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Spark (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

feature_store.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"
online_store:
    path: data/online_store.db

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)

write_logged_features (persist logged features to offline store)

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

export to SQL

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

export to data warehouse

export as Spark dataframe

yes

local execution of Python-based on-demand transforms

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

yes

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

Spark (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

feature_store.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"
online_store:
    path: data/online_store.db

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)

write_logged_features (persist logged features to offline store)

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

export to SQL

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

export to data warehouse

export as Spark dataframe

yes

local execution of Python-based on-demand transforms

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

yes

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