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Batch Materialization Engines

Please see Batch Materialization Engine for an explanation of batch materialization engines.

Snowflake

Description

The Snowflake batch materialization engine provides a highly scalable and parallel execution engine using a Snowflake Warehouse for batch materializations operations (materialize and materialize-incremental) when using a SnowflakeSource.

The engine requires no additional configuration other than for you to supply Snowflake's standard login and context details. The engine leverages custom (automatically deployed for you) Python UDFs to do the proper serialization of your offline store data to your online serving tables.

When using all three options together, snowflake.offline, snowflake.engine, and snowflake.online, you get the most unique experience of unlimited scale and performance + governance and data security.

Example

feature_store.yaml

...
offline_store:
  type: snowflake.offline
...
batch_engine:
  type: snowflake.engine
  account: snowflake_deployment.us-east-1
  user: user_login
  password: user_password
  role: sysadmin
  warehouse: demo_wh
  database: FEAST

AWS Lambda (alpha)

Description

The AWS Lambda batch materialization engine is considered alpha status. It relies on the offline store to output feature values to S3 via to_remote_storage, and then loads them into the online store.

See LambdaMaterializationEngineConfig for configuration options.

See also Dockerfile for a Dockerfile that can be used below with materialization_image.

Example

feature_store.yaml

...
offline_store:
  type: snowflake.offline
...
batch_engine:
  type: lambda
  lambda_role: [your iam role]
  materialization_image: [image uri of above Docker image]

Spark (contrib)

Description

The Spark batch materialization engine is considered alpha status. It relies on the offline store to output feature values to S3 via to_remote_storage, and then loads them into the online store.

See SparkMaterializationEngine for configuration options.

Example

feature_store.yaml

...
offline_store:
  type: snowflake.offline
...
batch_engine:
  type: spark.engine
  partitions: [optional num partitions to use to write to online store]

Example in Python

feature_store.py

from feast import FeatureStore, RepoConfig
from feast.repo_config import RegistryConfig
from feast.infra.online_stores.dynamodb import DynamoDBOnlineStoreConfig
from feast.infra.offline_stores.contrib.spark_offline_store.spark import SparkOfflineStoreConfig

repo_config = RepoConfig(
    registry="s3://[YOUR_BUCKET]/feast-registry.db",
    project="feast_repo",
    provider="aws",
    offline_store=SparkOfflineStoreConfig(
      spark_conf={
        "spark.ui.enabled": "false",
        "spark.eventLog.enabled": "false",
        "spark.sql.catalogImplementation": "hive",
        "spark.sql.parser.quotedRegexColumnNames": "true",
        "spark.sql.session.timeZone": "UTC"
      }
    ),
    batch_engine={
      "type": "spark.engine",
      "partitions": 10
    },
    online_store=DynamoDBOnlineStoreConfig(region="us-west-1"),
    entity_key_serialization_version=2
)

store = FeatureStore(config=repo_config)

Snowflake

Description

Example

feature_store.yaml

...
offline_store:
  type: snowflake.offline
...
batch_engine:
  type: snowflake.engine
  account: snowflake_deployment.us-east-1
  user: user_login
  password: user_password
  role: sysadmin
  warehouse: demo_wh
  database: FEAST

AWS Lambda (alpha)

Description

See LambdaMaterializationEngineConfig for configuration options.

See also Dockerfile for a Dockerfile that can be used below with materialization_image.

Example

feature_store.yaml

...
offline_store:
  type: snowflake.offline
...
batch_engine:
  type: lambda
  lambda_role: [your iam role]
  materialization_image: [image uri of above Docker image]

Spark (contrib)

Description

The Spark batch materialization engine is considered alpha status. It relies on the offline store to output feature values to S3 via to_remote_storage, and then loads them into the online store.

See SparkMaterializationEngine for configuration options.

Example

feature_store.yaml

...
offline_store:
  type: snowflake.offline
...
batch_engine:
  type: spark.engine
  partitions: [optional num partitions to use to write to online store]

Example in Python

feature_store.py

from feast import FeatureStore, RepoConfig
from feast.repo_config import RegistryConfig
from feast.infra.online_stores.dynamodb import DynamoDBOnlineStoreConfig
from feast.infra.offline_stores.contrib.spark_offline_store.spark import SparkOfflineStoreConfig

repo_config = RepoConfig(
    registry="s3://[YOUR_BUCKET]/feast-registry.db",
    project="feast_repo",
    provider="aws",
    offline_store=SparkOfflineStoreConfig(
      spark_conf={
        "spark.ui.enabled": "false",
        "spark.eventLog.enabled": "false",
        "spark.sql.catalogImplementation": "hive",
        "spark.sql.parser.quotedRegexColumnNames": "true",
        "spark.sql.session.timeZone": "UTC"
      }
    ),
    batch_engine={
      "type": "spark.engine",
      "partitions": 10
    },
    online_store=DynamoDBOnlineStoreConfig(region="us-west-1"),
    entity_key_serialization_version=2
)

store = FeatureStore(config=repo_config)