- KubeRay cluster deployed on Kubernetes

- CodeFlare SDK installed: `pip install codeflare-sdk`

- Access credentials for the Kubernetes cluster

**Configuration:**

1. Using configuration file:

```yaml

offline_store:

type: ray

use_kuberay: true

storage_path: s3://my-bucket/feast-data

kuberay_conf:

cluster_name: "feast-ray-cluster"

namespace: "feast-system"

auth_token: "${RAY_AUTH_TOKEN}"

auth_server: "https://api.openshift.com:6443"

skip_tls: false

enable_ray_logging: false

```

2. Using environment variables:

```bash

export FEAST_RAY_USE_KUBERAY=true

export FEAST_RAY_CLUSTER_NAME=feast-ray-cluster

export FEAST_RAY_AUTH_TOKEN=your-k8s-token

export FEAST_RAY_AUTH_SERVER=https://api.openshift.com:6443

export FEAST_RAY_NAMESPACE=feast-system

export FEAST_RAY_SKIP_TLS=false

# Then use standard Feast code

python your_feast_script.py

```

**Features:**

- The CodeFlare SDK handles cluster connection and authentication

- Automatic TLS certificate management

- Authentication with Kubernetes clusters

- Namespace isolation

- Secure communication between client and Ray cluster

- Automatic cluster discovery

### Data Source Validation

The Ray offline store validates data sources to ensure compatibility: