feast-dev · feast-ci-bot · Aug 15, 2022 · Jul 11, 2022 · Aug 2, 2022 · Aug 8, 2022
@@ -0,0 +1,5 @@
+# Batch materialization
+
+Please see [Batch Materialization Engine](../../getting-started/architecture-and-components/batch-materialization-engine.md) for an explanation of batch materialization engines.
+
+{% page-ref page="bytewax.md" %}
@@ -0,0 +1,59 @@
+# Bytewax
+
+## Description
+
+The [Bytewax](https://bytewax.io) batch materialization engine provides an execution
+engine for batch materializing operations (`materialize` and `materialize-incremental`).
+
+### Guide
+
+In order to use the Bytewax materialization engine, you will need a [Kubernetes](https://kubernetes.io/) cluster running version 1.22.10 or greater.
+
+#### Kubernetes Authentication
+
+The Bytewax materialization engine loads authentication and cluster information from the [kubeconfig file](https://kubernetes.io/docs/concepts/configuration/organize-cluster-access-kubeconfig/). By default, kubectl looks for a file named `config` in the `$HOME/.kube directory`. You can specify other kubeconfig files by setting the `KUBECONFIG` environment variable.
+
+#### Resource Authentication
+
+Bytewax jobs can be configured to access [Kubernetes secrets](https://kubernetes.io/docs/concepts/configuration/secret/) as environment variables to access online and offline stores during job runs.
+
+To configure secrets, first create them using `kubectl`:
+
+``` shell
+kubectl create secret generic -n bytewax aws-credentials --from-literal=aws-access-key-id='<access key id>' --from-literal=aws-secret-access-key='<secret access key>'
+```
+
+Then configure them in the batch_engine section of `feature_store.yaml`:
+
+``` yaml
+batch_engine:
+  type: bytewax
+  namespace: bytewax
+  env:
+    - name: AWS_ACCESS_KEY_ID
+      valueFrom:
+        secretKeyRef:
+          name: aws-credentials
+          key: aws-access-key-id
+    - name: AWS_SECRET_ACCESS_KEY
+      valueFrom:
+        secretKeyRef:
+          name: aws-credentials
+          key: aws-secret-access-key
+```
+
+#### Configuration
+
+The Bytewax materialization engine is configured through the The `feature_store.yaml` configuration file:
+
+``` yaml
+batch_engine:
+  type: bytewax
+  namespace: bytewax
+  image: bytewax/bytewax-feast:latest
+```
+
+The `namespace` configuration directive specifies which Kubernetes [namespace](https://kubernetes.io/docs/concepts/overview/working-with-objects/namespaces/) jobs, services and configuration maps will be created in.
+
+The `image` parameter specifies which container image to use when running the materialization job. To create a custom image based on this container, please see the [GitHub repository](https://github.com/bytewax/bytewax-feast) for this image.
+
@@ -310,6 +310,14 @@ Local Engine
 (Alpha) Lambda Based Engine
 ---------------------------
 
-.. autoclass:: feast.infra.materialization.lambda.lambda_engine
+.. automodule:: feast.infra.materialization.lambda.lambda_engine
+    :members:
+    :noindex:
+
+
+Bytewax Engine
+---------------------------
+
+.. automodule:: feast.infra.materialization.contrib.bytewax
     :members:
     :noindex:
@@ -0,0 +1,29 @@
+feast.infra.materialization.contrib.bytewax package
+=================================================================
+
+Submodules
+----------
+
+feast.infra.materialization.contrib.bytewax.bytewax\_materialization\_engine
+----------------------------------------------------------------------
+
+.. automodule:: feast.infra.materialization.contrib.bytewax.bytewax_materialization_engine
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+feast.infra.materialization.contrib.bytewax.bytewax\_materialization\_job
+----------------------------------------------------------------------
+
+.. automodule:: feast.infra.materialization.contrib.bytewax.bytewax_materialization_job
+   :members:
+   :undoc-members:
+   :show-inheritance:      
+
+Module contents
+---------------
+
+.. automodule:: feast.infra.materialization.contrib.bytewax
+   :members:
+   :undoc-members:
+   :show-inheritance:
@@ -0,0 +1,10 @@
+feast.infra.materialization.contrib package
+==========================================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   feast.infra.materialization.contrib.bytewax
@@ -313,3 +313,11 @@ Local Engine
 .. autoclass:: feast.infra.materialization.lambda.lambda_engine
     :members:
     :noindex:
+
+
+Bytewax Engine
+---------------------------
+
+.. automodule:: feast.infra.materialization.contrib.bytewax
+    :members:
+    :noindex:
@@ -0,0 +1,15 @@
+from .bytewax_materialization_dataflow import BytewaxMaterializationDataflow
+from .bytewax_materialization_engine import (
+    BytewaxMaterializationEngine,
+    BytewaxMaterializationEngineConfig,
+)
+from .bytewax_materialization_job import BytewaxMaterializationJob
+from .bytewax_materialization_task import BytewaxMaterializationTask
+
+__all__ = [
+    "BytewaxMaterializationTask",
+    "BytewaxMaterializationJob",
+    "BytewaxMaterializationDataflow",
+    "BytewaxMaterializationEngine",
+    "BytewaxMaterializationEngineConfig",
+]
@@ -0,0 +1,89 @@
+from typing import List
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+import s3fs
+from bytewax import Dataflow, cluster_main  # type: ignore
+from bytewax.inputs import AdvanceTo, Emit, ManualInputConfig, distribute
+from bytewax.parse import proc_env
+from tqdm import tqdm
+
+from feast import FeatureStore, FeatureView, RepoConfig
+from feast.utils import _convert_arrow_to_proto, _run_pyarrow_field_mapping
+
+
+class BytewaxMaterializationDataflow:
+    def __init__(
+        self,
+        config: RepoConfig,
+        feature_view: FeatureView,
+        paths: List[str],
+    ):
+        self.config = config
+        self.feature_store = FeatureStore(config=config)
+
+        self.feature_view = feature_view
+        self.paths = paths
+
+        self._run_dataflow()
+
+    def process_path(self, path):
+        fs = s3fs.S3FileSystem()
+        dataset = pq.ParquetDataset(path, filesystem=fs, use_legacy_dataset=False)
+        batches = []
+        for fragment in dataset.fragments:
+            for batch in fragment.to_table().to_batches():
+                batches.append(batch)
+
+        return batches
+
+    def input_builder(self, worker_index, worker_count, resume_epoch):
+        worker_paths = distribute(self.paths, worker_index, worker_count)
+        epoch = 0
+        for path in worker_paths:
+            yield AdvanceTo(epoch)
+            yield Emit(path)
+            epoch += 1
+
+        return
+
+    def output_builder(self, worker_index, worker_count):
+        def output_fn(epoch_batch):
+            _, batch = epoch_batch
+
+            table = pa.Table.from_batches([batch])
+
+            if self.feature_view.batch_source.field_mapping is not None:
+                table = _run_pyarrow_field_mapping(
+                    table, self.feature_view.batch_source.field_mapping
+                )
+
+            join_key_to_value_type = {
+                entity.name: entity.dtype.to_value_type()
+                for entity in self.feature_view.entity_columns
+            }
+
+            rows_to_write = _convert_arrow_to_proto(
+                table, self.feature_view, join_key_to_value_type
+            )
+            provider = self.feature_store._get_provider()
+            with tqdm(total=len(rows_to_write)) as progress:
+                provider.online_write_batch(
+                    config=self.config,
+                    table=self.feature_view,
+                    data=rows_to_write,
+                    progress=progress.update,
+                )
+
+        return output_fn
+
+    def _run_dataflow(self):
+        flow = Dataflow()
+        flow.flat_map(self.process_path)
+        flow.capture()
+        cluster_main(
+            flow,
+            ManualInputConfig(self.input_builder),
+            self.output_builder,
+            **proc_env(),
+        )