Spark offline store (contrib)

Description

The Spark offline store provides support for reading SparkSources.

Entity dataframes can be provided as a SQL query, Pandas dataframe or can be provided as a Pyspark dataframe. A Pandas dataframes will be converted to a Spark dataframe and processed as a temporary view.

Disclaimer

The Spark offline store does not achieve full test coverage. Please do not assume complete stability.

Getting started

In order to use this offline store, you'll need to run pip install 'feast[spark]'. You can get started by then running feast init -t spark.

Example

{% code title="feature_store.yaml" %}

yaml

project: my_project
registry: data/registry.db
provider: local
offline_store:
    type: spark
    spark_conf:
        spark.master: "local[*]"
        spark.ui.enabled: "false"
        spark.eventLog.enabled: "false"
        spark.sql.catalogImplementation: "hive"
        spark.sql.parser.quotedRegexColumnNames: "true"
        spark.sql.session.timeZone: "UTC"
        spark.sql.execution.arrow.fallback.enabled: "true"
        spark.sql.execution.arrow.pyspark.enabled: "true"
    # Optional: spill large materializations to the staging location instead of collecting in the driver
    staging_location: "s3://my-bucket/tmp/feast"
online_store:
    path: data/online_store.db

The staging_location can point to object storage (like S3, GCS, or Azure blobs) or a local filesystem directory (e.g., /tmp/feast/staging) to spill large materialization outputs before reading them back into Feast. {% endcode %}

The full set of configuration options is available in SparkOfflineStoreConfig.

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 Spark offline store.

	Spark
`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 SparkRetrievalJob.

	Spark
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.)	yes
export to data warehouse	no
export as Spark dataframe	yes
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
preview the query plan before execution	yes
read partitioned data	yes

To compare this set of functionality against other offline stores, please see the full functionality matrix.