The MsSQL offline store provides support for reading MsSQL Sources. Specifically, it is developed to read from Synapse SQL on Microsoft Azure
- Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe.
In order to use this offline store, you'll need to run pip install 'feast[azure]'. You can get started by then following this tutorial.
The MsSQL offline store does not achieve full test coverage. Please do not assume complete stability.
{% code title="feature_store.yaml" %}
registry:
registry_store_type: AzureRegistryStore
path: ${REGISTRY_PATH} # Environment Variable
project: production
provider: azure
online_store:
type: redis
connection_string: ${REDIS_CONN} # Environment Variable
offline_store:
type: mssql
connection_string: ${SQL_CONN} # Environment Variable{% 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 Spark offline store.
| MsSql | |
|---|---|
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) |
no |
write_logged_features (persist logged features to offline store) |
no |
Below is a matrix indicating which functionality is supported by MsSqlServerRetrievalJob.
| MsSql | |
|---|---|
| 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 |
| local execution of Python-based on-demand transforms | no |
| remote execution of Python-based on-demand transforms | no |
| persist results in the offline store | yes |
To compare this set of functionality against other offline stores, please see the full functionality matrix.