BigQuery

Description

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.

Getting started

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.

Example

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 full set of configuration options is available in BigQueryOfflineStoreConfig.

Functionality Matrix

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.

BigQuery

	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

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.

	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

BigQuery

export to dataframe

yes

export to arrow table

yes

export to arrow batches

export to SQL

yes

export to data lake (S3, GCS, etc.)

export to data warehouse

yes

export as Spark dataframe

local execution of Python-based on-demand transforms

yes

remote execution of Python-based on-demand transforms

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.

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