BigQuery
- 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.feature_store.yaml
project: my_feature_repo
registry: gs://my-bucket/data/registry.db
provider: gcp
offline_store:
type: bigquery
dataset: feast_bq_dataset
The set of functionality supported by offline stores is described in detail here. Below is a matrix indicating which functionality is supported by the BigQuery offline store.
Text | BigQuery |
|---|---|
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
BigQueryRetrievalJob.Text | BigQuery |
|---|---|
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 | 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 |
*See GitHub issue 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 functionality matrix.
Last modified 1mo ago