The BigQuery offline store provides support for reading BigQuerySources.
All joins happen within BigQuery.
Entity dataframes can be provided as a SQL query or can be provided as a Pandas dataframe. A Pandas dataframes will be uploaded to BigQuery as a table (marked for expiration) in order to complete join operations.
In order to use this offline store, you'll need to run pip install 'feast[gcp]'. You can get started by then running feast init -t gcp.
The full set of configuration options is available in .
The set of functionality supported by offline stores is described in detail . Below is a matrix indicating which functionality is supported by the BigQuery offline store.
Below is a matrix indicating which functionality is supported by BigQueryRetrievalJob.
*See for details on proposed solutions for enabling the BigQuery offline store to understand tables that use _PARTITIONTIME as the partition column.
To compare this set of functionality against other offline stores, please see the full .
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*
partial
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
export to dataframe
yes
export to arrow table
yes
export to arrow batches
no
export to SQL
yes
export to data lake (S3, GCS, etc.)
no
export to data warehouse
yes
export as Spark dataframe
project: my_feature_repo
registry: gs://my-bucket/data/registry.db
provider: gcp
offline_store:
type: bigquery
dataset: feast_bq_dataset