Adding a custom compute engine

Overview

Feast batch materialization operations (materialize and materialize-incremental), and get_historical_features are executed through a ComputeEngine.

Custom batch compute engines allow Feast users to extend Feast to customize the materialization and get_historical_features process. Examples include:

• Setting up custom materialization-specific infrastructure during feast apply (e.g. setting up Spark clusters or Lambda Functions)
• Launching custom batch ingestion (materialization) jobs (Spark, Beam, AWS Lambda)
• Tearing down custom materialization-specific infrastructure during feast teardown (e.g. tearing down Spark clusters, or deleting Lambda Functions)

Feast comes with built-in materialization engines, e.g, LocalComputeEngine, and an experimental LambdaComputeEngine. However, users can develop their own compute engines by creating a class that implements the contract in the ComputeEngine class.

Guide

The fastest way to add custom logic to Feast is to implement the ComputeEngine. The guide that follows will extend the LocalProvider with operations that print text to the console. It is up to you as a developer to add your custom code to the engine methods, but the guide below will provide the necessary scaffolding to get you started.

Step 1: Define an Engine class

The first step is to define a custom compute engine class. We've created the MyCustomEngine below. This python file can be placed in your feature_repo directory if you're following the Quickstart guide.

from typing import List, Sequence, Union

from feast.entity import Entity
from feast.feature_view import FeatureView
from feast.batch_feature_view import BatchFeatureView
from feast.stream_feature_view import StreamFeatureView
from feast.infra.common.retrieval_task import HistoricalRetrievalTask
from feast.infra.compute_engines.local.job import LocalMaterializationJob
from feast.infra.compute_engines.base import ComputeEngine 
from feast.infra.common.materialization_job import MaterializationTask
from feast.infra.offline_stores.offline_store import OfflineStore, RetrievalJob
from feast.infra.online_stores.online_store import OnlineStore
from feast.repo_config import RepoConfig


class MyCustomEngine(ComputeEngine):
    def __init__(
            self,
            *,
            repo_config: RepoConfig,
            offline_store: OfflineStore,
            online_store: OnlineStore,
            **kwargs,
    ):
        super().__init__(
            repo_config=repo_config,
            offline_store=offline_store,
            online_store=online_store,
            **kwargs,
        )

    def update(
            self,
            project: str,
            views_to_delete: Sequence[
                Union[BatchFeatureView, StreamFeatureView, FeatureView]
            ],
            views_to_keep: Sequence[
                Union[BatchFeatureView, StreamFeatureView, FeatureView]
            ],
            entities_to_delete: Sequence[Entity],
            entities_to_keep: Sequence[Entity],
    ):
        print("Creating new infrastructure is easy here!")
        pass

    def materialize(
        self, registry, tasks: List[MaterializationTask]
    ) -> List[LocalMaterializationJob]:
        print("Launching custom batch jobs or multithreading things is pretty easy...")
        return [
            self._materialize_one(
                registry,
                task.feature_view,
                task.start_time,
                task.end_time,
                task.project,
                task.tqdm_builder,
            )
            for task in tasks
        ]

    def get_historical_features(self, task: HistoricalRetrievalTask) -> RetrievalJob:
        raise NotImplementedError

Notice how in the above engine we have only overwritten two of the methods on the LocalComputeEngine, namely update and materialize. These two methods are convenient to replace if you are planning to launch custom batch jobs. If you want to use the compute to execute the get_historical_features method, you will need to implement the get_historical_features method as well.

Step 2: Configuring Feast to use the engine

Configure your feature_store.yaml file to point to your new engine class:

project: repo
registry: registry.db
batch_engine: feast_custom_engine.MyCustomEngine
online_store:
    type: sqlite
    path: online_store.db
offline_store:
    type: file

Notice how the batch_engine field above points to the module and class where your engine can be found.

Step 3: Using the engine

Now you should be able to use your engine by running a Feast command:

feast apply

Registered entity driver_id
Registered feature view driver_hourly_stats
Deploying infrastructure for driver_hourly_stats
Creating new infrastructure is easy here!

It may also be necessary to add the module root path to your PYTHONPATH as follows:

PYTHONPATH=$PYTHONPATH:/home/my_user/my_custom_engine feast apply

That's it. You should now have a fully functional custom engine!