The duckdb offline store provides support for reading FileSources. It can read both Parquet and Delta formats. DuckDB offline store uses ibis under the hood to convert offline store operations to DuckDB queries.
- Entity dataframes can be provided as a Pandas dataframe.
In order to use this offline store, you'll need to run pip install 'feast[duckdb]'.
{% code title="feature_store.yaml" %}
project: my_project
registry: data/registry.db
provider: local
offline_store:
type: duckdb
online_store:
path: data/online_store.db{% endcode %}
The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the DuckDB offline store.
| DuckdDB | |
|---|---|
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 IbisRetrievalJob.
| DuckDB | |
|---|---|
| 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 | no |
| read partitioned data | yes |
To compare this set of functionality against other offline stores, please see the full functionality matrix.