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‎.secrets.baseline‎

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"filename": "infra/feast-operator/api/v1/featurestore_types.go",
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"hashed_secret": "44e17306b837162269a410204daaa5ecee4ec22c",
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"is_verified": false,
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"line_number": 726
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"line_number": 729
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}
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],
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"infra/feast-operator/api/v1/zz_generated.deepcopy.go": [
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"filename": "infra/feast-operator/api/v1/zz_generated.deepcopy.go",
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"hashed_secret": "f914fc9324de1bec1ad13dec94a8ea2ddb41fc87",
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"is_verified": false,
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"line_number": 681
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"line_number": 686
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},
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{
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"type": "Secret Keyword",
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"filename": "infra/feast-operator/api/v1/zz_generated.deepcopy.go",
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"hashed_secret": "44e17306b837162269a410204daaa5ecee4ec22c",
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"is_verified": false,
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"line_number": 1249
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"line_number": 1254
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},
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{
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"type": "Secret Keyword",
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"filename": "infra/feast-operator/api/v1/zz_generated.deepcopy.go",
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"hashed_secret": "c2028031c154bbe86fd69bef740855c74b927dcf",
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"is_verified": false,
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"line_number": 1254
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"line_number": 1259
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}
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],
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"infra/feast-operator/api/v1alpha1/featurestore_types.go": [
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"filename": "infra/feast-operator/api/v1alpha1/featurestore_types.go",
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"hashed_secret": "44e17306b837162269a410204daaa5ecee4ec22c",
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"is_verified": false,
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"line_number": 647
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"line_number": 649
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}
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],
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"infra/feast-operator/api/v1alpha1/zz_generated.deepcopy.go": [
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"filename": "infra/feast-operator/api/v1alpha1/zz_generated.deepcopy.go",
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"hashed_secret": "f914fc9324de1bec1ad13dec94a8ea2ddb41fc87",
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"is_verified": false,
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"line_number": 590
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"line_number": 595
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},
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{
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"type": "Secret Keyword",
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"filename": "infra/feast-operator/api/v1alpha1/zz_generated.deepcopy.go",
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"hashed_secret": "44e17306b837162269a410204daaa5ecee4ec22c",
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"is_verified": false,
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"line_number": 1098
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"line_number": 1103
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},
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{
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"type": "Secret Keyword",
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"filename": "infra/feast-operator/api/v1alpha1/zz_generated.deepcopy.go",
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"hashed_secret": "c2028031c154bbe86fd69bef740855c74b927dcf",
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"is_verified": false,
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"line_number": 1103
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"line_number": 1108
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}
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],
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"infra/feast-operator/config/samples/v1_featurestore_db_persistence.yaml": [
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}
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]
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},
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"generated_at": "2026-03-05T15:25:10Z"
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"generated_at": "2026-03-14T16:01:28Z"
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}

‎docs/SUMMARY.md‎

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* [Trino (contrib)](reference/data-sources/trino.md)
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* [Azure Synapse + Azure SQL (contrib)](reference/data-sources/mssql.md)
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* [Couchbase (contrib)](reference/data-sources/couchbase.md)
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* [Oracle (contrib)](reference/data-sources/oracle.md)
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* [Offline stores](reference/offline-stores/README.md)
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* [Overview](reference/offline-stores/overview.md)
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* [Dask](reference/offline-stores/dask.md)
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* [Azure Synapse + Azure SQL (contrib)](reference/offline-stores/mssql.md)
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* [Clickhouse (contrib)](reference/offline-stores/clickhouse.md)
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* [Ray (contrib)](reference/offline-stores/ray.md)
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* [Oracle (contrib)](reference/offline-stores/oracle.md)
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* [Remote Offline](reference/offline-stores/remote-offline-store.md)
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* [Online stores](reference/online-stores/README.md)
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* [Overview](reference/online-stores/overview.md)

‎docs/getting-started/concepts/feast-types.md‎

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Feast supports the following categories of data types:
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- **Primitive types**: numerical values (`Int32`, `Int64`, `Float32`, `Float64`), `String`, `Bytes`, `Bool`, and `UnixTimestamp`.
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- **Domain-specific primitives**: `PdfBytes` (PDF binary data for RAG/document pipelines) and `ImageBytes` (image binary data for multimodal pipelines). These are semantic aliases over `Bytes` and must be explicitly declared in schema — no backend infers them.
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- **Array types**: ordered lists of any primitive type, e.g. `Array(Int64)`, `Array(String)`.
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- **Set types**: unordered collections of unique values for any primitive type, e.g. `Set(String)`, `Set(Int64)`.
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- **Set types**: unordered collections of unique values for any primitive type, e.g. `Set(String)`, `Set(Int64)`. Set types are not inferred by any backend and must be explicitly declared. They are best suited for online serving use cases.
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- **Map types**: dictionary-like structures with string keys and values that can be any supported Feast type (including nested maps), e.g. `Map`, `Array(Map)`.
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- **JSON type**: opaque JSON data stored as a string at the proto level but semantically distinct from `String` — backends use native JSON types (`jsonb`, `VARIANT`, etc.), e.g. `Json`, `Array(Json)`.
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- **Struct type**: schema-aware structured type with named, typed fields. Unlike `Map` (which is schema-free), a `Struct` declares its field names and their types, enabling schema validation, e.g. `Struct({"name": String, "age": Int32})`.

‎docs/reference/data-sources/README.md‎

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{% content-ref url="clickhouse.md" %}
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[clickhouse.md](clickhouse.md)
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{% endcontent-ref %}
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{% content-ref url="oracle.md" %}
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[oracle.md](oracle.md)
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{% endcontent-ref %}

‎docs/reference/data-sources/oracle.md‎

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# Oracle source (contrib)
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## Description
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Oracle data sources are Oracle database tables.
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These are specified by a table reference (e.g. `"TRANSACTION_FEATURES"` or `"SCHEMA.TABLE"`).
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## Disclaimer
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The Oracle data source does not achieve full test coverage.
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Please do not assume complete stability.
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## Examples
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Defining an Oracle source:
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```python
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from feast.infra.offline_stores.contrib.oracle_offline_store.oracle_source import (
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OracleSource,
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)
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driver_stats_source = OracleSource(
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name="driver_hourly_stats",
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table_ref="DRIVER_HOURLY_STATS",
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event_timestamp_column="EVENT_TIMESTAMP",
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created_timestamp_column="CREATED",
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)
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```
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**Note:** Oracle stores unquoted identifiers in uppercase. Reference columns using the casing shown by Oracle (e.g. `USER_ID` for unquoted identifiers).
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## Supported Types
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Oracle data sources support standard Oracle numeric, string, date, and timestamp types mapped through the ibis Oracle backend.
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For a comparison against other batch data sources, please see [here](overview.md#functionality-matrix).
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‎docs/reference/data-sources/overview.md‎

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In Feast, each batch data source is associated with corresponding offline stores.
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For example, a `SnowflakeSource` can only be processed by the Snowflake offline store, while a `FileSource` can be processed by both File and DuckDB offline stores.
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Otherwise, the primary difference between batch data sources is the set of supported types.
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Feast has an internal type system, and aims to support eight primitive types (`bytes`, `string`, `int32`, `int64`, `float32`, `float64`, `bool`, and `timestamp`) along with the corresponding array types.
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Feast has an internal type system that supports primitive types (`bytes`, `string`, `int32`, `int64`, `float32`, `float64`, `bool`, `timestamp`), array types, set types, map/JSON types, and struct types.
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However, not every batch data source supports all of these types.
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For more details on the Feast type system, see [here](../type-system.md).
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| `bool` | yes | yes | yes | yes | yes | yes | yes | yes |
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| `timestamp` | yes | yes | yes | yes | yes | yes | yes | yes |
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| array types | yes | yes | yes | no | yes | yes | yes | no |
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| `Map` | yes | no | yes | yes | yes | yes | yes | no |
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| `Json` | yes | yes | yes | yes | yes | no | no | no |
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| `Struct` | yes | yes | no | no | yes | yes | no | no |
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| set types | yes* | no | no | no | no | no | no | no |
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\* **Set types** are defined in Feast's proto and Python type system but are **not inferred** by any backend. They must be explicitly declared in the feature view schema and are best suited for online serving use cases. See [Type System](../type-system.md#set-types) for details.

‎docs/reference/offline-stores/README.md‎

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{% content-ref url="ray.md" %}
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[ray.md](ray.md)
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{% endcontent-ref %}
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{% content-ref url="oracle.md" %}
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[oracle.md](oracle.md)
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{% endcontent-ref %}

‎docs/reference/offline-stores/oracle.md‎

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# Oracle offline store (contrib)
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## Description
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The Oracle offline store provides support for reading [OracleSources](../data-sources/oracle.md).
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* Entity dataframes can be provided as a SQL query or as a Pandas dataframe.
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* Uses the [ibis](https://ibis-project.org/) Oracle backend (`ibis.oracle`) for all database interactions.
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* Only one of `service_name`, `sid`, or `dsn` may be set in the configuration.
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## Disclaimer
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The Oracle offline store does not achieve full test coverage.
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Please do not assume complete stability.
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## Getting started
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Install the Oracle extras:
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```bash
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pip install 'feast[oracle]'
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```
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## Example
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{% code title="feature_store.yaml" %}
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```yaml
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project: my_project
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registry: data/registry.db
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provider: local
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offline_store:
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type: oracle
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host: DB_HOST
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port: 1521
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user: DB_USERNAME
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password: DB_PASSWORD
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service_name: ORCL
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online_store:
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path: data/online_store.db
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```
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{% endcode %}
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Connection can alternatively use `sid` or `dsn` instead of `service_name`:
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```yaml
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# Using SID
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offline_store:
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type: oracle
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host: DB_HOST
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port: 1521
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user: DB_USERNAME
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password: DB_PASSWORD
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sid: ORCL
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# Using DSN
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offline_store:
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type: oracle
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host: DB_HOST
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port: 1521
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user: DB_USERNAME
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password: DB_PASSWORD
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dsn: "(DESCRIPTION=(ADDRESS=(PROTOCOL=TCP)(HOST=DB_HOST)(PORT=1521))(CONNECT_DATA=(SERVICE_NAME=ORCL)))"
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```
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### Configuration reference
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| Parameter | Required | Default | Description |
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| :------------- | :------- | :---------- | :------------------------------------------------------- |
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| `type` | yes | — | Must be set to `oracle` |
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| `user` | yes | — | Oracle database user |
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| `password` | yes | — | Oracle database password |
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| `host` | no | `localhost` | Oracle database host |
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| `port` | no | `1521` | Oracle database port |
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| `service_name` | no | — | Oracle service name (mutually exclusive with sid and dsn) |
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| `sid` | no | — | Oracle SID (mutually exclusive with service_name and dsn) |
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| `database` | no | — | Oracle database name |
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| `dsn` | no | — | Oracle DSN string (mutually exclusive with service_name and sid) |
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## Functionality Matrix
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The set of functionality supported by offline stores is described in detail [here](overview.md#functionality).
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Below is a matrix indicating which functionality is supported by the Oracle offline store.
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| | Oracle |
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| :----------------------------------------------------------------- | :----- |
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| `get_historical_features` (point-in-time correct join) | yes |
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| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes |
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| `pull_all_from_table_or_query` (retrieve a saved dataset) | yes |
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| `offline_write_batch` (persist dataframes to offline store) | yes |
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| `write_logged_features` (persist logged features to offline store) | yes |
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Below is a matrix indicating which functionality is supported by `OracleRetrievalJob`.
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| | Oracle |
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| ----------------------------------------------------- | ------ |
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| export to dataframe | yes |
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| export to arrow table | yes |
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| export to arrow batches | no |
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| export to SQL | no |
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| export to data lake (S3, GCS, etc.) | no |
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| export to data warehouse | no |
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| export as Spark dataframe | no |
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| local execution of Python-based on-demand transforms | yes |
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| remote execution of Python-based on-demand transforms | no |
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| persist results in the offline store | yes |
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| preview the query plan before execution | no |
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| read partitioned data | no |
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To compare this set of functionality against other offline stores, please see the full [functionality matrix](overview.md#functionality-matrix).
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