# File

## Description

The file offline store provides support for reading [FileSources](https://docs.feast.dev/v0.25-branch/reference/data-sources/file). It uses Dask as the compute engine.

{% hint style="warning" %}
All data is downloaded and joined using Python and therefore may not scale to production workloads.
{% endhint %}

## Example

{% code title="feature\_store.yaml" %}

```yaml
project: my_feature_repo
registry: data/registry.db
provider: local
offline_store:
  type: file
```

{% endcode %}

The full set of configuration options is available in [FileOfflineStoreConfig](https://rtd.feast.dev/en/latest/#feast.infra.offline_stores.file.FileOfflineStoreConfig).

## Functionality Matrix

The set of functionality supported by offline stores is described in detail [here](https://docs.feast.dev/v0.25-branch/reference/overview#functionality). Below is a matrix indicating which functionality is supported by the file offline store.

|                                                                    | File |
| ------------------------------------------------------------------ | ---- |
| `get_historical_features` (point-in-time correct join)             | yes  |
| `pull_latest_from_table_or_query` (retrieve latest feature values) | yes  |
| `pull_all_from_table_or_query` (retrieve a saved dataset)          | yes  |
| `offline_write_batch` (persist dataframes to offline store)        | yes  |
| `write_logged_features` (persist logged features to offline store) | yes  |

Below is a matrix indicating which functionality is supported by `FileRetrievalJob`.

|                                                       | File |
| ----------------------------------------------------- | ---- |
| export to dataframe                                   | yes  |
| export to arrow table                                 | yes  |
| export to arrow batches                               | no   |
| export to SQL                                         | no   |
| export to data lake (S3, GCS, etc.)                   | no   |
| export to data warehouse                              | no   |
| export as Spark dataframe                             | no   |
| local execution of Python-based on-demand transforms  | yes  |
| remote execution of Python-based on-demand transforms | no   |
| persist results in the offline store                  | yes  |
| preview the query plan before execution               | yes  |
| read partitioned data                                 | yes  |

To compare this set of functionality against other offline stores, please see the full [functionality matrix](https://docs.feast.dev/v0.25-branch/reference/overview#functionality-matrix).