Adding a custom batch materialization engine

Overview
Feast batch materialization operations (materialize and materialize-incremental) execute through a BatchMaterializationEngine.
Custom batch materialization engines allow Feast users to extend Feast to customize the materialization 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, LocalMaterializationEngine, and an experimental LambdaMaterializationEngine. However, users can develop their own materialization engines by creating a class that implements the contract in the BatchMaterializationEngine class.
Guide
The fastest way to add custom logic to Feast is to extend an existing materialization engine. The most generic engine is the LocalMaterializationEngine which contains no cloud-specific logic. 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 materialization engine class. We've created the MyCustomEngine below.
from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, 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.materialization import LocalMaterializationEngine, LocalMaterializationJob, MaterializationTask
from feast.infra.offline_stores.offline_store import OfflineStore
from feast.infra.online_stores.online_store import OnlineStore
from feast.repo_config import RepoConfig
​
​
class MyCustomEngine(LocalMaterializationEngine):
    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
        ]
Notice how in the above engine we have only overwritten two of the methods on the LocalMaterializatinEngine, namely update and materialize. These two methods are convenient to replace if you are planning to launch custom batch jobs.
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!
Last modified 24d ago